A control method and related equipment for a mobile lighting beacon

Abstract

The present application discloses a control method and related devices for a mobile lighting lighthouse. First, environmental data is detected by lighthouse sensors to form a sensor data set containing multi-dimensional information such as light intensity, temperature and humidity. Trend analysis is performed on it to obtain an environmental change prediction result including future lighting requirements. A lighting strategy model is constructed by combining the prediction result with the current environmental data, and a lighting control strategy is calculated. Next, sensitivity analysis is performed on the light intensity in the lighting control strategy to calculate the optimal lighting duration, and the strategy is optimized in combination with the lighthouse power limit information. Finally, the instructions from the user based on the intelligent device side are parsed, and control instructions are generated in combination with the optimal strategy. This method can master the environmental changes in real time by collecting and analyzing multi-dimensional environmental data, avoiding unstable lighting effects. It can dynamically adjust the lighting strategy, flexibly dim the light according to the comprehensive user requirements, improve the level of personalization and intelligence, and provide an efficient, energy-saving and intelligent solution for outdoor lighting.

Description

Technical Field

[0001] This application relates to the field of lighthouse control, and more specifically, to a control method and related equipment for a mobile lighting lighthouse. Background Art

[0002] In today's society, the demand for outdoor lighting shows a continuous growth trend. Especially in key fields such as emergency rescue, construction, and post-disaster recovery, mobile lighting lighthouses have become indispensable important lighting equipment. However, the current control technology of mobile lighting lighthouses on the market has obvious limitations. Its operation mode is basically a preset fixed scheme, which is difficult to sense and respond to the dynamic changes of environmental conditions in real time, and cannot automatically and accurately adjust the lighting intensity according to the actual situation, resulting in unstable lighting effects and being difficult to meet the complex and changeable actual use requirements.

[0003] From the perspective of the existing technology, the control system of mobile lighting lighthouses mainly relies on manual operation or runs with the help of a simple time scheduling program, and fails to fully explore and utilize environmental data to achieve intelligent control. Although some systems are equipped with sensors to monitor environmental parameters, these systems generally lack the function of forward-looking prediction and analysis of environmental change trends. Moreover, the mobile lighting lighthouses under the existing technology also have obvious shortcomings in the intelligent user interaction function. Most lighting control systems are difficult to make flexible and timely adjustments according to the real-time needs of users. Especially in emergency situations, the control needs of users are often urgent and changeable, while the existing lighting control systems are still limited to fixed preset modes or can only receive simple operation instructions, seriously lacking the ability to deeply analyze and customize according to user needs. As a result, there is often a disconnection between the actual needs of users and the lighting control strategy, which in turn leads to a series of problems such as unreasonable lighting time (too long or too short) and mismatched light intensity, having a negative impact on lighting efficiency and effect.

[0004] Based on this, this application provides a control solution for mobile lighting lighthouses, overcoming the deficiencies of the existing technology and effectively improving the lighting effect. Summary of the Invention

[0005] In view of this, this application provides a control method and related equipment for a mobile lighting lighthouse, aiming to overcome the technical problem that the existing technology cannot adjust the lighting intensity according to environmental conditions and user instructions, resulting in poor lighting effects.

[0006] A control method for a mobile lighting lighthouse includes:

[0007] Using sensors on the mobile lighting lighthouse to detect environmental data to obtain a sensor data set, where the sensor data set includes multi-dimensional data combining light intensity and temperature and humidity;

[0008] Perform trend analysis on the set of sensor data to obtain an environmental change prediction result, where the environmental change prediction result includes lighting demand information within a preset future time;

[0009] Construct a lighting strategy model by combining the environmental change prediction result and the current environmental data detected by the sensor, and calculate a lighting control strategy based on the lighting strategy model;

[0010] Perform sensitivity analysis on the light intensity in the lighting control strategy to obtain the influence weight of the light intensity on the lighting control strategy, and calculate the optimal lighting duration based on the influence weight;

[0011] Obtain the power limit information of the mobile lighting beacon, and optimize the lighting control strategy based on the optimal lighting duration and the power limit information to obtain an optimal lighting control strategy;

[0012] Perform comprehensive calculation according to the user demand information and the optimal lighting control strategy to generate a lighting control instruction, and control the mobile lighting beacon based on the lighting control instruction, where the user demand information is obtained by parsing the control instruction sent by the user based on the intelligent device side.

[0013] Optionally, using the sensors on the mobile lighting beacon to detect environmental data to obtain a set of sensor data, including:

[0014] Use the sensors on the mobile lighting beacon to detect the light intensity data and temperature and humidity data of the environment;

[0015] Perform hierarchical acquisition on the light intensity data to obtain light intensity data at multiple different heights and azimuths, and perform integration and weighting processing to obtain light intensity distribution data;

[0016] Perform multi-dimensional feature extraction on the temperature and humidity data to obtain the independent contribution degrees of temperature and humidity to the change of light intensity, and construct a regression model by combining the interaction of temperature and humidity to obtain global temperature and humidity prediction data;

[0017] Classify the environmental state at each moment according to the interaction relationship between the light intensity distribution data and the global temperature and humidity prediction data to obtain dynamic environmental state data;

[0018] Perform data fusion on the light intensity distribution data, the global temperature and humidity prediction data, and the dynamic environmental state data to obtain a set of sensor data.

[0019] Optionally, performing trend analysis on the set of sensor data to obtain an environmental change prediction result, including:

[0020] Perform time series decomposition processing on the light intensity data in the sensor data set to obtain a long-term trend component;

[0021] Perform dynamic state estimation on the temperature and humidity data in the sensor data set to obtain the state estimation value corresponding to the temperature and humidity data;

[0022] Perform spatio-temporal correlation analysis on the long-term trend component and the state estimation value to generate an environmental change trend;

[0023] Use the exponential smoothing method to predict the environmental change trend in the future time period to obtain the environmental change prediction result, where the environmental change prediction result includes lighting demand information within a preset future time.

[0024] Optionally, construct a lighting strategy model by combining the environmental change prediction result and the current environmental data detected by the sensor, and calculate a lighting control strategy based on the lighting strategy model, including:

[0025] Perform joint modeling processing on the environmental change prediction result and the current environmental data detected by the sensor to obtain a joint model of environmental change and lighting demand;

[0026] Use the joint model to perform feature importance analysis on each input feature, calculate the contribution degree of each input feature to the lighting demand prediction result, and obtain the key factors affecting the lighting control strategy;

[0027] Construct a dynamic weight allocation mechanism based on the key factors to obtain a lighting control strategy weight model;

[0028] Optimize the lighting control strategy weight model based on the particle swarm optimization algorithm to generate the lighting control strategy.

[0029] Optionally, perform sensitivity analysis on the light intensity in the lighting control strategy to obtain the influence weight of the light intensity on the lighting control strategy, and calculate the optimal lighting duration based on the influence weight, including:

[0030] Perform weight allocation processing on the light intensity in the lighting control strategy based on the light intensity data to form a weight allocation result;

[0031] Perform sensitivity evaluation on the weight allocation result, calculate the influence degree of the environmental factors in the weight allocation result on the lighting control strategy, and obtain an analysis result, where the environmental factors include light intensity data and temperature and humidity data;

[0032] Multiply the influence weight of each environmental factor in the analysis result by the lighting demand in the lighting control strategy to obtain the lighting intensity demand for each time period, and calculate the optimal lighting duration according to the lighting intensity demand.

[0033] Optionally, obtain the power limit information of the mobile lighting tower, and optimize the lighting control strategy based on the optimal lighting duration and the power limit information to obtain the optimal lighting control strategy, including:

[0034] Obtain the power limit information of the mobile lighting tower, where the power limit information at least includes the current battery power, battery discharge curve, power consumption history, maximum device output power, and predetermined operation time limit;

[0035] Extract features from the power limit information to obtain feature data of the power consumption rate, power load fluctuation, remaining power prediction value, and maximum working duration, and form a power limit feature vector;

[0036] Perform weighted processing on each feature in the power limit feature vector to form a power limit condition, and obtain the value range of the optimal lighting duration under the power limit condition;

[0037] Generate a dynamic adjustment strategy according to the value range, and adjust the lighting control strategy based on the dynamic adjustment strategy to obtain a power scheduling function;

[0038] Perform timing adjustment in the simulation environment according to the power scheduling function to obtain the optimal lighting control strategy.

[0039] Optionally, perform comprehensive calculation based on the user demand information and the optimal lighting control strategy to generate a lighting control instruction, and control the mobile lighting tower based on the lighting control instruction, including:

[0040] Obtain the control instruction sent by the user based on the intelligent device side, parse and extract the key information in the control instruction to form a demand data set;

[0041] Perform feature mapping on the demand data set to obtain a multi-dimensional user demand feature vector;

[0042] Based on the optimal lighting control strategy and the multi-dimensional user demand feature vector, obtain a lighting control scheme that meets the user's needs through matching analysis;

[0043] Optimize the optimal lighting control strategy according to the lighting control scheme to obtain an optimization result;

[0044] Generate a lighting control instruction according to the optimization result, and control the mobile lighting tower based on the lighting control instruction.

[0045] A control device for a mobile lighting lighthouse, comprising:

[0046] A collection module, configured to detect environmental data by using sensors on the mobile lighting lighthouse to obtain a set of sensor data, wherein the set of sensor data includes multi-dimensional data combining light intensity and temperature and humidity;

[0047] An analysis module, configured to perform trend analysis on the set of sensor data to obtain an environmental change prediction result, wherein the environmental change prediction result includes lighting demand information within a preset future time;

[0048] A construction module, configured to construct a lighting strategy model by combining the environmental change prediction result and the current environmental data detected by the sensors, and calculate a lighting control strategy based on the lighting strategy model;

[0049] A calculation module, configured to perform sensitivity analysis on the light intensity in the lighting control strategy to obtain the influence weight of the light intensity on the lighting control strategy, and calculate an optimal lighting duration based on the influence weight;

[0050] An optimization module, configured to obtain the power limit information of the mobile lighting lighthouse, and optimize the lighting control strategy based on the optimal lighting duration and the power limit information to obtain an optimal lighting control strategy;

[0051] A control module, configured to perform comprehensive calculation based on user demand information and the optimal lighting control strategy to generate a lighting control instruction, and control the mobile lighting lighthouse based on the lighting control instruction, wherein the user demand information is obtained by parsing a control instruction sent by the user based on the smart device side.

[0052] A control device for a mobile lighting lighthouse, comprising a memory and a processor;

[0053] The memory is used for storing programs;

[0054] The processor is configured to execute the programs to implement each step of the control method for the mobile lighting lighthouse as described in any one of the above.

[0055] A readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each step of the control method for the mobile lighting lighthouse as described in any one of the above is implemented.

[0056] As can be seen from the above technical solutions, a control method and related devices for a mobile lighting lighthouse provided by an embodiment of the present application first use sensors on the lighthouse to detect environmental data, forming a sensor data set containing multi-dimensional data such as light intensity, temperature, and humidity. Then, trend analysis is performed on these data to obtain an environmental change prediction result containing lighting demand information within a preset future time. After that, a lighting strategy model is constructed by combining the environmental change prediction result with the current environmental data, and then a lighting control strategy is calculated. To optimize the lighting scheme, sensitivity analysis is performed on the light intensity in the strategy to determine its influence weight and calculate the optimal lighting duration. Then, the power limit information of the lighthouse is obtained, and the strategy is optimized in combination with the optimal lighting duration to obtain the optimal lighting control strategy. Finally, the user demand information is obtained by parsing the control instructions sent by the user based on the intelligent device side, and it is comprehensively calculated with the optimal lighting control strategy to generate a lighting control instruction to control the lighthouse.

[0057] By collecting and analyzing multi-dimensional environmental data such as light intensity, temperature, and humidity, the present application can grasp the trend of environmental changes in real time, avoiding the problem of unstable lighting effects caused by the existing technology relying on fixed patterns or manual adjustment. Combining environmental change prediction with current environmental data to dynamically adjust the lighting strategy, calculating the optimal lighting duration based on sensitivity analysis, and being able to parse user instructions and integrate user demands with the optimal strategy, the lighting effect can be flexibly adjusted according to different scenarios and user demands, improving the personalization and intelligence level of the lighting system. In case of emergencies, it can quickly respond to the real-time needs of users, avoiding the problem of being unable to adjust the lighting mode in time, effectively improving the lighting efficiency, effect, and energy utilization rate, greatly enhancing the intelligence level of the mobile lighting lighthouse, and providing an efficient, energy-saving, and intelligent solution for changing outdoor lighting needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0059] Figure 1 It is a flowchart of a control method for a mobile lighting lighthouse disclosed in an embodiment of the present application;

[0060] Figure 2 It is a schematic structural diagram of a mobile lighting lighthouse disclosed in an embodiment of the present application;

[0061] Figure 3 It is a schematic diagram of a control device for a mobile lighting lighthouse disclosed in an embodiment of the present application;

[0062] Figure 4 This is a hardware structure block diagram of a control device for a mobile lighting beacon disclosed in an embodiment of the present application. Detailed implementation manners

[0063] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0064] The present application can be used in many general or special computing device environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor devices, distributed computing environments including any of the above devices or equipment, and so on.

[0065] Next, the solution of the present application will be introduced. The present application proposes the following technical solution. For details, please refer to the following text.

[0066] Figure 1 This is a flowchart of a control method for a mobile lighting beacon disclosed in an embodiment of the present application.

[0067] As Figure 1 shown, the method may include:

[0068] Step S1: Use sensors on the mobile lighting beacon to detect environmental data to obtain a sensor data set, where the sensor data set includes multi-dimensional data combining light intensity and temperature and humidity.

[0069] Specifically, the ambient light intensity sensor mainly collects data by sensing the light intensity. Such sensors are usually equipped with optoelectronic sensor elements, which can sensitively detect the light intensity generated by natural light or artificial light sources in the surrounding environment. Subsequently, the detected light intensity is converted into an electrical signal and transmitted to the control system after digital processing. The change of light intensity is closely related to factors such as time, weather, and seasonal changes, and can accurately reflect the current actual lighting demand.

[0070] The temperature and humidity sensor is responsible for monitoring the temperature and humidity data in the environment. It uses a thermosensitive element and a humidity-sensitive element to detect the temperature and humidity of the surrounding air, converts them into corresponding electrical signals, and finally transmits them to the data processing unit.

[0071] During the entire detection process, the raw sensor data obtained will be integrated into a multidimensional data set in time series. Because the ambient light intensity and temperature and humidity data will continue to change over time, these data will be continuously recorded to form a multidimensional data set containing ambient light intensity and temperature and humidity values.

[0072] Step S2: Perform trend analysis on the sensor data set to obtain environmental change prediction results, wherein the environmental change prediction results include lighting demand information within a preset time in the future.

[0073] Specifically, trend analysis is a data processing technology that focuses on in-depth analysis of the historical records of raw sensor data to uncover potential patterns or trends. First, the raw data must be preprocessed, such as removing noise and filling missing values. Then, historical data is used to perform time series analysis to identify periodic fluctuations or sudden changes in the data.

[0074] For example, the sliding window technology can be used to analyze the data in segments, calculate the change pattern of light intensity, temperature and humidity in each period of time, and then infer the possible short-term and long-term trends. At the same time, it can also combine regression analysis, moving average, exponential smoothing and other methods to predict environmental changes in a certain period of time in the future.

[0075] In the process of trend analysis, by analyzing the changing trends of ambient light intensity and temperature and humidity, it is possible to estimate the lighting demand in the future preset time period. For example, if the light intensity is expected to drop significantly or the temperature and humidity values ​​are abnormal during the preset time period, it can be predicted that the lighting demand will increase; if the ambient light intensity is expected to rise or the temperature and humidity remain stable, then the lighting demand may decrease or remain at a low level. In order to make the prediction results more accurate, historical data and real-time environmental data can be combined for analysis, so that real-time adjustment and optimization of the prediction results can be achieved.

[0076] Step S3: construct a lighting strategy model in combination with the environmental change prediction result and the current environmental data detected by the sensor, and calculate a lighting control strategy based on the lighting strategy model.

[0077] Specifically, the current environmental data is derived from the sensor data set collected in step S1, including real-time ambient light intensity, temperature and humidity, etc. Combining the current environmental data with the prediction results of future environmental changes actually enables the lighting system to comprehensively and dynamically respond to different possible situations.

[0078] In actual operation, building an illumination strategy model requires comprehensive consideration of multiple factors. Mathematical modeling or artificial intelligence algorithms can be used to deeply analyze the current environmental data and prediction results to determine the key factors affecting illumination requirements. For example, an increase in temperature may reduce the illumination requirement, and an increase in humidity is also related to the change in light demand. Based on these factors, a multi-factor mathematical model is built, and these input variables are used to predict the optimal illumination plan.

[0079] To improve the accuracy and adaptability of the model, a dynamic adjustment mechanism can be added to the model so that the illumination control strategy can respond in real time to the rapid changes in the environment. In terms of model design, the intensity, time, and energy consumption of illumination should be taken into account. For example, when the ambient light intensity is high, the illumination intensity can be appropriately reduced to reduce energy waste; while in an environment with low temperature and high humidity, the illumination duration may need to be extended to ensure sufficient brightness. Through such considerations, a set of illumination parameters to be adjusted can be calculated.

[0080] Based on the built illumination strategy model, the specific illumination control strategy can be calculated. This process is actually the result of model solving. The current environmental data and predicted change trends are input into the illumination strategy model, and then a set of specific control parameters are generated, including illumination intensity, on and off times, illumination duration, etc. By calculating these control parameters, the illumination system can automatically adjust the illumination state according to environmental changes in the future for a period of time, which not only meets the established illumination requirements but also ensures the efficient use of energy.

[0081] Step S4: Conduct a sensitivity analysis on the illumination intensity in the illumination control strategy to obtain the influence weight of the illumination intensity on the illumination control strategy, and calculate the optimal illumination duration based on the influence weight.

[0082] Specifically, the sensitivity analysis aims to explore how different illumination intensity parameters affect the effectiveness and energy efficiency of the overall strategy in the illumination control strategy. This process uses mathematical and statistical methods to measure the influence of illumination intensity on the illumination control strategy in different scenarios. By using indicators such as the coefficient of variation and sensitivity index, the illumination intensity is gradually adjusted, and then the changes in other elements of the illumination control strategy (such as energy consumption, illumination time, etc.) are observed.

[0083] For example, when the illumination intensity increases or decreases, it may change the illumination duration and even affect the power consumption efficiency. By gradually changing the illumination intensity, the system can quantify the contribution of each illumination intensity to the final control effect, thereby clarifying the weight of each change to the illumination control strategy.

[0084] After sensitivity analysis, an impact weight index can be obtained, which can reflect the actual impact degree of different light intensities on the overall lighting control strategy. Subsequently, optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) can be used to calculate the optimal lighting duration after simulating the energy efficiency and lighting requirements under different light intensities.

[0085] In this way, while meeting the lighting requirements, the lighting lighthouse can maximize energy conservation and extend the service life. It can even automatically adjust the lighting strategy according to user needs and environmental conditions, and continuously adjust and optimize the lighting system through continuous monitoring and feedback mechanisms to adapt to the complex and changing actual environment.

[0086] Step S5: Obtain the power limit information of the mobile lighting lighthouse, and optimize the lighting control strategy based on the optimal lighting duration and the power limit information to obtain the optimal lighting control strategy.

[0087] Specifically, the power limit information refers to the maximum power output that the mobile lighting lighthouse can support during operation, which is jointly determined by the hardware performance of the lighting lighthouse, the device battery capacity, the energy supply conditions, and other external limiting factors. There are various ways to obtain the power limit information. It can be directly detected by sensors or obtained by means of set parameters and the power dispatching model in the control system. These information usually cover data such as the current battery power, power consumption mode, and maximum load power.

[0088] Based on this power limit information, the lighting control strategy needs to be further optimized. Among them, the optimal lighting duration is obtained through the previous light intensity sensitivity analysis. It is the ideal duration that can maximize the satisfaction of lighting requirements and avoid energy waste under specific environmental conditions. However, in actual situations, due to power resource limitations, this optimal lighting duration may not be directly applicable and needs to be adjusted according to the power limit information.

[0089] For example, when the sensor detects that the battery power of the lighting lighthouse is approaching the lower limit, it is necessary to reduce the lighting intensity or shorten the lighting time according to the power limit information to ensure that the lighting lighthouse continues to work and meets the minimum lighting requirements. In some special scenarios, if the environment changes violently or the user needs change suddenly, it is also necessary to dynamically adjust the lighting strategy to adapt to these changes. In the future period, based on the current power limit conditions, the optimal lighting time and lighting intensity that can be supported without exceeding the power resource limit need to be calculated.

[0090] Step S6: Based on the user demand information and the optimal lighting control strategy, perform comprehensive calculations to generate a lighting control instruction, and control the mobile lighting lighthouse based on the lighting control instruction, where the user demand information is obtained by parsing the control instruction sent by the user based on the intelligent device side.

[0091] Specifically, first, it is necessary to parse the control instructions sent by the user through the intelligent device side (such as smartphones, tablets, or other networked devices). The control instructions sent by the user to the mobile lighting lighthouse through these intelligent devices can involve adjustments to aspects such as lighting intensity, lighting mode, and switch status. These control instructions usually carry clear demand parameters, such as the required light intensity and the on / off time of lighting.

[0092] Next, perform comprehensive calculations on the user demand information and the previously obtained optimal lighting control strategy. First, convert the user demand into specific lighting control parameters, such as light intensity and duration, and then compare them with the optimal light intensity and optimal lighting duration set in the lighting control strategy. If the user demand matches the existing strategy, the control instruction can be directly generated based on the optimal control strategy. If the user demand exceeds the scope supported by the strategy, the system will automatically adjust and recalculate the most suitable lighting parameters, and then generate a lighting control instruction that includes adjusting the lighthouse light intensity, switch status, operation duration, etc. This instruction will directly drive the lighthouse to perform the corresponding lighting tasks.

[0093] In this way, the intelligent control method can not only ensure that the lighthouse automatically adjusts according to environmental conditions but also flexibly respond to the real-time needs of users. This real-time interaction between users and the system significantly enhances the intelligence and flexibility of the lighting system.

[0094] From the above technical solution, it can be seen that the control method and related equipment of a mobile lighting lighthouse provided in the embodiment of the present application first use the sensors on the lighthouse to detect environmental data to form a sensor data set containing multi-dimensional data such as light intensity and temperature and humidity. Then, perform trend analysis on these data to obtain an environmental change prediction result containing lighting demand information within a preset future time. After that, combine the environmental change prediction result with the current environmental data to construct a lighting strategy model, and then calculate the lighting control strategy. In order to optimize the lighting scheme, perform sensitivity analysis on the light intensity in this strategy, determine its influence weight, and calculate the optimal lighting duration. Then, obtain the power limit information of the lighthouse, and optimize the strategy in combination with the optimal lighting duration to obtain the optimal lighting control strategy. Finally, parse the control instruction sent by the user based on the intelligent device side to obtain the user demand information, and perform comprehensive calculations on it and the optimal lighting control strategy to generate a lighting control instruction to control the lighthouse.

[0095] By collecting and analyzing multi-dimensional environmental data such as light intensity, temperature and humidity, this application can grasp the environmental change trend in real time, avoiding the problem of unstable lighting effects caused by the existing technology relying on fixed patterns or manual adjustment. Combining environmental change prediction with current environmental data to dynamically adjust the lighting strategy, calculating the optimal lighting duration based on sensitivity analysis, and also being able to parse user instructions and integrate user needs with the optimal strategy, it can flexibly adjust the lighting effect according to different scenarios and user needs, improving the personalization and intelligence level of the lighting system. In case of emergency, it can quickly respond to the real-time needs of users, avoiding the problem of being unable to adjust the lighting mode in time, effectively improving the lighting efficiency, effect and energy utilization rate, greatly enhancing the intelligence level of the mobile lighting tower, and providing an efficient, energy-saving and intelligent solution for the changeable outdoor lighting needs.

[0096] Reference Figure 2 , in one embodiment, the mobile lighting tower includes support legs 3, a generator 4, a lamp 1 and a lamp height adjuster 2. The support legs 3 are connected to the base through a hydraulic lifting device, and this hydraulic lifting device can adjust the height and angle of the support legs 3 as needed.

[0097] The support legs 3 are connected to the tower body 5 through hinge connectors. The hinge connection allows the support legs 3 to have a certain degree of rotational freedom in the vertical direction, so that when lifting, the support legs 3 can freely follow the movement of the tower body 5 and ensure the stability of the tower. During operation, the connection of the support legs 3 to the tower body 5 through hinges can be quickly folded or unfolded as needed, facilitating transportation and deployment.

[0098] The generator 4 is connected to the electrical control box through multi-point bolt connections. This connection method provides strength and stability, ensuring that the generator 4 can be firmly connected to the electrical control system during operation. Through this connection, the electrical control box can monitor the working state of the generator 4 in real time and adjust the power output according to actual needs to ensure the normal operation of the lamp 1 and other systems.

[0099] The lamp 1 is connected to the lamp height adjuster 2 through a gear transmission device. The gear transmission device can accurately adjust the lifting and angle of the lamp 1, enabling it to adjust the light direction and height according to environmental needs. The adjuster controls the vertical lifting of the lamp 1 by rotating the gear to ensure that the tower can provide the best lighting effect.

[0100] Specifically, the lamp height adjuster 2 is connected to the adjusting rod through internal threads, and the lamp 1 is connected to the hole groove of the adjusting rod through a pin shaft. In this embodiment, through the above control instructions, the lifting of the support legs 3, the on / off of the generator 4, and the on / off of the lamp 1 can be controlled.

[0101] In addition, the mobile lighting tower also has a function of self-checking for faults and alarming. When a component of the tower, such as the generator 4, the lamp 1, or the lifting device, malfunctions, the intelligent control system can quickly identify the type of the fault and send an alarm message to the user through the intelligent device terminal, specifying in detail the fault location and possible solutions. This instant feedback mechanism greatly shortens the fault response time and improves the maintenance efficiency of the tower.

[0102] In some embodiments of the present application, step S1 is further introduced and specifically may include:

[0103] Step S11: Use the sensors on the mobile lighting tower to detect the light intensity data and temperature and humidity data of the environment;

[0104] Step S12: Perform hierarchical acquisition on the light intensity data to obtain light intensity data at multiple different heights and azimuths, and perform integration and weighting processing to obtain light intensity distribution data;

[0105] Step S13: Perform multi-dimensional feature extraction on the temperature and humidity data to obtain the independent contribution degrees of temperature and humidity to the change in light intensity, and construct a regression model in combination with the interaction between temperature and humidity to obtain global temperature and humidity prediction data;

[0106] Step S14: Classify the environmental state at each moment according to the interaction relationship between the light intensity distribution data and the global temperature and humidity prediction data to obtain dynamic environmental state data;

[0107] Step S15: Perform data fusion on the light intensity distribution data, the global temperature and humidity prediction data, and the dynamic environmental state data to obtain a sensor data set.

[0108] Specifically, the mobile lighting tower is equipped with multiple sensors, whose function is to monitor and collect relevant data in the environment in real time. The light intensity sensor can accurately measure the light intensity at different positions and heights according to the environmental conditions; the temperature and humidity sensor can record the temperature and humidity levels of the environment.

[0109] When collecting light intensity data, data is collected at different heights and azimuth angles. For example, the light intensity values measured by sensors at three different heights, namely the ground, mid-air, and top, may be different. When integrating these data, the influence of different positions on the overall environmental illumination is fully considered. After hierarchical acquisition, data integration and weighting processing are performed. The purpose is to synthesize the light intensity data at different acquisition positions into a light intensity distribution data that can represent the entire area. Specifically, the light intensity data at different heights and directions can be weighted by a weight coefficient, and the distribution of the weight coefficient needs to be set according to the illumination characteristics and requirements in the actual environment.

[0110] Furthermore, a regression model will be established to explore the effects of temperature and humidity on light intensity, taking into account the independent and interactive effects of temperature and humidity on light intensity, i.e., how the combination of temperature and humidity affects light illumination. For example, low temperature may increase the air density, thus affecting the refraction and propagation of light; high humidity may increase the water vapor content in the air, affecting the scattering and absorption of light. Through these feature extractions, global temperature and humidity prediction data can be obtained and classified. Analyze the environmental data at each moment to determine the current environmental state, such as sufficient light, moderate temperature and humidity, or weak light, etc.

[0111] Finally, all the collected data, including light intensity distribution data, global temperature and humidity prediction data, and dynamic environmental state data, are fused to combine different types of data into a complete set of original sensor data. This fusion process can be achieved through Bayesian fusion algorithm or neural network to ensure that global environmental information can be obtained, and then the lighting effect and energy efficiency can be optimized.

[0112] In some embodiments of the present application, step S2 is further introduced, which may specifically include:

[0113] Step S21: Perform time series decomposition processing on the light intensity data in the sensor data set to obtain a long-term trend component;

[0114] Step S22: Perform dynamic state estimation on the temperature and humidity data in the sensor data set to obtain the state estimation values corresponding to the temperature and humidity data;

[0115] Step S23: Perform spatio-temporal correlation analysis on the long-term trend component and the state estimation values to generate an environmental change trend;

[0116] Step S24: Use the exponential smoothing method to predict the environmental change trend in the future time period to obtain the environmental change prediction result, where the environmental change prediction result includes lighting demand information within a preset future time.

[0117] Specifically, time series decomposition processing is performed on the ambient light intensity data in the original sensor data set. Time series analysis is a method specifically for analyzing data collected in chronological order. In this process, the original ambient light intensity data is decomposed into several components as a whole, generally including a trend component, a seasonal component, and a residual component. In this embodiment, the focus is on the long-term trend component, aiming to obtain the long-term change pattern of the light intensity, such as the long-term effects of factors such as seasonal alternation and weather patterns on the light intensity. For example, if the light intensity data in a certain area shows a gradually decreasing trend, this may be due to the shortening of daylight hours in winter, and this trend is relatively stable and persistent. By performing time series decomposition processing on the light intensity data, this long-term trend can be accurately extracted.

[0118] Furthermore, dynamic state estimation is performed on the temperature and humidity data to obtain a state estimation value. Dynamic state estimation is a method for inferring the current environmental state based on temperature and humidity data that changes over time. The temperature and humidity data are processed through a specific model or algorithm to estimate the state changes at different time points. Common methods include Kalman filtering, particle filtering, or other state estimation algorithms. These methods combine the temperature and humidity data with other factors in the environment to obtain the current estimated state value.

[0119] Spatio-temporal correlation analysis can capture the interdependent relationships between environmental data by comprehensively considering spatial distribution and time series. Through spatio-temporal correlation analysis, the long-term trend of light intensity and the dynamic state estimation value of temperature and humidity can be correlated to find the correlation between them, and then a comprehensive environmental change trend can be generated.

[0120] Finally, exponential smoothing method is used to predict the future environmental changes to obtain the lighting demand information within a preset future time period. Exponential smoothing method belongs to a time series prediction method, which gradually estimates the future trend by assigning different weights to historical data. Specifically, higher weights are given to recent data, while lower weights are given to older data. In lighting demand prediction, the exponential smoothing method can effectively predict the lighting demand in the future time period based on the previous environmental change trend.

[0121] In some embodiments of the present application, step S3 is further introduced, which may specifically include:

[0122] Step S31: Joint modeling processing is performed on the environmental change prediction result and the current environmental data detected by the sensor to obtain a joint model of environmental change and lighting demand;

[0123] Step S32: Use the joint model to perform feature importance analysis on each input feature, calculate the contribution of each input feature to the lighting demand prediction result, and obtain the key factors affecting the lighting control strategy;

[0124] Step S33: Based on the key factors, construct a dynamic weight allocation mechanism to obtain a lighting control strategy weight model;

[0125] Step S34: Optimize the lighting control strategy weight model based on the particle swarm optimization algorithm to generate the lighting control strategy.

[0126] Specifically, combine the environmental change prediction result with the current environmental data to construct a joint model. This model can integrate various environmental information such as light intensity, temperature and humidity, and time series trends, so as to achieve a more comprehensive lighting demand prediction. Based on this joint model, perform feature importance analysis to evaluate the contribution of each input feature to the final prediction result. Further, sort each input feature through an algorithm to analyze which factors have the most significant impact on the lighting demand. For example, the impact of temperature change on lighting demand may far exceed that of humidity change, or the change in light intensity affects lighting demand more directly than other environmental data. Through such analysis, the factors that have an important impact on optimizing the lighting control strategy can be identified, and a dynamic weight allocation mechanism is constructed based on these factors.

[0127] The core of the dynamic weight allocation mechanism is to dynamically adjust the weights of different factors in the lighting control strategy according to the influence degree of each key factor. On this basis, in this embodiment, the particle swarm optimization (PSO) algorithm is used to optimize the weight model of the lighting control strategy. This algorithm simulates the flight process of particles in the search space to find the optimal solution. Its advantage is that it can handle multi-dimensional complex problems and has a powerful global search ability. In this embodiment, the particle swarm optimization algorithm mainly optimizes the effect of the lighting control strategy by adjusting the weight parameters. Through multiple iterations of optimizing the weights in the model, the particle swarm algorithm can find an optimal strategy to ensure that the lighting demand can be most effectively predicted and controlled under various environmental change conditions. During the particle swarm optimization process, each particle represents a potential solution, and through simulating the cooperation and information transmission between particles, the most suitable weight combination is finally determined.

[0128] Furthermore, the calculation expression of the above embodiment is: ,

[0129] where represents the output of the final lighting control strategy (i.e., the predicted value of the lighting demand), and this value will be used to determine the working state (on / off) of each lighting device and its brightness adjustment. represents the weight coefficient of the i-th feature, It represents the functional form of the input feature i with respect to the lighting demand, and E is the environmental data vector. It represents the dynamic adjustment factor of the i-th feature, and θ is the time parameter of environmental change. It represents the correction factor of feature i under the current environmental state, where is the real-time environmental data, is the environmental prediction data. It represents the optimized value in the optimization result of the i-th particle swarm optimization, which reflects the adjustment effect of the particle swarm optimization algorithm on the strategy weights. The entire formula adjusts the weights of each feature through the particle swarm optimization algorithm, enabling the lighting control strategy to continuously self-optimize with the changes of time and environment, ensuring the best lighting effect under any environmental conditions. In practical applications, this calculation formula will perform real-time calculations through real-time environmental data (such as temperature, humidity, light intensity, etc. obtained by sensors) and pre-set environmental prediction data, and continuously update the lighting control strategy.

[0130] In some embodiments of the present application, step S4 is further introduced, which may specifically include:

[0131] Step S41: Perform weight assignment processing on the light intensity in the lighting control strategy based on the light intensity data to form a weight assignment result;

[0132] Step S42: Evaluate the sensitivity of the weight assignment result, calculate the influence degree of the environmental factors in the weight assignment result on the lighting control strategy, and obtain an analysis result, where the environmental factors include light intensity data and temperature and humidity data;

[0133] Step S43: Multiply the influence weights of each environmental factor in the analysis result by the lighting demand in the lighting control strategy to obtain the light intensity demands for each time period, and calculate the optimal lighting duration according to the light intensity demands.

[0134] Specifically, first, according to the real-time or preset environmental light intensity data, it is necessary to clarify the influence degree of the environmental light intensity on the light intensity in the lighting control strategy in different time periods. This requires modeling and analyzing the environmental light intensity in different time periods, and a functional relationship between the environmental light intensity and the indoor light intensity demand can be constructed by means of regression analysis or other machine learning methods.

[0135] Next, evaluate the preliminary weight distribution results and deeply analyze the specific impacts of different environmental factors (such as ambient light intensity, temperature and humidity values) on the lighting intensity requirements. For example, the impact of temperature and humidity on lighting requirements is not direct. However, changes in temperature and humidity will affect people's comfort, which in turn indirectly affects lighting requirements. Through sensitivity analysis, the specific impact degree of each environmental factor on the lighting control strategy can be obtained, that is, the sensitivity index of each factor. For factors with higher sensitivity, higher weights will be assigned to them; while for factors with lower sensitivity, their influence on the strategy will be reduced. Using this method, the lighting control strategy can be automatically adjusted according to changes in multiple factors such as ambient light intensity, temperature and humidity, so as to achieve accurate prediction of lighting requirements.

[0136] Finally, according to the lighting intensity requirements in each time period and combining the influence weights of environmental factors on lighting requirements, the lighting intensity required for each time period can be determined, and finally the most suitable lighting duration for each time period can be calculated.

[0137] In some embodiments of the present application, step S5 is further introduced, which may specifically include:

[0138] Step S51, obtain the power limit information of the mobile lighting beacon, where the power limit information at least includes the current battery power, battery discharge curve, power consumption history, maximum output power of the device, and predetermined operation time limit;

[0139] Step S52, extract features from the power limit information to obtain feature data of power consumption rate, power load fluctuation, predicted remaining power value, and maximum working duration, and form a power limit feature vector;

[0140] Step S53, perform weighted processing on each feature in the power limit feature vector to form a power limit condition, and obtain the value range of the optimal lighting duration under the power limit condition;

[0141] Step S54, generate a dynamic adjustment strategy according to the value range, and adjust the lighting control strategy based on the dynamic adjustment strategy to obtain a power scheduling function;

[0142] Step S55, perform timing adjustment in a simulation environment according to the power scheduling function to obtain the optimal lighting control strategy.

[0143] Specifically, first obtain the power limit information of the mobile lighting tower. This data set covers the current battery power, battery discharge curve, equipment power consumption history, maximum equipment output power, and predetermined operation time limit, etc. Then perform feature extraction on the power limit data set to generate a power limit feature vector. Feature extraction aims to extract representative parameters from the original power limit data, which helps to deeply analyze the relationship between battery power change and equipment power output. The extracted features at least include power consumption rate, power load fluctuation, predicted remaining power value, and maximum working duration.

[0144] The power consumption rate reflects the power usage speed of the equipment under different load conditions; the power load fluctuation reflects the change of equipment power consumption in different time periods, which is crucial for dynamically adjusting the lighting duration because the power load fluctuation may affect the battery discharge curve. The predicted remaining power value is the prediction of the remaining power of the battery in the future period based on the existing data, which helps the system to judge whether it can continuously provide lighting services. The maximum working duration is the longest time that the equipment can continuously work calculated based on the existing power and power consumption rate.

[0145] Subsequently, perform weighted processing on each feature in the power limit feature vector, and assign different weights according to the influence degree of each feature on the final lighting duration in the optimization process. For example, the remaining battery power and the maximum working duration will be assigned higher weights because the power consumption rate and load fluctuation have relatively less impact on the battery life. Through weighted processing, the obtained power limit conditions can accurately reflect the optimal value range of the lighting duration under specific battery states and equipment load conditions.

[0146] Based on these power limit conditions, generate a dynamic adjustment strategy. This strategy will adjust the lighting control strategy according to the real-time change of battery power, power consumption rate, and other influencing factors to avoid battery power depletion while meeting the lighting requirements. The core advantage of the dynamic adjustment strategy lies in its flexibility and adaptability. Generate a power scheduling function according to this strategy. This function is the core tool for controlling the power distribution and adjustment of the lighting system in different time periods, and can adjust the power output in real time according to environmental changes and equipment states to ensure the most suitable lighting intensity under power limits.

[0147] Finally, perform timing adjustment in the simulation environment based on the power scheduling function to test and optimize the optimal lighting control strategy.

[0148] In some embodiments of this application, step S6 is further introduced, which may specifically include:

[0149] Step S61: Obtain the control instruction sent by the user based on the intelligent device side, parse and extract the key information in the control instruction, and form a demand data set;

[0150] Step S62: Perform feature mapping on the demand data set to obtain a multi-dimensional user demand feature vector;

[0151] Step S63: Based on the optimal lighting control strategy and the multi-dimensional user demand feature vector, obtain a lighting control scheme that meets the user's needs through matching analysis;

[0152] Step S64: Optimize the optimal lighting control strategy according to the lighting control scheme to obtain an optimization result;

[0153] Step S65: Generate a lighting control instruction according to the optimization result, and control the mobile lighting tower based on the lighting control instruction.

[0154] Specifically, obtaining the control instruction sent by the user based on the intelligent device side is the starting point of the entire process. The user can send control instructions to the lighting system through the application program, voice assistant or other means on the intelligent device side. These instructions cover the user's lighting needs, such as brightness, lighting duration, lighting mode, etc. After extracting the key information from the instructions, a demand data set is formed. The content of this set may include the light intensity requested by the user, the desired lighting time, the required lighting mode (such as constant brightness, gradual change, flicker, etc.) and other environmental requirements.

[0155] Next, perform feature mapping on the demand data set to obtain a multi-dimensional user demand feature vector. The role of feature mapping is to transform the user's needs from the original control instruction into numerical features that can be quantified and processed. These feature vectors may include the light intensity range required by the user, the upper and lower limits of the lighting duration, and the selected lighting mode, etc. The feature mapping process can use methods such as standardization, normalization, and vectorization in machine learning to ensure that the generated feature vectors can effectively reflect the multi-dimensional characteristics of the user's needs.

[0156] After that, the optimal lighting control strategy is matched and analyzed with the user's requirements. The differences between the user's demand feature vector and each feature in the optimal lighting control strategy are compared to determine whether adjustments need to be made to the lighting control strategy. After completing the matching of demand and strategy, the optimal lighting control strategy is optimized to generate a final lighting control solution that meets the user's requirements. The optimization process adjusts the lighting control strategy according to the user's requirements. For example, if the user requires a longer lighting duration, the power output strategy needs to be adjusted to extend the lighting time; if the user's demand exceeds the power output capacity of the device, the light intensity or lighting mode will be automatically adjusted to avoid excessive battery power consumption while ensuring the lighting effect. The goal of optimization is to find a balance point to meet the user's lighting needs as much as possible without exceeding the device power and battery limits.

[0157] After optimization, the finally generated lighting control instructions will include specific lighting parameters such as lighting intensity, duration, and control mode, and the mobile lighting tower will be controlled according to the instructions. The generation of the control instructions takes into account both the actual needs of the user and fully optimizes factors such as the power consumption and remaining battery power of the device to ensure the reliability and stability of the lighting system.

[0158] Next, a control device for a mobile lighting tower provided by an embodiment of the present application will be described. The control device for a mobile lighting tower described below can be correspondingly referred to the control method for a mobile lighting tower described above.

[0159] See Figure 3 , Figure 3 which is a schematic diagram of a control device for a mobile lighting tower disclosed in an embodiment of the present application.

[0160] As Figure 3 shown, the control device for a mobile lighting tower may include:

[0161] A collection module 110, configured to detect environmental data by using sensors on the mobile lighting tower to obtain a set of sensor data, where the set of sensor data includes multi-dimensional data combining light intensity and temperature and humidity;

[0162] An analysis module 120, configured to perform trend analysis on the set of sensor data to obtain an environmental change prediction result, where the environmental change prediction result includes lighting demand information within a preset future time;

[0163] A construction module 130, configured to construct a lighting strategy model by combining the environmental change prediction result and the current environmental data detected by the sensors, and calculate a lighting control strategy based on the lighting strategy model;

[0164] A calculation module 140 is configured to perform a sensitivity analysis on the light intensity in the lighting control strategy to obtain an influence weight of the light intensity on the lighting control strategy, and calculate an optimal lighting duration based on the influence weight;

[0165] An optimization module 150 is configured to obtain power limit information of the mobile lighting lighthouse, and optimize the lighting control strategy based on the optimal lighting duration and the power limit information to obtain an optimal lighting control strategy;

[0166] A control module 160 is configured to perform a comprehensive calculation based on user demand information and the optimal lighting control strategy to generate a lighting control instruction, and control the mobile lighting lighthouse based on the lighting control instruction, where the user demand information is obtained by parsing a control instruction sent by a user based on a smart device. As can be seen from the above technical solution, a control method and related device for a mobile lighting lighthouse provided by an embodiment of the present application first use sensors on the lighthouse to detect environmental data to form a sensor data set including multi-dimensional data such as light intensity, temperature, and humidity. Then, trend analysis is performed on these data to obtain an environmental change prediction result including lighting demand information within a preset future time. After that, a lighting strategy model is constructed by combining the environmental change prediction result with the current environmental data, and then a lighting control strategy is calculated. To optimize the lighting scheme, a sensitivity analysis is performed on the light intensity in the strategy to determine its influence weight and calculate the optimal lighting duration. Then, the power limit information of the lighthouse is obtained, and the strategy is optimized in combination with the optimal lighting duration to obtain an optimal lighting control strategy. Finally, the user demand information is obtained by parsing a control instruction sent by a user based on a smart device, and it is comprehensively calculated with the optimal lighting control strategy to generate a lighting control instruction to control the lighthouse.

[0167] By collecting and analyzing multi-dimensional environmental data such as light intensity, temperature, and humidity, the present application can grasp the environmental change trend in real time, and avoid the problem of unstable lighting effects caused by the existing technology relying on fixed patterns or manual adjustment. By dynamically adjusting the lighting strategy by combining environmental change prediction and current environmental data, calculating the optimal lighting duration based on sensitivity analysis, and being able to parse user instructions and integrate user demands with the optimal strategy, the lighting effect can be flexibly adjusted according to different scenarios and user demands, improving the personalization and intelligence level of the lighting system. In case of an emergency, it can quickly respond to the real-time demands of users, avoid the problem of being unable to adjust the lighting mode in time, effectively improve the lighting efficiency, effect, and energy utilization rate, greatly improve the intelligence level of the mobile lighting lighthouse, and provide an efficient, energy-saving, and intelligent solution for changing outdoor lighting demands.

[0168] The control device for a mobile lighting lighthouse provided by an embodiment of the present application can be applied to a control device of a mobile lighting lighthouse. Figure 4shows the hardware structure block diagram of the control device of the mobile lighting tower. Refer to Figure 4 , the hardware structure of the control device of the mobile lighting tower may include: at least one processor 10, at least one communication interface 20, at least one memory 30, and at least one communication bus 40;

[0169] In the embodiments of the present application, the number of the processor 10, the communication interface 20, the memory 30, and the communication bus 40 is at least one, and the processor 10, the communication interface 20, and the memory 30 complete mutual communication through the communication bus 40;

[0170] The processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.;

[0171] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory;

[0172] Among them, the memory stores a program, and the processor can call the program stored in the memory. The program is used for:

[0173] Using the sensors on the mobile lighting tower to detect environmental data to obtain a set of sensor data, where the set of sensor data includes multi-dimensional data combining light intensity and temperature and humidity;

[0174] Performing trend analysis on the set of sensor data to obtain an environmental change prediction result, where the environmental change prediction result includes lighting demand information within a preset future time;

[0175] Combining the environmental change prediction result and the current environmental data detected by the sensor to construct a lighting strategy model, and calculating a lighting control strategy based on the lighting strategy model;

[0176] Performing sensitivity analysis on the light intensity in the lighting control strategy to obtain the influence weight of the light intensity on the lighting control strategy, and calculating the optimal lighting duration based on the influence weight;

[0177] Obtaining the power limit information of the mobile lighting tower, and optimizing the lighting control strategy based on the optimal lighting duration and the power limit information to obtain an optimal lighting control strategy;

[0178] Based on the comprehensive calculation of the user demand information and the optimal lighting control strategy, a lighting control instruction is generated, and the mobile lighting lighthouse is controlled based on the lighting control instruction, wherein the user demand information is obtained by parsing the control instruction sent by the user based on the smart device side.

[0179] Optionally, the refinement functions and extension functions of the program can be referred to the above description.

[0180] The embodiment of the present application also provides a readable storage medium, which can store a program suitable for execution by a processor, and the program is used for:

[0181] Detect environmental data using sensors on the mobile lighting lighthouse to obtain a set of sensor data, wherein the set of sensor data includes multi-dimensional data combining light intensity and temperature and humidity;

[0182] Perform trend analysis on the set of sensor data to obtain an environmental change prediction result, wherein the environmental change prediction result includes lighting demand information within a preset future time;

[0183] Construct a lighting strategy model by combining the environmental change prediction result and the current environmental data detected by the sensors, and calculate the lighting control strategy based on the lighting strategy model;

[0184] Perform sensitivity analysis on the light intensity in the lighting control strategy to obtain the influence weight of the light intensity on the lighting control strategy, and calculate the optimal lighting duration based on the influence weight;

[0185] Obtain the power limit information of the mobile lighting lighthouse, and optimize the lighting control strategy based on the optimal lighting duration and the power limit information to obtain the optimal lighting control strategy;

[0186] Based on the comprehensive calculation of the user demand information and the optimal lighting control strategy, a lighting control instruction is generated, and the mobile lighting lighthouse is controlled based on the lighting control instruction, wherein the user demand information is obtained by parsing the control instruction sent by the user based on the smart device side.

[0187] Optionally, the refinement functions and extension functions of the program can be referred to the above description.

[0188] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0189] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other.

[0190] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method for a mobile lighting beacon, characterized in that, Including: Using sensors on a mobile lighting tower to detect environmental data, obtaining a set of sensor data, where the set of sensor data includes multi-dimensional data combining light intensity and temperature / humidity; Performing trend analysis on the set of sensor data to obtain an environmental change prediction result, where the environmental change prediction result includes lighting demand information within a preset future time; Combining the environmental change prediction result and the current environmental data detected by the sensors to construct a lighting strategy model, and calculating a lighting control strategy based on the lighting strategy model; Performing sensitivity analysis on the light intensity in the lighting control strategy to obtain the influence weight of the light intensity on the lighting control strategy, and calculating the optimal lighting duration based on the influence weight; Obtaining the power limit information of the mobile lighting tower, and optimizing the lighting control strategy based on the optimal lighting duration and the power limit information to obtain an optimal lighting control strategy; Performing comprehensive calculation based on the user demand information and the optimal lighting control strategy to generate a lighting control instruction, and controlling the mobile lighting tower based on the lighting control instruction, where the user demand information is obtained by parsing the control instruction sent by the user based on the intelligent device side.

2. The method according to claim 1, wherein The using sensors on a mobile lighting tower to detect environmental data, obtaining a set of sensor data, includes: Using the sensors on the mobile lighting tower to detect the light intensity data and temperature / humidity data of the environment; Performing hierarchical acquisition on the light intensity data to obtain light intensity data at multiple different heights and azimuths, and performing integration and weighting processing to obtain light intensity distribution data; Performing multi-dimensional feature extraction on the temperature / humidity data to obtain the independent contribution degrees of temperature and humidity to the change in light intensity, and constructing a regression model by combining the interaction of temperature and humidity to obtain global temperature / humidity prediction data; Classifying the environmental state at each moment according to the interaction relationship between the light intensity distribution data and the global temperature / humidity prediction data to obtain dynamic environmental state data; Performing data fusion on the light intensity distribution data, the global temperature / humidity prediction data, and the dynamic environmental state data to obtain a set of sensor data.

3. The method according to claim 1, wherein Performing trend analysis on the set of sensor data to obtain an environmental change prediction result, including: Performing time series decomposition processing on the light intensity data in the set of sensor data to obtain a long-term trend component; Performing dynamic state estimation on the temperature / humidity data in the set of sensor data to obtain the state estimation value corresponding to the temperature / humidity data; Performing spatio-temporal correlation analysis on the long-term trend component and the state estimation value to generate an environmental change trend; Using the exponential smoothing method to predict the environmental change trend in a future time period to obtain the environmental change prediction result, where the environmental change prediction result includes lighting demand information within a preset future time.

4. The method according to claim 1, wherein Combining the environmental change prediction result and the current environmental data detected by the sensors to construct a lighting strategy model, and calculating a lighting control strategy based on the lighting strategy model, including: Jointly model the predicted results of the environmental changes and the current environmental data detected by the sensors to obtain a joint model of environmental changes and lighting requirements; Use the joint model to analyze the feature importance of each input feature, calculate the contribution of each input feature to the predicted results of lighting requirements, and obtain the key factors affecting the lighting control strategy; Construct a dynamic weight allocation mechanism based on the key factors to obtain a lighting control strategy weight model; Optimize the lighting control strategy weight model based on the particle swarm optimization algorithm to generate the lighting control strategy.

5. The method according to claim 1, characterized in that Conduct a sensitivity analysis on the light intensity in the lighting control strategy to obtain the influence weight of the light intensity on the lighting control strategy, and calculate the optimal lighting duration based on the influence weight, including: Perform weight allocation processing on the light intensity in the lighting control strategy based on the light intensity data to form a weight allocation result; Evaluate the sensitivity of the weight allocation result, calculate the influence degree of the environmental factors in the weight allocation result on the lighting control strategy, and obtain an analysis result, where the environmental factors include light intensity data and temperature and humidity data; Multiply the influence weight of each environmental factor in the analysis result by the lighting requirements in the lighting control strategy to obtain the light intensity requirements for each time period, and calculate the optimal lighting duration based on the light intensity requirements.

6. The method according to claim 1, characterized in that, Obtain the power limit information of the mobile lighting tower, and optimize the lighting control strategy based on the optimal lighting duration and the power limit information to obtain the optimal lighting control strategy, including: Obtain the power limit information of the mobile lighting tower, where the power limit information at least includes the current battery power, battery discharge curve, power consumption history, maximum output power of the device, and predetermined operation time limit; Extract features from the power limit information to obtain feature data of the power consumption rate, power load fluctuation, predicted remaining power value, and maximum working duration, and form a power limit feature vector; Perform weighted processing on each feature in the power limit feature vector to form a power limit condition, and obtain the value range of the optimal lighting duration under the power limit condition; Generate a dynamic adjustment strategy according to the value range, and adjust the lighting control strategy based on the dynamic adjustment strategy to obtain a power scheduling function; Perform timing adjustment in the simulation environment according to the power scheduling function to obtain the optimal lighting control strategy.

7. The method according to claim 1, characterized in that Perform comprehensive calculation based on the user demand information and the optimal lighting control strategy to generate a lighting control instruction, and control the mobile lighting tower based on the lighting control instruction, including: Obtain the control instruction sent by the user based on the intelligent device terminal, parse and extract the key information in the control instruction to form a demand data set; Perform feature mapping on the demand data set to obtain a multi-dimensional user demand feature vector; Based on the optimal lighting control strategy and the multi-dimensional user demand feature vector, obtain a lighting control solution that meets the user's needs through matching analysis; Optimize the optimal lighting control strategy according to the lighting control scheme to obtain an optimization result; Generate a lighting control instruction according to the optimization result, and control the mobile lighting tower based on the lighting control instruction.

8. A control device for a mobile lighting beacon, characterized in that, Comprising: A collection module, configured to use sensors on the mobile lighting tower to detect environmental data, obtaining a set of sensor data, wherein the set of sensor data includes multi-dimensional data combining light intensity and temperature and humidity; An analysis module, configured to perform a trend analysis on the set of sensor data to obtain an environmental change prediction result, wherein the environmental change prediction result includes lighting demand information within a preset future time; A construction module, configured to construct a lighting strategy model by combining the environmental change prediction result and the current environmental data detected by the sensor, and calculate a lighting control strategy based on the lighting strategy model; A calculation module, configured to perform a sensitivity analysis on the light intensity in the lighting control strategy to obtain an influence weight of the light intensity on the lighting control strategy, and calculate an optimal lighting duration based on the influence weight; An optimization module, configured to obtain the power limit information of the mobile lighting tower, and optimize the lighting control strategy based on the optimal lighting duration and the power limit information to obtain an optimal lighting control strategy; A control module, configured to perform a comprehensive calculation based on the user demand information and the optimal lighting control strategy, generate a lighting control instruction, and control the mobile lighting tower based on the lighting control instruction, wherein the user demand information is obtained by parsing a control instruction sent by the user based on the smart device side.

9. A control device for a mobile lighting beacon, characterized in that, Comprising a memory and a processor; The memory is used for storing programs; The processor is configured to execute the program to implement each step of the control method of the mobile lighting tower according to any one of claims 1-7.

10. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, each step of the control method of the mobile lighting tower according to any one of claims 1-7 is implemented.

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