Efficient wireless image transmission method and system supporting virtual live broadcast, and storage medium

Abstract

The invention relates to the technical field of wireless communication, and discloses an efficient wireless image transmission method and system supporting virtual live broadcast and a storage medium, and the method comprises the steps: generating a signal intensity thermodynamic diagram through collecting indoor environment signal intensity distribution data and equipment position information, and carrying out the path planning and optimization based on the thermodynamic diagram; a path optimization algorithm is combined with a signal stability index and a speed constraint, a path is screened and optimized, and a speed adaptive path model is generated; a dynamic planning algorithm is adopted to fuse the signal interruption risk and the transmission smoothness, and a final rotation path planning scheme is generated; by inserting a standby signal enhancement point into the path, the stability and the signal strength of the path are further improved, and the transmission interruption risk is reduced; the deviation between the actual track and the planned path of the equipment is corrected in real time, and a rotation control instruction for real-time adjustment is generated, so that accurate navigation of the equipment is realized; according to the invention, the stability and efficiency of wireless image transmission can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, specifically to an efficient wireless image transmission method and system supporting virtual live streaming, and a storage medium, which is particularly suitable for wireless image transmission optimization in a virtual live streaming environment, and aims to improve the stability and efficiency of wireless image transmission in virtual live streaming through technical means such as signal strength heat map, path planning, and speed adaptive adjustment. BACKGROUND

[0002] With the rapid development of virtual live streaming technology, especially in indoor environments, the requirements for wireless image transmission systems are increasingly high. In a virtual live streaming environment, devices need to achieve stable and low-latency image transmission in complex indoor spaces to ensure that viewers can have a smooth viewing experience. However, current wireless image transmission technology faces many challenges in virtual live streaming, especially in environments with large signal fluctuations. Traditional path planning and signal transmission techniques cannot effectively guarantee the stability of the signal and the continuity of the image.

[0003] In a complex indoor environment, factors such as walls, obstacles, and multipath effects can cause wireless signal attenuation and interference, further exacerbating signal fluctuations. These signal fluctuations can cause data transmission interruptions or delays, severely affecting the effectiveness of virtual live streaming and the viewing experience. Traditional wireless communication methods do not fully consider the dynamic changes in signal strength and the impact of environmental factors, resulting in unstable signals during device movement and ineffective guarantee of image transmission quality.

[0004] Therefore, how to intelligently optimize the device path in a dynamically changing environment, balance signal stability and transmission efficiency, has become a key issue in improving the quality of virtual live streaming image transmission. Existing technologies are unable to provide stable and predictable wireless image transmission paths in high-interference and high-fluctuation environments, so there is an urgent need for a new method to solve these problems and improve the performance of wireless image transmission systems, especially in virtual live streaming scenarios. SUMMARY

[0005] To solve the above technical problems, the present application provides an efficient wireless image transmission method and system supporting virtual live streaming, which adjusts the path planning and speed control of the device in real time by combining signal strength heat map, path optimization algorithm, and speed adaptive control, to cope with signal fluctuations in complex indoor environments and ensure the stability and efficiency of wireless image transmission.

[0006] In a first aspect, the present application provides an efficient wireless image transmission method supporting virtual live streaming, which comprises: Step S1: Obtain signal strength distribution data and device location information in a virtual live streaming environment, and generate a signal strength heat map; Step S2: based on the signal intensity heat map, a set of possible paths from the starting point to the target point is generated, and a signal stability index is calculated for each path; Step S3: based on the signal stability index, the paths are screened, and a path optimization algorithm is used to comprehensively weight and optimize the signal intensity and transmission efficiency of the screened paths to obtain a preliminary optimized path sequence; Step S4: based on the preliminary optimized path sequence, a speed adaptive path model is generated, which considers the relationship between device speed and signal fluctuation; Step S5: using the speed adaptive path model, a final optimized graph transmission path is generated by combining signal interruption risk and transmission smoothness through a dynamic programming algorithm; Step S6: based on the final optimized graph transmission path, the signal intensity of the key transmission turning points is extracted, and a backup signal enhancement point is added to generate an enhanced graph transmission path; Step S7: real-time monitoring of the deviation between the actual transmission path of the device and the planned path, correction of the deviation by using a filtering algorithm, and generation of real-time adjusted graph transmission control instructions.

[0007] In a second aspect, the present application provides an efficient wireless graph transmission system supporting virtual live streaming, comprising: A heat map generation unit for obtaining signal intensity distribution data and device location information in a virtual live streaming environment and generating a signal intensity heat map; An index calculation unit for generating a set of possible paths from the starting point to the target point based on the signal intensity heat map, and calculating a signal stability index for each path; A path screening unit for screening paths based on the signal stability index, and using a path optimization algorithm to comprehensively weight and optimize the signal intensity and transmission efficiency of the screened paths to obtain a preliminary optimized path sequence; A path model generation unit for generating a speed adaptive path model based on the preliminary optimized path sequence, which considers the relationship between device speed and signal fluctuation; A final path generation unit for using the speed adaptive path model to generate a final optimized graph transmission path by combining signal interruption risk and transmission smoothness through a dynamic programming algorithm; An enhanced path generation unit for extracting the signal intensity of key transmission turning points based on the final optimized graph transmission path, and adding a backup signal enhancement point to generate an enhanced graph transmission path; A control instruction generation unit for real-time monitoring of the deviation between the actual transmission path of the device and the planned path, correction of the deviation by using a filtering algorithm, and generation of real-time adjusted graph transmission control instructions.

[0008] The third aspect of the present application provides a computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer is caused to perform the above-mentioned efficient wireless image transmission method supporting virtual live streaming.

[0009] Compared with the prior art, the beneficial effects of the present application are at least as follows: In the technical solution provided by the present application, the wireless image transmission path optimization in virtual live streaming is realized by combining signal strength heat map, path optimization algorithm and speed adaptive control. Through real-time generation of the signal strength heat map, the device can automatically adjust the path according to the signal strength of the current environment, avoid signal attenuation or interference area, and thus ensure the stability of the device during movement. In addition, the path optimization algorithm comprehensively considers distance, signal strength and transmission efficiency, improving the intelligence and reliability of path selection.

[0010] By inserting a backup signal enhancement point in the path, the time of the device staying in a weak signal area is effectively reduced, the signal stability is improved, and thus the risk of transmission interruption is further reduced. This method is particularly suitable for virtual live streaming and other application scenarios with high requirements for image transmission quality, and can ensure smooth transmission of image data and avoid live streaming picture freezing or loss caused by unstable signals.

[0011] By using Kalman filtering algorithm to correct the device deviation, the accuracy of path planning is further improved. By monitoring the deviation between the device and the planned path in real time, the speed and path of the device can be adjusted in time to adapt to environmental changes, ensure smooth and stable completion of the path, and reduce the path deviation caused by device errors or environmental interference. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0013] Figure 1 The flow chart of the efficient wireless image transmission method supporting virtual live streaming in the embodiment of the present application is shown in the figure. Figure 2 The structure diagram of the efficient wireless image transmission system supporting virtual live streaming in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0014] The embodiments of the present application provide a high-efficiency wireless image transmission method, system and storage medium supporting virtual live broadcast. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment one: For ease of understanding, the specific process of the embodiments of the present application is described below. Please refer to Figure 1 One embodiment of a high-efficiency wireless image transmission method supporting virtual live broadcast in the embodiments of the present application includes: Step S1: Obtain signal strength distribution data and device position information in a virtual live broadcast environment, and generate a signal strength heat map; including: by collecting signal strength data and device position information in the virtual live broadcast environment, using a gridding mapping method to generate a two-dimensional signal strength heat map, and integrating the preset maximum allowed speed and minimum safety speed into the signal strength heat map, generating a signal strength heat map for path evaluation.

[0016] Specifically, in the virtual live broadcast environment, first, uniformly deploy multiple signal sensors in the indoor environment to collect signal strength values in real time; for example, in an office scenario, set a sensor point every 1 meter, record the received signal strength indication (RSSI) value of each point as the signal strength distribution data; at the same time, the device built-in GPS module obtains the current position coordinate information of the device; then, the collected signal strength distribution data is mapped to a predefined indoor grid model, which divides the indoor space into uniform squares, and the size of each square is 0.5 meters x 0.5 meters. In each square, the signal strength value of each grid point is generated by averaging the signal strength values of all sampling points, and based on this signal strength value, a color gradient representing the signal strength is generated, thereby forming a two-dimensional signal strength heat map, in which higher signal strength is represented by red color and lower signal strength is represented by blue color. In this way, the signal strength heat map can intuitively show the signal distribution in the virtual live broadcast environment, especially the signal coverage blind area, so as to identify areas with strong or weak signal strength when planning the path.

[0017] Based on the preset maximum allowed speed and minimum safe speed, the speed constraint is integrated into the two-dimensional signal strength heat map to generate a signal strength heat map for path evaluation; specifically, assuming that the preset maximum allowed speed is 2 m / s and the minimum safe speed is 0.5 m / s, wherein the maximum allowed speed and the minimum safe speed values are determined based on the device type; for example, for a mobile robot, it is ensured that it can accelerate in areas with high signal strength and needs to decelerate in areas with weak signal to maintain stable wireless connection; by calculating the corresponding relationship between the signal strength of each grid and the speed, the linear interpolation method is used to superimpose the speed constraint on each grid point of the two-dimensional signal strength heat map; for example, if the signal strength of a certain area is higher than -60 dBm, the device is allowed to approach the maximum speed; while in the area with weak signal such as below -70 dBm, the speed of the device is limited to the minimum value, thereby adding a speed layer in the signal strength heat map; this speed constraint serves as an additional dimension, labeling the feasible speed range of each grid with a numerical label, and the fused signal strength heat map not only shows the signal strength distribution, but also combines the speed constraint to provide a more accurate basis for path evaluation.

[0018] The signal strength heat map generated by the above steps can ensure that the influence of signal strength and speed is considered comprehensively during path planning, thereby reducing the risk of transmission interruption; for example, in office path planning, if the signal strength heat map shows that the signal strength of the corridor area is -70 dBm, the speed constraint of this area will be set to 1 m / s, which is lower than the area with stronger signal strength (such as 2 m / s), thereby improving the signal stability of the device in this area and avoiding connection or data loss due to high-speed movement.

[0019] In the extended embodiment, different parameter settings are also considered. In the narrow channel office sub-scene, the minimum safety speed is adjusted to 0.3 m / s, and the signal strength heat map is smoothed to eliminate noise using Gaussian filtering, thereby improving the accuracy of the signal strength heat map. This processing helps to further optimize path evaluation, making path planning more reliable and reducing the device's pause time in weak signal areas. For example, assuming that the original signal strength of a certain grid in the signal strength heat map is -65 dBm, after smoothing, it is adjusted to -62 dBm, and the upper limit of its speed will be increased from 1.5 m / s to 1.8 m / s, thereby supporting more stable movement of the device, further ensuring the stability of the signal in the wireless image transmission process. Through the above technical means, the present application can effectively improve the planning and optimization of the image transmission path in the virtual live broadcast process, ensure the stable movement of the device in different signal strength areas, and also maintain signal stability by dynamically adjusting the device speed, greatly reducing the risk of transmission interruption caused by signal fluctuations or speed changes, effectively improving the efficiency and stability of wireless image transmission, especially in complex indoor environments, which can provide a high-quality virtual live broadcast experience.

[0020] Step S2: based on the signal strength heat map, a set of possible paths from the starting point to the target point is generated, and a signal stability index is calculated for each path; including: based on the signal strength heat map, a set of possible paths from the starting point to the target point is generated, and the average and variance of the signal strength on each path are calculated, and based on the signal fluctuation threshold and the real-time fluctuation amplitude, the signal stability index of each path is determined.

[0021] Specifically, first, based on the signal strength heat map, a set of possible paths from the starting point to the target point is generated; for this purpose, a depth-first search algorithm is used to traverse the two-dimensional matrix, and it is ensured that the path avoids areas with signal strength below a preset threshold, thereby generating a non-repeating path sequence; for each generated path, the signal strength data on the path is extracted, and the average and variance of the signal strength on the path are calculated to quantify the stability and signal quality of the path. The average is obtained by summing the signal strength sequence and dividing by the number of points, and the sum of the squared differences between each signal strength value and the average is calculated to further obtain the variance; according to the calculated average and variance of the signal strength, the preset signal fluctuation threshold and the real-time fluctuation amplitude, the signal stability index of each path is determined, and the path is selected according to the index to exclude unstable paths to ensure the stability and reliability of the path planning; the calculation formula of the signal stability index is: index = average / (variance + |real-time fluctuation amplitude - threshold|), where the denominator reflects the signal fluctuation and instability of the path, and the higher the value, the more stable the path.

[0022] In specific embodiments, the generated path set, the signal strength heat map is based on indoor WiFi signal collection, the starting point is the current location of the device, and the target point is the specified room. Through the depth-first search algorithm, the generated path avoids areas with signal strength lower than -70 dBm, ensuring that the device always remains in a stable signal coverage area. In addition, the number of paths is controlled within 50, optimizing the calculation load and improving the path planning efficiency. The process of calculating the mean and variance can effectively quantify the signal quality of the path, especially in some complex environments such as offices or conference rooms. When the path contains multiple grid points with signal strengths of -50 dBm, -52 dBm, and -48 dBm, the calculated mean is -50 dBm, and the variance is 4, indicating that the signal is relatively uniform and can effectively reduce the risk of transmission interruption.

[0023] By combining the signal fluctuation threshold with the real-time fluctuation amplitude, unstable paths can be effectively identified and removed. For example, in a high-interference indoor environment such as electrical interference, the real-time fluctuation amplitude is 15 dB, which exceeds the preset signal fluctuation threshold of 10 dB, reducing the signal stability index of the path. Therefore, this path is removed, avoiding the device staying in a weak signal area and causing connection loss. This can improve the reliability of path planning and ensure that the device always selects the most stable signal path for data transmission. Furthermore, adaptive adjustments can be made based on different interference environments. For example, in a low-interference environment, the minimum signal fluctuation threshold can be set to 8 dB, while in a high-interference environment, the threshold can be set to 10 dB. Optimizing these two parameters can ensure the adaptability and flexibility of path planning.

[0024] The above steps can effectively evaluate and select paths by combining the mean, variance, and real-time fluctuation amplitude of signal strength, solving the problem of unstable image transmission caused by signal fluctuations in virtual live streaming environments. By comprehensively considering the stability of the path, the device can select the best transmission path in complex indoor environments, improving the efficiency of wireless image transmission and reducing the risk of connection interruption or data loss caused by unstable signals, effectively ensuring the image transmission quality and real-time performance in virtual live streaming.

[0025] Step S3: Based on the signal stability index, the low-stability paths are removed from the possible path set to obtain the remaining paths. The path optimization algorithm is used to comprehensively weight and optimize the distance, signal strength, and transmission efficiency of the remaining paths to obtain the preliminary optimized path sequence.

[0026] Specifically, each path in the set of possible paths is traversed, and its signal stability index is compared with a preset threshold value, such as setting the signal stability index threshold value to 0.8. When the signal stability index of a path is lower than the threshold value, it is marked as unstable and removed from the path set. In this way, only paths with higher signal stability are included in the path set, thereby reducing the risk of signal interruption during device movement and improving the stability and reliability of wireless image transmission.

[0027] For the remaining paths, a heuristic search algorithm is used for comprehensive weighted optimization of distance, signal strength, and transmission efficiency. Specifically, the remaining paths are used as the search space of the heuristic search algorithm, and a heuristic function is defined, in which the weight of distance accounts for 60%, and the weight of the average value of signal strength accounts for 40%. According to this weight setting, the comprehensive cost of each path is calculated, and the total distance d and the average strength s of the signal of the path are substituted into the cost function, and the calculation formula is: cost = 0.6 d + 0.4 (1 / s), to find the optimal path by minimizing the comprehensive cost. During the iteration process of the heuristic search algorithm, nodes with higher signal strength are preferentially expanded, and signal strength heat map data is integrated to ensure that the path avoids low signal areas, thereby further improving the stability and efficiency of image transmission. After outputting the optimized path list, the paths are sorted by comprehensive cost, and the path with the minimum cost is selected as the final path. This process considers the distance, signal strength, and transmission efficiency of the path to achieve more optimized path selection, ensuring efficient image transmission of the device during virtual live streaming.

[0028] According to the speed adjustment interval and the fluctuation detection frequency, a preliminary optimized path sequence is generated, which refers to obtaining the speed adjustment interval and setting appropriate fluctuation detection frequencies according to the signal stability and device performance of each path segment. Specifically, the speed adjustment interval can be set to adjust every 10 meters, and the signal fluctuation is detected every second. Adjustment points are inserted on the optimized path, the path segments are divided according to the interval, and the expected speed range and fluctuation monitoring requirements are marked for each path segment. The path segments are serialized to form a preliminary optimized path sequence, which includes the position coordinates of the device, speed constraints, and detection parameters. This process dynamically adjusts and optimizes the path to enable the device to adapt to signal changes in real time during movement, thereby improving the stability of wireless image transmission and avoiding image transmission interruption caused by signal fluctuations or sudden interference. This method can effectively support efficient path planning of the device in complex indoor environments and ensure the stability of signal quality.

[0029] Step S4: generating a speed adaptive path model based on the preliminary optimized path sequence, the speed adaptive path model considering the relationship between the device speed and the signal fluctuation; wherein, generating the speed adaptive path model comprises: obtaining the speed constraint model and the fluctuation data sampling rate of the preliminary optimized path sequence, and calculating the real-time signal fluctuation amplitude, if the real-time signal fluctuation amplitude exceeds the preset signal fluctuation threshold, inserting a speed adjustment node in the path segment, and processing the path segment data based on the path sampling point and the real-time position coordinate, to generate the speed adaptive path model.

[0030] Specifically, generating the speed adaptive path model comprises obtaining the speed constraint model and the fluctuation data sampling rate of the preliminary optimized path sequence, and calculating the real-time signal fluctuation amplitude; if the real-time fluctuation amplitude exceeds the preset signal fluctuation threshold, inserting a speed adjustment node in the corresponding path segment, and processing the path segment data based on the path sampling point and the real-time position coordinate to generate the speed adaptive path model; specifically, first obtain the speed constraint model and the fluctuation data sampling rate of the preliminary optimized path sequence, the speed constraint model is obtained by integrating the maximum allowed speed and the minimum safe speed as the initial constraint, which is used to define the acceptable speed range of the device on the path; the fluctuation data sampling rate represents the frequency of collecting signal fluctuation data per second, for example, set to 10 times per second, to ensure effective monitoring of real-time signal fluctuation.

[0031] If the real-time fluctuation amplitude exceeds the preset signal fluctuation threshold, a speed adjustment node is inserted in the path segment; for example, the real-time signal fluctuation amplitude on the path can be calculated by collecting the difference between the current environmental signal intensity change value and the average value, if the fluctuation amplitude exceeds the signal fluctuation threshold, such as 5dB, it is considered that the signal is unstable, which may cause data transmission interruption, at this time, a speed adjustment node is inserted in the path segment to adjust the device speed to reduce the interruption risk; the process of inserting the speed adjustment node comprises: first, identifying the specific position in the path segment where the fluctuation amplitude exceeds the threshold; second, calculating the adjustment speed at the inserted node, for example, based on the speed constraint model, adjusting the speed from the initial 10 meters / second to 5 meters / second, thereby improving the stability of the signal and ensuring the quality of image transmission.

[0032] Further, based on the trajectory sampling points and real-time position coordinates, process the path segment data to generate a speed adaptive path model; specifically, the path sampling points are collected from the current position information of the device at fixed intervals, such as 1 meter, and the real-time position coordinates are obtained through a positioning system, such as GPS or indoor positioning; when processing the path segment data, the average value and variance of the signal strength of each path segment are calculated, and the speed adjustment node information is combined to generate a speed adaptive path model; this model can dynamically adjust the device speed according to real-time fluctuation data and ensure the continuity and adaptability of the path, thereby effectively improving the stability of the image transmission; for example, in an indoor warehouse environment, the device moves from the starting point to the target point, the sampling points are 20 points on the path, the real-time position coordinates show that it is currently at the 10th point, and a node is inserted after the signal fluctuation amplitude exceeds the threshold value; after processing, the model shows that the speed adjustment for this segment is 4 meters per second, which reduces the interruption risk by 20% and improves the accuracy of path evaluation; for path segments with low signal strength, the maximum allowed speed constraint is also considered when processing the data, for example, if the model calculates that the adjusted speed is 3 meters per second but the upper limit of the constraint is 8 meters per second, then the intermediate value of 5 meters per second is taken, thereby ensuring the balance between safety and efficiency of the device in path planning.

[0033] This method can effectively solve the transmission interruption problem caused by wireless image transmission signal fluctuation in virtual live streaming, and ensure stable movement of the device in different environmental conditions through speed adaptive control, which is beneficial to improve the image transmission quality and the scheduling success rate of the mobile device, and ensure efficient and stable data transmission in virtual live streaming.

[0034] Step S5: using the speed adaptive path model, generate the final optimized image transmission path through a dynamic programming algorithm combined with signal interruption risk and transmission smoothness; including: based on the speed adaptive path model, calculate the expected signal interruption risk of each path segment, and use a dynamic programming algorithm to combine the signal interruption risk and path smoothness to generate the final optimized image transmission path.

[0035] Specifically, based on the speed adaptive path model, first calculate the expected signal interruption risk of each path segment; for this, in the above step, extract the signal strength average value, variance and speed adjustment node data from the speed adaptive path model, wherein the speed adaptive path model is a path representation obtained by inserting speed adjustment nodes in the previous path sequence and fusing fluctuation data sampling rate, containing signal stability indicators and real-time position coordinates; for each path segment, based on the extracted data, calculate its signal interruption risk value; for example, by dividing the signal strength variance by the average value to get the fluctuation coefficient, and then multiplying it by the ratio of the path segment length and the speed, finally quantifying the interruption risk of the path; this interruption risk value reflects the probability of transmission interruption caused by weak signal areas, the higher the risk value, the greater the signal instability.

[0036] The dynamic programming algorithm is also adopted, and the calculated signal interruption risk sequence is taken as a state variable of the dynamic programming; the dynamic programming algorithm optimizes path selection by decomposing the problem into sub-problems and storing the sub-problem solutions to avoid repeated calculation; specifically, the moving smoothness is defined as the minimization of the sum of the absolute difference of the speed changes of adjacent path segments, which is taken as part of the objective function; by recursive calculation, the sub-path with the minimum weighted sum of interruption risk and smoothness is selected, for example, with a 0.6 interruption risk weight and a 0.4 smoothness weight, thereby constructing the overall optimal path; the fused path sequence is also output, and the selected path can reduce the interruption risk and maintain good speed stability according to the comprehensive cost ranking.

[0037] In an embodiment, the application scenario of the dynamic programming algorithm can be effectively implemented in an indoor environment, such as an office or a warehouse; taking an office as an example, the dynamic programming recursively calculates from the starting point to the target point, if the interruption risk of a path segment is 0.5 and the smoothness difference is 0.2, the total value is 0.6*0.5+0.4*0.2=0.38, and this path is selected as the optimal path to ensure the transmission stability and speed smoothness of the device movement; for path segments with large signal fluctuations, the weight proportion can be adjusted, for example, in a high interference area, the weight of the interruption risk is increased to 0.7 and the weight of the smoothness is reduced to 0.3, thereby optimizing the path selection and ensuring the stability and adaptability of the path.

[0038] In addition, a final turning path planning scheme is further generated according to the planned path nodes and the deviation threshold setting; specifically, the key nodes in the fused path sequence are extracted, and a deviation threshold is set, such as 0.5 meters; if the deviation of the path node exceeds the threshold, the node position is fine-tuned to ensure the execution accuracy of the path; in an office environment, if the node deviation exceeds 0.5 meters, it will be adjusted to generate a final turning path planning scheme, thereby improving the accuracy and execution stability of the path planning.

[0039] Through this method, an efficient path planning method can be provided in a virtual live environment, which combines the speed adaptability of the device and the signal fluctuation characteristics, optimizes the path selection in real time, reduces the risk of transmission interruption, and ensures the efficiency and stability of the image transmission process; it can effectively solve the problems of unstable signal and inaccurate path planning in wireless image transmission in virtual live broadcast, so that the device can maintain stable and efficient movement in a complex environment, and provide reliable technical support for virtual live broadcast.

[0040] Step S6: Based on the finally optimized graph transmission path, the signal strength of the key transmission turning point is extracted, and a backup signal enhancement point is added to generate an enhanced graph transmission path; wherein, the generation of the enhanced graph transmission path comprises: extracting the signal strength of the key transmission point from the finally optimized graph transmission path, if the signal strength of the key transmission point is lower than the average value of the signal strength in the transmission path, a backup signal enhancement point is added in the path to generate an enhanced graph transmission path.

[0041] Specifically, the signal strength of each key transmission turning point is extracted from the finally optimized graph transmission path, the key transmission turning point refers to the position where the path direction changes significantly, and the signal strength value at these turning points is obtained according to the previously generated signal strength heat map; if the signal strength of a certain turning point is lower than the average value of the signal strength in the transmission path, it is considered that the signal at the turning point is weak, which may cause transmission interruption; then in this case, the signal stability of the path is improved by obtaining a backup signal enhancement point and integrating it into the path to ensure the smoothness and reliability of wireless graph transmission in virtual live broadcast; The specific steps include, first, calculating the average value of the signal strength of all key transmission turning points, and comparing the signal strength of each turning point with the average value, if the signal strength of a certain turning point is lower than the average value, searching for the nearest backup signal enhancement point from the pre-stored indoor environment signal distribution data; the backup signal enhancement point is a position with higher signal strength, which can be accessed by temporarily deviating from the original path, thereby improving the stability of the overall signal; then when inserting the backup signal enhancement point, it is ensured that the increase of the total length of the path does not exceed the preset proportion, so as to avoid excessive reduction of the path efficiency; for example, in the actual scene of indoor device scheduling, if the final turning path planning scheme contains three key turning points with signal strengths of -70dBm, -85dBm and -60dBm, and the average value of the signal strength is -71.67dBm, the signal strength of the turning point of -85dBm is lower than the average value, then the nearest backup signal enhancement point to the turning point is selected from the pre-stored data, the signal strength of the point is -50dBm, and it is integrated into the path, by increasing a short detour section before and after the turning point, the risk of transmission interruption is reduced to ensure the stability of the path.

[0042] In an extended embodiment, in view of the influence of real-time environmental changes such as indoor dynamic obstacles, the enhanced point insertion path without obstacles is preferentially selected to ensure the adaptability of the path and the reliability of the signal; further, a deviation calculation formula and a path correction algorithm are used to correct the path integrated with the standby signal enhancement point, to ensure the continuity of the path and the stability of the movement; the specific steps include: first, using the deviation calculation formula to evaluate the deviation between the path integrated with the standby signal enhancement point and the original planned path, the deviation calculation formula is the sum of the Euclidean distance of the path point coordinates divided by the path length, to obtain the deviation value; if the deviation value exceeds the set threshold, the path correction algorithm is applied to smooth the path, the path point position is adjusted using the Bezier curve method to ensure that the path is still continuous and smooth after correction; through this method, an enhanced type image transmission path is generated to ensure the signal enhancement effect of the path, especially at the turning point; for example, during the movement of the device from the starting point to the target point, assuming that the deviation value of the path integrated with the standby signal enhancement point is 0.15 meters, if the deviation value exceeds the set deviation threshold of 0.1 meters, the path correction algorithm will insert an intermediate point to adjust the path, so that the deviation is reduced to 0.08 meters, thereby generating an enhanced type image transmission path; the path corrected by this method can effectively reduce the trajectory deviation in the actual movement of the device, improve the accuracy and efficiency in the device scheduling process, and ensure the stability and reliability of the image transmission in the virtual live broadcast process.

[0043] In addition, according to the planned path node and the deviation threshold setting, a final turning path planning scheme is generated to ensure the accuracy and stability of the path when executed in the actual environment, which can effectively improve the reliability of wireless image transmission in the virtual live broadcast environment, reduce transmission interruptions caused by signal fluctuations, device movement or environmental interference, and provide efficient and stable image transmission path for the virtual live broadcast process.

[0044] Step S7: Real-time monitoring of the deviation between the actual transmission path of the device and the planned path, using a filtering algorithm to correct the deviation, and generating a real-time adjusted image transmission control instruction; wherein generating a real-time adjusted image transmission control instruction includes: based on the enhanced image transmission path, real-time monitoring of the deviation between the actual transmission path of the device and the planned path, using a Kalman filtering algorithm to correct the path deviation and update the path parameters, and generating a real-time adjusted image transmission control instruction.

[0045] Specifically, based on the enhanced map transmission path, first, the deviation of the actual transmission path of the device from the planned path is monitored in real time; through the positioning sensor on the device, the actual movement trajectory coordinate data of the device is collected, and compared with the planned coordinates in the enhanced map transmission path point by point, the deviation value of each sampling point is calculated, and then a deviation sequence is formed for correction; in order to ensure real-time, the monitoring process can be performed within a fixed time interval, for example, collecting deviation data every 0.5 seconds, which is particularly important in areas with low signal strength, and can help to discover path deviation in time and prevent the risk of signal interruption.

[0046] The Kalman filtering algorithm is used to correct the deviation and update the path parameters; the Kalman filtering algorithm estimates the state of the device recursively, and minimizes the variance of the deviation according to the prediction and observation results. In this embodiment, the prediction step of Kalman filtering is performed by using a motion model of the device, such as an assumed uniform motion model, to predict the state at the next time point using a state transition matrix, and to calculate the predicted covariance; then in the update step, the Kalman gain is obtained by fusing the actual deviation observation value and the predicted value to obtain the optimal state estimation, and the path parameters are updated based on the corrected state, such as adjusting the path node coordinates or the speed constraint; this method can effectively filter the noise in the positioning sensor, improve the accuracy of the path parameters, and reduce the deviation of the device in the signal fluctuation area, ensuring the stability of the rotation path.

[0047] According to the monitoring time interval and the deviation alarm mechanism, a real-time adjusted map transmission control instruction is generated; specifically, the monitoring time interval is set, for example, deviation data is collected every 0.5 seconds, and it is checked whether the corrected deviation exceeds a preset threshold, such as a threshold of 0.1 meters; if the deviation exceeds the threshold, the deviation alarm mechanism is triggered, the alarm level is calculated, and an alarm is generated based on the deviation amplitude, for example, when the deviation amplitude is greater than 0.2 meters, it is considered as a high alarm level; according to the alarm level and the monitoring time interval, an adjustment instruction is generated, such as reducing the speed or inserting a correction node into the path, to reduce the deviation of the path; finally, the generated adjustment instruction is sent to the device controller to adjust the movement path and speed of the device in real time; for example, in the indoor warehouse device scheduling scene, if the deviation alarm mechanism detects that the deviation exceeds the threshold for three consecutive times, an instruction is generated to reduce the device to the minimum safe speed, so as to effectively reduce the risk of transmission interruption and improve the reliability of path planning; at the same time, by dynamically adjusting the path parameters and the speed, the device can realize more stable and efficient navigation in a complex signal environment, thereby ensuring the efficiency and stability of wireless map transmission in virtual live streaming.

[0048] In an embodiment, the deviation alarm mechanism can dynamically adjust the threshold according to the signal strength heat map; for example, in a low signal strength area, the threshold can be set to 0.05 meters to respond more sensitively to potential path deviation, thereby optimizing instruction generation to ensure that the device can achieve optimal rotation control in different indoor environments; through this method, the application can correct the deviation of the device path in real time and optimize the path control instruction, greatly improving the autonomous navigation efficiency of the device in a complex signal environment and ensuring stable and reliable wireless image transmission in virtual live streaming.

[0049] Embodiment two: The above describes an efficient wireless image transmission method supporting virtual live streaming in an embodiment of the application, and the following describes an efficient wireless image transmission system supporting virtual live streaming in an embodiment of the application. Please refer to Figure 2 An embodiment of the efficient wireless image transmission system supporting virtual live streaming in an embodiment of the application includes: A heat map generation unit configured to obtain signal strength distribution data and device position information in a virtual live streaming environment and generate a signal strength heat map; An index calculation unit configured to generate a set of possible paths from a starting point to a target point based on the signal strength heat map and calculate a signal stability index for each path; A path screening unit configured to screen paths based on the signal stability index, use a path optimization algorithm to comprehensively and weightedly optimize the signal strength and transmission efficiency of the screened paths, and obtain a preliminary optimized path sequence; A path model generation unit configured to generate a speed adaptive path model based on the preliminary optimized path sequence, the speed adaptive path model taking into account the relationship between the speed of the device and signal fluctuations; A final path generation unit configured to use the speed adaptive path model to generate a final optimized image transmission path by using a dynamic programming algorithm in combination with signal interruption risks and transmission smoothness; An enhanced path generation unit configured to extract the signal strength of key transmission turning points based on the final optimized image transmission path and add backup signal enhancement points to generate an enhanced image transmission path; A control instruction generation unit configured to monitor the deviation of the actual transmission path of the device from the planned path in real time, correct the deviation using a filtering algorithm, and generate real-time adjusted image transmission control instructions.

[0050] Through the synergistic cooperation of the above-mentioned components, the stability and efficiency of wireless image transmission in virtual live streaming are further improved.

[0051] Embodiment three: The application further provides a computer readable storage medium, which can be a nonvolatile computer readable storage medium or a volatile computer readable storage medium, and the computer readable storage medium stores instructions, which, when executed on a computer, cause the computer to perform the steps of the method for supporting efficient wireless image transmission in virtual live streaming.

[0052] In summary, the wireless image transmission method provided by the application can effectively improve the efficiency and stability of wireless image transmission in virtual live streaming, reduce the decline in live streaming quality caused by signal fluctuation or transmission interruption, and ensure the efficiency and reliability of image transmission in a virtual live streaming environment.

[0053] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, system and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0054] The integrated unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program code storage media.

[0055] The above-described embodiments are only used to illustrate the technical solutions of the application, rather than limit them. Although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A highly efficient wireless image transmission method supporting virtual live streaming, characterized in that, The method includes: Step S1: Obtain signal strength distribution data and device location information in the virtual live streaming environment, and generate a signal strength heatmap; Step S2: Based on the signal strength heatmap, generate a set of possible paths from the starting point to the target point, and calculate the signal stability index for each path; Step S3: Based on the signal stability index, the path is screened, and the path optimization algorithm is used to perform a comprehensive weighted optimization of the signal strength and transmission efficiency of the screened path to obtain a preliminary optimized path sequence; Step S4: Based on the preliminary optimized path sequence, generate a speed adaptive path model, which takes into account the relationship between device speed and signal fluctuation; Step S5: Using the speed adaptive path model, the final optimized image transmission path is generated by combining signal interruption risk and transmission smoothness through a dynamic programming algorithm; Step S6: Based on the final optimized image transmission path, extract the signal strength of key transmission turning points, add backup signal enhancement points, and generate an enhanced image transmission path; Step S7: Monitor the deviation between the actual transmission path and the planned path of the equipment in real time, use a filtering algorithm to correct the deviation, and generate the real-time adjusted image transmission control command.

2. The method according to claim 1, characterized in that, Step S1 further includes: By collecting signal strength data and device location information in the virtual live streaming environment, a two-dimensional signal strength heatmap is generated using a gridded mapping method. The preset maximum allowable speed and minimum safe speed are then integrated into the signal strength heatmap to generate a signal strength heatmap for path evaluation.

3. The method according to claim 1, characterized in that, Step S2 further includes: Based on the signal strength heatmap, a set of possible paths from the starting point to the target point is generated, and the average signal strength and variance of each path are calculated. Furthermore, based on the signal fluctuation threshold and real-time fluctuation amplitude, the signal stability index of each path is determined.

4. The method according to claim 1, characterized in that, Step S3 further includes: Based on the signal stability index, low-stability paths are removed from the set of possible paths to obtain the remaining paths. The remaining paths are then optimized using a path optimization algorithm that comprehensively considers distance, signal strength, and transmission efficiency to obtain a preliminary optimized path sequence.

5. The method according to claim 1, characterized in that, The step S4 of generating the speed adaptive path model includes: The speed constraint model and fluctuation data sampling rate of the preliminary optimized path sequence are obtained, and the real-time signal fluctuation amplitude is calculated. If the real-time signal fluctuation amplitude exceeds the preset signal fluctuation threshold, a speed adjustment node is inserted in the path segment, and the path segment data is processed based on the path sampling points and real-time position coordinates to generate a speed adaptive path model.

6. The method according to claim 1, characterized in that, Step S5 further includes: Based on the speed adaptive path model, the expected signal interruption risk for each path segment is calculated, and a dynamic programming algorithm is used to combine the signal interruption risk with the path smoothness to generate the final optimized image transmission path.

7. The method according to claim 1, characterized in that, The step S6 of generating the enhanced image transmission path includes: The signal strength of key transmission points is extracted from the final optimized image transmission path. If the signal strength of the key transmission point is lower than the average signal strength in the transmission path, a backup signal enhancement point is added to the path to generate an enhanced image transmission path.

8. The method according to claim 1, characterized in that, The step S7, which generates real-time adjusted image transmission control commands, includes: Based on the enhanced image transmission path, the deviation between the actual transmission path and the planned path of the device is monitored in real time. The Kalman filter algorithm is used to correct the path deviation and update the path parameters, generating real-time adjusted image transmission control commands.

9. A high-efficiency wireless image transmission system supporting virtual live streaming, used to implement the high-efficiency wireless image transmission method supporting virtual live streaming as described in any one of claims 1-8, characterized in that, The system includes: The heatmap generation unit is used to acquire signal strength distribution data and device location information in the virtual live streaming environment, and generate a signal strength heatmap. The index calculation unit is used to generate a set of possible paths from the starting point to the target point based on the signal strength heatmap, and to calculate a signal stability index for each path. The path selection unit is used to select paths based on the signal stability index, and to perform a comprehensive weighted optimization of the selected paths based on signal strength and transmission efficiency using a path optimization algorithm to obtain a preliminary optimized path sequence. The path model generation unit is used to generate a speed adaptive path model based on the preliminary optimized path sequence, wherein the speed adaptive path model takes into account the relationship between device speed and signal fluctuation. The final path generation unit is used to generate the final optimized image transmission path by using the speed adaptive path model and combining signal interruption risk and transmission smoothness through a dynamic programming algorithm. The enhanced path generation unit is used to extract the signal strength of key transmission turning points based on the final optimized image transmission path, and add backup signal enhancement points to generate an enhanced image transmission path. The control command generation unit is used to monitor the deviation between the actual transmission path and the planned path of the equipment in real time, use a filtering algorithm to correct the deviation, and generate the image transmission control command after real-time adjustment.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instruction is executed by the processor, it implements an efficient wireless image transmission method supporting virtual live streaming as described in any one of claims 1-8.

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