Explosion-proof intelligent lighting remote monitoring method and system based on internet of things

Abstract

The application discloses an explosion-proof intelligent lighting remote monitoring method and system based on an Internet of Things, and relates to the technical field of lighting monitoring.The method comprises the following steps: obtaining real-time sensor information through dynamic monitoring of a sensor network; activating an edge controller to analyze the real-time sensor information and obtaining real-time lighting decisions; transmitting the real-time lighting decisions and the real-time sensor information to a remote monitoring center through a ZigBee wireless radio frequency device, and analyzing the real-time lighting decisions and the real-time sensor information by the remote monitoring center to obtain a predicted heat index; if the predicted heat index reaches a predetermined limit value, a remote heat dissipation instruction is sent, and the explosion-proof intelligent lighting equipment is subjected to remote heat dissipation based on the remote heat dissipation instruction.The technical problems of untimely monitoring of the running state of the explosion-proof lighting equipment and slow response of heat dissipation control in the prior art are solved, the remote monitoring of the explosion-proof intelligent lighting equipment is realized through the Internet of Things technology, and the technical effects of improving the heat dissipation response speed and ensuring stable operation of the equipment are achieved.

Description

Technical Field

[0001] This invention relates to the field of lighting monitoring technology, specifically to a remote monitoring method and system for explosion-proof smart lighting based on the Internet of Things. Background Technology

[0002] In flammable and explosive environments such as petrochemical, power, and coal mines, explosion-proof lighting equipment serves as a crucial infrastructure for ensuring workplace safety, and its operational stability and reliability are paramount. However, existing explosion-proof lighting systems generally suffer from problems such as untimely monitoring of operational status, delayed fault detection, and ineffective temperature control. On the one hand, traditional lighting systems rely heavily on manual inspections to obtain equipment status information, lacking real-time sensing and dynamic response capabilities. On the other hand, when lighting equipment operates continuously for extended periods or when ambient temperatures rise, the cooling system often fails to start and stop as needed, easily leading to equipment overheating, shortened lifespan, and even safety hazards. Summary of the Invention

[0003] This application provides a remote monitoring method and system for explosion-proof smart lighting based on the Internet of Things, which solves the technical problems of untimely monitoring of the operating status of explosion-proof lighting equipment and delayed response of heat dissipation control in the prior art.

[0004] The first aspect of this application provides a remote monitoring method for explosion-proof smart lighting based on the Internet of Things, the method comprising:

[0005] Real-time sensor information is obtained through dynamic monitoring via a sensor network, wherein the sensor network is deployed in the explosion-proof smart lighting equipment; an edge controller is activated to analyze the real-time sensor information to obtain a real-time lighting decision; the real-time lighting decision and the real-time sensor information are transmitted to a remote monitoring center via a ZigBee wireless radio frequency device, and the remote monitoring center analyzes the data to obtain a predicted thermal index; if the predicted thermal index reaches a predetermined limit, a remote heat dissipation command is issued, and the explosion-proof smart lighting equipment is remotely cooled based on the remote heat dissipation command.

[0006] A second aspect of this application provides an explosion-proof smart lighting remote monitoring system based on the Internet of Things, the system comprising:

[0007] Monitoring module: obtains real-time sensor information through dynamic monitoring via a sensor network, wherein the sensor network is deployed in the explosion-proof smart lighting equipment; First analysis module: activates the edge controller to analyze the real-time sensor information and obtain real-time lighting decisions; Second analysis module: transmits the real-time lighting decisions and the real-time sensor information to a remote monitoring center via a ZigBee wireless radio frequency device, and the remote monitoring center analyzes and obtains a predicted heat index; Control module: if the predicted heat index reaches a predetermined limit, issues a remote heat dissipation command and performs remote heat dissipation on the explosion-proof smart lighting equipment based on the remote heat dissipation command.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, real-time sensor information is obtained through dynamic monitoring via a sensor network deployed within the explosion-proof smart lighting equipment. Next, an edge controller is activated to analyze the real-time sensor information and obtain real-time lighting decisions. Then, the real-time lighting decisions and real-time sensor information are transmitted to a remote monitoring center via a ZigBee wireless radio frequency device, where the predicted heat index is analyzed. If the predicted heat index reaches a predetermined limit, a remote heat dissipation command is issued, and the explosion-proof smart lighting equipment is remotely cooled based on this command. This solves the technical problems of untimely monitoring of the operating status of explosion-proof lighting equipment and lagging heat dissipation control response in existing technologies. By using IoT technology to achieve remote monitoring of explosion-proof smart lighting equipment, it achieves the technical effects of improving heat dissipation response speed and ensuring stable equipment operation. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic diagram of the process for a remote monitoring method for explosion-proof smart lighting based on the Internet of Things provided in this application embodiment;

[0012] Figure 2 A schematic diagram of the structure of an explosion-proof smart lighting remote monitoring system based on the Internet of Things provided in this application embodiment.

[0013] Explanation of reference numerals in the attached diagram: Monitoring module 11, First analysis module 12, Second analysis module 13, Control module 14. Detailed Implementation

[0014] This application provides a remote monitoring method and system for explosion-proof smart lighting based on the Internet of Things, which solves the technical problems of untimely monitoring of the operating status of explosion-proof lighting equipment and delayed response of heat dissipation control in the prior art.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, this application provides a remote monitoring method for explosion-proof smart lighting based on the Internet of Things, wherein the method includes:

[0018] Real-time sensor information is obtained through dynamic monitoring via a sensor network, wherein the sensor network is deployed in the explosion-proof smart lighting equipment.

[0019] Multiple sensor nodes are deployed at strategic locations on the surface and inside the casing of the explosion-proof smart lighting equipment to construct a sensor network. Each sensor node includes at least a light intensity sensor, a temperature sensor, a humidity sensor, an airflow velocity sensor, and a particulate matter concentration sensor. Each sensor node is connected to a local edge controller via wired or wireless means, forming a data acquisition and transmission link. This sensor network continuously collects multi-dimensional parameter information from the lighting equipment's operating environment at a preset sampling period, generating real-time sensor information including light intensity, equipment surface temperature, ambient temperature and humidity, airflow status, and particulate matter concentration. After preliminary local processing, the real-time sensor information is aggregated and sent to the edge controller for subsequent lighting decision analysis and remote heat dissipation control.

[0020] The edge controller is activated to analyze the real-time sensor information and obtain real-time lighting decisions.

[0021] Upon receiving real-time sensor information uploaded from the sensor network, the edge controller deployed near the explosion-proof smart lighting equipment automatically activates its processing module to analyze and classify the real-time sensor information. The edge controller integrates an edge computing unit and an edge storage unit. Based on a preset lighting control strategy model, the edge computing unit performs multi-dimensional correlation calculations on parameters such as illuminance, ambient temperature and humidity, and air velocity to determine whether the current environment requires lighting adjustment. If the illuminance is detected to be below a preset threshold, or if the pedestrian / vehicle traffic density increases to the trigger condition for enhanced lighting, the edge controller generates a corresponding lighting enhancement decision based on the strategy model. If the illuminance exceeds the target limit or the ambient temperature rises abnormally, a decision command is generated to reduce the lighting intensity or activate heat dissipation preparation.

[0022] Furthermore, before activating the edge controller to analyze the real-time sensor information and obtain real-time lighting decisions, the process also includes:

[0023] The system iterates through the real-time sensor information to obtain the real-time illuminance monitored by the illuminance sensors in the sensor network; it determines whether the real-time illuminance exceeds the predetermined illuminance limit of the explosion-proof smart lighting device; if it does, it issues an edge control command and controls the illuminance reduction of the explosion-proof smart lighting device based on the edge control command.

[0024] After receiving real-time sensor information uploaded from the sensor network, the edge controller processes various sensor data. During this process, it extracts monitoring data from the illuminance sensor to obtain the real-time illuminance value of the environment surrounding the explosion-proof smart lighting equipment. The edge controller compares the real-time illuminance with a predetermined illuminance limit, which is the maximum safe illuminance threshold set based on equipment safety specifications, environmental characteristics, or historical fault records. When the real-time illuminance exceeds the predetermined illuminance limit, the edge controller immediately triggers control logic, generating and issuing an edge control command. The edge control command includes illuminance reduction parameters, such as the target dimming level, adjustment method (continuous dimming or stepped dimming), and execution duration. The edge control command is sent by the edge controller to the lighting driver module via a local communication interface, causing the explosion-proof smart lighting equipment to reduce its luminous intensity according to control requirements, achieving local adaptive dimming.

[0025] Furthermore, before determining whether the real-time illuminance exceeds the predetermined illuminance limit of the explosion-proof smart lighting device, the following steps are included:

[0026] Obtain historical fault records of the same type and model of the explosion-proof smart lighting equipment; obtain the first record in the historical fault records, wherein the first record includes a first fault illuminance; sort the first fault illuminance in ascending order and determine the target fault illuminance, and use the target fault illuminance as the predetermined illuminance limit.

[0027] The edge controller retrieves historical operating data of explosion-proof smart lighting equipment from the database and extracts historical fault records that are consistent with the current equipment category and model. Among them, the historical fault records include relevant illuminance data when the equipment malfunctioned under different operating environments.

[0028] Multiple records associated with illuminance anomalies are retrieved from historical fault records, including at least one first record containing a first fault illuminance, indicating that the equipment experienced an anomaly or malfunction at that illuminance level. All first fault illuminance values ​​are compiled into an illuminance sample set and sorted in ascending order to identify the critical conditions for equipment anomalies under low tolerance. The smallest fault illuminance value is extracted from the sorted sample set as the target fault illuminance, representing the lowest illuminance threshold at which the equipment first malfunctioned during its historical operation. This target fault illuminance is set as a predetermined illuminance limit required for current real-time illuminance judgment, used to subsequently determine whether the current environment in which the equipment is located is at potential risk of illuminance anomalies.

[0029] Furthermore, activating the edge controller to analyze the real-time sensor information to obtain real-time lighting decisions includes:

[0030] If the limit is not exceeded, historical monitoring records are retrieved, wherein the historical monitoring records are stored in the edge storage component of the edge controller; a predetermined illuminance demand factor is read, and the historical monitoring records are traversed based on the predetermined illuminance demand factor to obtain a historical factor parameter set; the historical factor parameter set is weighted and calculated to obtain the historical demand illuminance; when the historical demand illuminance does not exceed the predetermined illuminance limit, the historical demand illuminance is used as the real-time lighting demand; when the historical demand illuminance exceeds the predetermined illuminance limit, the target fault illuminance is used as the real-time lighting demand; the real-time lighting decision is generated based on the real-time lighting demand.

[0031] Furthermore, the predetermined illuminance requirements include at least natural illuminance, pedestrian traffic, and vehicle traffic.

[0032] When it is determined that the current real-time illuminance does not exceed the predetermined illuminance limit of the explosion-proof smart lighting device, the edge controller calls the historical monitoring records stored in its built-in edge storage component. The historical monitoring records include illuminance data, device operating status parameters, environmental condition records, and external influencing factors such as pedestrian flow and vehicle flow within the corresponding historical time period.

[0033] The edge controller reads a set of predetermined illuminance demand factors. This set of factors measures the actual lighting requirements under specific environmental conditions. The predetermined illuminance demand factors include at least natural light intensity, pedestrian density, vehicle traffic intensity, and task intensity. Based on these illuminance demand factors, the edge controller iterates through historical monitoring records, extracting historical parameters that match the current environmental state to form a historical factor parameter set. A weighted calculation is performed on this historical factor parameter set, where the weighting coefficients can be set according to a preset environmental sensitivity model. This calculates the historical required illuminance, which serves as a reference lighting value under similar environmental conditions.

[0034] If the historical illuminance demand does not exceed the predetermined illuminance limit, the historical illuminance demand is directly used as the real-time lighting demand. If the historical illuminance demand exceeds the predetermined illuminance limit, to ensure the stability and safety of equipment operation, the historical illuminance demand is not used; instead, the previously determined target fault illuminance is used as the current real-time lighting demand. The edge controller generates lighting decisions based on the real-time lighting demand, and these decisions include parameters such as dimming level, lighting mode, and response time.

[0035] The real-time lighting decisions and real-time sensor information are transmitted to a remote monitoring center via a ZigBee wireless radio frequency device, and the predicted heat index is obtained by the analysis of the remote monitoring center.

[0036] Real-time lighting decisions and real-time sensor information are synchronously transmitted to a remote monitoring center via a ZigBee wireless radio frequency device. The ZigBee radio frequency device is integrated into the communication module of the explosion-proof smart lighting equipment, featuring low power consumption, short range, and high reliability data transmission characteristics, making it suitable for stable and safe wireless information exchange in explosion-proof environments. Real-time lighting decisions include command parameters such as the target lighting power, dimming level, and execution duration generated at the current moment. Real-time sensor information includes equipment operating status parameters, environmental monitoring data such as ambient temperature, humidity, air velocity, and particulate matter concentration.

[0037] After receiving the above information, the remote monitoring center constructs a lighting demand time-series curve based on the current timestamp and lighting decision content. Combining this with equipment operation history records and a heat load database, it analyzes potential heat accumulation trends for the equipment in the current or future periods. The monitoring center maps the lighting demand curve to an estimated heat load value by introducing a load-heat dissipation factor model, and then corrects for thermal effects using environmental factors, ultimately obtaining a predicted heat index. This predicted heat index is used to characterize the thermal risk level of the lighting system under current operating conditions.

[0038] Furthermore, the real-time lighting decisions and the real-time sensor information are transmitted to a remote monitoring center via a ZigBee wireless radio frequency device, and the predicted heat index is obtained by the remote monitoring center through analysis, including:

[0039] Based on the correspondence between the real-time lighting decision and the real-time time, a lighting demand time series is formed; the remote monitoring center traverses the lighting demand time series in the lighting demand database to obtain the target historical time series, and the target historical time series corresponds to the target historical load; the load-heat dissipation factor is introduced to adjust the target historical load to obtain the predicted heat index.

[0040] Based on the timestamp information accompanying the real-time lighting decisions, the remote monitoring center extracts the correspondence between lighting power changes and real-time moments, constructs a lighting demand time-series curve, and uses the lighting demand time-series curve to reflect the power output change trend of the equipment over a future period of time.

[0041] The remote monitoring center traverses and matches historical time-series data in its built-in lighting demand database, using a similarity comparison algorithm to find the target historical time sequence that is closest to the current lighting demand time sequence. The target historical time sequence is a known lighting power scheduling curve from a historical operating scenario, and corresponds to a set of historical operating load parameters, i.e., the target historical load. The target historical load includes the average temperature rise data, operating current, voltage fluctuations, and environmental thermal parameters of the equipment under that historical time sequence.

[0042] After obtaining the target historical load, the remote monitoring center further introduces a load-heat dissipation factor model to adjust the target historical load. The load-heat dissipation factor is a weighted correction factor established based on predetermined parameters such as the physical characteristics of the lighting system, the thermal conductivity of the equipment materials, the efficiency of the radiator, the ambient temperature, and the airflow velocity. It can effectively reflect the actual contribution of the load to the temperature rise under different environmental and system conditions. Finally, the adjusted load data is mapped to a thermal model to output a predicted thermal index. The predicted thermal index is used to reflect the potential heat accumulation trend under the current operating conditions of the equipment.

[0043] The remote monitoring center employs a curve similarity-based comparison algorithm for time-series matching. Specifically: First, the current lighting demand time-series and each historical lighting time-series data entry in the database are standardized to ensure curves with different time spans and power scales have uniform dimensions. Next, curve fitting is performed on the current lighting demand time-series and historical lighting time-series data to form the current lighting demand curve and the target historical curve. Then, a first similarity value is calculated between the two, which can be measured using cosine similarity, Euclidean distance, or dynamic time warping algorithms. When the similarity value between a historical lighting curve and the current lighting demand curve reaches or exceeds a predetermined similarity limit (e.g., 0.95), its corresponding historical time-series is determined as the target historical time-series.

[0044] To improve the environmental adaptability and accuracy of heat load prediction, a load-heat dissipation factor model is introduced to correct the target historical load.

[0045] Load-heat dissipation factor Modeling can be performed using the following function: ;in, The current ambient temperature. For ambient humidity, air velocity, This refers to the particulate matter concentration. This is the heat dissipation efficiency factor of the equipment. , , , , These are empirical weighting coefficients, which can be obtained through simulation or experimental regression based on the actual deployment environment.

[0046] The heat index is predicted using the following formula: , The thermal power parameters in the target historical load.

[0047] Furthermore, the remote monitoring center traverses the lighting demand time series in the lighting demand database to obtain the target historical time series, including:

[0048] Extract the first time series from the lighting demand database; sequentially perform curve processing on the lighting demand time series and the first time series to obtain the lighting demand curve and the first curve respectively; obtain the first similarity between the lighting demand curve and the first curve; when the first similarity reaches a predetermined similarity limit, the first time series is used as the target historical time series.

[0049] A historical time-series sample, designated as the first time-series, is extracted from the lighting demand database. This first time-series contains lighting power scheduling data recorded during historical operation and possesses the same time resolution and duration as the current lighting demand time-series structure. Both the current lighting demand time-series and the first time-series are then processed using curve normalization techniques. Specifically, data standardization is achieved through interpolation resampling, normalization, and smoothing filtering to obtain the lighting demand curve and the first curve. A first similarity score is calculated between the lighting demand curve and the first curve. This similarity score can be calculated using various similarity measurement methods, such as dynamic time warping, cosine similarity, Euclidean distance, or weighted sliding window alignment. The similarity score reflects the degree of consistency between the current lighting demand trend and the historical operating pattern. When the first similarity score reaches a preset similarity limit, indicating a high correlation between the first time-series and the current lighting demand, the first time-series is selected as the target historical time-series.

[0050] Furthermore, after adjusting the target historical load by introducing a load-heat dissipation factor to obtain the predicted thermal index, the process includes:

[0051] A predetermined thermal factor is read, and the real-time sensor information is traversed based on the predetermined thermal factor to obtain real-time thermal parameters; the remote monitoring center weights the variation of the real-time thermal parameters to obtain a real-time thermal influence coefficient; the predicted thermal index is calibrated using the real-time thermal influence coefficient as a weight.

[0052] Furthermore, the predetermined thermal factors include ambient temperature, ambient humidity, air velocity, and particulate matter concentration.

[0053] First, predetermined thermal factors are read, including but not limited to physical environmental parameters closely related to equipment heat accumulation and heat dissipation efficiency, such as ambient temperature, ambient humidity, air velocity, and particulate matter concentration. Then, based on these predetermined thermal factors, relevant data fields from real-time sensor information are traversed and extracted one by one to obtain the real-time thermal parameters at the corresponding time. Next, the remote monitoring center performs a variation-weighted calculation on the real-time thermal parameters. Specifically, based on the sensitivity of each thermal factor to heat accumulation, corresponding weighting factors are assigned to form a multi-factor weighted model, which is then used to calculate the real-time thermal influence coefficient. The real-time thermal influence coefficient reflects the thermal response deviation under current environmental conditions relative to historical environmental benchmarks. Finally, using the real-time thermal influence coefficient as a weighted calibration factor, the initially predicted thermal index value is dynamically corrected to obtain the final calibrated predicted thermal index.

[0054] If the predicted thermal index reaches a predetermined limit, a remote heat dissipation command is issued, and the explosion-proof smart lighting device is remotely cooled based on the remote heat dissipation command.

[0055] If the predicted thermal index reaches the predetermined limit, the remote monitoring center will automatically issue a remote heat dissipation command. The predicted thermal index is a dynamic thermal risk assessment value calculated based on the target historical load, load-heat dissipation factor, and real-time thermal impact coefficient. When this value reaches or exceeds the set thermal safety threshold, it indicates that the current explosion-proof smart lighting equipment has an overheating hazard or is nearing a critical operating state.

[0056] The remote cooling command is a set of control commands that can be transmitted to the target lighting equipment via a ZigBee wireless radio frequency device. Control signals include start / stop control signals for the equipment's cooling module, cooling power level adjustment signals, and air-cooling / liquid-cooling control mode switching signals. The remote cooling command is recognized and triggered by the local receiving unit at the device end, which then schedules the cooling execution module to respond. After executing the remote cooling command, the equipment activates an active cooling mechanism, such as activating the high-efficiency air-cooling components inside the explosion-proof cavity, adjusting the ventilation area of ​​the heat-sensitive material's heat dissipation windows, activating the liquid-cooled microchannel system, or adjusting the heat conduction path, ensuring that the equipment's temperature rise rate is controlled and heat accumulation is released in a timely manner.

[0057] In summary, the embodiments of this application have at least the following technical effects:

[0058] First, real-time sensor information is obtained through dynamic monitoring via a sensor network deployed within the explosion-proof smart lighting equipment. Next, an edge controller is activated to analyze the real-time sensor information and obtain real-time lighting decisions. Then, the real-time lighting decisions and real-time sensor information are transmitted to a remote monitoring center via a ZigBee wireless radio frequency device, where the predicted heat index is analyzed. If the predicted heat index reaches a predetermined limit, a remote heat dissipation command is issued, and the explosion-proof smart lighting equipment is remotely cooled based on this command. This solves the technical problems of untimely monitoring of the operating status of explosion-proof lighting equipment and lagging heat dissipation control response in existing technologies. By using IoT technology to achieve remote monitoring of explosion-proof smart lighting equipment, it achieves the technical effects of improving heat dissipation response speed and ensuring stable equipment operation.

[0059] Example 2, based on the same inventive concept as the IoT-based remote monitoring method for explosion-proof smart lighting in the foregoing examples, such as... Figure 2 As shown, this application provides an explosion-proof smart lighting remote monitoring system based on the Internet of Things, wherein the system includes:

[0060] Monitoring module 11: obtains real-time sensor information through dynamic monitoring via a sensor network, wherein the sensor network is deployed in the explosion-proof smart lighting equipment; First analysis module 12: activates the edge controller to analyze the real-time sensor information and obtain a real-time lighting decision; Second analysis module 13: transmits the real-time lighting decision and the real-time sensor information to a remote monitoring center via a ZigBee wireless radio frequency device, and the remote monitoring center analyzes and obtains a predicted heat index; Control module 14: if the predicted heat index reaches a predetermined limit, issues a remote heat dissipation command and performs remote heat dissipation on the explosion-proof smart lighting equipment based on the remote heat dissipation command.

[0061] Furthermore, the first analysis module 12 is used to perform the following methods:

[0062] The system iterates through the real-time sensor information to obtain the real-time illuminance monitored by the illuminance sensors in the sensor network; it determines whether the real-time illuminance exceeds the predetermined illuminance limit of the explosion-proof smart lighting device; if it does, it issues an edge control command and controls the illuminance reduction of the explosion-proof smart lighting device based on the edge control command.

[0063] Furthermore, the first analysis module 12 is used to perform the following methods:

[0064] Obtain historical fault records of the same type and model of the explosion-proof smart lighting equipment; obtain the first record in the historical fault records, wherein the first record includes a first fault illuminance; sort the first fault illuminance in ascending order and determine the target fault illuminance, and use the target fault illuminance as the predetermined illuminance limit.

[0065] Furthermore, the first analysis module 12 is used to perform the following methods:

[0066] If the limit is not exceeded, historical monitoring records are retrieved, wherein the historical monitoring records are stored in the edge storage component of the edge controller; a predetermined illuminance demand factor is read, and the historical monitoring records are traversed based on the predetermined illuminance demand factor to obtain a historical factor parameter set; the historical factor parameter set is weighted and calculated to obtain the historical demand illuminance; when the historical demand illuminance does not exceed the predetermined illuminance limit, the historical demand illuminance is used as the real-time lighting demand; when the historical demand illuminance exceeds the predetermined illuminance limit, the target fault illuminance is used as the real-time lighting demand; the real-time lighting decision is generated based on the real-time lighting demand.

[0067] Furthermore, the first analysis module 12 is used to perform the following methods:

[0068] The predetermined illuminance requirements include at least natural illuminance, pedestrian traffic, and vehicle traffic.

[0069] Furthermore, the second analysis module 13 is used to perform the following methods:

[0070] Based on the correspondence between the real-time lighting decision and the real-time time, a lighting demand time series is formed; the remote monitoring center traverses the lighting demand time series in the lighting demand database to obtain the target historical time series, and the target historical time series corresponds to the target historical load; the load-heat dissipation factor is introduced to adjust the target historical load to obtain the predicted heat index.

[0071] Furthermore, the second analysis module 13 is used to perform the following methods:

[0072] Extract the first time series from the lighting demand database; sequentially perform curve processing on the lighting demand time series and the first time series to obtain the lighting demand curve and the first curve respectively; obtain the first similarity between the lighting demand curve and the first curve; when the first similarity reaches a predetermined similarity limit, the first time series is used as the target historical time series.

[0073] Furthermore, the second analysis module 13 is used to perform the following methods:

[0074] A predetermined thermal factor is read, and the real-time sensor information is traversed based on the predetermined thermal factor to obtain real-time thermal parameters; the remote monitoring center weights the variation of the real-time thermal parameters to obtain a real-time thermal influence coefficient; the predicted thermal index is calibrated using the real-time thermal influence coefficient as a weight.

[0075] Furthermore, the second analysis module 13 is used to perform the following methods:

[0076] The predetermined thermal factors include ambient temperature, ambient humidity, air velocity, and particulate matter concentration.

[0077] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0078] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0079] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A remote monitoring method for explosion-proof smart lighting based on the Internet of Things, characterized in that, The method includes: Real-time sensor information is obtained through dynamic monitoring via a sensor network, wherein the sensor network is deployed in the explosion-proof smart lighting equipment; The edge controller is activated to analyze the real-time sensor information and obtain real-time lighting decisions; The real-time lighting decision and the real-time sensor information are transmitted to a remote monitoring center via a ZigBee wireless radio frequency device, and the predicted heat index is obtained by the analysis of the remote monitoring center. If the predicted heat index reaches a predetermined limit, a remote heat dissipation command is issued, and the explosion-proof smart lighting device is remotely cooled based on the remote heat dissipation command. The real-time lighting decisions and real-time sensor information are transmitted to a remote monitoring center via a ZigBee wireless radio frequency device, where the predicted heat index is analyzed and obtained, including: Based on the correspondence between the real-time lighting decisions and real-time moments, a lighting demand time sequence is formed; The remote monitoring center traverses the lighting demand time series in the lighting demand database to obtain the target historical time series, and the target historical time series corresponds to the target historical load. The target historical load is adjusted by introducing a load-heat dissipation factor to obtain the predicted thermal index; Before activating the edge controller to analyze the real-time sensor information and obtain a real-time lighting decision, the process also includes: By traversing through the real-time sensor information, the real-time illuminance monitored by the illuminance sensors in the sensor network is obtained; Determine whether the real-time illuminance exceeds the predetermined illuminance limit of the explosion-proof smart lighting device; If the light intensity is exceeded, an edge control command is issued, and the illuminance of the explosion-proof smart lighting device is reduced based on the edge control command. Before determining whether the real-time illuminance exceeds the predetermined illuminance limit of the explosion-proof smart lighting device, the process includes: Obtain historical fault records of the same type and model of the explosion-proof smart lighting equipment; Obtain the first record from the historical fault records, and the first record includes the first fault illuminance; The first fault illuminance is sorted in ascending order and the target fault illuminance is determined, and the target fault illuminance is used as the predetermined illuminance limit. The edge controller is activated to analyze the real-time sensor information to obtain real-time lighting decisions, including: If the time limit is not exceeded, retrieve historical monitoring records, which are stored in the edge storage component of the edge controller; Read the predetermined illuminance demand factors, and iterate through the historical monitoring records based on the predetermined illuminance demand factors to obtain the historical factor parameter set; The historical illuminance demand is obtained by weighting the set of historical factor parameters. When the historical required illuminance does not exceed the predetermined illuminance limit, the historical required illuminance is used as the real-time lighting requirement; when the historical required illuminance exceeds the predetermined illuminance limit, the target fault illuminance is used as the real-time lighting requirement. The real-time lighting decision is generated based on the real-time lighting requirements.

2. The remote monitoring method for explosion-proof smart lighting based on the Internet of Things according to claim 1, characterized in that, The predetermined illuminance requirements include at least natural illuminance, pedestrian traffic, and vehicle traffic.

3. The remote monitoring method for explosion-proof smart lighting based on the Internet of Things according to claim 1, characterized in that, The remote monitoring center iterates through the lighting demand time series in the lighting demand database to obtain the target historical time series, including: Extract the first time series from the lighting demand database; The lighting demand time series and the first time series are sequentially processed into curves to obtain the lighting demand curve and the first curve, respectively. Obtain the first similarity between the lighting demand curve and the first curve; When the first similarity reaches a predetermined similarity limit, the first time series is taken as the target historical time series.

4. The remote monitoring method for explosion-proof smart lighting based on the Internet of Things according to claim 1, characterized in that, After adjusting the target historical load by introducing a load-heat dissipation factor to obtain the predicted thermal index, the process includes: Read the predetermined thermal factors, and traverse the real-time sensor information based on the predetermined thermal factors to obtain real-time thermal parameters; The remote monitoring center obtains the real-time thermal influence coefficient by weighting the real-time thermal parameter variations. The predicted thermal index is calibrated using the real-time thermal influence coefficient as a weight.

5. The remote monitoring method for explosion-proof smart lighting based on the Internet of Things according to claim 4, characterized in that, The predetermined thermal factors include ambient temperature, ambient humidity, air velocity, and particulate matter concentration.

6. An explosion-proof smart lighting remote monitoring system based on the Internet of Things, characterized in that, For implementing the IoT-based remote monitoring method for explosion-proof smart lighting as described in any one of claims 1-5, the system comprises: Monitoring module: obtains real-time sensor information through dynamic monitoring via a sensor network, wherein the sensor network is deployed in the explosion-proof smart lighting equipment; First analysis module: Activate the edge controller to analyze the real-time sensor information and obtain real-time lighting decisions; The second analysis module transmits the real-time lighting decision and the real-time sensor information to the remote monitoring center via a ZigBee wireless radio frequency device, and the remote monitoring center analyzes the data to obtain the predicted heat index. Control module: If the predicted heat index reaches a predetermined limit, a remote heat dissipation command is issued, and the explosion-proof smart lighting device is remotely cooled based on the remote heat dissipation command.

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