A sunshade control method for large-span electric track based on multi-source light environment sensing

Abstract

The present invention relates to the technical field of sunshade equipment, and in particular to a sunshade control method for a large-span electric track based on multi-source light environment sensing. The multi-source light environment data sensed and detected by the sensor provided on the large-span electric track sunshade equipment is obtained, the sunshade range of the large-span electric track sunshade equipment is set, and the light environment radiation space of the sunshade range is constructed according to the multi-source light environment data. The light irradiation analysis is performed through the spatial light interference between the light environment radiation space and the object required for sunshade protection by the large-span electric track sunshade equipment, and the track operation of the large-span electric track sunshade equipment and the extension, contraction and tension of the sunshade canopy are controlled according to the analysis results. The present invention can perform accurate and reasonable sunshade analysis on the weather conditions of the large-span electric track sunshade equipment according to the multi-source light environment sensing data, so as to improve the control accuracy and sunshade performance of the large-span electric track sunshade equipment.

Description

Technical Field

[0001] The invention relates to the technical field of sunshade equipment, and in particular to a sunshade control method for a large-span electric track based on multi-source light environment sensing. Background Art

[0002] With the increasing demand for energy conservation and environmental protection in modern buildings, intelligent shading systems have been widely used in architectural design. The shading system can not only effectively adjust the outdoor light environment and improve outdoor comfort, but also reduce building energy consumption. At present, track shading equipment has become an important part of building shading design due to its stability and flexibility. In particular, the large-span electric track shading system is suitable for large-space buildings such as commercial complexes, large exhibition halls, and airport terminals. However, the changes in the multi-source light environment caused by different time, location and climatic conditions have put forward higher requirements for the intelligent control of shading equipment.

[0003] The existing control methods for large-span electric rail awnings are usually only able to achieve control with a single sensor, and it is difficult to fully perceive the complex changes in the multi-source light environment, such as the combined effects of direct light, scattered light, and reflected light, resulting in insufficient control accuracy of the awnings; and most large-span electric rail awning control systems only support control based on preset schedules or simple threshold judgments, and it is difficult to respond to rapid changes in the external light environment in real time, such as changes in clouds or dynamic movement of the sun's position, which may lead to unsatisfactory shading effects or energy waste. Secondly, it is difficult for the existing large-span electric rail awning control system to accurately identify smooth weather and severe weather and make corresponding coordinated control decisions, which makes it easy to have problems with insufficient or excessive local shading, and may even cause damage to the shading objects due to weather, which is not conducive to the user's shading safety. Summary of the invention

[0004] The present invention overcomes the shortcomings of the prior art and provides a large-span electric track sunshade control method based on multi-source light environment sensing.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] A first aspect of the present invention provides a large-span electric track sunshade control method based on multi-source light environment sensing, comprising the following steps:

[0007] S102: Acquire a number of light environment sensing data of a specified shading area of ​​a large-span electric rail awning and evaluate the Worley noise function, adjust the frequency of the Worley noise function so that the noise varies with time and space, generate spatiotemporal noise items for each data, introduce each spatiotemporal noise item into the dynamic light environment stochastic differential equation rendering of the Worley noise function multi-layer noise layer superposition mapping, and generate a dynamic light environment simulation field;

[0008] S104: dynamically slicing the dynamic light environment simulation field according to the activity plan information of the target shading subject to obtain a number of multi-source meteorological samples and sample features, slicing the corresponding multi-source meteorological samples based on the random slicing points of the sample features and recursively splitting them to obtain abnormal score values ​​of the multi-source meteorological samples, analyzing the abnormal score values ​​through the abnormal score intervals of the severe meteorological samples, and obtaining the multi-source shading meteorological recognition results;

[0009] S106: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric rail awning is at a stable weather activity node, the light propagation of the dynamic light environment simulation field is calculated by finite difference differentiation to obtain a time-series light propagation path, a dynamic factor model is constructed based on the time-series light propagation path and future light propagation is predicted to obtain the light environment radiation range, and whether the light environment radiation range interferes with the activity planning range is determined, and shading control is performed on the large-span electric rail awning to generate a first shading control strategy;

[0010] S108: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric rail awning is at a severe weather activity node, then based on the current sensing monitoring readings of each severe weather type, the radiation space planning and median vertical line division of the radiation point source of the specified shading area are carried out, a Voronoi radiation space unit is constructed, and dangerous radiation analysis and control are performed to generate a second shading control strategy.

[0011] More specifically, the step S102 includes the following steps:

[0012] Obtain a specified shading area of ​​the large-span electric track awning, and perform time-series light environment monitoring on the specified shading area through a sensor array integrated with multiple sensors of the large-span electric track awning to obtain light environment sensing data in a plurality of continuous time series;

[0013] Introducing the Worley noise method to perform noise smoothness analysis and evaluation on the light environment sensing data on the plurality of continuous time series, so as to determine the Worley noise function of each light environment sensing data, and obtain the sensing timestamp corresponding to each light environment sensing data when it is monitored;

[0014] Preset an expected frequency threshold, and obtain the actual frequency value of each light environment sensing data based on the Worley noise function. If the actual frequency value is lower than the expected frequency value, adjust the actual frequency value of the light environment sensing data according to the sensing timestamp until it is higher than or equal to the expected frequency value, so as to obtain the spatiotemporal noise term of each light environment sensing data;

[0015] Acquire sensing targets and type information of the sensing targets of the sensor array, and obtain multiple noise layers contained in the Worley noise function corresponding to each light environment sensing data through the actual frequency value according to the preset parameter superposition rule of the sensing targets and the type information;

[0016] Based on the parameter superposition rule, the multiple noise layers corresponding to each light environment sensing data are mapped and superimposed to obtain the dynamic light environment stochastic differential equation of the specified shading area, and the spatiotemporal noise terms of each light environment sensing data are introduced into the dynamic light environment stochastic differential equation;

[0017] After the introduction is completed, the dynamic light environment stochastic differential equation is solved to obtain a number of white noises and color noises of the light environment sensing data, and the light rendering engine software is used to encode and render the white noises and color noises to finally generate a dynamic light environment simulation field.

[0018] More specifically, the step S104 includes the following steps:

[0019] Obtain the target shading body of the large-span electric track awning and the activity plan information of the target shading body, and preset the activity nodes and the activity time range of each activity node according to the activity plan information;

[0020] Based on the activity period range, the dynamic light environment simulation field is dynamically time-series sliced ​​at each activity node to obtain a plurality of multi-source meteorological samples of the dynamic light environment at each activity node within the activity period range and sample features of each multi-source meteorological sample;

[0021] Construct a root node, obtain a reasonable value range of the sample feature, randomly select a sample feature from the root node as the starting point, determine the segmentation position based on the reasonable value range, obtain a random segmentation point for each sample feature, and segment and recursively split the multi-source meteorological samples corresponding to the sample feature based on the random segmentation point;

[0022] Preset severe weather samples, obtain abnormal sample features of the severe weather samples and the abnormal value range of each abnormal sample feature through the big data network, preset the isolation tree depth based on the abnormal value range, and stop the recursive splitting if the current depth of the recursive splitting reaches the isolation tree depth, and obtain the recursive path length of each multi-source weather sample;

[0023] Obtain an abnormal score interval of the severe weather sample, and determine the abnormal score value of each multi-source weather sample according to the recursive path length calculation; if the abnormal score value does not exist in the abnormal score interval, mark the active node of the dynamic slice corresponding to the multi-source weather sample with the abnormal score value as a stable weather active node;

[0024] If the anomaly score value exists in the anomaly score range, the active node of the dynamic slice corresponding to the multi-source meteorological sample with the anomaly score value is marked as a severe weather active node, and the multi-source shading meteorological recognition result is obtained.

[0025] More specifically, the step S106 includes the following steps:

[0026] If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric track awning is at a stable weather activity node, the space-time grid is discretized and the dynamic light environment simulation field is finitely differentiated to construct a differential equation for dynamic light propagation, the differential equation is iteratively solved and described and deduced, and the time-series light propagation path in the dynamic light environment simulation field is obtained;

[0027] The current active period of the target sunshade subject is obtained, and several groups of continuous time-series light propagation paths displayed in the dynamic light environment simulation field during the current active period are extracted, which are defined as the current time-series light propagation paths;

[0028] Obtain the future activity plan of the target sunshade subject through the activity plan information of the target sunshade subject, and set the future activity period of the target sunshade subject in the large-span electric track awning according to the future activity plan;

[0029] Based on the current activity period, the influence of the potential dynamic factors on the time series on the light propagation variables of several groups of continuous current time series light propagation paths is calculated to generate a dynamic factor model; the dynamic factor model is analyzed according to the future activity period to determine the number of future potential dynamic factors in the dynamic factor model for the future activity period, and a factor loading matrix is ​​constructed by estimating the number of future potential dynamic factors;

[0030] The Kalman filter method is introduced to construct a state space model, and each future potential dynamic factor in the factor loading matrix is ​​estimated using the state space model to obtain a dynamic factor estimation value of each future potential dynamic factor;

[0031] Presetting an iteration depth based on the number of future potential dynamic factors, updating and iterating the dynamic factor model based on the dynamic factor estimation value of each future potential dynamic factor, and stopping updating after reaching the iteration depth to obtain a new dynamic factor model;

[0032] By analyzing the new dynamic factor model, the light propagation path displayed by the dynamic light environment simulation field when the target shading body is in the large-span electric track awning during the future activity period is obtained, which is defined as the future time-series light propagation path, and the light environment radiation range is preset according to the future time-series light propagation path;

[0033] Based on the activity plan information, the activity planning range of the target shading body within the future activity period is obtained, and it is determined whether there is interference between the light environment radiation range and the activity planning range. If there is interference, the large-span electric track awning is controlled to generate the first shading control strategy.

[0034] More specifically, if the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric track awning is at a stable weather activity node, the space-time grid is discretized and the dynamic light environment simulation field is finitely differentiated to construct a differential equation for dynamic light propagation, the differential equation is iteratively solved and described and deduced, and the time-series light propagation path in the dynamic light environment simulation field is obtained, which specifically includes the following steps:

[0035] If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric track awning is at a stable weather activity node, the light environment sensing law of each sensor in the sensor array is obtained, and a uniform simulation step length of the light environment is preset based on the light environment sensing law;

[0036] Discretely set the space and time of the dynamic light environment simulation field based on the uniform simulation step, generate a discrete space-time grid, and discretize the dynamic light environment simulation field at each uniform simulation step according to the discrete space-time grid to obtain the dynamic light propagation partial differential equation at each uniform simulation step;

[0037] The finite difference algorithm is introduced to approximate the derivatives in the partial differential equation of dynamic light propagation at each uniform simulation step with a differential form, which approximately describes the dynamic light propagation in the dynamic light environment simulation field, so as to construct the differential equation of dynamic light propagation.

[0038] Preset the maximum iteration frequency according to the uniform simulation step size, extract the initial position, initial velocity and boundary behavior of the light based on the dynamic light environment simulation field, solve the differential equation with the initial position and initial velocity set as initial conditions and the boundary behavior of the light as boundary conditions, and obtain the solution of the light propagation differential equation;

[0039] The deduced position of the light description under each uniform simulation step is iteratively calculated by solving the light propagation differential equation until the current iteration frequency reaches the maximum iteration frequency, thereby generating a temporal light propagation path in the dynamic light environment simulation field.

[0040] More specifically, the step S108 includes the following steps:

[0041] If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric rail awning is at a bad weather activity node, then one or more bad weather types occurring in the current activity period and the current sensing monitoring readings of each bad weather type are obtained through the dynamic light environment simulation field;

[0042] Obtain the preset shading control decision of the large-span electric track awning and the limit sensing range of the sensor array, construct a radiation space of the specified shading area based on the limit sensing range, divide the radiation space into N sub-radiation spaces according to the preset shading control decision, and obtain the monitoring points in each sub-radiation space that sense each current sensing monitoring reading;

[0043] Through the dynamic light environment simulation field, one or more severe weather types are used to identify and generate multiple interference points in each sub-radiation space, which are defined as radiation point sources. The Euclidean distance between each radiation point source and each monitoring point is calculated, and only the monitoring points whose Euclidean distance is less than the preset Euclidean distance are extracted to obtain the set of monitoring points contained in each radiation point source. The intersection of the Voronoi radiation polygon is constructed according to the set of monitoring points.

[0044] Based on the preset critical planning threshold of the current sensing monitoring readings, the perpendicular bisector is used to divide the median perpendicular line of each radiation point source in the sub-radiation space into regions until the critical planning threshold is reached, and the planning is stopped. The intersection of the median perpendicular lines of each radiation point source in the radiation space is obtained, and the spatial boundary of the Voronoi radiation polygon is determined according to the intersection of the median perpendicular lines of each radiation point source in the radiation space.

[0045] Constructing Voronoi radiation space units for the specified shading area affected by each severe weather type according to the intersection of the Voronoi radiation polygon and the spatial boundary of the Voronoi radiation polygon, and obtaining the radiation point distance between each radiation point source and each boundary point of the spatial boundary in the Voronoi radiation space unit;

[0046] Obtain material information of the large-span electric rail awning, retrieve the maximum damage critical value of the large-span electric rail awning caused by various severe weather types in the big data network based on the material information, and preset a maximum danger distance threshold based on the maximum damage critical value;

[0047] If the radiation point distance is greater than the maximum dangerous distance threshold, the radiation point source corresponding to the radiation point distance is marked as a dangerous radiation point source, and all dangerous radiation point sources are integrated to output a dangerous radiation point source pattern network. The large-span electric track awning is controlled according to the dangerous radiation point source pattern network to generate a second shading control strategy.

[0048] A second aspect of the present invention provides a large-span electric track shading control system based on multi-source light environment sensing, the large-span electric track shading control system includes a memory and a processor, the memory stores a large-span electric track shading control method program based on multi-source light environment sensing, when the large-span electric track shading control method program is executed by the processor, any one of the steps of the large-span electric track shading control method is implemented.

[0049] The present invention solves the technical defects existing in the background technology, and the beneficial technical effects of the present invention are:

[0050] By evaluating the Worley noise function and adjusting the frequency of several light environment sensing data of the long-span electric rail awning, the spatiotemporal noise terms of each data are introduced into the dynamic light environment stochastic differential equation mapped by the Worley noise function multi-layer noise layer superposition mapping to generate a dynamic light environment simulation field; the corresponding multi-source meteorological samples are divided into random cutting points based on the sample characteristics and recursively split to obtain the abnormal score values ​​of the multi-source meteorological samples, and the abnormal score values ​​are analyzed through the abnormal score interval of the severe meteorological samples to obtain the multi-source shading meteorological recognition results; if the multi-source shading meteorological recognition results show that the shading of the large-span electric rail awning for the target shading body is at a stable weather activity node, the finite difference Differentiate and calculate the time-series light propagation path of the dynamic light environment simulation field, build a dynamic factor model based on the time-series light propagation path to predict the radiation range of the light environment, determine whether the radiation range of the light environment interferes with the activity planning range, and control the shading of the large-span electric track awning to generate a first shading control strategy; if the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric track awning is at the activity node of the bad weather, then based on the current sensing monitoring readings of each bad weather type, the radiation space planning of the radiation point source and the median vertical line division are carried out for the specified shading area, a Voronoi radiation space unit is constructed, and dangerous radiation analysis and control are performed to generate a second shading control strategy. The present invention can perform accurate and reasonable shading analysis on the weather conditions of the large-span electric track shading device according to the multi-source light environment sensing data, so as to improve the control accuracy and shading performance of the large-span electric track shading device. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0052] Figure 1A first method flow chart of a large-span electric track sunshade control method based on multi-source light environment sensing is shown;

[0053] Figure 2 A second method flow chart of a large-span electric track sunshade control method based on multi-source light environment sensing is shown;

[0054] Figure 3 The system framework diagram of a large-span electric track sunshade control system based on multi-source light environment sensing is shown. DETAILED DESCRIPTION

[0055] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0057] The first aspect of the present invention provides a large-span electric track sunshade control method based on multi-source light environment sensing, such as Figure 1 As shown, the following steps are included:

[0058] S102: Acquire a number of light environment sensing data of a specified shading area of ​​a large-span electric rail awning and evaluate the Worley noise function, adjust the frequency of the Worley noise function so that the noise varies with time and space, generate spatiotemporal noise items for each data, introduce each spatiotemporal noise item into the dynamic light environment stochastic differential equation rendering of the Worley noise function multi-layer noise layer superposition mapping, and generate a dynamic light environment simulation field;

[0059] S104: dynamically slicing the dynamic light environment simulation field according to the activity plan information of the target shading subject to obtain a number of multi-source meteorological samples and sample features, slicing the corresponding multi-source meteorological samples based on the random slicing points of the sample features and recursively splitting them to obtain abnormal score values ​​of the multi-source meteorological samples, analyzing the abnormal score values ​​through the abnormal score intervals of the severe meteorological samples, and obtaining the multi-source shading meteorological recognition results;

[0060] S106: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric rail awning is at a stable weather activity node, the light propagation of the dynamic light environment simulation field is calculated by finite difference differentiation to obtain a time-series light propagation path, a dynamic factor model is constructed based on the time-series light propagation path and future light propagation is predicted to obtain the light environment radiation range, and whether the light environment radiation range interferes with the activity planning range is determined, and shading control is performed on the large-span electric rail awning to generate a first shading control strategy;

[0061] S108: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric rail awning is at a severe weather activity node, then based on the current sensing monitoring readings of each severe weather type, the radiation space planning and median vertical line division of the radiation point source of the specified shading area are carried out, a Voronoi radiation space unit is constructed, and dangerous radiation analysis and control are performed to generate a second shading control strategy.

[0062] More specifically, the step S102 includes the following steps:

[0063] Obtain a specified shading area of ​​the large-span electric track awning, and perform time-series light environment monitoring on the specified shading area through a sensor array integrated with multiple sensors of the large-span electric track awning to obtain light environment sensing data in a plurality of continuous time series;

[0064] Introducing the Worley noise method to perform noise smoothness analysis and evaluation on the light environment sensing data on the plurality of continuous time series, so as to determine the Worley noise function of each light environment sensing data, and obtain the sensing timestamp corresponding to each light environment sensing data when it is monitored;

[0065] Preset an expected frequency threshold, and obtain the actual frequency value of each light environment sensing data based on the Worley noise function. If the actual frequency value is lower than the expected frequency value, adjust the actual frequency value of the light environment sensing data according to the sensing timestamp until it is higher than or equal to the expected frequency value, so as to obtain the spatiotemporal noise term of each light environment sensing data;

[0066] Acquire sensing targets and type information of the sensing targets of the sensor array, and obtain multiple noise layers contained in the Worley noise function corresponding to each light environment sensing data through the actual frequency value according to the preset parameter superposition rule of the sensing targets and the type information;

[0067] Based on the parameter superposition rule, the multiple noise layers corresponding to each light environment sensing data are mapped and superimposed to obtain the dynamic light environment stochastic differential equation of the specified shading area, and the spatiotemporal noise terms of each light environment sensing data are introduced into the dynamic light environment stochastic differential equation;

[0068] After the introduction is completed, the dynamic light environment stochastic differential equation is solved to obtain a number of white noises and color noises of the light environment sensing data, and the light rendering engine software is used to encode and render the white noises and color noises to finally generate a dynamic light environment simulation field.

[0069] It should be noted that the light environment sensing data includes temperature, humidity, light intensity, wind speed, rainfall and cloud density. The large-span electric rail awning is equipped with multiple sensors to sense the light environment conditions in the shading area, so that the shading operation of the large-span electric rail awning can be controlled according to the analysis results of the light environment. However, it is difficult for traditional control methods to intuitively reflect the changes in light and the surrounding environment for these collected light environment data, making it difficult for large-span electric rail awnings to maintain a high control accuracy rate in response to light changes and harsh environmental changes, and it is impossible to achieve high-quality shading effects. Therefore, this method uses Worley noise to evaluate each light environment sensing data obtained, which is used to abstractly simulate and restore the irregularity and complexity of the light environment in nature, thereby improving the accuracy and authenticity of the subsequent simulation field construction. The inconsistent spatiotemporal noise frequencies between different light environment sensing data may cause noise interference in the dynamic simulation of different light environments, resulting in dislocation or jagged effects on the display effect of the final simulation field. To this end, this method adjusts the actual frequency value of each light environment sensing data to be higher than or equal to the expected frequency value, thereby generating spatiotemporal noise terms to ensure that the noise generation effect between each light environment sensing data changes dynamically with time and space, revealing the possible impact of light environment noise on dynamic evolution. Finally, based on the parameter superposition rule, each light environment sensing data is mapped and superimposed with the multiple noise layers contained in the Worley noise function, and the spatiotemporal noise terms of each data are introduced into the dynamic light environment stochastic differential equation to output white noise and color noise for encoding and rendering. Through mapping, the noise display is no longer an abstract value, but directly affects the physical effect of the simulation, so that the generated weather, lighting and other effects are physically reasonable, forming a more intuitive and accurate dynamic light environment simulation field.

[0070] It should be noted that this method can calculate the spatiotemporal noise of the sensed light environment data and introduce mapping superposition to construct a simulation field that dynamically expresses the changes in the light environment. On the one hand, it improves the control accuracy of the large-span electric rail awning in response to changes in light and harsh environment, and provides accurate and reliable shading analysis basis for the subsequent control of large-span electric rail awnings, further optimizing the shading control effect; on the other hand, it breaks the dilemma of manually inspecting the shading environment on the spot, reduces a lot of manpower cost output, and improves the analysis efficiency of shading control. Among them, the Chinese name of the Worley noise function is the Worley noise function, which is used to evaluate the smoothness expression of the simulation characteristics between different light environment sensing data, so as to achieve a more realistic restoration of the light environment simulation visual effect and ensure the accuracy of the light environment analysis.

[0071] More specifically, the step S104 includes the following steps:

[0072] Obtain the target shading body of the large-span electric track awning and the activity plan information of the target shading body, and preset the activity nodes and the activity time range of each activity node according to the activity plan information;

[0073] Based on the activity period range, the dynamic light environment simulation field is dynamically time-series sliced ​​at each activity node to obtain a plurality of multi-source meteorological samples of the dynamic light environment at each activity node within the activity period range and sample features of each multi-source meteorological sample;

[0074] Construct a root node, obtain a reasonable value range of the sample feature, randomly select a sample feature from the root node as the starting point, determine the segmentation position based on the reasonable value range, obtain a random segmentation point for each sample feature, and segment and recursively split the multi-source meteorological samples corresponding to the sample feature based on the random segmentation point;

[0075] Preset severe weather samples, obtain abnormal sample features of the severe weather samples and the abnormal value range of each abnormal sample feature through the big data network, preset the isolation tree depth based on the abnormal value range, and stop the recursive splitting if the current depth of the recursive splitting reaches the isolation tree depth, and obtain the recursive path length of each multi-source weather sample;

[0076] Obtain an abnormal score interval of the severe weather sample, and determine the abnormal score value of each multi-source weather sample according to the recursive path length calculation; if the abnormal score value does not exist in the abnormal score interval, mark the active node of the dynamic slice corresponding to the multi-source weather sample with the abnormal score value as a stable weather active node;

[0077] If the anomaly score value exists in the anomaly score range, the active node of the dynamic slice corresponding to the multi-source meteorological sample with the anomaly score value is marked as a severe weather active node, and the multi-source shading meteorological recognition result is obtained.

[0078] It should be noted that the large-span electric track awning of the present invention can activate different early warning protection measures according to the real-time shading operation status and meteorological environment changes, so as to achieve the effect of protecting the safety of shading users. However, the large-span electric track awning adopts the traditional control method for the analysis adaptability or analysis accuracy of abnormal weather in the dynamic light environment simulation field, which means that the large-span electric track awning has a large recognition error for the dynamic light environment simulation field, and it is difficult to identify from the dynamic light environment simulation field whether the meteorological environment when the awning protects the target shading object is stable or severe, which leads to a significant reduction in the automatic discrimination control accuracy of the large-span electric track awning with different meteorological environment change functions. Therefore, this method first obtains the multi-source meteorological samples displayed in the dynamic light environment simulation field during the activity time period and the sample characteristics of each multi-source meteorological sample according to the activity plan of the target shading subject, so that the large-span electric track awning can be highly targeted in the shading meteorological analysis of different shading subject activities, and effectively optimize the subsequent control accuracy. Then, we randomly select sample features from the root node and divide and recursively split the corresponding multi-source meteorological samples based on the random split points within their reasonable value range, so as to continuously build an isolation tree for each multi-source meteorological sample. By splitting the split points of random sample features, we can make data with different characteristic distributions robust. For each data point, the recursive depth of the branches of the isolation tree is the length of the path that the point takes to reach the leaf node. Since the abnormal point is far away from other data points, it is usually isolated earlier, so the path of the data point is shorter. Normal points are located in dense areas of the data set, so they usually require more splits to be isolated. Therefore, the recursive depth of the branches of the isolation tree reflects the degree of abnormality of the multi-source meteorological sample.

[0079] It should be noted that if the abnormal score value does not exist in the abnormal score interval of the severe weather sample, it means that the dynamic light environment simulation field shows that the multi-source meteorological sample in which the current large-span electric rail awning is located does not belong to the severe category, that is, the shading activity time node for the target shading body is in a stable weather. On the contrary, it means that the multi-source meteorological sample belongs to the severe category, and its shading activity time node is in a severe weather environment. Therefore, the current awning is affected by severe weather and may need to implement emergency protection measures. This method enables the large-span electric rail awning to accurately identify and analyze whether the meteorological sample is abnormal based on the changes in the dynamic light environment simulation field, so that the dynamic light environment simulation field can be used to quickly and accurately determine the control response strategy that the current large-span electric rail awning should implement, improve the shading pertinence under different meteorological changes, improve the recognition adaptability and reliability of the awning control for the light environment simulation field, and effectively improve the shading control level.

[0080] More specifically, the step S106 includes the following steps:

[0081] If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric track awning is at a stable weather activity node, the space-time grid is discretized and the dynamic light environment simulation field is finitely differentiated to construct a differential equation for dynamic light propagation, the differential equation is iteratively solved and described and deduced, and the time-series light propagation path in the dynamic light environment simulation field is obtained;

[0082] The current active period of the target sunshade subject is obtained, and several groups of continuous time-series light propagation paths displayed in the dynamic light environment simulation field during the current active period are extracted, which are defined as the current time-series light propagation paths;

[0083] Obtain the future activity plan of the target sunshade subject through the activity plan information of the target sunshade subject, and set the future activity period of the target sunshade subject in the large-span electric track awning according to the future activity plan;

[0084] Based on the current activity period, the influence of the potential dynamic factors on the time series on the light propagation variables of several groups of continuous current time series light propagation paths is calculated to generate a dynamic factor model; the dynamic factor model is analyzed according to the future activity period to determine the number of future potential dynamic factors in the dynamic factor model for the future activity period, and a factor loading matrix is ​​constructed by estimating the number of future potential dynamic factors;

[0085] The Kalman filter method is introduced to construct a state space model, and each future potential dynamic factor in the factor loading matrix is ​​estimated using the state space model to obtain a dynamic factor estimation value of each future potential dynamic factor;

[0086] Presetting an iteration depth based on the number of future potential dynamic factors, updating and iterating the dynamic factor model based on the dynamic factor estimation value of each future potential dynamic factor, and stopping updating after reaching the iteration depth to obtain a new dynamic factor model;

[0087] By analyzing the new dynamic factor model, the light propagation path displayed by the dynamic light environment simulation field when the target shading body is in the large-span electric track awning during the future activity period is obtained, which is defined as the future time-series light propagation path, and the light environment radiation range is preset according to the future time-series light propagation path;

[0088] Based on the activity plan information, the activity planning range of the target shading body within the future activity period is obtained, and it is determined whether there is interference between the light environment radiation range and the activity planning range. If there is interference, the large-span electric track awning is controlled to generate the first shading control strategy.

[0089] It should be noted that if the multi-source shading meteorological identification results show that the shading of the target shading body by the large-span electric rail awning is at a stable weather activity node, it means that the current large-span electric rail awning is at a stable weather activity time node, and will not be affected by bad weather at this time. Therefore, the awning's shading control is more inclined to shading light in stable weather. However, the light irradiation and propagation in stable weather are not fixed in position, but a dynamic propagation process determined by environmental changes. Therefore, this method first discretizes the continuously changing dynamic light environment simulation field and divides it into a finite number of discrete space-time grids for finite difference differentiation, so that a differential equation expressing dynamic light propagation can be constructed for the dynamic light environment simulation field. The light that continuously iterates to solve the differential equation can describe the deduction process of its continuous dynamic propagation, so that the path of light propagation in the continuous time series can be clarified. This method can be used to continuously deduce and describe the propagation of light in the dynamic light environment simulation field in a discretized finite difference manner, providing a stable, reliable and credible basis for light shading capture analysis for the precise shading control of large-span electric track awnings under subsequent stable weather conditions.

[0090] It should be noted that after obtaining the light propagation path of the large-span electric rail awning under stable weather conditions in the dynamic light environment simulation field, the awning's shading must sense the light changes in the meteorological environment in advance to make corresponding shading decisions to improve timely shading response performance. If shading response control is performed after the light propagation changes, it may cause the light to irradiate the target shading body in advance and cause damage to it. Therefore, it is necessary to perform a predictive analysis of the light propagation in the future to further control the predictive shading control of the awning. In view of this, this method first obtains the continuous propagation path of light in the current activity period of the large-span electric rail awning, that is, the current time-series light propagation path, and then calculates the influence of the light propagation variables on the time series potential dynamic factors on several groups of continuous current time-series light propagation paths based on the current activity period, and generates a dynamic factor model that expresses the influence of the potential randomness of light propagation on certain potential dynamic factors, where the potential dynamic factors include humidity, temperature, propagation medium and light intensity, so that the prediction of the dynamic factor model conforms to the randomness of light propagation; then, according to the future activity period, the dynamic factor model is analyzed to determine the number of future potential dynamic factors for the future activity period, and a factor loading matrix is ​​estimated to be constructed. The factor loading matrix can clearly describe the contribution or influence of future potential dynamic factors on the change of light propagation, which can effectively improve the prediction accuracy of the dynamic factor model. Finally, the dynamic factor estimation value of each future potential dynamic factor is estimated through the factor loading matrix to update the iterative dynamic factor model, ensure the real-time light propagation prediction of the dynamic factor model, avoid the shading control error caused by inaccurate light propagation prediction, and prevent damage to the shading body. Among them, if there is interference between the radiation range of the light environment and the activity planning range, it means that the light propagation under stable weather conditions in the future will illuminate the activities of the target shading body and affect it, so it is necessary to control the shading of the large-span electric track awning. This method can analyze the real-time shading control needs of the large-span electric track awning under stable weather conditions by dynamically predicting light propagation, thereby improving the large-span electric track awning's response to the random changes in light in stable weather, and improving the shading accuracy and advance response efficiency.

[0091] More specifically, if the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric track awning is at a stable weather activity node, the space-time grid is discretized and the dynamic light environment simulation field is finitely differentiated to construct a differential equation for dynamic light propagation, the differential equation is iteratively solved and described, and the time-series light propagation path in the dynamic light environment simulation field is obtained, such as Figure 2 As shown, the specific steps include:

[0092] S202: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric track awning is at a stable weather activity node, the light environment sensing law of each sensor in the sensor array is obtained, and a uniform simulation step length of the light environment is preset based on the light environment sensing law;

[0093] S204: discretizing the space and time of the dynamic light environment simulation field based on the uniform simulation step, generating a discrete space-time grid, and performing discretization calculation on the dynamic light environment simulation field at each uniform simulation step according to the discrete space-time grid to obtain a dynamic light propagation partial differential equation at each uniform simulation step;

[0094] S206: introducing a finite difference algorithm, and using the finite difference algorithm to approximately replace the derivative in the dynamic light propagation partial differential equation at each uniform simulation step with a differential form, so as to approximately describe the dynamic light propagation in the dynamic light environment simulation field, so as to construct a differential equation for dynamic light propagation;

[0095] S208: Preset a maximum iteration frequency according to a uniform simulation step size, extract the initial position, initial velocity, and boundary behavior of the light based on the dynamic light environment simulation field, solve the differential equation with the initial position and initial velocity set as initial conditions and the boundary behavior of the light as boundary conditions, and obtain a solution to the light propagation differential equation;

[0096] S210: Iteratively calculate the deduced position of the light description under each uniform simulation step by solving the light propagation differential equation until the current iteration frequency reaches the maximum iteration frequency, and generate a time-series light propagation path in the dynamic light environment simulation field.

[0097] It should be noted that for the differential solution of the dynamic light environment simulation field expressing the propagation of light, the uniform simulation step of the light environment is preset according to the light environment sensing law of each sensor to discretize the space and time of the dynamic light environment simulation field, which can ensure that the displacement or movement description of light propagation is based on the sensing law of the sensor, improve the light propagation sensing performance of the sensor and the description accuracy of the light propagation deduction, and ensure that the light propagation calculation is accurate. By introducing the finite difference algorithm, the original partial differential equation describing the dynamic light propagation on the uniform simulation step is replaced by a differential form, so that the recursive relationship between the discrete space-time grid points can be more clearly defined, and the deduction propagation of light can be more approximately described, making the calculation of the light propagation path more accurate. The initial condition is the position and speed of the light at the initial irradiation moment, which serves as the starting point of the iterative calculation; the boundary condition stipulates the behavior of the light on the spatial boundary, such as reflection, refraction or diffraction, etc. The constraint of the boundary condition can prevent the occurrence of no solution or divergence of solution in the light propagation deduction process, and ensure the authenticity and reliability of the deduction of the light propagation path. Finally, the displacement of the light at each time step is calculated iteratively step by step to digitize the motion trajectory of the light in the environmental medium. This method can perform time-series dynamic differential deduction and calculation of light propagation under stable weather conditions, thereby providing a basis for determining and analyzing the shading control of a large-span electric track awning under stable weather conditions, making the large-span electric track awning more accurate in capturing the displacement or movement of light under stable weather conditions, thereby further making the awning's shading effect control in response to the ever-changing light more ideal and efficient.

[0098] More specifically, the step S108 includes the following steps:

[0099] If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric rail awning is at a bad weather activity node, then one or more bad weather types occurring in the current activity period and the current sensing monitoring readings of each bad weather type are obtained through the dynamic light environment simulation field;

[0100] Obtain the preset shading control decision of the large-span electric track awning and the limit sensing range of the sensor array, construct a radiation space of the specified shading area based on the limit sensing range, divide the radiation space into N sub-radiation spaces according to the preset shading control decision, and obtain the monitoring points in each sub-radiation space that sense each current sensing monitoring reading;

[0101] Through the dynamic light environment simulation field, one or more severe weather types are used to identify and generate multiple interference points in each sub-radiation space, which are defined as radiation point sources. The Euclidean distance between each radiation point source and each monitoring point is calculated, and only the monitoring points whose Euclidean distance is less than the preset Euclidean distance are extracted to obtain the set of monitoring points contained in each radiation point source. The intersection of the Voronoi radiation polygon is constructed according to the set of monitoring points.

[0102] Based on the preset critical planning threshold of the current sensing monitoring readings, the perpendicular bisector is used to divide the median perpendicular line of each radiation point source in the sub-radiation space into regions until the critical planning threshold is reached, and the planning is stopped. The intersection of the median perpendicular lines of each radiation point source in the radiation space is obtained, and the spatial boundary of the Voronoi radiation polygon is determined according to the intersection of the median perpendicular lines of each radiation point source in the radiation space.

[0103] Constructing Voronoi radiation space units for the specified shading area affected by each severe weather type according to the intersection of the Voronoi radiation polygon and the spatial boundary of the Voronoi radiation polygon, and obtaining the radiation point distance between each radiation point source and each boundary point of the spatial boundary in the Voronoi radiation space unit;

[0104] Obtain material information of the large-span electric rail awning, retrieve the maximum damage critical value of the large-span electric rail awning caused by various severe weather types in the big data network based on the material information, and preset a maximum danger distance threshold based on the maximum damage critical value;

[0105] If the radiation point distance is greater than the maximum dangerous distance threshold, the radiation point source corresponding to the radiation point distance is marked as a dangerous radiation point source, and all dangerous radiation point sources are integrated to output a dangerous radiation point source pattern network. The large-span electric track awning is controlled according to the dangerous radiation point source pattern network to generate a second shading control strategy.

[0106] It should be noted that if the multi-source shading meteorological identification results show that the shading of the target shading body by the large-span electric rail awning is at a bad weather activity node, it means that the current large-span electric rail awning is at a bad weather activity time node, and the surrounding meteorological environment is showing a bad trend, such as typhoons, heavy rains or hail weather. The operation of the large-span electric rail awning in these environments will definitely be affected. Therefore, the awning shading control at this time is more inclined to consider how to avoid the damage caused by the current bad weather environment, such as starting an emergency warning and starting corresponding protection measures by controlling the extension, contraction and tension of the awning. Due to the precision and intelligence of the shading control of the large-span electric rail awning, the awning can respond to the degree of influence of the bad weather environment in different regions and carry out corresponding regional defense control on the premise of ensuring that the shading body is not damaged by bad weather. However, improper control may lead to a significant decrease in regional defense performance under bad weather conditions, causing bad weather to affect the shading body. In view of this, this method obtains one or more severe weather types and corresponding current induction monitoring readings during the current active period of the large-span electric rail awning, and determines the intersection of the Voronoi radiation polygons and the spatial boundary of each severe weather type in the radiation space of the specified shading area according to the current induction monitoring readings, so as to realize the construction of a Voronoi radiation space unit reflecting the radiation intensity of the severe weather affecting the specified shading area of ​​the large-span electric rail awning based on the current induction monitoring readings; wherein the intersection of the Voronoi radiation polygon is for each radiation point source, all the positions closest to the radiation point source constitute a framework, reflecting the regional distribution of each severe weather and the intensity of the distributed radiation. The boundary of the Voronoi radiation polygon is the perpendicular bisector between adjacent radiation point sources, ensuring that the division of each area does not overlap and completely covers the entire space, thereby improving the expression accuracy of the regional radiation intensity of severe weather.

[0107] It should be noted that the Voronoi radiation space unit can be understood as a regional radiation distribution map showing the magnitude of severe weather radiation for a specified shading area. The radiation point distance between each radiation point source and each boundary point of the spatial boundary displayed in this Voronoi radiation space unit can reveal the magnitude of the radiation. Then, according to the preset maximum danger distance threshold of the maximum damage critical value that causes damage to the large-span electric rail awning, these radiation point distances are compared and analyzed one by one, so that it can be clear which areas are greatly affected by severe weather and need the awning to initiate emergency warnings and protective measures. Among them, if the radiation point distance is greater than the maximum danger distance threshold, it means that the area where the radiation point source corresponding to the radiation point distance is located is more dangerous due to the damage radiation of severe weather. For example, the wind index in this area is high and may cause damage to the shading body, so the radiation point source corresponding to the radiation point distance is marked as a dangerous radiation point source. This method can be used to conduct a regional analysis of the radiation destructiveness of the large-span electric rail awnings affected by severe weather, thereby improving the control accuracy of the emergency shading warning and regional protection measures of the large-span electric rail awnings under severe weather conditions, enabling the awnings to cope with the shading protection performance of different severe weather distributions, ensuring the safety of users' shading, and optimizing the control rationality and reliability of the large-span electric rail awnings in response to severe weather. Among them, the Voronoi diagram is a mathematical model that divides a plane into multiple regions. The data size of the severe weather type is represented by the principle of the closest distance to the radiation intensity distribution of the specified shading area. Compared with the traditional radiation intensity distribution map, the description of the radiation intensity of each area is more accurate and reliable.

[0108] In addition, a large-span electric track sunshade control method based on multi-source light environment sensing also includes the following steps:

[0109] Acquire multiple tension data of the large-span electric track awning in emergency shading within a preset time period through sensor monitoring, perform control response calculation based on multiple tension data, and obtain the tension control response rate within the preset time period;

[0110] If the tension control response rate is greater than the preset tension control response rate, it means that the tension control response rate of the awning is significantly increased due to certain specific wind and meteorological factors. Based on the big data, several wind and meteorological elements that cause the tension control response rate of the large-span electric track awning to increase when performing emergency shading are obtained and marked as a type of wind and meteorological elements;

[0111] If the tension control response rate is less than the preset tension control response rate, it means that the tension control response rate of the awning is significantly reduced due to certain specific wind and meteorological factors. Based on the big data, several wind and meteorological elements that cause the tension control response rate of the large-span electric track awning to be reduced when performing emergency shading are obtained, and are marked as Class II wind and meteorological elements;

[0112] The information gain algorithm is introduced to calculate the information gain ratio between the tension control response rate and each first-class wind meteorological element through the information gain algorithm to obtain multiple first-class element gain correlations, and the information gain ratio between the tension control response rate and each second-class wind meteorological element is calculated to obtain multiple second-class element gain correlations;

[0113] If the gain correlation of a class of elements is greater than the preset element gain correlation, then extract the class of wind meteorological elements corresponding to the gain correlation of the class of elements, mark them as a class of associated wind meteorological elements, and integrate all the extracted class of associated wind meteorological elements to obtain a class of associated wind meteorological element group;

[0114] If the gain correlation of the second-class element is greater than the preset element gain correlation, extract the first-class wind meteorological element corresponding to the gain correlation of the second-class element, mark it as the second-class associated wind meteorological element, and integrate all the extracted second-class associated wind meteorological elements to obtain the second-class associated wind meteorological element group;

[0115] The operating condition control parameters of the actual wind meteorological element set are analyzed and extracted through the first-class associated wind meteorological element group and the second-class associated wind meteorological element group, and the driving fault is estimated based on the extracted operating condition control parameters to determine the fault module of the large-span electric track awning.

[0116] It should be noted that the large-span electric track awning of the present invention integrates a variety of sensors, which can monitor the operating status of the awning and changes in the meteorological environment in real time, including wind monitoring, awning tension monitoring, and motor operation monitoring. However, due to long-term operation or external environmental factors, the monitoring performance of the sensor may have errors, which may make the monitored wind and meteorological environment data inaccurate, and then cause the motor to control the awning tension. The control response rate is too fast or too slow, resulting in the awning being over-controlled or failing to reach the target shading stroke, which is not conducive to the precise shading effect of the large-span electric track awning. Therefore, this method can first calculate and analyze the tension control response rate of the large-span electric rail awning, so as to obtain some wind and meteorological elements that cause the awning tension control to be too fast or too slow, and then further refine the calculation of the potential correlation between these wind and meteorological elements and the tension control response rate, and only collect the associated wind and meteorological elements whose element gain correlation is greater than the preset element gain correlation to require the sensor to focus on monitoring, thereby greatly improving the accuracy of the sensor's monitoring of wind and meteorological factors that affect the abnormal tension control of the awning, providing a reliable data analysis basis for the subsequent awning fault tracing, and effectively optimizing the performance of the awning wind monitoring and the credibility of the tension control.

[0117] In addition, the analysis and extraction of working condition control parameters of the actual wind meteorological element set by the first-class associated wind meteorological element group and the second-class associated wind meteorological element group, and the estimation of the driving fault based on the extracted working condition control parameters to determine the fault module of the large-span electric track awning specifically include the following steps:

[0118] Obtaining an actual wind meteorological element set monitored by the large-span electric track awning, if the actual wind meteorological element set contains at least one type of associated wind meteorological element in any one type of associated wind meteorological element group, extracting a motor operating condition control parameter when the tension control response rate of the actual extension or contraction of the large-span electric track awning increases due to the one type of associated wind meteorological element, and marking it as a first operating condition control parameter;

[0119] If the actual wind meteorological element set contains at least one second-type associated wind meteorological element in any second-type associated wind meteorological element group, the operating condition control parameter of the motor when the tension control response rate of the actual extension or contraction of the large-span electric track awning is reduced due to the second-type associated wind meteorological element is extracted and marked as the second operating condition control parameter;

[0120] The maximum driving characteristics of each driving module of the motor in the large-span electric track awning are obtained, and the grey correlation analysis method is introduced to calculate the grey correlation between the first working condition control parameter and the second working condition control parameter and the maximum driving characteristics of each driving module;

[0121] Determine the driving abnormality correlation degree of each driving module when the first working condition control parameter and the second working condition control parameter are cultivated according to the grey correlation degree, extract the driving module corresponding to the driving abnormality correlation degree greater than the preset driving abnormality correlation degree, and mark it as a suspicious driving module;

[0122] Acquire the actual control variables of each suspicious drive module within a preset time period, introduce the maximum likelihood algorithm to perform maximum likelihood estimation on the actual control variables of each suspicious drive module within the preset time period, and obtain the probability value of drive control abnormality of each suspicious drive module;

[0123] If the probability value of the suspected driving module having a driving control abnormality is greater than a preset probability value, the suspected driving module is marked as a faulty module, and the faulty module is uploaded to the operation and maintenance log of the large-span electric track awning.

[0124] It should be noted that if the sensor detects any associated wind meteorological element of the first group of associated wind meteorological elements or the second group of associated wind meteorological elements, it means that the wind environment in which the current large-span electric rail awning is located may cause its shading response rate to be too fast or too slow. This may be due to the improper involvement of wind force affecting the internal motor drive module. The fault caused. Therefore, this method obtains the motor's operating condition control parameters when the tension control response rate that causes the actual extension or contraction of the large-span electric rail awning increases or decreases, that is, the first operating condition control parameter and the second operating condition control parameter. These two operating condition control parameters can reflect the potential cause of the motor's failure, and then the two are respectively associated with the maximum driving characteristics of each drive module of the motor for calculation, so as to preliminarily identify the drive modules with suspected failures, and finally use the actual control performance of these identified suspicious drive modules to estimate the probability of failure, so as to accurately trace the fault module according to the probability value analysis. This method can be used to perform accurate and efficient automatic troubleshooting and tracing for abnormal tension control of large-span electric rail awnings caused by wind and meteorological factors, thereby improving the convenience and reliability of subsequent awning maintenance, reducing the tedious steps of manually checking and marking one by one, saving time and effort, ensuring timely discovery and warning of abnormal use of large-span electric rail awnings, and optimizing service life.

[0125] The second aspect of the present invention provides a large-span electric track sunshade control system based on multi-source light environment sensing, such as Figure 3 As shown, the large-span electric track shading control system includes a memory 31 and a processor 32. The memory 31 stores a large-span electric track shading control method program based on multi-source light environment sensing. When the large-span electric track shading control method program is executed by the processor 32, any one of the steps of the large-span electric track shading control method is implemented.

[0126] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A sunshade control method for a large-span electric track based on multi-source light environment sensing, characterized in that: The following steps are involved: S102: Acquire a number of light environment sensing data of a specified shading area of ​​a large-span electric rail awning and evaluate the Worley noise function, adjust the frequency of the Worley noise function so that the noise varies with time and space, generate spatiotemporal noise items for each data, introduce each spatiotemporal noise item into the dynamic light environment stochastic differential equation rendering of the Worley noise function multi-layer noise layer superposition mapping, and generate a dynamic light environment simulation field; S104: dynamically slicing the dynamic light environment simulation field according to the activity plan information of the target shading subject to obtain a number of multi-source meteorological samples and sample features, slicing the corresponding multi-source meteorological samples based on the random slicing points of the sample features and recursively splitting them to obtain abnormal score values ​​of the multi-source meteorological samples, analyzing the abnormal score values ​​through the abnormal score intervals of the severe meteorological samples, and obtaining the multi-source shading meteorological recognition results; S106: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric rail awning is at a stable weather activity node, the light propagation of the dynamic light environment simulation field is calculated by finite difference differentiation to obtain a time-series light propagation path, a dynamic factor model is constructed based on the time-series light propagation path and future light propagation is predicted to obtain the light environment radiation range, and whether the light environment radiation range interferes with the activity planning range is determined, and shading control is performed on the large-span electric rail awning to generate a first shading control strategy; S108: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric rail awning is at a severe weather activity node, then based on the current sensing monitoring readings of each severe weather type, the radiation space planning and median vertical line division of the radiation point source of the specified shading area are carried out, a Voronoi radiation space unit is constructed, and dangerous radiation analysis and control are performed to generate a second shading control strategy.

2. According to claim 1, a large-span electric track sunshade control method based on multi-source light environment sensing is characterized in that: The step S102 specifically includes the following steps: Obtain a specified shading area of ​​the large-span electric track awning, and perform time-series light environment monitoring on the specified shading area through a sensor array integrated with multiple sensors of the large-span electric track awning to obtain light environment sensing data in a plurality of continuous time series; Introducing the Worley noise method to perform noise smoothness analysis and evaluation on the light environment sensing data on the plurality of continuous time series, so as to determine the Worley noise function of each light environment sensing data, and obtain the sensing timestamp corresponding to each light environment sensing data when it is monitored; Preset an expected frequency threshold, and obtain the actual frequency value of each light environment sensing data based on the Worley noise function. If the actual frequency value is lower than the expected frequency value, adjust the actual frequency value of the light environment sensing data according to the sensing timestamp until it is higher than or equal to the expected frequency value, so as to obtain the spatiotemporal noise term of each light environment sensing data; Acquire sensing targets and type information of the sensing targets of the sensor array, and obtain multiple noise layers contained in the Worley noise function corresponding to each light environment sensing data through the actual frequency value according to the preset parameter superposition rule of the sensing targets and the type information; Based on the parameter superposition rule, the multiple noise layers corresponding to each light environment sensing data are mapped and superimposed to obtain the dynamic light environment stochastic differential equation of the specified shading area, and the spatiotemporal noise terms of each light environment sensing data are introduced into the dynamic light environment stochastic differential equation; After the introduction is completed, the dynamic light environment stochastic differential equation is solved to obtain a number of white noises and color noises of the light environment sensing data, and the light rendering engine software is used to encode and render the white noises and color noises to finally generate a dynamic light environment simulation field.

3. According to claim 1, a large-span electric track sunshade control method based on multi-source light environment sensing is characterized in that: The step S104 specifically includes the following steps: Obtain the target shading body of the large-span electric track awning and the activity plan information of the target shading body, and preset the activity nodes and the activity time range of each activity node according to the activity plan information; Based on the activity period range, the dynamic light environment simulation field is dynamically time-series sliced ​​at each activity node to obtain a plurality of multi-source meteorological samples of the dynamic light environment at each activity node within the activity period range and sample features of each multi-source meteorological sample; Construct a root node, obtain a reasonable value range of the sample feature, randomly select a sample feature from the root node as the starting point, determine the segmentation position based on the reasonable value range, obtain a random segmentation point for each sample feature, and segment and recursively split the multi-source meteorological samples corresponding to the sample feature based on the random segmentation point; Preset severe weather samples, obtain abnormal sample features of the severe weather samples and the abnormal value range of each abnormal sample feature through the big data network, preset the isolation tree depth based on the abnormal value range, and stop the recursive splitting if the current depth of the recursive splitting reaches the isolation tree depth, and obtain the recursive path length of each multi-source weather sample; Obtain an abnormal score interval of the severe weather sample, and determine the abnormal score value of each multi-source weather sample according to the recursive path length calculation; if the abnormal score value does not exist in the abnormal score interval, mark the active node of the dynamic slice corresponding to the multi-source weather sample with the abnormal score value as a stable weather active node; If the anomaly score value exists in the anomaly score range, the active node of the dynamic slice corresponding to the multi-source meteorological sample with the anomaly score value is marked as a severe weather active node, and the multi-source shading meteorological recognition result is obtained.

4. The method for controlling sunshade of a large-span electric track based on multi-source light environment sensing according to claim 1 is characterized in that: The step S106 specifically includes the following steps: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric track awning is at a stable weather activity node, the space-time grid is discretized and the dynamic light environment simulation field is finitely differentiated to construct a differential equation for dynamic light propagation, the differential equation is iteratively solved and described and deduced, and the time-series light propagation path in the dynamic light environment simulation field is obtained; The current active period of the target sunshade subject is obtained, and several groups of continuous time-series light propagation paths displayed in the dynamic light environment simulation field during the current active period are extracted, which are defined as the current time-series light propagation paths; Obtain the future activity plan of the target sunshade subject through the activity plan information of the target sunshade subject, and set the future activity period of the target sunshade subject in the large-span electric track awning according to the future activity plan; Based on the current activity period, the influence of the potential dynamic factors on the time series on the light propagation variables of several groups of continuous current time series light propagation paths is calculated to generate a dynamic factor model; the dynamic factor model is analyzed according to the future activity period to determine the number of future potential dynamic factors in the dynamic factor model for the future activity period, and a factor loading matrix is ​​constructed by estimating the number of future potential dynamic factors; The Kalman filter method is introduced to construct a state space model, and each future potential dynamic factor in the factor loading matrix is ​​estimated using the state space model to obtain a dynamic factor estimation value of each future potential dynamic factor; Presetting an iteration depth based on the number of future potential dynamic factors, updating and iterating the dynamic factor model based on the dynamic factor estimation value of each future potential dynamic factor, and stopping updating after reaching the iteration depth to obtain a new dynamic factor model; By analyzing the new dynamic factor model, the light propagation path displayed by the dynamic light environment simulation field when the target shading body is in the large-span electric track awning during the future activity period is obtained, which is defined as the future time-series light propagation path, and the light environment radiation range is preset according to the future time-series light propagation path; Based on the activity plan information, the activity planning range of the target shading body within the future activity period is obtained, and it is determined whether there is interference between the light environment radiation range and the activity planning range. If there is interference, the large-span electric track awning is controlled to generate the first shading control strategy.

5. The method for controlling sunshade of a large-span electric track based on multi-source light environment sensing according to claim 4 is characterized in that: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric track awning is at a stable weather activity node, the space-time grid is discretized and the dynamic light environment simulation field is finitely differentiated to construct a differential equation for dynamic light propagation, the differential equation is iteratively solved and described and deduced, and the time-series light propagation path in the dynamic light environment simulation field is obtained, which specifically includes the following steps: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric track awning is at a stable weather activity node, the light environment sensing law of each sensor in the sensor array is obtained, and a uniform simulation step length of the light environment is preset based on the light environment sensing law; Discretely set the space and time of the dynamic light environment simulation field based on the uniform simulation step, generate a discrete space-time grid, and discretize the dynamic light environment simulation field at each uniform simulation step according to the discrete space-time grid to obtain the dynamic light propagation partial differential equation at each uniform simulation step; The finite difference algorithm is introduced to approximate the derivatives in the partial differential equation of dynamic light propagation at each uniform simulation step with a differential form, which approximately describes the dynamic light propagation in the dynamic light environment simulation field, so as to construct the differential equation of dynamic light propagation. Preset the maximum iteration frequency according to the uniform simulation step size, extract the initial position, initial velocity and boundary behavior of the light based on the dynamic light environment simulation field, solve the differential equation with the initial position and initial velocity set as initial conditions and the boundary behavior of the light as boundary conditions, and obtain the solution of the light propagation differential equation; The deduced position of the light description under each uniform simulation step is iteratively calculated by solving the light propagation differential equation until the current iteration frequency reaches the maximum iteration frequency, thereby generating a temporal light propagation path in the dynamic light environment simulation field.

6. The method for controlling sunshade of a large-span electric track based on multi-source light environment sensing according to claim 1 is characterized in that: The step S108 specifically includes the following steps: If the multi-source shading meteorological identification result shows that the shading of the target shading body by the large-span electric rail awning is at a bad weather activity node, then one or more bad weather types occurring in the current activity period and the current sensing monitoring readings of each bad weather type are obtained through the dynamic light environment simulation field; Obtain the preset shading control decision of the large-span electric track awning and the limit sensing range of the sensor array, construct a radiation space of the specified shading area based on the limit sensing range, divide the radiation space into N sub-radiation spaces according to the preset shading control decision, and obtain the monitoring points in each sub-radiation space that sense each current sensing monitoring reading; Through the dynamic light environment simulation field, one or more severe weather types are used to identify and generate multiple interference points in each sub-radiation space, which are defined as radiation point sources. The Euclidean distance between each radiation point source and each monitoring point is calculated, and only the monitoring points whose Euclidean distance is less than the preset Euclidean distance are extracted to obtain the set of monitoring points contained in each radiation point source. The intersection of the Voronoi radiation polygon is constructed according to the set of monitoring points. Based on the preset critical planning threshold of the current sensing monitoring readings, the perpendicular bisector is used to divide the median perpendicular line of each radiation point source in the sub-radiation space into regions until the critical planning threshold is reached, and the planning is stopped. The intersection of the median perpendicular lines of each radiation point source in the radiation space is obtained, and the spatial boundary of the Voronoi radiation polygon is determined according to the intersection of the median perpendicular lines of each radiation point source in the radiation space. Constructing Voronoi radiation space units for the specified shading area affected by each severe weather type according to the intersection of the Voronoi radiation polygon and the spatial boundary of the Voronoi radiation polygon, and obtaining the radiation point distance between each radiation point source and each boundary point of the spatial boundary in the Voronoi radiation space unit; Obtain material information of the large-span electric rail awning, retrieve the maximum damage critical value of the large-span electric rail awning caused by various severe weather types in the big data network based on the material information, and preset a maximum danger distance threshold based on the maximum damage critical value; If the radiation point distance is greater than the maximum dangerous distance threshold, the radiation point source corresponding to the radiation point distance is marked as a dangerous radiation point source, and all dangerous radiation point sources are integrated to output a dangerous radiation point source pattern network. The large-span electric track awning is controlled according to the dangerous radiation point source pattern network to generate a second shading control strategy.

7. A large-span electric track sunshade control system based on multi-source light environment sensing, characterized in that: The large-span electric track shading control system includes a memory and a processor. The memory stores a large-span electric track shading control method program based on multi-source light environment sensing. When the large-span electric track shading control method program is executed by the processor, the large-span electric track shading control method steps as described in any one of claims 1-6 are implemented.

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