Intelligent lighting explosion-proof single lamp controller optimization scheduling method and system

Abstract

The invention relates to the technical field of intelligent illumination, and discloses an intelligent illumination explosion-proof single lamp controller optimization scheduling method and system, and the method comprises the steps: firstly obtaining the deployment position of a controller, determining a target illumination region, collecting ambient light data to construct an illumination distribution diagram, and recognizing a distribution control link; monitoring real-time energy consumption of a link, analyzing load characteristics and calculating an optimal dimming coefficient; acquiring a signal response frequency band based on the dimming coefficient, fitting to generate a dimming response curve, identifying a defect response section and calculating a harmonic distortion rate; illumination compensation is carried out according to the harmonic distortion rate, scheduling grouping is carried out after a compensation threshold value is determined, high-load nodes are identified, and power is corrected; and finally, acquiring the temperature change gradient of the lamp according to the load correction parameter, generating a regulation and control instruction, and formulating an optimal scheduling strategy. According to the invention, the scheduling optimization of the explosion-proof single lamp can be improved.

Description

Technical Field

[0001] The present invention relates to an optimized scheduling method and system for an intelligent lighting explosion-proof single lamp controller, belonging to the technical field of intelligent lighting. Background Art

[0002] An explosion-proof single lamp for lighting refers to an explosion-proof lighting device specifically used in dangerous environments such as flammable and explosive environments (such as petrochemical, mine, gas station, etc.). Its core feature is having an explosion-proof structure design (such as flameproof type, increased safety type, etc.), which can prevent internal electrical sparks or high temperatures from igniting external explosive gases or dust, ensuring the safety and reliability of the lighting process.

[0003] The existing technology mainly triggers explosion protection through fixed thresholds or conducts dimming based on a single sensor (such as illuminance), lacking the collaborative analysis of multi-dimensional environmental parameters (temperature and humidity, combustible gas concentration, personnel location, etc.), and the scheduling strategy is rigid. For example, high-brightness lighting is still maintained in low-personnel-density scenarios, or the emergency lighting response speed fails to be adjusted according to the real-time explosion risk level, resulting in limited prediction ability for emergencies and adaptive scheduling efficiency, and prone to causing energy waste and safety hazards. Summary of the Invention

[0004] The present invention provides an optimized scheduling method and system for an intelligent lighting explosion-proof single lamp controller, and its main purpose is to improve the scheduling optimization of the explosion-proof single lamp.

[0005] To achieve the above object, an optimized scheduling method for an intelligent lighting explosion-proof single lamp controller provided by the present invention includes:

[0006] Obtain the deployment location corresponding to the explosion-proof single lamp controller, determine the target lighting area corresponding to the deployment location, collect the ambient light data in the target lighting area, construct a light intensity distribution map corresponding to the ambient light data, and based on the light intensity distribution map, identify the distributed control link corresponding to the explosion-proof single lamp controller;

[0007] Monitor the real-time energy consumption data in the distributed control link, conduct a load analysis on the real-time energy consumption data to obtain the lighting load characteristics, and based on the lighting load characteristics, calculate the optimal dimming coefficient corresponding to the explosion-proof single lamp controller;

[0008] Based on the optimal dimming coefficient, collect the signal response frequency band in the target lighting area, conduct curve fitting on the signal response frequency band to generate a dimming response curve, identify the defective response section in the dimming response curve, and calculate the harmonic distortion rate corresponding to the response points in the defective response section;

[0009] Based on the harmonic distortion rate, perform light compensation on the defect response segment to obtain a light compensation threshold. Based on the light compensation threshold, schedule and group the explosion-proof single lamp controllers to generate a priority scheduling group list, and identify the high-load nodes in the priority scheduling group list;

[0010] Perform power correction on the high-load nodes to obtain load correction parameters. Based on the load correction parameters, collect the temperature change gradient in the lamp cavity corresponding to the explosion-proof single lamp controller in real time, and generate a temperature control instruction corresponding to the temperature change gradient. Based on the temperature control instruction, formulate an optimized scheduling strategy corresponding to the explosion-proof single lamp controller.

[0011] Optionally, the identifying the distributed control link corresponding to the explosion-proof single lamp controller based on the light distribution map includes:

[0012] Analyze the brightness gradient data corresponding to the light distribution map;

[0013] Based on the brightness gradient data, locate the light control area corresponding to the explosion-proof single lamp controller;

[0014] Collect the signal transmission nodes in the light control area;

[0015] Determine the topological link relationship corresponding to the signal transmission nodes;

[0016] Based on the topological link relationship, identify the distributed control link corresponding to the explosion-proof single lamp controller.

[0017] Optionally, the performing load analysis on the real-time energy consumption data to obtain lighting load characteristics includes:

[0018] Extract the lighting fluctuation signal from the real-time energy consumption data;

[0019] Fit an independent energy consumption curve corresponding to the lighting fluctuation signal;

[0020] Identify the lighting start and stop points in the independent energy consumption curve;

[0021] Analyze the start and stop distribution data corresponding to the lighting start and stop points;

[0022] Perform load analysis on the start and stop distribution data to obtain lighting load characteristics.

[0023] Optionally, the calculating the optimal dimming coefficient corresponding to the explosion-proof single lamp controller based on the lighting load characteristics includes:

[0024] Use the following formula to calculate the optimal dimming coefficient corresponding to the explosion-proof single lamp controller:

[0025]

[0026] Among them, K opt represents the optimal dimming coefficient corresponding to the explosion-proof single lamp controller, t1 and t2 respectively represent the start time and end time of the statistical period, n represents the total number of lighting devices, i represents the number index of the lighting devices, and P i (t) represents the real-time power of the i-th lighting device at time t, represents the average power of all lighting devices within the statistical period, m represents the total number of times the lighting devices are turned on, j represents the number index of the times the lighting devices are turned on, and T on,j represents the duration of the j-th turn-on of the lighting device, represents the average duration of the lighting devices being turned on within the statistical period, l represents the total number of times the lighting devices are turned off, k represents the number index of the times the lighting devices are turned off, and T off,k represents the duration of the k-th turn-off of the lighting device, represents the average duration of the lighting devices being turned off within the statistical period.

[0027] Optionally, collecting the signal response frequency band in the target lighting area based on the optimal dimming coefficient includes:

[0028] Determining the spectral sampling interval corresponding to the target lighting area according to the optimal dimming coefficient;

[0029] Scanning the band reflection intensity corresponding to each frequency band in the spectral sampling interval;

[0030] Filtering out the signal frequency bands in the band reflection intensity that exceed the preset threshold;

[0031] Extracting the frequency wave response points in the signal frequency bands;

[0032] Collecting the signal response frequency band in the target lighting area based on the frequency wave response points.

[0033] Optionally, calculating the harmonic distortion rate corresponding to the response points in the defect response segment includes:

[0034] Calculating the harmonic distortion rate corresponding to the response points in the defect response segment using the following formula:

[0035]

[0036] Among them, THD represents the harmonic distortion rate corresponding to the response points in the defect response segment, C represents the set highest harmonic order, v represents the harmonic order index, and A v represents the component amplitude of the v-th harmonic, and A1 represents the fundamental wave component amplitude.

[0037] Optionally, performing illumination compensation on the defect response segment based on the harmonic distortion rate to obtain an illumination compensation threshold includes:

[0038] Extracting high-frequency distortion components in the harmonic distortion rate;

[0039] Analyzing the illumination attenuation gradient corresponding to the defect response segment according to the high-frequency distortion components;

[0040] Dividing compensation cells in the defect response segment based on the illumination attenuation gradient;

[0041] Mapping illumination compensation coefficients corresponding to the compensation cells;

[0042] Performing illumination compensation on the defect response segment according to the illumination compensation coefficients to obtain an illumination compensation threshold.

[0043] Optionally, scheduling and grouping the explosion-proof single lamp controllers based on the illumination compensation threshold to generate a priority scheduling group list includes:

[0044] Querying the illumination regulation identifier corresponding to the illumination compensation threshold;

[0045] Analyzing the illumination compensation requirements corresponding to the illumination regulation identifier;

[0046] Dividing the dynamic scheduling levels corresponding to the explosion-proof single lamp controllers based on the illumination compensation requirements;

[0047] Extracting core scheduling nodes in the dynamic scheduling levels;

[0048] Generating a priority scheduling group list corresponding to the explosion-proof single lamp controllers based on the core scheduling nodes.

[0049] Optionally, performing power correction on the high-load nodes to obtain load correction parameters includes:

[0050] Analyzing the instantaneous power spectrum corresponding to the high-load nodes;

[0051] Extracting power feature clusters in the instantaneous power spectrum;

[0052] Analyzing the feature correlation index between features in the power feature clusters;

[0053] Calculating power compensation coefficients corresponding to the high-load nodes based on the feature correlation index;

[0054] Performing power correction on the high-load nodes based on the power compensation coefficients to obtain load correction parameters.

[0055] To solve the above problems, the present invention also provides an optimized scheduling system for an intelligent lighting explosion-proof single lamp controller, and the system includes:

[0056] A link recognition module, configured to obtain the deployment location corresponding to the explosion-proof single lamp controller, determine the target lighting area corresponding to the deployment location, collect ambient light data in the target lighting area, construct a light distribution map corresponding to the ambient light data, and identify the distribution control link corresponding to the explosion-proof single lamp controller based on the light distribution map;

[0057] A coefficient calculation module, configured to monitor the real-time energy consumption data in the distribution control link, perform load analysis on the real-time energy consumption data to obtain lighting load characteristics, and calculate the optimal dimming coefficient corresponding to the explosion-proof single lamp controller based on the lighting load characteristics;

[0058] A distortion rate calculation module, configured to collect the signal response frequency band in the target lighting area based on the optimal dimming coefficient, perform curve fitting on the signal response frequency band to generate a dimming response curve, identify the defective response segment in the dimming response curve, and calculate the harmonic distortion rate corresponding to the response points in the defective response segment;

[0059] A node recognition module, configured to perform light compensation on the defective response segment based on the harmonic distortion rate to obtain a light compensation threshold, perform scheduling grouping on the explosion-proof single lamp controller based on the light compensation threshold to generate a priority scheduling group list, and identify the high-load nodes in the priority scheduling group list;

[0060] A strategy formulation module, configured to perform power correction on the high-load nodes to obtain load correction parameters, collect the temperature change gradient in the lamp cavity corresponding to the explosion-proof single lamp controller in real time based on the load correction parameters, generate a temperature control instruction corresponding to the temperature change gradient, and formulate an optimized scheduling strategy for the explosion-proof single lamp controller based on the temperature control instruction.

[0061] Compared with the problems described in the background art, the present invention can accurately delimit the lighting range by obtaining the deployment location corresponding to the explosion-proof single lamp controller and determining the target lighting area corresponding to the deployment location, avoiding overlapping or omission of the lighting area and reducing energy waste. At the same time, it can realize refined and intelligent control of lighting, effectively improving the safety and energy-saving efficiency of the lighting system in a dangerous environment. The present invention can optimize the working mode of lighting equipment by monitoring the real-time energy consumption data in the distributed control link, achieving accurate energy conservation on the premise of meeting the lighting requirements, reducing unnecessary energy waste, and improving energy utilization efficiency. Further, based on the optimal dimming coefficient, the present invention collects the signal response frequency band in the target lighting area, can accurately master the interaction feedback between the lighting system and the environment, and can timely detect potential faults of lighting equipment, such as abnormal frequency bands indicating lamp aging or electrical problems, facilitating early maintenance. Further, based on the harmonic distortion rate, the present invention performs light compensation on the defect response section to obtain a light compensation threshold, which can effectively correct the light abnormality caused by harmonic interference, thereby accurately compensating for problems such as light intensity deviation and color temperature fluctuation, ensuring uniform and stable lighting. Finally, the present invention corrects the power of the high-load node to obtain a load correction parameter, which can significantly improve the operation stability of the explosion-proof lighting system, avoid the risk of equipment overload by dynamically adjusting the load parameters, and can achieve accurate energy consumption redistribution, optimizing the overall energy efficiency while ensuring the lighting quality of key areas. Therefore, the intelligent lighting explosion-proof single lamp controller optimization scheduling method and system provided by the embodiments of the present invention can improve the scheduling optimization of explosion-proof single lamps. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic flowchart of an intelligent lighting explosion-proof single lamp controller optimization scheduling method provided by an embodiment of the present invention;

[0063] Figure 2 It is a schematic architecture diagram of an intelligent lighting explosion-proof single lamp controller optimization scheduling method provided by an embodiment of the present invention;

[0064] Figure 3 It is a schematic module diagram of an intelligent lighting explosion-proof single lamp controller optimization scheduling system provided by an embodiment of the present invention.

[0065] The realization, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0067] The embodiments of the present application provide an optimized scheduling method for an intelligent lighting explosion-proof single lamp controller. The execution subject of the optimized scheduling method for the intelligent lighting explosion-proof single lamp controller includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiments of the present application. In other words, the optimized scheduling method for the intelligent lighting explosion-proof single lamp controller can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0068] Embodiment 1:

[0069] Referring to Figure 1 As shown, it is a schematic flowchart of an optimized scheduling method for an intelligent lighting explosion-proof single lamp controller provided by an embodiment of the present invention. In this embodiment, the optimized scheduling method for the intelligent lighting explosion-proof single lamp controller includes:

[0070] S1. Obtain the deployment location corresponding to the explosion-proof single lamp controller, determine the target lighting area corresponding to the deployment location, collect the ambient light data in the target lighting area, construct a light distribution map corresponding to the ambient light data, and identify the distribution control link corresponding to the explosion-proof single lamp controller based on the light distribution map.

[0071] By obtaining the deployment location corresponding to the explosion-proof single lamp controller and determining the target lighting area corresponding to the deployment location, the present invention can accurately delimit the lighting range, avoid overlapping or omission of the lighting area, and reduce energy waste; at the same time, it can achieve refined and intelligent control of lighting, effectively improving the safety and energy-saving efficiency of the lighting system in a dangerous environment.

[0072] Among them, the explosion-proof single-lamp controller refers to the core device used to control explosion-proof lighting equipment in dangerous environments such as flammable and explosive areas. It can achieve intelligent dimming, switch control, and status monitoring of explosion-proof single lamps according to environmental parameters and lighting requirements, ensuring safe and stable lighting through precise regulation and avoiding the risk of explosion caused by electrical sparks. For example, in a petrochemical plant area, the explosion-proof single-lamp controller can automatically adjust the brightness of explosion-proof lamps according to the concentration of combustible gases, personnel activities, etc., ensuring that the lighting can meet the operation requirements while reducing energy consumption. The deployment location refers to the installation location of the explosion-proof single-lamp controller in a specific scenario. The determination of this location needs to comprehensively consider factors such as the scope of the target lighting area, environmental characteristics, and equipment distribution. A reasonable deployment location can enable the controller to effectively cover and manage lighting equipment, achieving precise and efficient control. For example, in the coal mine underground roadway, according to the roadway orientation, bends, and the distribution of working areas, deploying the explosion-proof single-lamp controller at key nodes can ensure its effective regulation of surrounding explosion-proof lamps and guarantee underground lighting safety. The target lighting area refers to the specific spatial range responsible for lighting by the explosion-proof single-lamp controller. It is delimited based on the deployment location and environmental requirements, aiming to meet the lighting requirements for personnel operations and equipment operation in this area. For example, in the storage tank area of a gas station, the target lighting area covers the periphery of the storage tank and the inspection path, ensuring sufficient and safe lighting for staff during inspections while avoiding ineffective energy consumption. Optionally, the acquisition of the deployment location corresponding to the explosion-proof single-lamp controller can be achieved through GPS positioning technology. For example, using a GNSS module to collect the longitude and latitude coordinates of the device in real time to finally obtain the deployment location. The determination of the target lighting area corresponding to the deployment location can be achieved through light simulation analysis. For example, using DIALux software to calculate the light coverage range of the lamps to finally obtain the target lighting area.

[0073] Furthermore, by collecting the ambient light data in the target lighting area and constructing a light distribution map corresponding to the ambient light data, the present invention can reasonably plan the dimming strategy of the explosion-proof single-lamp controller, achieve lighting on demand, precisely meet the lighting requirements in different scenarios, and improve the intelligence and energy-saving level of the lighting system.

[0074] Among them, the ambient light data refers to various types of light-related information collected by sensors in the target lighting area, covering data such as light intensity, light direction, and spectral composition. For example, in a mine, the ambient light data can reflect the weakness of natural light in the tunnel, the direction of mining lamps, and the spectral distribution; in a petrochemical workshop, the light intensity in different areas can be monitored to provide basic information for lighting control; the light distribution map refers to a two-dimensional or three-dimensional graph generated based on the collected ambient light data using data processing and visualization technology, which displays the spatial distribution of light intensity in the target lighting area in an intuitive form. Areas of different colors or brightness correspond to different light intensity. Intensity value, for example, in a gas station, the light distribution map can clearly show the difference in light intensity in various places such as the tank area and the loading and unloading area, so that the staff can quickly understand the lighting conditions. Optionally, the collection of ambient light data in the target lighting area can be achieved through an optical sensor, such as: using the TSL2591 high dynamic range digital light sensor to perform real-time data collection, and finally obtain an accurate ambient light illumination value; the construction of the light distribution map corresponding to the ambient light data can be achieved through a geographic information system tool, such as: using the spatial analysis module of ArcGIS to grid the collected data and render a heat map, and finally obtain an intuitive light intensity distribution map.

[0075] Furthermore, based on the illumination distribution diagram, the present invention identifies the distributed control link corresponding to the explosion-proof single lamp controller, can optimize the working logic of the controller according to the illumination distribution characteristics, enable each controller to allocate lighting resources as needed, achieve precise dimming, and effectively reduce energy consumption.

[0076] Among them, the distributed control link refers to a set of signal transmission paths formed based on topological link relationships and used to control the working status of single explosion-proof lamps. It clarifies the entire process from the issuance of control instructions to the response of lamps, so that the system can accurately control various lighting equipment. For example, in complex mine lighting systems, the distributed control link can ensure that the instructions from the ground control center are accurately transmitted to the lamps in the corresponding lanes through controllers and transmission nodes at all levels, thereby realizing on-demand lighting.

[0077] As an embodiment of the present invention, identifying the distributed control link corresponding to the explosion-proof single lamp controller based on the light distribution map includes: parsing the brightness gradient data corresponding to the light distribution map; locating the light control area corresponding to the explosion-proof single lamp controller based on the brightness gradient data; collecting signal transmission nodes in the light control area; determining the topological link relationship corresponding to the signal transmission nodes; and identifying the distributed control link corresponding to the explosion-proof single lamp controller based on the topological link relationship.

[0078] Among them, the brightness gradient data refers to the degree and direction information of the change in illumination brightness between different positions in the illumination distribution map. It is obtained by analyzing the difference and change trend of the illumination intensity between adjacent regions, and can intuitively reflect the non-uniformity of the illumination distribution. For example, in a large petrochemical warehouse, by analyzing the brightness gradient data, the difference in illumination intensity between the deep part of the shelf and the passage can be found; the illumination regulation area refers to a specific spatial range where the explosion-proof single-lamp controller is responsible for adjusting the illumination. Based on the brightness gradient data, the areas where the illumination intensity needs to be adjusted can be located, and these areas are the illumination regulation areas. For example, at the turning point of a mine roadway, if the brightness gradient data shows insufficient illumination here, this area will be designated as an illumination regulation area and the brightness will be adjusted by the corresponding controller; the signal transmission node refers to a key position point in the lighting system for transmitting control signals and data information. It can be devices such as explosion-proof single-lamp controllers, sensors, and lamps. For example, in the lighting system of a gas station, the combustible gas concentration sensor, single-lamp controller, and lighting lamp are all signal transmission nodes, and these nodes are responsible for collecting data, transmitting instructions, and performing operations; the topological link relationship refers to the connection method and logical relationship between each signal transmission node. It describes how signals are transmitted between nodes and reflects the architecture form of the system. For example, in the factory lighting network, each explosion-proof single-lamp controller and lamp are connected by wired or wireless means, and the network structure formed by these connection relationships is the topological link relationship.

[0079] Furthermore, the analysis of the brightness gradient data corresponding to the illumination distribution map can be realized by computer vision algorithms. For example, the image gradient calculation method in the OpenCV library is used to process the illumination distribution map, and finally an accurate brightness gradient matrix is obtained; the positioning of the illumination regulation area corresponding to the explosion-proof single-lamp controller can be realized by the illumination intensity analysis method. For example, based on the preset illumination standard threshold, the effective illumination boundary is determined, and finally the illumination regulation range of the lamp is delimited; the collection of the signal transmission nodes in the illumination regulation area can be realized by network detection technology. For example, the Nmap network scanning tool is used to actively detect the network devices in the area, and finally the IP addresses and device information of all signal transmission nodes are obtained; the determination of the topological link relationship corresponding to the signal transmission node can be realized by network topology discovery technology. For example, based on the SNMP protocol, the device connection information is collected, and finally a complete network topology connection relationship diagram is generated; the identification of the distributed control link corresponding to the explosion-proof single-lamp controller can be realized by device communication protocol analysis. For example, the communication messages of the Modbus or DALI protocol are parsed, and finally the topological structure of the complete device control link is restored.

[0080] S2. Monitor the real-time energy consumption data in the distributed control link, perform load analysis on the real-time energy consumption data to obtain the lighting load characteristics, and calculate the optimal dimming coefficient corresponding to the explosion-proof single-lamp controller based on the lighting load characteristics.

[0081] By monitoring the real-time energy consumption data in the distributed control link, the present invention can optimize the working mode of lighting devices, achieve precise energy conservation, reduce unnecessary energy waste, and improve energy utilization efficiency on the premise of meeting lighting requirements.

[0082] Among them, the real-time energy consumption data refers to the power consumption-related data collected in real time at very short time intervals (such as second-level or millisecond-level) through devices such as smart meters and power sensors during the operation of the explosion-proof single-lamp control system. It covers parameters such as the instantaneous power, cumulative power consumption, current, and voltage of a single lamp or lighting area. For example, in a petrochemical workshop, the real-time energy consumption data can accurately reflect the power consumption of each explosion-proof single lamp at different times and the instant power consumption of the entire area lighting system. Optionally, the monitoring of the real-time energy consumption data in the distributed control link can be achieved through a smart meter acquisition system. For example, an RS485 bus is used to connect to a DL / T645 protocol meter to read the power consumption of each node in real time, and finally, energy consumption monitoring data accurate to the second level is obtained.

[0083] Specifically, to further intuitively understand the link architecture corresponding to the distributed control link in this application, reference can be made to Figure 2 the image shown, which is a schematic diagram of the link architecture provided by the present invention. It should be noted that in the present invention, Figure 2 the presented architecture schematic diagram is only used to show the sub-links corresponding to the distributed control link. Among them, the sub-links can be simply divided into Link 1 to Link 5. Link 1 includes loose power supply line connection ends and damaged power supply line connection ends. Link 2 includes damaged power supply lines and over-bent power supply lines. Link 3 includes damaged or aged power supply components, production quality problems, and harsh environmental conditions. Link 4 includes sudden voltage and current fluctuations and external power supply failures. Link 5 includes ineffective monitoring of power supply failures and control circuit failures. The control circuit failures can include poor contact, line short circuit, and unreasonable circuit design. Each sub-link directly affects the normal use conditions of the overall explosion-proof single lamp, and it is not limited to the relationship analysis of the sub-links in the distributed control link in actual different application scenarios.

[0084] Furthermore, by performing load analysis on the real-time energy consumption data, the present invention obtains lighting load characteristics, can accurately understand the power consumption law and load characteristics of the lighting system, clearly identify high-energy-consuming periods and regions, can detect abnormal power consumption situations, such as sudden power changes caused by equipment failures, quickly locate the fault points, and ensure the stable operation of the lighting system.

[0085] Among them, the lighting load characteristics refer to the electricity consumption laws and load characteristics of the lighting system summarized through in-depth analysis of the start-stop distribution data, which cover information such as peak and valley periods of equipment electricity consumption, average load, and load change trends.

[0086] As an embodiment of the present invention, the load analysis of the real-time energy consumption data to obtain the lighting load characteristics includes: extracting the lighting fluctuation signal in the real-time energy consumption data; fitting the independent energy consumption curve corresponding to the lighting fluctuation signal; identifying the lighting start-stop points in the independent energy consumption curve; analyzing the start-stop distribution data corresponding to the lighting start-stop points; and performing load analysis on the start-stop distribution data to obtain the lighting load characteristics.

[0087] Among them, the lighting fluctuation signal refers to the information of the fluctuation of the energy consumption value caused by the operation of lighting equipment such as switching and dimming or environmental changes in the real-time energy consumption data. For example, when the explosion-proof single-lamp controller automatically adjusts the brightness according to the entry and exit of personnel, the energy consumption data will produce corresponding fluctuations, and the signals formed by these fluctuations contain key clues about the operation status of the equipment and the changes in lighting requirements; the independent energy consumption curve refers to the curve drawn by mathematically fitting the extracted lighting fluctuation signal, which reflects the change trend of the energy consumption of a single lighting equipment or area over time. With time as the horizontal axis and the energy consumption value as the vertical axis, the discrete energy consumption data is transformed into a continuous and intuitive curve form, which is convenient for clearly observing the energy consumption change law, such as the energy consumption difference curve performance between the low peak period at night and the high peak period during the day; the lighting start-stop point refers to the characteristic point on the independent energy consumption curve that can clearly identify the opening or closing moment of the lighting equipment. When the lighting equipment is turned on, there will be an obvious upward inflection point on the energy consumption curve; when it is turned off, there will be a downward inflection point on the energy consumption curve, and these inflection points are the lighting start-stop points; the start-stop distribution data refers to the data set obtained by statistically sorting out the relevant information of the lighting start-stop points, including start-stop time, interval duration, start-stop times, etc. For example, in the lighting system of the mine roadway, by counting the start-stop times and continuous durations of the lighting at different times, the correlation between the work shift and the lighting use can be analyzed.

[0088] Further, the extraction of the lighting fluctuation signal from the real-time energy consumption data can be achieved through signal processing algorithms. For example, wavelet transform is used for time-frequency analysis to separate the lighting feature signal, and finally a pure lighting fluctuation signal after denoising is obtained; the fitting of the independent energy consumption curve corresponding to the lighting fluctuation signal can be achieved through regression analysis methods. For example, the least squares method is used to construct an energy consumption-time function model, and finally a fitting curve representing the energy consumption change law of a single lamp is obtained; the identification of the lighting start-stop points in the independent energy consumption curve can be achieved through mutation detection algorithms. For example, the CUSUM (cumulative sum) control chart is applied to detect the step change points of the energy consumption curve, and finally the opening and closing moments of the lamps are accurately located; the analysis of the start-stop distribution data corresponding to the lighting start-stop points can be achieved through time series analysis methods. For example, the ARIMA model is used to perform periodic decomposition on the start-stop moments, and finally the spatio-temporal distribution law of the lamp operation is obtained; the load analysis of the start-stop distribution data can be achieved through feature engineering methods. For example, key load characteristic parameters are extracted through principal component analysis (PCA), and finally a lighting load feature vector including peak power and average operation duration is formed.

[0089] Based on the lighting load characteristics, the present invention calculates the optimal dimming coefficient corresponding to the explosion-proof single lamp controller, which can accurately adapt to the actual lighting requirements in different scenarios, and can reduce the frequent start-stop and overuse of lamps, extend the service life of lighting equipment, and reduce maintenance costs.

[0090] Among them, the optimal dimming coefficient is a key parameter used to guide the explosion-proof single lamp controller to adjust the light brightness, which comprehensively considers the energy consumption fluctuation of lighting equipment within a certain period of time (reflected by the relationship between real-time power and average power) and the start-stop time distribution characteristics of lighting equipment.

[0091] As an embodiment of the present invention, the calculation of the optimal dimming coefficient corresponding to the explosion-proof single lamp controller based on the lighting load characteristics includes:

[0092] The optimal dimming coefficient corresponding to the explosion-proof single lamp controller is calculated using the following formula:

[0093]

[0094] Among them, K opt represents the optimal dimming coefficient corresponding to the explosion-proof single lamp controller, t1 and t2 respectively represent the start time and end time of the statistical period, n represents the total number of lighting equipment, i represents the number index of lighting equipment, P i (t) represents the real-time power of the i-th lighting equipment at time t, represents the average power of all lighting equipment within the statistical period, m represents the total number of times the lighting equipment is turned on, j represents the number index of the times the lighting equipment is turned on, Ton,j represents the duration of the jth lighting device being turned on, represents the average duration of lighting device on in the statistical period, l represents the total number of times lighting device is turned off, k represents the number of times lighting device is turned off, T off,k represents the duration of the kth lighting device being turned off, Indicates the average duration of lighting equipment being turned off during the statistical period.

[0095] In detail, the statistical period refers to a period of time selected when performing lighting load characteristic analysis. During this period, relevant lighting data (such as the power, start and stop time of lighting equipment, etc.) are collected and counted, which is the time basis for calculating the optimal dimming coefficient. Different statistical periods may obtain different lighting load characteristics, which in turn affect the calculation result of the optimal dimming coefficient. For example, the statistical period can be set according to different time lengths such as one day, one week or one month to meet different lighting usage law analysis needs; the lighting equipment refers to a device used to provide lighting functions in an explosion-proof environment (such as a place with flammable and explosive gases and dust). Specifically in this context, it can be understood as an explosion-proof single lamp. These lamps need to have explosion-proof functions to prevent the electric sparks generated during the working process from causing explosion hazards. These lamps are controlled by an explosion-proof single lamp controller to meet the lighting needs of the place by adjusting the brightness, etc.; the real-time power refers to the actual electric power consumed by the i-th lighting device at a specific time t, and the unit is usually watt (W). It reflects the instantaneous energy consumption of the lighting equipment during operation, and will change with the opening, closing, dimming and other operations of the lighting equipment and environmental factors. By monitoring the real-time power, the working status of the lighting equipment can be understood.

[0096] S3. Based on the optimal dimming coefficient, the signal response frequency band in the target lighting area is collected, and curve fitting is performed on the signal response frequency band to generate a dimming response curve, and the defective response segment in the dimming response curve is identified, and the harmonic distortion rate corresponding to the response point in the defective response segment is calculated.

[0097] The present invention collects the signal response frequency band in the target lighting area based on the optimal dimming coefficient, can accurately grasp the interactive feedback between the lighting system and the environment, and can promptly discover potential faults of lighting equipment. For example, abnormal frequency bands indicate lamp aging or electrical problems, which is convenient for early maintenance.

[0098] Among them, the signal response frequency band refers to a set of light frequency bands determined based on the frequency wave response point, which can reflect the comprehensive optical response characteristics of the target lighting area. It integrates information such as the luminous characteristics of lighting equipment in the area and the reflection characteristics of environmental objects, and is the lighting system for further optimizing dimming strategies and diagnosing potential problems.

[0099] As an embodiment of the present invention, collecting the signal response frequency band in the target lighting area based on the optimal dimming coefficient includes: determining the spectral sampling interval corresponding to the target lighting area according to the optimal dimming coefficient; scanning the band reflection intensity corresponding to each frequency band in the spectral sampling interval; screening the signal frequency bands in the band reflection intensity that exceed a preset threshold; extracting the frequency wave response points in the signal frequency bands; and collecting the signal response frequency band in the target lighting area based on the frequency wave response points.

[0100] Among them, the spectral sampling interval refers to a spectral range determined based on the optimal dimming coefficient. The dimming coefficient reflects the working state and lighting requirements of the lighting device. Based on this, the defined spectral sampling interval covers the spectral range that may be involved in the light emitted by the lighting device in the target lighting area and the ambient reflected light. The band reflection intensity refers to the intensity of the light reflected back after the light of different frequency bands in the spectral sampling interval irradiates the objects in the target lighting area, which measures the reflection of light in a specific frequency band in this area. Different objects and environments have different reflection abilities for different frequency bands of the incident light. This intensity data is an important basis for judging the frequency band characteristics of light. The signal frequency band refers to the range of the light frequency band selected from the band reflection intensity data, where the reflection intensity exceeds the preset threshold. The preset threshold is set according to the application scenario of the lighting system, the characteristics of the lighting device, etc. The frequency band that exceeds this threshold means that its reflection situation has special significance in this lighting environment and may be closely related to the lighting effect, environmental characteristics, etc. The frequency wave response point refers to a frequency-response characteristic point with specific physical significance in the signal frequency band, which reflects the response characteristics of the light in this frequency band when interacting with objects and the environment in the target lighting area. For example, at a specific frequency wave response point, it may correspond to the absorption or reflection peak of a specific substance in the environment for the lighting light, which is a key node for in-depth analysis of the signal response in the lighting area.

[0101] Further, the determination of the spectral sampling interval corresponding to the target illumination area can be achieved by a spectral analyzer. For example, the Ocean Insight HDX spectrometer is used to collect the visible light band of 400 - 700 nm, and finally a standardized spectral sampling interval is obtained; the scanning of the band reflection intensity corresponding to each frequency band in the spectral sampling interval can be achieved by a spectrophotometer. For example, the Shimadzu UV-2600 instrument is used for multi-band reflectivity measurement, and finally the accurate reflection intensity values of each frequency band are obtained; the screening of the signal frequency bands exceeding the preset threshold in the band reflection intensity can be achieved by digital signal processing technology. For example, the Butterworth filter is applied for band threshold screening, and finally the effective reflection signal frequency bands are extracted; the extraction of the frequency response points in the signal frequency bands can be achieved by a peak detection algorithm. For example, the Savitzky-Golay filter is used in combination with local extreme value detection, and finally the characteristic response points of each frequency band are located; the collection of the signal response frequency bands in the target illumination area can be achieved by a multi-spectral imaging system. For example, the Specim IQ intelligent spectral camera is used for area scanning, and finally a complete signal response frequency band distribution map is obtained.

[0102] The present invention generates a dimming response curve by curve fitting the signal response frequency bands, and identifies the defective response segments in the dimming response curve, which can intuitively present the dimming characteristics of the lighting system, accurately locate abnormal areas such as sudden brightness changes and slow responses during the dimming process, and timely discover problems such as equipment performance degradation and control algorithm defects.

[0103] Among them, the dimming response curve refers to a curve obtained by curve fitting the signal response frequency bands, with the dimming parameter (such as the dimming coefficient) as the horizontal axis and the corresponding response of the lighting system (such as brightness, light frequency band change, etc.) as the vertical axis, which shows the change law of the lighting system response during the dimming process. For example, when adjusting the brightness of an explosion-proof lamp, different dimming coefficients are used as inputs, and the corresponding brightness values are recorded, and the connected curve is the dimming response curve; the defective response segment refers to the part of the dimming response curve that does not conform to the expected dimming law. For example, under normal circumstances, the dimming should change linearly, but there is a sudden large fluctuation in brightness in a certain segment of the curve, or the brightness does not change significantly when the dimming coefficient changes, and this segment is the defective response segment, which reflects that there is an abnormality in the dimming of the lighting system and needs to be checked and repaired. Optionally, the curve fitting of the signal response frequency bands can be achieved by a non-linear regression algorithm. For example, the Levenberg-Marquardt algorithm is used to fit the spectral data with a Gaussian function, and finally a smooth dimming response curve is obtained; the identification of the defective response segments in the dimming response curve can be achieved by an anomaly detection algorithm. For example, the DBSCAN clustering algorithm is used to detect the outliers in the curve, and finally the defective frequency band intervals with response anomalies are located.

[0104] Furthermore, by calculating the harmonic distortion rate corresponding to the response points in the defect response section, the present invention can quantitatively evaluate the distortion degree of the current and voltage waveforms of the lighting system, accurately identify power quality problems caused by equipment aging, line faults or improper control, timely detect potential safety hazards, and avoid serious accidents such as equipment overheating, shortened lifespan or even explosion caused by harmonic interference.

[0105] Among them, the harmonic distortion rate is an index that measures the degree of deviation of an electrical signal (such as current and voltage) from a sine wave. In a lighting system, an ideal electrical signal should be a sine wave, but in actual operation, harmonics will be generated due to equipment characteristics, load changes, etc. Its harmonic distortion rate reflects the proportion of harmonic content in the electrical signal in the form of a percentage by calculating the relationship between the amplitudes of each harmonic component and the amplitude of the fundamental component, and reflects the distortion degree of the signal waveform, which is an important basis for evaluating power quality.

[0106] As an embodiment of the present invention, calculating the harmonic distortion rate corresponding to the response points in the defect response section includes:

[0107] Calculating the harmonic distortion rate corresponding to the response points in the defect response section using the following formula:

[0108]

[0109] Among them, THD represents the harmonic distortion rate corresponding to the response points in the defect response section, C represents the set highest harmonic order, v represents the harmonic order index, A v represents the amplitude of the v-th harmonic component, and A1 represents the amplitude of the fundamental component.

[0110] Specifically, the highest harmonic order is a boundary value set in harmonic analysis, which is used to limit the range of harmonic orders involved in calculating the harmonic distortion rate. Since the actual number of harmonic orders can theoretically be infinite, but the influence of higher-order harmonics on signal distortion gradually decreases, and considering calculation complexity and practical significance, a reasonable highest harmonic order will be set, such as the common 25th, 50th, etc., to determine the harmonic range involved in the calculation; the component amplitude refers to the amplitude of the harmonic signal at a specific harmonic order (v-th), and in an electrical signal, harmonics of different harmonic orders have their own energy intensities, and this amplitude reflects the proportion of the corresponding harmonic in the entire signal; the fundamental component amplitude refers to the amplitude of the fundamental component in the electrical signal. The fundamental frequency is usually the standard frequency of the power supply (such as the mains 50Hz), which is the basic component of the electrical signal, and its amplitude is the reference benchmark when calculating the harmonic distortion rate. The amplitudes of other harmonic components are compared with it to obtain the proportion of harmonics relative to the fundamental wave, reflecting the distortion degree of the signal.

[0111] S4. Based on the harmonic distortion rate, perform light compensation on the defect response segment to obtain a light compensation threshold. Based on the light compensation threshold, schedule and group the explosion-proof single lamp controllers to generate a priority scheduling group list, and identify the high-load nodes in the priority scheduling group list.

[0112] Based on the harmonic distortion rate, the present invention performs light compensation on the defect response segment to obtain a light compensation threshold, which can effectively correct the abnormal light caused by harmonic interference, thereby accurately compensating for problems such as light intensity deviation and color temperature fluctuation, and ensuring uniform and stable lighting.

[0113] Among them, the light compensation threshold refers to a limit value of light intensity or related parameters obtained after performing light compensation operations on each compensation cell of the defect response segment. It marks the light standard reached after compensation, ensures that the light in this area meets the normal use requirements, avoids the adverse effects of light caused by harmonics, and ensures stable and appropriate light output of the lighting system.

[0114] As an embodiment of the present invention, performing light compensation on the defect response segment based on the harmonic distortion rate to obtain a light compensation threshold includes: extracting the high-frequency distortion component in the harmonic distortion rate; analyzing the light attenuation gradient corresponding to the defect response segment according to the high-frequency distortion component; dividing the compensation cells in the defect response segment based on the light attenuation gradient; mapping the light compensation coefficient corresponding to the compensation cells; and performing light compensation on the defect response segment according to the light compensation coefficient to obtain a light compensation threshold.

[0115] Among them, the high-frequency distortion component refers to the harmonic components with relatively high frequencies in the harmonic distortion rate. In the power system, high-order harmonics (generally with frequencies several times higher than the fundamental frequency) can cause power quality problems. For example, in lighting scenarios, the high-frequency distortion components can interfere with the normal operation of lighting devices and affect the lighting stability, such as causing flickering, uneven brightness, etc.; the lighting attenuation gradient refers to the rate of change of the lighting intensity with space or time within the defect response section obtained based on the analysis of the high-frequency distortion components. For example, in a specific area, due to the interference of high-frequency harmonics, the lighting intensity gradually decreases within a certain distance or time, and the degree of this decrease is the lighting attenuation gradient, which is used to measure the trend of the lighting deteriorating due to harmonic influence; the compensation cell refers to the small unit areas obtained by dividing the defect response section according to factors such as the lighting attenuation gradient. By dividing the compensation cells, the lighting problems in different local areas can be processed more precisely, facilitating the subsequent determination of appropriate lighting compensation strategies for each small area respectively to achieve precise compensation; the lighting compensation coefficient refers to a value set for each compensation cell, which is used to determine the amount of lighting that needs to be increased or adjusted within the cell. It comprehensively considers factors such as the lighting attenuation situation and the degree of high-frequency distortion influence at the location of the cell, and is the quantitative basis for achieving precise lighting compensation. Different cells may correspond to different lighting compensation coefficients.

[0116] Furthermore, the extraction of the high-frequency distortion components in the harmonic distortion rate can be achieved through digital signal processing techniques. For example, the fast Fourier transform (FFT) combined with a Butterworth band-pass filter is used to separate the high-frequency components, and finally, high-frequency distortion components above 5 kHz are obtained; the analysis of the lighting attenuation gradient corresponding to the defect response section can be achieved through image processing algorithms. For example, the Sobel operator is used to calculate the rate of change of the light intensity gradient in the defect area, and finally, the attenuation gradient matrix is quantified; the division of the compensation cells in the defect response section can be achieved through a grid segmentation method. For example, the Voronoi diagram algorithm is applied to automatically generate the optimal compensation cells according to the attenuation gradient, and finally, uniformly distributed compensation cells are obtained; the mapping of the lighting compensation coefficients corresponding to the compensation cells can be achieved through machine learning methods. For example, the XGBoost regression model is used to establish the gradient-compensation coefficient mapping relationship, and finally, the precise compensation coefficients of each cell are output; the lighting compensation for the defect response section can be achieved through an adaptive control algorithm. For example, based on a PID controller, the light output power of the compensation cells is dynamically adjusted, and finally, the lighting compensation threshold meeting the preset standard is achieved.

[0117] Based on the lighting compensation threshold, the present invention schedules and groups the explosion-proof single-lamp controllers to generate a priority scheduling group list, which can significantly improve the response efficiency and energy consumption optimization level of the lighting system, ensure that key areas obtain precise lighting compensation first, and at the same time achieve a reasonable allocation of equipment resources.

[0118] Among them, the priority scheduling group column refers to the sequence formed by arranging and grouping explosion-proof single-lamp controllers in an orderly manner according to factors such as the lighting compensation requirements, the scheduling levels of each controller, and the core scheduling nodes. In this sequence, the regulation priorities of each controller are clarified, and the controllers with urgent lighting compensation requirements and in important areas are operated preferentially to ensure the efficient and reasonable operation of the lighting system and meet the lighting requirements of different areas.

[0119] In an embodiment of the invention, scheduling and grouping the explosion-proof single-lamp controllers based on the lighting compensation threshold to generate a priority scheduling group column includes: querying the lighting regulation identifier corresponding to the lighting compensation threshold; analyzing the lighting compensation requirements corresponding to the lighting regulation identifier; dividing the dynamic scheduling levels corresponding to the explosion-proof single-lamp controllers based on the lighting compensation requirements; extracting the core scheduling nodes in the dynamic scheduling levels; and generating the priority scheduling group column corresponding to the explosion-proof single-lamp controllers based on the core scheduling nodes.

[0120] Among them, the lighting regulation identifier refers to a specific symbol, code, or parameter set associated with the lighting compensation threshold, which represents information such as the lighting adjustment rules and directions corresponding to the threshold, and is used to indicate how the explosion-proof single-lamp controller should adjust the lighting. It is the key link connecting the lighting compensation threshold and subsequent regulation operations and can intuitively convey the specific requirements of lighting regulation; the lighting compensation requirement refers to the specific need for lighting compensation in the target area reflected by the lighting regulation identifier, including requirements for the amplitude of increasing or decreasing the lighting intensity, color temperature adjustment requirements, improvement of lighting uniformity, etc.; the dynamic scheduling level refers to dividing the explosion-proof single-lamp controllers into different scheduling levels according to the lighting compensation requirements. These levels are not fixed and will be dynamically adjusted according to factors such as lighting conditions and equipment status. Different levels represent different regulation priorities and response speeds. For example, the controllers near the key operation area are at a higher level and can obtain resource allocation preferentially; the core scheduling node refers to the explosion-proof single-lamp controller or control unit that plays a key role in the dynamic scheduling level. It undertakes important regulation tasks, is the hub of the entire scheduling system, coordinates the work of surrounding controllers, and has a significant impact on the lighting compensation effect. Usually, the controllers located in important areas, with strong control capabilities, or with special functions are selected as the core scheduling nodes.

[0121] Further, the query of the lighting regulation identifier corresponding to the lighting compensation threshold can be achieved through key-value database query technology. For example, the HGET command of Redis is used to retrieve the pre-stored regulation identifier with the compensation threshold as the key, and finally the corresponding lighting regulation coding identifier is obtained. The analysis of the lighting compensation requirements corresponding to the lighting regulation identifier can be achieved through a decision tree classification algorithm. For example, the C4.5 algorithm is used to analyze the mapping relationship between the identifier features and the compensation requirements, and finally the quantified compensation requirement level is output. The division of the dynamic scheduling levels corresponding to the explosion-proof single-lamp controller can be achieved through a clustering analysis method. For example, the K-means++ algorithm is used to perform multi-dimensional clustering based on the device location and compensation requirements, and finally a differentiated three-level scheduling level is formed. The extraction of the core scheduling nodes in the dynamic scheduling levels can be achieved through a graph theory centrality algorithm. For example, based on the PageRank algorithm, the influence score of the nodes in the network topology is calculated, and finally the core scheduling nodes with the highest connectivity are selected. The generation of the priority scheduling group list corresponding to the explosion-proof single-lamp controller can be achieved through a multi-objective optimization algorithm. For example, the NSGA-II algorithm is used to balance the response time and energy consumption indicators, and finally the device scheduling sequence sorted by priority is output.

[0122] By identifying the high-load nodes in the priority scheduling group list, the present invention can specifically optimize the scheduling strategy, reasonably allocate control tasks, balance the system load, improve the overall operation stability and reliability, and can also provide accurate basis for equipment maintenance and upgrade, extend the service life of the equipment, reduce the operation and maintenance cost, and ensure the continuous and efficient operation of the lighting system.

[0123] Among them, the high-load node refers to an explosion-proof single-lamp controller or related equipment node in the priority scheduling group list that undertakes control tasks, data processing volume, energy consumption, etc. exceeding its rated working ability or the system average load level. Such nodes usually slow down the operation speed, have abnormal heat generation, response delay, etc. due to being responsible for lighting regulation in key areas, connecting a large number of lighting devices, or unreasonable scheduling and distribution. They are the objects that need to be monitored and optimized key points in the system operation. Optionally, the identification of the high-load nodes in the priority scheduling group list can be achieved through a load balancing analysis algorithm. For example, the EWMA (Exponentially Weighted Moving Average) algorithm is applied to calculate the weighted average of the historical loads of each node, and finally the nodes that continuously exceed the threshold are selected and marked as high-load nodes.

[0124] S5. Perform power correction on the high-load nodes to obtain load correction parameters. Based on the load correction parameters, continuously collect the temperature change gradient in the lamp cavity corresponding to the explosion-proof single-lamp controller, and generate a temperature regulation instruction corresponding to the temperature change gradient. Based on the temperature regulation instruction, formulate an optimized scheduling strategy for the explosion-proof single-lamp controller.

[0125] By performing power correction on the high-load node, the load correction parameters are obtained, which can significantly improve the operating stability of the explosion-proof lighting system. By dynamically adjusting the load parameters, the risk of equipment overload is avoided. It can achieve precise energy consumption redistribution, optimize the overall energy efficiency while ensuring the lighting quality in key areas.

[0126] Among them, the load correction parameters refer to a set of parameters used to describe the load state of the node after power correction for the high-load node. These parameters include the corrected power value, load rate, working current, etc.

[0127] As an embodiment of the present invention, performing power correction on the high-load node to obtain load correction parameters includes: analyzing the instantaneous power spectrum corresponding to the high-load node; extracting the power feature clusters in the instantaneous power spectrum; analyzing the feature correlation index between the features in the power feature clusters; calculating the power compensation coefficient corresponding to the high-load node based on the feature correlation index; and performing power correction on the high-load node based on the power compensation coefficient to obtain load correction parameters.

[0128] Among them, the instantaneous power spectrum refers to the spectrum of the power distribution of the high-load node over frequency at a very short instant. With frequency as the horizontal axis and power amplitude as the vertical axis, it intuitively presents the power energy distribution of the node at that instant. By analyzing the instantaneous power spectrum, the power consumption characteristics of the node at different frequencies can be understood, and abnormal power fluctuation frequency bands can be discovered. It is the basic data for studying the power characteristics of high-load nodes. The power feature clusters refer to the set of power data with similar or related characteristics extracted from the instantaneous power spectrum. These data sets reflect specific patterns or rules in the power consumption of the high-load node. For example, the area where power is concentrated within a specific frequency band, or the combination of power data with similar change trends, which helps to summarize and analyze the power consumption behavior of the node. The feature correlation index refers to a quantitative index used to measure the tightness of the mutual relationship between the power features in the power feature clusters. It is obtained through mathematical calculations, and the numerical value reflects the correlation strength between different power features, such as positive correlation, negative correlation, or no correlation. The feature correlation index can help identify the internal connection between power features. The power compensation coefficient refers to the parameter used to adjust the power of the high-load node calculated based on the feature correlation index. It comprehensively considers the correlation of the node power features and is calculated through a specific algorithm. It is used to determine the power ratio that needs to be increased or decreased when performing power correction on the high-load node to achieve precise control of the node power.

[0129] Further, the parsing of the instantaneous power spectrum corresponding to the high-load node can be achieved through spectrum analysis techniques. For example, short-time Fourier transform (STFT) is used to perform time-frequency analysis on the real-time power signal, and finally an accurate instantaneous power spectrum distribution is obtained. The extraction of the power feature clusters in the instantaneous power spectrum can be achieved through clustering algorithms. For example, the DBSCAN density clustering algorithm is used to identify the high-density energy regions in the power spectrum, and finally the power clusters with significant features are separated. The analysis of the feature correlation index between the features in the power feature clusters can be achieved through correlation analysis methods. For example, the Pearson correlation coefficient is used to calculate the statistical correlation degree between the feature clusters, and finally the feature correlation index in the range of 0-1 is obtained by quantification. The calculation of the power compensation coefficient corresponding to the high-load node can be achieved through a regression prediction model. For example, a mapping relationship between the load characteristics and the compensation requirements is established based on the XGBoost algorithm, and finally a dynamically adjusted power compensation coefficient is output. The power correction of the high-load node can be achieved through an adaptive control algorithm. For example, a fuzzy PID controller is applied to adjust the output power in real time according to the compensation coefficient, and finally an optimal set of load correction parameters is generated.

[0130] Based on the load correction parameters, the present invention can collect the temperature change gradient in the lamp cavity corresponding to the explosion-proof single-lamp controller in real time and generate a temperature control instruction corresponding to the temperature change gradient, which can realize the active thermal management of the explosion-proof lighting system, effectively prevent the risk of equipment overheating, and can accurately adjust the working state of the lamp by dynamically generating the temperature control instruction, optimizing the heat dissipation efficiency while ensuring the lighting performance.

[0131] Among them, the temperature change gradient refers to the rate and trend of temperature change in the lamp cavity corresponding to the explosion-proof single lamp controller over time or with the change of spatial position. It is measured by the temperature change value per unit time (such as °C / min) or the temperature difference per unit spatial distance (such as °C / m), reflecting the severity of temperature change in different regions or at different times of the lamp cavity. It is a key indicator for judging whether there is an overheating risk in the lamp and whether the heat distribution is uniform; the temperature control instruction refers to a control command generated based on the temperature change gradient, used to adjust the operating state of the lamp to achieve temperature control. This instruction can control the explosion-proof single lamp controller to adjust the lamp power, turn on or enhance the heat dissipation device (such as fan speed adjustment, heat sink angle adjustment), or link with an external cooling system, etc., aiming to maintain the temperature of the lamp cavity within a safe and reasonable range, ensure the stable operation of the lighting equipment. Optionally, the real-time acquisition of the temperature change gradient in the lamp cavity corresponding to the explosion-proof single lamp controller can be achieved through distributed temperature sensing technology, such as: using a DS18B20 digital temperature sensor array for multi-point synchronous sampling to finally obtain the cavity temperature gradient distribution with spatio-temporal characteristics; the generation of the temperature control instruction corresponding to the temperature change gradient can be achieved through a fuzzy control algorithm, such as: establishing a mapping rule between the temperature gradient and the control parameters based on a Mamdani-type fuzzy inference system, and finally outputting a temperature control instruction set including fan speed and dimming level.

[0132] Furthermore, based on the temperature control instruction, the present invention formulates an optimized scheduling strategy corresponding to the explosion-proof single lamp controller, which can realize the intelligent thermal balance management of the explosion-proof lighting system, effectively improve the reliability of equipment operation, significantly reduce the overheating risk while ensuring the lighting quality, and extend the service life of the equipment.

[0133] Among them, the optimized scheduling strategy refers to a set of dynamic and intelligent control schemes formulated based on temperature control instructions, combined with the operating characteristics of explosion-proof single-lamp controllers and lamps. The purpose of this strategy is to ensure equipment safety, reduce energy consumption, and improve overall performance while meeting usage requirements by reasonably allocating system resources and precisely adjusting the operating state of lamps. It covers various optimization arrangements such as lamp power adjustment, switch control, and linkage of heat dissipation devices, and will be flexibly adjusted according to real-time temperature data and preset thresholds. For example, when the temperature change gradient in the lamp cavity of the explosion-proof single-lamp exceeds the preset value and triggers a temperature control instruction, the optimized scheduling strategy will first reduce the power of the lamp. If the temperature continues to rise, an external strong heat dissipation device will be further linked and started. At the same time, according to the lighting requirements of the entire area, the brightness of surrounding lamps will be dynamically adjusted to avoid insufficient local illumination caused by a sudden drop in the power of a single lamp while ensuring the overall lighting effect. Optionally, the optimized scheduling strategy corresponding to the explosion-proof single-lamp controller can be implemented through a multi-objective optimization algorithm. For example, the NSGA-III algorithm is used to simultaneously optimize three objective functions: temperature control, energy consumption management, and lighting quality, and finally generate a Pareto optimal solution set as the scheduling strategy.

[0134] Compared with the problems described in the background art, the present invention can accurately delimit the lighting range by obtaining the deployment location corresponding to the explosion-proof single lamp controller and determining the target lighting area corresponding to the deployment location, avoiding overlapping or omission of the lighting area and reducing energy waste. At the same time, it can realize refined and intelligent control of lighting, effectively improving the safety and energy-saving efficiency of the lighting system in a dangerous environment. By monitoring the real-time energy consumption data in the distributed control link, the present invention can optimize the working mode of lighting equipment, achieve accurate energy conservation on the premise of meeting the lighting requirements, reduce unnecessary energy waste, and improve energy utilization efficiency. Further, based on the optimal dimming coefficient, the present invention collects the signal response frequency band in the target lighting area, can accurately grasp the interaction feedback between the lighting system and the environment, and can timely detect potential faults of lighting equipment, such as abnormal frequency bands indicating lamp aging or electrical problems, which is convenient for early maintenance. Further, based on the harmonic distortion rate, the present invention performs light compensation on the defect response section to obtain a light compensation threshold, which can effectively correct the light abnormality caused by harmonic interference, thereby accurately compensating for problems such as light intensity deviation and color temperature fluctuation, and ensuring uniform and stable lighting. Finally, by correcting the power of the high-load node to obtain a load correction parameter, the present invention can significantly improve the operation stability of the explosion-proof lighting system, avoid the risk of equipment overload by dynamically adjusting the load parameters, and can achieve accurate energy consumption redistribution, optimizing the overall energy efficiency while ensuring the lighting quality of key areas. Therefore, the intelligent lighting explosion-proof single lamp controller optimization scheduling method and system provided by the embodiments of the present invention can improve the scheduling optimization of explosion-proof single lamps. The intelligent lighting explosion-proof single lamp controller optimization scheduling method and system provided by the embodiments of the present invention can improve the scheduling optimization of explosion-proof single lamps.

[0135] Embodiment 2:

[0136] As Figure 3 shown, it is a functional module diagram of an intelligent lighting explosion-proof single lamp controller optimization scheduling system of the present invention.

[0137] The intelligent lighting explosion-proof single lamp controller optimization scheduling system 200 of the present invention can be installed in an electronic device. According to the functions achieved, the intelligent lighting explosion-proof single lamp controller optimization scheduling system can include a link identification module 201, a coefficient calculation module 202, a distortion rate calculation module 203, a node identification module 204, and a strategy formulation module 205. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0138] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0139] The link identification module 201 is configured to obtain the deployment location corresponding to the explosion-proof single lamp controller, determine the target lighting area corresponding to the deployment location, collect the ambient light data in the target lighting area, construct a light distribution map corresponding to the ambient light data, and identify the distribution control link corresponding to the explosion-proof single lamp controller based on the light distribution map;

[0140] The coefficient calculation module 202 is configured to monitor the real-time energy consumption data in the distribution control link, perform load analysis on the real-time energy consumption data to obtain lighting load characteristics, and calculate the optimal dimming coefficient corresponding to the explosion-proof single lamp controller based on the lighting load characteristics;

[0141] The distortion rate calculation module 203 is configured to collect the signal response frequency band in the target lighting area based on the optimal dimming coefficient, perform curve fitting on the signal response frequency band to generate a dimming response curve, identify the defective response segment in the dimming response curve, and calculate the harmonic distortion rate corresponding to the response points in the defective response segment;

[0142] The node identification module 204 is configured to perform light compensation on the defective response segment based on the harmonic distortion rate to obtain a light compensation threshold, perform scheduling grouping on the explosion-proof single lamp controller based on the light compensation threshold to generate a priority scheduling group list, and identify the high-load nodes in the priority scheduling group list;

[0143] The policy formulation module 205 is configured to perform power correction on the high-load nodes to obtain load correction parameters, collect the temperature change gradient in the lamp cavity corresponding to the explosion-proof single lamp controller in real time based on the load correction parameters, generate a temperature control instruction corresponding to the temperature change gradient, and formulate an optimal scheduling policy corresponding to the explosion-proof single lamp controller based on the temperature control instruction.

[0144] Specifically, each module in the intelligent lighting explosion-proof single lamp controller optimization scheduling system 200 in the embodiments of the present invention adopts the same technical means as those in the Figure 1 intelligent lighting explosion-proof single lamp controller optimization scheduling method described above, and can produce the same technical effects, which will not be elaborated here.

[0145] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An optimized scheduling method for an intelligent lighting explosion-proof single lamp controller, characterized in that, The method includes: Obtaining the deployment location corresponding to the explosion-proof single lamp controller, determining the target lighting area corresponding to the deployment location, collecting the ambient light data in the target lighting area, constructing a light intensity distribution map corresponding to the ambient light data, and identifying the distribution control link corresponding to the explosion-proof single lamp controller based on the light intensity distribution map; Monitoring the real-time energy consumption data in the distribution control link, performing a load analysis on the real-time energy consumption data to obtain lighting load characteristics, and calculating the optimal dimming coefficient corresponding to the explosion-proof single lamp controller based on the lighting load characteristics; Based on the optimal dimming coefficient, collecting the signal response frequency band in the target lighting area, performing curve fitting on the signal response frequency band to generate a dimming response curve, identifying the defective response segment in the dimming response curve, and calculating the harmonic distortion rate corresponding to the response points in the defective response segment; Based on the harmonic distortion rate, performing light compensation on the defective response segment to obtain a light compensation threshold, and scheduling and grouping the explosion-proof single lamp controllers based on the light compensation threshold to generate a priority scheduling group list, and identifying the high-load nodes in the priority scheduling group list; Performing power correction on the high-load nodes to obtain load correction parameters, based on the load correction parameters, collecting the temperature change gradient in the lamp cavity corresponding to the explosion-proof single lamp controller in real time, generating a temperature control instruction corresponding to the temperature change gradient, and formulating an optimized scheduling strategy corresponding to the explosion-proof single lamp controller based on the temperature control instruction.

2. The optimized scheduling method for the intelligent lighting explosion-proof single lamp controller according to claim 1, characterized in that The identifying the distribution control link corresponding to the explosion-proof single lamp controller based on the light intensity distribution map includes: Analyzing the brightness gradient data corresponding to the light intensity distribution map; Based on the brightness gradient data, positioning the light intensity regulation area corresponding to the explosion-proof single lamp controller; Collecting the signal transmission nodes in the light intensity regulation area; Determining the topological link relationship corresponding to the signal transmission nodes; Identifying the distribution control link corresponding to the explosion-proof single lamp controller based on the topological link relationship.

3. The optimized scheduling method of the intelligent lighting explosion-proof single lamp controller according to claim 1, characterized in that, The performing a load analysis on the real-time energy consumption data to obtain lighting load characteristics includes: Extracting the lighting fluctuation signal in the real-time energy consumption data; Fitting an independent energy consumption curve corresponding to the lighting fluctuation signal; Identifying the lighting start and stop points in the independent energy consumption curve; Analyzing the start and stop distribution data corresponding to the lighting start and stop points; Performing a load analysis on the start and stop distribution data to obtain lighting load characteristics.

4. The optimized scheduling method for the intelligent lighting explosion-proof single lamp controller according to claim 1, characterized in that, The calculating the optimal dimming coefficient corresponding to the explosion-proof single lamp controller based on the lighting load characteristics includes: Calculating the optimal dimming coefficient corresponding to the explosion-proof single lamp controller using the following formula: Among them, K opt represents the optimal dimming coefficient corresponding to the explosion-proof single lamp controller, t1 and t2 respectively represent the start time and end time of the statistical period, n represents the total number of lighting devices, i represents the number index of the lighting device, P i (t) represents the real-time power of the i-th lighting device at time t, represents the average power of all lighting devices within the statistical period, m represents the total number of times the lighting device is turned on, j represents the number index of the lighting device being turned on, T on,j represents the duration of the j-th lighting device being turned on, represents the average duration of the lighting device being turned on within the statistical period, l represents the total number of times the lighting device is turned off, k represents the number index of the lighting device being turned off, T off,k represents the duration of the k-th lighting device being turned off, represents the average duration of the lighting device being turned off within the statistical period.

5. The optimized scheduling method for the intelligent lighting explosion-proof single lamp controller according to claim 1, characterized in that The collecting the signal response frequency band in the target lighting area based on the optimal dimming coefficient includes: Determining the spectral sampling interval corresponding to the target lighting area according to the optimal dimming coefficient; Scanning the band reflection intensity corresponding to each frequency band in the spectral sampling interval; Filtering out the signal frequency bands in the band reflection intensity that exceed a preset threshold; Extracting the frequency wave response points in the signal frequency bands. Based on the frequency-wave response points, collect the signal response frequency bands in the target illumination area.

6. The optimized scheduling method of the intelligent lighting explosion-proof single lamp controller according to claim 1, characterized in that, Calculating the harmonic distortion rate corresponding to the response points in the defect response segment includes: Calculating the harmonic distortion rate corresponding to the response points in the defect response segment using the following formula: Among them, THD represents the harmonic distortion rate corresponding to the response point in the defect response segment, C represents the set maximum harmonic order, v represents the harmonic order index, and A v represents the component amplitude of the v-th harmonic, and A1 represents the fundamental wave component amplitude.

7. The optimized scheduling method for the intelligent lighting explosion-proof single lamp controller according to claim 1, characterized in that Based on the harmonic distortion rate, performing light compensation on the defect response segment to obtain a light compensation threshold, including: Extract the high-frequency distortion components in the harmonic distortion rate; Analyze the light attenuation gradient corresponding to the defect response segment according to the high-frequency distortion components; Divide the compensation cells in the defect response segment based on the light attenuation gradient; Map the light compensation coefficients corresponding to the compensation cells; Perform light compensation on the defect response segment according to the light compensation coefficients to obtain a light compensation threshold.

8. The optimized scheduling method for the intelligent lighting explosion-proof single lamp controller according to claim 1, characterized in that Based on the light compensation threshold, performing scheduling grouping on the explosion-proof single-lamp controller to generate a priority scheduling group list, including: Query the light control identifier corresponding to the light compensation threshold; Analyze the light compensation requirements corresponding to the light control identifier; Divide the dynamic scheduling levels corresponding to the explosion-proof single-lamp controller based on the light compensation requirements; Extract the core scheduling nodes in the dynamic scheduling levels; Generate a priority scheduling group list corresponding to the explosion-proof single-lamp controller based on the core scheduling nodes.

9. The optimized scheduling method for the intelligent lighting explosion-proof single lamp controller according to claim 1, characterized in that, Performing power correction on the high-load node to obtain a load correction parameter, including: Analyze the instantaneous power spectrum corresponding to the high-load node; Extract the power feature clusters in the instantaneous power spectrum; Analyze the feature correlation index between the features in the power feature clusters; Calculate the power compensation coefficient corresponding to the high-load node based on the feature correlation index; Perform power correction on the high-load node based on the power compensation coefficient to obtain a load correction parameter.

10. An optimized scheduling system for an intelligent lighting explosion-proof single lamp controller, characterized in that, The system includes: A link identification module, configured to obtain the deployment location corresponding to the explosion-proof single-lamp controller, determine the target illumination area corresponding to the deployment location, collect the ambient light data in the target illumination area, construct a light distribution map corresponding to the ambient light data, and identify the distributed control link corresponding to the explosion-proof single-lamp controller based on the light distribution map; A coefficient calculation module, configured to monitor the real-time energy consumption data in the distributed control link, perform load analysis on the real-time energy consumption data to obtain lighting load characteristics, and calculate the optimal dimming coefficient corresponding to the explosion-proof single-lamp controller based on the lighting load characteristics; A distortion rate calculation module, configured to collect the signal response frequency bands in the target illumination area based on the optimal dimming coefficient, perform curve fitting on the signal response frequency bands to generate a dimming response curve, identify the defect response segment in the dimming response curve, and calculate the harmonic distortion rate corresponding to the response points in the defect response segment; A node identification module, configured to perform light compensation on the defect response segment based on the harmonic distortion rate to obtain a light compensation threshold, perform scheduling grouping on the explosion-proof single-lamp controller based on the light compensation threshold to generate a priority scheduling group list, and identify the high-load nodes in the priority scheduling group list; A strategy formulation module, which is used to perform power correction on the high-load node to obtain a load correction parameter. Based on the load correction parameter, it collects the temperature change gradient in the lamp cavity corresponding to the explosion-proof single-lamp controller in real time, and generates a temperature control instruction corresponding to the temperature change gradient. Based on the temperature control instruction, it formulates an optimized scheduling strategy corresponding to the explosion-proof single-lamp controller.

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