Emergency rescue remote control method, system, equipment and medium

Abstract

The invention relates to an emergency rescue remote control method, system and device and a medium. The method comprises the following steps: acquiring environment signal intensity and initial positioning data; obtaining enhanced multi-source positioning data according to the initial positioning coordinates; extracting moving speed and altitude information from the enhanced multi-source positioning data, and determining a real-time dynamic trajectory path; generating an encryption key sequence according to the real-time dynamic track path to obtain an encrypted transmission packet; when the encrypted transmission packet exceeds the channel capacity, segmenting the encrypted transmission packet into a sub-packet sequence, adding a check code, judging the integrity of the sub-packet according to the check code, and retransmitting a missing part to obtain a complete backhaul data set; analyzing the complete returned data set, analyzing a potential dangerous area, and determining an adjusted track display parameter; and obtaining dynamic rescue guidance information according to the adjusted track display parameters. By adopting the method, the remote rescue efficiency and safety can be improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and particularly relates to an emergency rescue remote control method, system, device and medium. BACKGROUND

[0002] The field of emergency rescue is crucial in modern society, especially in natural disasters, missing events or sudden accidents. Quickly locating and obtaining key information of the rescued person is directly related to life safety. Whether it is a sudden medical event in the city or an outdoor accident in remote areas, timely obtaining the location, environmental sound or video information of the rescued person can provide decision basis for rescue personnel and greatly improve rescue efficiency.

[0003] However, the current technical solution often fails to meet the real-time, accuracy and multi-scene adaptation requirements when dealing with complex scenarios, limiting the effectiveness of emergency rescue. Existing methods have significant limitations in positioning and information acquisition. Many solutions rely on a single positioning technology, such as using GPS alone, resulting in a significant decrease in positioning accuracy in signal-limited environments such as indoors, tunnels or high-rise dense areas, or even complete failure. In addition, existing systems have difficulty in simultaneously collecting multi-dimensional information such as location, movement speed and environmental data when remotely controlling the rescued person's device. Rescue personnel often cannot fully grasp the dynamics of the rescued person, which not only increases the blindness of rescue operations, but also may delay the rescue opportunity due to insufficient information.

[0004] In terms of technology, the core difficulty lies in how to achieve seamless integration of multi-source positioning technology and efficient transmission of real-time data. First, multi-source positioning technology needs to integrate GPS, Beidou, base station triangulation and WiFi hotspots, etc. However, the signal strength and accuracy of these technologies vary greatly in different environments. How to dynamically select and optimize the positioning method to ensure continuous high accuracy is a key challenge. Second, even if the location data is successfully obtained, how to transmit the latitude, longitude, altitude, and movement speed information through an encrypted channel to the rescue end in real time and dynamically present the trajectory on the map when remotely controlling the relative's phone still faces technical bottlenecks. For example, in a mountain rescue, the rescue personnel may not be able to receive the movement trajectory of the rescued person in real time due to unstable mobile phone signals, resulting in the inability to determine whether they are close to a dangerous area. SUMMARY

[0005] Therefore, it is necessary to provide an emergency rescue remote control method, system, device and medium that can achieve dynamic integration of multi-source positioning technology in complex environments while ensuring real-time encrypted transmission of multi-dimensional data and dynamic trajectory presentation.

[0006] In a first aspect, the application provides an emergency rescue remote control method, comprising:

[0007] acquiring environment signal strength and initial positioning data;

[0008] obtaining enhanced multi-source positioning data according to the initial positioning coordinates;

[0009] extracting moving speed and altitude information from the enhanced multi-source positioning data to determine a real-time dynamic trajectory path;

[0010] generating an encryption key sequence according to the real-time dynamic trajectory path to obtain an encrypted transmission packet;

[0011] when the encrypted transmission packet exceeds the channel capacity, splitting it into a sub-packet sequence and adding a check code, judging the integrity of the sub-packet according to the check code and retransmitting the missing part to obtain a complete return data set;

[0012] parsing the complete return data set, analyzing potential dangerous areas, and determining adjusted trajectory display parameters;

[0013] obtaining dynamic rescue guidance information according to the adjusted trajectory display parameters; the dynamic rescue guidance information is used to indicate the moving trajectory of the rescued party and the surrounding environment information.

[0014] In one embodiment, acquiring environment signal strength and initial positioning data includes:

[0015] acquiring environment signal strength and positioning data through multi-source sensors to obtain an original data set;

[0016] fusing global navigation system base station positioning data and communication network positioning data to obtain initial positioning coordinates;

[0017] when the deviation of the initial positioning coordinates exceeds a preset threshold, performing smoothing processing on the initial positioning coordinates to obtain smoothed coordinates;

[0018] obtaining optimized coordinates according to the smoothed coordinates and the environment signal strength;

[0019] when the deviation of the optimized coordinates and historical positioning data exceeds a preset threshold, recalibrating the optimized coordinates through the environment signal strength to obtain calibrated coordinates;

[0020] updating the positioning data according to the calibrated coordinates and the real-time signal strength collected by the sensor to obtain updated coordinates;

[0021] correcting the time deviation according to the updated coordinates and the time synchronization data of the global navigation system to obtain the initial positioning coordinates.

[0022] In one embodiment, obtaining enhanced multi-source positioning data according to the initial positioning coordinates includes:

[0023] The environment signal strength and the initial positioning data are weighted and fused to obtain enhanced positioning coordinates;

[0024] The enhanced positioning coordinates are smoothed to obtain smoothed positioning coordinates;

[0025] The smoothed positioning coordinates are optimized based on the environment signal strength to obtain optimized positioning coordinates;

[0026] The optimized positioning coordinates are calibrated for coordinate deviation to obtain calibrated positioning coordinates;

[0027] According to the calibrated positioning coordinates, enhanced multi-source positioning data is obtained.

[0028] In one of the embodiments, the mobile speed and altitude information are extracted from the enhanced multi-source positioning data to determine the real-time dynamic trajectory path, including:

[0029] The speed information and altitude data are obtained from the enhanced multi-source positioning data to generate an initial trajectory path;

[0030] The initial trajectory path is smoothed to obtain a smoothed trajectory path;

[0031] According to the smoothed trajectory path and the timestamp data, a corrected trajectory path is obtained;

[0032] Based on the environment signal, the corrected trajectory path is identified for trajectory abnormal points to obtain abnormal point markers;

[0033] When the number of abnormal point markers exceeds a preset threshold, the corrected trajectory path is re-adjusted to obtain an adjusted trajectory path;

[0034] Based on real-time data, the adjusted trajectory path is updated to obtain an updated trajectory path;

[0035] Based on the updated trajectory path, a real-time dynamic trajectory path is obtained.

[0036] In one of the embodiments, when the encrypted transmission packet exceeds the channel capacity, it is divided into a sub-packet sequence and a check code is added, the integrity of the sub-packet is judged according to the check code and the missing part is retransmitted to obtain a complete return data set, including:

[0037] When the data amount of the encrypted transmission packet exceeds the channel capacity, the encrypted transmission packet is decomposed into a sub-packet sequence to obtain a sub-packet sequence data set;

[0038] Based on the cyclic redundancy check code, the integrity of the sub-packet sequence data set is checked to obtain a sub-packet integrity check result; the sub-packet integrity check result is used to judge whether the sub-packet is complete;

[0039] When the sub-packet integrity check result is that the sub-packet is not complete, the incomplete sub-packet is re-sent to obtain an updated sub-packet sequence;

[0040] According to the update sub-packet sequence, the sub-packet transmission order is adjusted to obtain a synchronization sub-packet data set;

[0041] According to the synchronization sub-packet data set, a reorganized data packet is obtained;

[0042] The data integrity of the reorganized data packet is verified to obtain a data integrity verification result; the data integrity verification result is used to indicate whether the reorganized data packet is complete;

[0043] When the data integrity verification result is that the reorganized data packet is complete, a complete backhaul data set is obtained.

[0044] In one of the embodiments, the complete backhaul data set is parsed, a potential dangerous area is analyzed, and an adjusted trajectory display parameter is determined, including:

[0045] The complete backhaul data set is extracted to obtain a feature vector set;

[0046] The feature vector set is grouped into risk areas to obtain a region division result;

[0047] According to the region division result, a region risk assessment value is calculated;

[0048] According to the region risk assessment value, a trajectory adjustment parameter is optimized to obtain a preliminary adjustment parameter set;

[0049] Based on the preliminary adjustment parameter set, the trajectory display order is calibrated to obtain a synchronization trajectory parameter;

[0050] According to the synchronization trajectory parameter, an adjusted trajectory display parameter is generated.

[0051] In one of the embodiments, according to the region division result, a region risk assessment value is calculated, including:

[0052] Obtain the geographical information and risk related data of the region division to obtain a structured data set;

[0053] According to the structured data set, the region features are extracted, and the initial risk value of each region is calculated using the following formula:

[0054]

[0055] wherein, represents the risk score of the i-th region, represents the total number of risk assessment indexes, represents the weight value of the i-th risk index, represents the i-th region in the j-th risk index, represents the i-th region in the j-th risk index, represents the i-th region in the j-th risk index, represents the i-th region in the j-th risk index, a standardized value on the risk indicator;

[0056] an adjusted risk value is obtained based on the initial risk value and the region boundary;

[0057] a risk level distribution is obtained by dividing the region into risk levels according to the adjusted risk value;

[0058] a region risk assessment value is obtained according to the risk level distribution.

[0059] In a second aspect, the present application further provides an emergency rescue remote control system, comprising:

[0060] a data acquisition module, configured to acquire environmental signal strength and initial positioning data;

[0061] a positioning data optimization module, configured to obtain enhanced multi-source positioning data according to the initial positioning coordinates;

[0062] a dynamic trajectory path generation module, configured to extract moving speed and altitude information from the enhanced multi-source positioning data and determine a real-time dynamic trajectory path;

[0063] a data encryption module, configured to generate an encryption key sequence according to the real-time dynamic trajectory path and obtain an encrypted transmission packet;

[0064] a data transmission module, configured to split the encrypted transmission packet into a sub-packet sequence and add a check code when the encrypted transmission packet exceeds a channel capacity, judge the integrity of the sub-packet according to the check code and retransmit the missing part, and obtain a complete return data set;

[0065] a data analysis module, configured to analyze the complete return data set, analyze potential dangerous areas, and determine adjusted trajectory display parameters;

[0066] a rescue guidance module, configured to obtain dynamic rescue guidance information according to the adjusted trajectory display parameters; the dynamic rescue guidance information is used to indicate the moving trajectory of the rescued party and the surrounding environmental information.

[0067] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the method of the first aspect.

[0068] The aforementioned emergency rescue remote control method, system, equipment, and medium, through the fusion of multi-source positioning technology, break free from reliance on single positioning and ensure positioning effectiveness even in signal-constrained environments such as indoors and tunnels, improving positioning accuracy and stability. It can simultaneously collect multi-dimensional information such as location, movement speed, and environment, allowing rescuers to fully grasp the dynamics of the person being rescued. It achieves seamless fusion of multi-source positioning and efficient real-time data transmission, transmitting latitude, longitude, altitude, and other information in real-time via encrypted channels and dynamically presenting the trajectory, providing rescuers with sufficient decision-making basis, reducing blind spots in rescue efforts, avoiding delays, and improving rescue efficiency. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a schematic diagram illustrating the application environment of an emergency rescue remote control method in one embodiment.

[0071] Figure 2 This is a flowchart illustrating an emergency rescue remote control method in one embodiment;

[0072] Figure 3 This is a schematic diagram of the structure of an emergency rescue remote control system in one embodiment. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0074] This application provides an emergency rescue remote control method that can be applied to, for example... Figure 1In the application environment shown, the rescued terminal 101 and the remote rescue terminal 102 communicate with the server 103 via a network. A data storage system can store the data that the server 103 needs to process. The data storage system can be integrated onto the server 103 or placed on the cloud or other network servers. The rescued terminal 101 and the remote rescue terminal 102 can be, but are not limited to, smartphones, various personal computers, laptops, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. The server 103 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0075] In one exemplary embodiment, such as Figure 2 As shown, an emergency rescue remote control method is provided, which can be applied to... Figure 1 Taking the rescued terminal (hereinafter referred to as the terminal) as an example, the following steps are included:

[0076] S1, acquire environmental signal strength and initial positioning data.

[0077] For example, the rescued terminal acquires the transmitted environmental signal strength and initial positioning data. The acquisition of environmental signal strength relies on the signal receiving module on the rescued terminal, which can collect various types of signal strength data, including communication base station signals and satellite navigation signals, to reflect the communication link quality of the terminal's environment. The acquisition of initial positioning data is achieved through the positioning receiving unit integrated into the terminal, which can receive raw positioning information from different positioning systems. During this process, the collected environmental signal strength data needs to be filtered to remove abnormal signal values ​​caused by transient interference. Simultaneously, a preliminary validity assessment of the initial positioning data is performed, excluding positioning data that clearly exceeds a reasonable geographical range. The positioning system can be a Global Navigation Satellite System (GNSS), which may include the Global Positioning System (GPS), the BeiDou Navigation Satellite System (BDS), and other positioning systems.

[0078] S2, based on the initial positioning coordinates, obtain the enhanced multi-source positioning data.

[0079] For example, the terminal being rescued obtains enhanced multi-source positioning data by fusing GNSS positioning data, base station positioning data, and Wi-Fi (Wireless Fidelity) positioning data based on the specific coordinate information of the initial positioning coordinates. By fusing the initial positioning coordinates with positioning data from other sources, the shortcomings of a single positioning method in certain environments can be compensated for, improving the accuracy and reliability of the positioning data.

[0080] S3 extracts movement speed and altitude information from the enhanced multi-source positioning data to determine the real-time dynamic trajectory path.

[0081] For example, the rescued party's terminal extracts movement speed and altitude information from the enhanced multi-source positioning data to determine a real-time dynamic trajectory path. Movement speed is extracted by calculating the timestamps and location coordinates in the enhanced multi-source positioning data. The instantaneous movement speed is obtained by dividing the coordinate difference between two adjacent positioning points by the time difference, and then smoothed using a moving average algorithm to eliminate abrupt changes in speed data. The terminal extracts altitude information from the elevation data field included in the enhanced multi-source positioning data, and simultaneously calibrates the altitude information using air pressure data collected by a barometer to improve the accuracy of the altitude data. After acquiring movement speed and altitude information, continuous positioning points, corresponding movement speeds, and altitude information are correlated and integrated along the time axis to construct an initial trajectory model. This initial trajectory model is then optimized using a trajectory fitting algorithm to correct abnormal inflection points in the trajectory, ultimately determining the real-time dynamic trajectory path. This dynamic trajectory path accurately reflects the movement state of the rescued party. The trajectory fitting algorithm can be polynomial fitting, spline interpolation, or linear regression.

[0082] S4 generates an encryption key sequence based on the real-time dynamic trajectory path to obtain an encrypted transmission packet.

[0083] For example, the terminal being rescued generates an encryption key sequence based on the real-time dynamic trajectory path, thereby obtaining an encrypted transmission packet. The generation of the encryption key sequence employs dynamic key generation technology, using characteristic parameters from the real-time dynamic trajectory path as seed information for key generation. A time-sensitive encryption key sequence is generated through a symmetric encryption algorithm, ensuring the uniqueness and security of the key. These characteristic parameters can be trajectory inflection point coordinates, movement speed variation patterns, etc.; the symmetric encryption algorithm can be the Advanced Encryption Standard (AES). After generating the encryption key sequence, the terminal organizes and packages the real-time dynamic trajectory path data according to a preset data format to form an original data transmission packet. Then, it uses the generated encryption key sequence to encrypt the original data transmission packet, converting the plaintext data in the original data transmission packet into ciphertext data using an encryption algorithm. Simultaneously, data header information is added to the encrypted transmission packet, ultimately forming an encrypted transmission packet. The encrypted transmission packet effectively prevents data from being illegally stolen and tampered with during transmission. The data header information can include data length, encryption algorithm identifier, key version, etc.

[0084] S5. When the encrypted transmission packet exceeds the channel capacity, it is divided into a sub-packet sequence and a check code is added. The integrity of the sub-packet is judged based on the check code and the missing part is retransmitted to obtain the complete return dataset.

[0085] For example, the rescued terminal compares the size of the encrypted transmission packet with the capacity of the current communication channel. If the size of the encrypted transmission packet exceeds the channel capacity, a data segmentation algorithm is used to evenly divide the encrypted transmission packet into several sub-packet sequences according to the channel's Maximum Transmission Unit (MTU). Each sub-packet sequence contains identification information such as sub-packet number, total number of sub-packets, and data length. Next, a checksum is added to each sub-packet sequence using Cyclic Redundancy Check (CRC). By performing a CRC operation on the data content of each sub-packet sequence, a corresponding checksum is generated and appended to the end of the sub-packet. The terminal sends the sub-packet sequences sequentially to the server. The server performs integrity checks on each sub-packet based on the checksum. If the checksum indicates the presence of missing or corrupted sub-packets, the server returns the missing sub-packet number to the rescued terminal. The rescued terminal retransmits the missing sub-packet according to the feedback information until all sub-packets pass the integrity check. Finally, all complete sub-packets are reassembled in order of their sub-packet numbers to obtain the complete return dataset.

[0086] S6 parses the complete returned dataset, analyzes potential danger areas, and determines the adjusted trajectory display parameters.

[0087] For example, the remote rescue terminal retrieves the complete returned dataset from the server, parses the data, extracts key information such as positioning trajectory data and environmental parameter data, and converts the parsed data into a standardized data format. When analyzing potential hazardous areas, it performs correlation analysis between the parsed environmental parameter data and the positioning trajectory data, based on preset hazardous area determination rules. Spatial analysis algorithms identify potential hazardous areas around the positioning trajectory and mark the extent and hazard level of these areas. When determining the adjusted trajectory display parameters, the color, line thickness, and labeling information of the trajectory display are adjusted according to the distribution of potential hazardous areas. For example, trajectory segments near hazardous areas are displayed with thick red lines and hazard warning labels are added to ensure that rescuers can clearly identify the risk information in the trajectory. The final adjusted trajectory display parameters are then determined. Environmental parameter data includes information on surrounding obstacles and terrain data; hazardous area determination rules can include criteria such as steep slope areas, water areas, and areas with dense obstacles; and spatial analysis algorithms can include Dijkstra's algorithm, Floyd's algorithm, etc.

[0088] S7 obtains dynamic rescue guidance information based on the adjusted trajectory display parameters.

[0089] Specifically, the remote rescue terminal generates dynamic rescue guidance information based on the adjusted trajectory display parameters. This dynamic rescue guidance information is used to indicate the movement trajectory and surrounding environmental information of the person being rescued. First, the remote rescue terminal visualizes the real-time dynamic trajectory path according to the adjusted display style, forming a trajectory visualization image. Simultaneously, the analyzed potential danger zone information and surrounding environmental auxiliary information are integrated with the trajectory visualization image according to a preset information layout format to generate initial rescue guidance information containing trajectory display, hazard warnings, and environmental guidance. The initial rescue guidance information is dynamically updated based on the real-time movement status of the person being rescued. For example, when the person being rescued approaches a new danger zone, a warning about that danger zone is added to the rescue guidance information; when the person being rescued deviates from the recommended route, route correction suggestions are given in the information. Finally, dynamic rescue guidance information is generated, which can provide movement guidance and environmental prompts to the person being rescued in real time and accurately. The display style includes color, line thickness, etc.; the surrounding environmental auxiliary information includes the location of safe shelters and traversable routes, etc.

[0090] In the aforementioned emergency rescue remote control method, the fusion of multi-source positioning technologies overcomes the limitations of single technologies in signal-constrained environments, enabling continuous and accurate positioning in different scenarios. Simultaneously, it comprehensively collects multi-dimensional information such as location, movement speed, and environment, allowing rescuers to fully grasp the dynamics of the person being rescued. Furthermore, it efficiently encrypts and processes real-time data, enabling the rescue end to acquire and dynamically present trajectory information in real time, providing sufficient basis for rescue decisions, improving rescue efficiency, better adapting to various emergency rescue scenarios, and facilitating timely and efficient rescue operations.

[0091] In an optional embodiment, acquiring environmental signal strength and preliminary location data includes the following steps:

[0092] S11 collects environmental signal strength and positioning data through multi-source sensors to obtain the raw dataset.

[0093] For example, the rescued terminal collects environmental signal strength and location data through multi-source sensors to obtain a raw dataset. Multi-source sensors refer to various types of sensors equipped on the terminal, such as signal strength sensors and location sensors. These sensors are responsible for collecting different types of signals and location-related data from the environment. Environmental signal strength data reflects the strength of various signals, while location data contains the terminal's location information. This data, directly collected by the sensors, is then aggregated and integrated to form the unprocessed raw dataset.

[0094] S12, integrates global navigation system base station positioning data and communication network positioning data to obtain initial positioning coordinates.

[0095] For example, the terminal being rescued fuses GPS base station positioning data and communication network positioning data to obtain initial positioning coordinates. GPS base station positioning data is based on positioning information provided by GPS base stations, obtained by receiving satellite signals and using base station-assisted calculations. Communication network positioning data is location information obtained using methods such as triangulation from base stations in mobile communication networks. Fusing these two types of positioning data leverages their respective advantages, using data fusion algorithms to complement and correct them, thereby obtaining more accurate initial positioning coordinates and improving the reliability of the initial positioning.

[0096] S13 When the initial positioning coordinate deviation exceeds the preset threshold, the initial positioning coordinate is smoothed to obtain smooth coordinates.

[0097] For example, when the initial positioning coordinate deviation exceeds a preset threshold, the terminal being rescued smooths the initial positioning coordinates to obtain smoothed coordinates. The preset threshold is a numerical standard pre-set in the system to determine whether the positioning coordinate deviation is acceptable. When the deviation between the initial positioning coordinates and the actual location exceeds this threshold, it indicates insufficient positioning accuracy. The smoothing process uses algorithms such as moving average and Kalman filtering to process the initial positioning coordinate sequence, eliminating abnormal fluctuations and noise interference, making the coordinate changes more stable, and ultimately obtaining smoothed coordinates, thus improving the stability of positioning.

[0098] S14. Optimized coordinates are obtained based on the smoothed coordinates and the environmental signal strength.

[0099] Specifically, the rescued terminal operates based on the correlation between environmental signal strength and positioning accuracy. Different environmental signal strengths correspond to different positioning environments, resulting in varying positioning error characteristics. The terminal categorizes and analyzes environmental signal strength data, classifying it into strong, medium, and weak levels. Each level corresponds to a different positioning error correction coefficient: the stronger the signal, the smaller the correction coefficient; conversely, the weaker the signal, the larger the correction coefficient. Then, smoothed coordinates are matched with the corresponding environmental signal strength level to obtain the positioning error correction coefficient for that level. Following a pre-defined error correction model, the smoothed coordinates and the correction coefficient are calculated to compensate for system errors in the smoothed coordinates, resulting in optimized coordinates. For example, when the environmental signal strength is weak, the smoothed coordinates are adjusted appropriately based on the correction coefficient to offset the positioning offset caused by the weak signal, ultimately yielding optimized coordinates. By optimizing the smoothed coordinates, positioning accuracy is improved.

[0100] S15, when the deviation between the optimized coordinates and the historical positioning data exceeds a preset threshold, the optimized coordinates are recalibrated by the environmental signal strength to obtain the calibrated coordinates.

[0101] For example, when the deviation between the optimized coordinates and historical positioning data exceeds a preset threshold, the rescued terminal recalibrates the optimized coordinates using environmental signal strength to obtain calibrated coordinates. Historical positioning data is the positioning information previously recorded by the terminal, containing the terminal's historical movement trajectory characteristics. When the deviation between the optimized coordinates and historical positioning data exceeds the preset threshold, it indicates that the current optimized coordinates may be abnormal. At this time, the optimized coordinates are recalibrated using the environmental characteristics reflected by the environmental signal strength, adjusting the coordinate values ​​to better reflect the actual movement, ultimately obtaining the calibrated coordinates.

[0102] S16. Based on the calibration coordinates and the real-time signal strength collected by the sensor, update the positioning data to obtain the updated coordinates.

[0103] For example, the remote rescue terminal updates its positioning data based on the calibrated coordinates and the real-time signal strength collected by the sensor, thus obtaining updated coordinates. The calibrated coordinates are relatively accurate coordinate information after calibration, while the real-time signal strength collected by the sensor represents the latest signal status in the environment at the current moment. By combining these two, and using a certain algorithm to update the original positioning data, the positioning data can reflect the latest location and environmental signal conditions, thereby obtaining updated coordinates and ensuring the real-time nature and accuracy of the positioning information.

[0104] S17. Based on the updated coordinates and the time synchronization data of the global navigation system, the time deviation is corrected to obtain the initial positioning coordinates.

[0105] For example, the rescued terminal corrects for time deviation by matching the updated coordinates with the time synchronization data of the Global Navigation Satellite System (GNSS) to obtain the initial positioning coordinates. The GNSS time synchronization data provides information with a high-precision time reference, ensuring that the terminal's time is consistent with the standard time. The time corresponding to the updated coordinates may have some deviation. By comparing and correcting the updated coordinates with this time synchronization data, the time error is eliminated, ensuring accurate correspondence between coordinate and time information, ultimately yielding precise initial positioning coordinates.

[0106] In an optional embodiment, the enhanced multi-source positioning data is obtained based on the initial positioning coordinates, including the following steps:

[0107] S21, The environmental signal strength and the initial positioning data are weighted and fused to obtain the enhanced positioning coordinates.

[0108] For example, the rescued device performs a weighted fusion of environmental signal strength and initial positioning data to obtain enhanced positioning coordinates. Weighted fusion assigns different weight values ​​to the environmental signal strength and the initial positioning data based on their reliability. Higher environmental signal strength indicates a more stable positioning signal, allowing for a higher weight to be set for the corresponding positioning data; conversely, lower environmental signal strength results in a lower weight. By combining the two through weighted calculation, the influence of different factors on positioning is comprehensively considered, thus obtaining more accurate enhanced positioning coordinates.

[0109] S22, smooth the enhanced positioning coordinates to obtain smooth positioning coordinates.

[0110] For example, the remote rescue terminal smooths the enhanced positioning coordinates to obtain smoothed positioning coordinates. Although the enhanced positioning coordinates have been enhanced, in reality, due to environmental interference and other factors, their sequence may still have jumps or fluctuations. Smoothing processing uses a filtering algorithm to process the enhanced positioning coordinate sequence, weakening the influence of outliers and making the coordinate change trend more gradual, thus obtaining smoothed positioning coordinates and improving the stability and continuity of positioning. The filtering algorithm can be a moving average method.

[0111] S23, based on the environmental signal strength, optimize the smooth positioning coordinates to obtain optimized positioning coordinates.

[0112] For example, the rescued terminal optimizes the smoothed positioning coordinates based on the environmental signal strength to obtain optimized positioning coordinates. Changes in environmental signal strength reflect changes in the terminal's environment, thus affecting positioning accuracy. When the environmental signal strength is strong, the positioning accuracy is relatively high, and the smoothed positioning coordinates can be fine-tuned to further improve accuracy; when the signal strength is weak, it may be necessary to combine historical data and environmental characteristics to correct the coordinates, optimizing the smoothed positioning coordinates to obtain optimized positioning coordinates that better reflect the actual situation.

[0113] S24, calibrate the coordinate deviation of the optimized positioning coordinates to obtain the calibrated positioning coordinates.

[0114] For example, the terminal being rescued calibrates the coordinate deviation of the optimized positioning coordinates to obtain calibrated positioning coordinates. The optimized positioning coordinates may still have some deviation, which could be due to factors such as sensor errors or environmental interference. The coordinate deviation is calibrated by comparing it with the coordinates of a known reference point, calculating the deviation value, and adjusting the optimized positioning coordinates based on this deviation value to eliminate errors, thereby obtaining the calibrated positioning coordinates and further improving positioning accuracy.

[0115] S25. Based on the calibrated positioning coordinates, the enhanced multi-source positioning data is obtained.

[0116] For example, the rescued terminal obtains enhanced multi-source positioning data based on the calibrated positioning coordinates. The calibrated positioning coordinates already possess high accuracy and stability. The terminal further integrates multi-dimensional auxiliary data related to the calibrated positioning coordinates to form enhanced multi-source positioning data, which is then uploaded to the server. This auxiliary data includes the timestamp corresponding to the calibrated positioning coordinates, detailed parameters of the environmental signal strength at the time the coordinates were acquired, terminal motion status data, and surrounding environment perception data. Following a preset data structure specification, the terminal associates and integrates the calibrated positioning coordinates with the aforementioned auxiliary data, formats the data, and adds fields such as data identifiers and data verification information to ensure data integrity and identifiability. The resulting enhanced multi-source positioning data contains precise location information and rich environmental and motion status information. Detailed parameters of the environmental signal strength include the signal-to-noise ratio and signal strength change rate of each frequency band; terminal motion status data includes movement speed and acceleration; and surrounding environment perception data includes obstacle distance and terrain type.

[0117] In an optional embodiment, the movement speed and altitude information are extracted from the enhanced multi-source positioning data to determine the real-time dynamic trajectory path, including the following steps:

[0118] S31: Obtain speed and altitude information from the enhanced multi-source positioning data to generate an initial trajectory path.

[0119] For example, the remote rescue terminal obtains enhanced multi-source positioning data from the server and extracts speed and altitude data to generate an initial trajectory path. The enhanced multi-source positioning data contains location coordinates at different time points. By calculating the ratio of the change in coordinates between adjacent time points to the time interval, speed information, including speed magnitude and direction, can be obtained. Altitude data can be directly extracted from the positioning data, reflecting the height of different locations. These speed information, altitude data, and corresponding time and location coordinates are arranged in chronological order and connected into a line to generate an initial trajectory path, used to initially demonstrate the movement trajectory.

[0120] S32, smooth the initial trajectory path to obtain a smooth trajectory path.

[0121] For example, the remote rescue terminal smooths the initial trajectory path to obtain a smoothed trajectory path. The initial trajectory path may be discontinuous or jagged due to minor fluctuations in the positioning data. The smoothing process adjusts the points on the initial trajectory path using methods such as curve fitting and filtering, making the trajectory lines smoother and eliminating local abrupt changes and noise interference, thus more realistically reflecting the actual movement trajectory trend and obtaining a smoothed trajectory path.

[0122] S33, based on the smooth trajectory path and timestamp data, obtain the corrected trajectory path.

[0123] For example, the remote rescue terminal obtains a corrected trajectory path based on the smoothed trajectory path and timestamp data. The timestamp data records the specific time information corresponding to each location point and is an important component of the trajectory. The smoothed trajectory path may have inaccurate time-location correspondences. By precisely matching and correcting each point on the smoothed trajectory path with its corresponding timestamp data, the positional changes on the trajectory are ensured to remain consistent with the passage of time, thus obtaining the corrected trajectory path and improving the time accuracy of the trajectory.

[0124] S34, based on environmental signals, identifies abnormal points in the corrected trajectory path and obtains abnormal point markers.

[0125] For example, the remote rescue terminal identifies abnormal points on the corrected trajectory path based on environmental signals, thus obtaining anomaly point markers. Sudden changes or anomalies in environmental signals may indicate significant changes in the terminal's environment, potentially leading to abnormal positioning data, which is reflected on the trajectory as anomaly points. By analyzing the characteristics of environmental signals, such as sudden and significant changes in signal strength or signal loss, and combining this with changes in position on the corrected trajectory path, points that do not conform to normal movement patterns are identified and marked, resulting in anomaly point markers.

[0126] S35, when the number of abnormal point markers exceeds the preset threshold, the correction trajectory path is readjusted to obtain the adjusted trajectory path.

[0127] For example, when the number of anomaly markers exceeds a preset threshold, the remote rescue terminal readjusts the correction trajectory path to obtain an adjusted trajectory path. The preset threshold is a standard for judging whether the number of anomaly markers has a significant impact on the reliability of the trajectory. When the number of anomaly markers exceeds this threshold, it indicates that there are many unreliable points in the correction trajectory path, requiring readjustment. During the adjustment process, information such as historical trajectory characteristics and environmental signal change trends is combined to correct or remove anomalies, and the trajectory path is reconstructed to obtain the adjusted trajectory path.

[0128] S36, based on real-time data, updates the adjusted trajectory path to obtain the updated trajectory path.

[0129] For example, the remote rescue terminal updates the adjusted trajectory path based on real-time data to obtain an updated trajectory path. Real-time data includes newly acquired positioning data, environmental signal data, etc., reflecting the terminal's latest movement status and environmental conditions. As time progresses, the terminal continuously generates new real-time data, which is integrated into the adjusted trajectory path to extend and correct the trajectory, ensuring that the trajectory reflects the terminal's latest movement in real time, thus obtaining an updated trajectory path.

[0130] S37, based on the updated trajectory path, obtains the real-time dynamic trajectory path.

[0131] For example, the remote rescue terminal obtains a real-time dynamic trajectory path based on the updated trajectory path. The updated trajectory path contains the latest movement trajectory information up to the present, but the real-time dynamic trajectory path emphasizes the dynamism and real-time nature of the trajectory. By refreshing and dynamically displaying the updated trajectory path in real time, it is ensured that the trajectory can be updated instantly as the terminal moves, accurately reflecting the terminal's positional changes at every moment, ultimately resulting in a real-time dynamic trajectory path that provides real-time trajectory reference for rescue guidance.

[0132] In an optional embodiment, when the encrypted transmission packet exceeds the channel capacity, it is segmented into a sub-packet sequence and a checksum is added. The integrity of the sub-packets is determined based on the checksum, and the missing parts are retransmitted to obtain the complete return dataset. This includes the following steps:

[0133] S41, when the data volume of the encrypted transmission packet exceeds the channel capacity, the encrypted transmission packet is decomposed into a sub-packet sequence to obtain a sub-packet sequence dataset.

[0134] For example, when the data volume of the encrypted transmission packet exceeds the channel capacity, the terminal being rescued decomposes the encrypted transmission packet into a sequence of sub-packets, obtaining a sub-packet sequence dataset. Channel capacity is the maximum data volume that a data transmission channel can accommodate; when the size of the encrypted transmission packet exceeds this limit, it cannot be transmitted directly. In this case, according to a preset segmentation rule, the encrypted transmission packet is evenly or logically divided into multiple smaller sub-packets. These sub-packets together constitute the sub-packet sequence dataset. Each sub-packet contains a portion of the original data and identification information for reassembly, so that the server can correctly reconstruct the original data packet.

[0135] S42, based on the cyclic redundancy check code, performs integrity verification on the sub-packet sequence dataset to obtain the sub-packet integrity verification result.

[0136] For example, the terminal being rescued performs integrity verification on the sub-packet sequence dataset based on a Cyclic Redundancy Check (CRC) code, obtaining a sub-packet integrity verification result. This result is used to determine whether a sub-packet is complete. A Cyclic Redundancy Check (CRC) code is a checksum obtained by performing polynomial operations on the data, possessing strong error detection capabilities. For each sub-packet in the sub-packet sequence dataset, the CRC value of each sub-packet is calculated and compared with a pre-generated checksum. If they match, the sub-packet is considered complete; otherwise, it is considered incomplete, thus obtaining the sub-packet integrity verification result.

[0137] S43, when the sub-packet integrity check result is that the sub-packet is incomplete, resend the incomplete sub-packet to obtain the updated sub-packet sequence.

[0138] For example, when the sub-packet integrity check result indicates that the sub-packet is incomplete, the terminal being rescued retransmits the incomplete sub-packet to obtain an updated sub-packet sequence. Sub-packet incompleteness may be due to data loss or corruption caused by signal interference, network congestion, or other reasons during transmission. When an incomplete sub-packet is detected, the terminal triggers a retransmission mechanism to retransmit these incomplete sub-packets. The server then receives them again and forms an updated sub-packet sequence containing the newly transmitted sub-packets to compensate for previous omissions or errors.

[0139] S44. Based on the updated sub-packet sequence, adjust the sub-packet transmission order to obtain the synchronized sub-packet dataset.

[0140] For example, the server adjusts the transmission order of sub-packets according to the updated sub-packet sequence to obtain a synchronized sub-packet dataset. During transmission, sub-packets may arrive at the receiving end in a different order than they were sent due to network latency or other reasons, resulting in out-of-order delivery. Based on the reassembly identifier information contained in the sub-packets, the sub-packets in the updated sub-packet sequence are sorted to restore their original sending order, ensuring the correct order between sub-packets and thus obtaining a synchronized sub-packet dataset, laying the foundation for subsequent data packet reassembly.

[0141] S45, based on the synchronization sub-packet dataset, obtain the reassembled data packet.

[0142] For example, the server obtains the reconstructed data packet based on the synchronized sub-packet dataset. The synchronized sub-packet dataset is a collection of sub-packets arranged in the correct order, each sub-packet containing a portion of the original encrypted transmission packet's data. The data from each sub-packet is sequentially concatenated and integrated according to their order to reconstruct data consistent with the content of the original encrypted transmission packet, thereby obtaining the reconstructed data packet and achieving complete data recovery.

[0143] S46 verifies the data integrity of the reassembled data packet and obtains the data integrity verification result.

[0144] For example, the server verifies the integrity of the reassembled data packet to obtain a data integrity verification result; the data integrity verification result indicates whether the reassembled data packet is complete. Integrity verification of the reassembled data packet can be performed using methods similar to sub-packet verification, such as calculating the CRC value of the entire reassembled data packet and comparing it with the checksum of the original encrypted transmission packet. If they match, it indicates that the reassembled data packet is complete; if they do not match, it indicates a problem in the reassembly process, the data packet is incomplete, and a data integrity verification result is obtained.

[0145] S47. When the data integrity verification result shows that the reassembled data packet is complete, the complete return dataset is obtained.

[0146] For example, when the data integrity verification result indicates that the reconstructed data packet is complete, the server receives the complete return dataset. A complete reconstructed data packet means that it contains all the information from the original encrypted transmission packet, and no data loss or corruption has occurred. After decryption and other processing, this complete reconstructed data packet is integrated into the return data to form a complete return dataset. This dataset contains all the valid information that needs to be returned and can be used for subsequent data analysis and processing.

[0147] In an optional embodiment, the complete returned dataset is parsed to analyze potential hazardous areas and determine the adjusted trajectory display parameters, including the following steps:

[0148] S51. Extract key feature vectors from the complete returned dataset to obtain a feature vector set.

[0149] For example, the remote rescue terminal extracts key feature vectors from the complete transmitted dataset to obtain a feature vector set. The complete transmitted dataset contains a large amount of raw data, and the key feature vectors are information extracted from this data that reflects the core characteristics of the data, such as the rate of change of location coordinates, the intensity distribution of environmental signals, and regional terrain features. Through feature extraction algorithms, such as principal component analysis and feature selection, these key features are selected from the complete transmitted dataset and transformed into vector form to form a feature vector set for subsequent risk analysis.

[0150] S52, group the feature vector set into risk regions to obtain the region division results.

[0151] For example, the remote rescue terminal groups the feature vector set into risk areas to obtain regional division results. Risk area grouping divides the geographic space into different regions based on the feature information in the feature vector set. The division criteria include factors such as the degree of environmental hazard reflected by the feature vectors and the complexity of the terrain. By using clustering algorithms, regions with similar risk characteristics are grouped together, so that regions within each group have similar risk attributes, thereby obtaining regional division results and providing a regional basis for subsequent risk assessment.

[0152] S53. Calculate the regional risk assessment value based on the regional division results.

[0153] Specifically, the remote rescue terminal calculates regional risk assessment values ​​based on the regional division results. The calculation process is based on the common characteristics of each risk area in the regional division results, combined with a pre-set risk assessment indicator system, with each dimension corresponding to one or more feature vectors. The remote rescue terminal standardizes each risk assessment indicator, converting the feature values ​​of different areas on the same indicator into a unified scoring range, eliminating dimensional differences between different indicators. Based on the importance of each risk assessment indicator in emergency rescue, a corresponding weight is assigned to each indicator. The weight values ​​are determined through expert evaluation or the analytic hierarchy process, with the sum of the weights being 1. A weighted summation method is used to multiply the standardized scores of each area on each indicator by their corresponding weights and then sum them to obtain the comprehensive risk score for that area, i.e., the regional risk assessment value. The higher the regional risk assessment value, the greater the rescue risk in that area, requiring focused avoidance or countermeasures in subsequent trajectory planning. The risk assessment indicator system includes multiple risk assessment dimensions, including terrain hazard, communication conditions, and accessibility.

[0154] S54. Based on the regional risk assessment value, optimize the trajectory adjustment parameters to obtain a preliminary set of adjustment parameters.

[0155] For example, the remote rescue terminal optimizes the trajectory adjustment parameters based on the regional risk assessment value to obtain a preliminary adjustment parameter set. The trajectory adjustment parameters include the recommended direction of the trajectory and the priority of areas to be avoided. A higher regional risk assessment value indicates a greater degree of danger in the area, and a higher priority should be given for avoiding it during trajectory adjustment. The trajectory adjustment parameters are optimized according to the risk assessment values ​​of different areas to reflect the risk differences between areas, thus obtaining the preliminary adjustment parameter set.

[0156] S55, based on the initial adjustment of the parameter set, calibrates the trajectory display order, and obtains the synchronous trajectory parameters.

[0157] For example, the remote rescue terminal calibrates the trajectory display order based on a preliminary adjustment parameter set to obtain synchronized trajectory parameters. The preliminary adjustment parameter set may contain inconsistencies between parameters or mismatches with actual display requirements. The trajectory display order refers to the order and hierarchy of trajectory presentation on the display interface. By calibrating the trajectory display order according to the preliminary adjustment parameter set, the parameters are ensured to match the display order, enabling the trajectory display to accurately reflect the adjustment intent and thus obtaining synchronized trajectory parameters.

[0158] S56 generates adjusted trajectory display parameters based on the synchronized trajectory parameters.

[0159] For example, the remote rescue terminal generates adjusted trajectory display parameters based on the synchronized trajectory parameters. The synchronized trajectory parameters integrate various information and display order requirements for trajectory adjustment. On this basis, the specific display attributes of the trajectory are further determined, such as the trajectory color corresponding to different risk areas (red for high-risk areas and green for low-risk areas), line thickness (thicker lines for high-risk areas to highlight them), etc., and finally the adjusted trajectory display parameters are generated, making the trajectory display more targeted and intuitive.

[0160] In an optional embodiment, based on the regional division results, a regional risk assessment value is calculated, including:

[0161] S61, obtain geographic information and risk-related data for regional division to obtain a structured dataset.

[0162] For example, the remote rescue terminal acquires geographic information and risk-related data of the area division to obtain a structured dataset. The geographic information of the area division includes spatial information such as the boundary coordinates, terrain type, and geographical location of the area, while the risk-related data covers potential hazards within the area, such as obstacle distribution, climate conditions, and historical accident data. This information is then organized, classified, and standardized to form a structured dataset with a unified format and structure, facilitating subsequent data analysis and calculation.

[0163] S62, Extract regional features from the structured dataset and calculate the initial risk value for each region using the following formula:

[0164]

[0165] in, Indicates the first Risk score for each region This indicates the total number of risk assessment indicators. Indicates the first The weight values ​​of each risk indicator, Indicates the first The region in the first Standardized values ​​for each risk indicator.

[0166] Specifically, the remote rescue terminal extracts regional features from a structured dataset and calculates the initial risk value for each region using a preset formula. Regional features are key indicators extracted from the structured dataset that reflect the regional risk status, such as obstacle density and climate hazard level. The preset formula comprehensively considers these regional features, assigning corresponding weight values ​​to each risk indicator and combining the region's standardized values ​​for each indicator. It then calculates the initial risk value for each region through a weighted summation, thus initially quantifying the region's risk level. In the above formula for calculating the initial risk value, Indicates the first The risk score of each region, for the first The quantitative result of the overall risk level of the region shows that the higher the score, the higher the risk level of the region. This indicates the total number of risk assessment indicators, meaning that when assessing regional risks, indicators will be selected from multiple different dimensions (such as terrain hazards, communication stability, obstacle density, etc.) to comprehensively measure the risks. Indicates the first The weight value of the first risk indicator is used to reflect the first risk indicator. The importance of each risk indicator in the overall risk assessment. Indicates the first The region in the first Standardized values ​​for each risk indicator are used to eliminate differences in dimensions, make different indicators comparable, and facilitate weighted summation calculations.

[0167] S63, based on the initial risk value and the area boundary, yields the adjusted risk value.

[0168] Specifically, the remote rescue terminal derives an adjusted risk value based on an initial risk value and regional boundaries. The initial risk value is a preliminary result calculated based on regional characteristics, but different regional boundaries may lead to variations in the distribution of risk between regions. For example, areas near regional boundaries may be affected by risks from adjacent areas. By incorporating regional boundary information, the initial risk value is adjusted to more accurately reflect the actual risk distribution within the region, resulting in the adjusted risk value.

[0169] S64 uses the adjusted risk values ​​to classify the region into risk levels, thus obtaining the risk level distribution.

[0170] For example, the remote rescue terminal classifies a region into risk levels based on adjusted risk values, thus obtaining a risk level distribution. According to the adjusted risk values ​​and a preset classification standard, the region is divided into different risk levels, such as high risk, medium risk, and low risk. Each level corresponds to a certain range of risk values. By comparing the adjusted risk values ​​of each region with these ranges, the corresponding risk level is determined, ultimately resulting in a risk level distribution that visually displays the differences in risk levels across regions.

[0171] S65, based on the risk level distribution, yields the regional risk assessment value.

[0172] For example, the remote rescue terminal obtains a regional risk assessment value based on the risk level distribution. The risk level distribution reflects the risk level of each region, with different risk levels corresponding to different quantitative risk values. The risk level of each region is converted into a corresponding quantitative value, i.e., the regional risk assessment value. The regional risk assessment value integrates various risk characteristics and boundary effects of the region, and is the final quantitative representation of the degree of risk in the region.

[0173] The aforementioned emergency rescue remote control method effectively solves the problems of existing technologies, such as reliance on single positioning, poor adaptability to multiple scenarios, and limited information acquisition dimensions. By fusing and optimizing multi-source positioning data, it overcomes the limitations of single technologies in signal-constrained environments, achieving accurate positioning in complex scenarios; it integrates multi-dimensional information collection, allowing rescuers to fully grasp the dynamics of those being rescued; it utilizes encrypted transmission and sub-packet verification and retransmission to ensure real-time and secure data transmission in complex networks; and it combines risk area analysis and trajectory parameter adjustment to provide clear guidance for rescue efforts, improving efficiency and targeting, adapting to various rescue scenarios such as urban and remote areas, and facilitating efficient rescue operations.

[0174] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0175] Based on the same inventive concept, this application also provides an emergency rescue remote control system for implementing the aforementioned emergency rescue remote control method. The solution provided by this system is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the emergency rescue remote control system provided below can be found in the limitations of the emergency rescue remote control method described above, and will not be repeated here.

[0176] In one exemplary embodiment, such as Figure 3 As shown, an emergency rescue remote control system 200 is provided, including:

[0177] Data acquisition module 201 is used to acquire environmental signal strength and initial positioning data;

[0178] The positioning data optimization module 202 is used to obtain enhanced multi-source positioning data based on the initial positioning coordinates;

[0179] The dynamic trajectory path generation module 203 is used to extract movement speed and altitude information from the enhanced multi-source positioning data to determine the real-time dynamic trajectory path.

[0180] Data encryption module 204 is used to generate an encryption key sequence based on the real-time dynamic trajectory path to obtain an encrypted transmission packet;

[0181] The data transmission module 205 is used to divide the encrypted transmission packet into a sub-packet sequence and add a check code when the encrypted transmission packet exceeds the channel capacity. The integrity of the sub-packet is judged based on the check code and the missing part is retransmitted to obtain the complete return data set.

[0182] Data parsing module 206 is used to parse the complete returned dataset, analyze potential danger areas, and determine the adjusted trajectory display parameters;

[0183] The rescue guidance module 207 is used to obtain dynamic rescue guidance information based on the adjusted trajectory display parameters; the dynamic rescue guidance information is used to indicate the movement trajectory and surrounding environment information of the person being rescued.

[0184] Furthermore, the data acquisition module 201 is also used for:

[0185] The raw dataset is obtained by collecting environmental signal strength and positioning data through multi-source sensors;

[0186] The initial positioning coordinates are obtained by integrating global navigation system base station positioning data and communication network positioning data;

[0187] When the initial positioning coordinate deviation exceeds a preset threshold, the initial positioning coordinates are smoothed to obtain smoothed coordinates.

[0188] Optimized coordinates are obtained based on smoothed coordinates and environmental signal strength;

[0189] When the deviation between the optimized coordinates and historical positioning data exceeds a preset threshold, the optimized coordinates are recalibrated using environmental signal strength to obtain the calibrated coordinates.

[0190] The positioning data is updated based on the calibration coordinates and the real-time signal strength collected by the sensor to obtain the updated coordinates;

[0191] Based on the updated coordinates and the time synchronization data of the global navigation system, the time deviation is corrected to obtain the initial positioning coordinates.

[0192] Furthermore, the positioning data optimization module 202 is also used for:

[0193] The environmental signal strength and the initial positioning data are weighted and fused to obtain the enhanced positioning coordinates;

[0194] The enhanced positioning coordinates are smoothed to obtain smoothed positioning coordinates.

[0195] Based on the environmental signal strength, the smooth positioning coordinates are optimized to obtain the optimized positioning coordinates;

[0196] The coordinate deviation of the optimized positioning coordinates is calibrated to obtain the calibrated positioning coordinates;

[0197] Enhanced multi-source positioning data is obtained based on the calibrated positioning coordinates.

[0198] Furthermore, the dynamic trajectory path generation module 203 is also used for:

[0199] Velocity and altitude data are obtained from the enhanced multi-source positioning data to generate an initial trajectory path;

[0200] The initial trajectory path is smoothed to obtain a smoothed trajectory path;

[0201] The corrected trajectory path is obtained based on the smoothed trajectory path and timestamp data;

[0202] Based on environmental signals, abnormal points are identified in the corrected trajectory path, and abnormal point markers are obtained.

[0203] When the number of outlier markers exceeds a preset threshold, the correction trajectory path is readjusted to obtain the adjusted trajectory path.

[0204] Based on real-time data, the adjusted trajectory path is updated to obtain the updated trajectory path;

[0205] Based on the updated trajectory path, the real-time dynamic trajectory path is obtained.

[0206] Furthermore, the data transmission module 205 is also used for:

[0207] When the amount of data in the encrypted transmission packet exceeds the channel capacity, the encrypted transmission packet is decomposed into a sub-packet sequence to obtain a sub-packet sequence dataset;

[0208] Based on cyclic redundancy check (CRC) codes, integrity checks are performed on the sub-packet sequence dataset to obtain sub-packet integrity check results; these results are used to determine whether the sub-packets are complete.

[0209] When the sub-packet integrity check result is that the sub-packet is incomplete, the incomplete sub-packet is resent to obtain the updated sub-packet sequence;

[0210] Based on the updated sub-packet sequence, adjust the sub-packet transmission order to obtain the synchronized sub-packet dataset;

[0211] Based on the synchronization sub-packet dataset, the reassembled data packets are obtained;

[0212] The integrity of the reassembled data packet is verified to obtain the data integrity verification result; the data integrity verification result is used to indicate whether the reassembled data packet is complete.

[0213] When the data integrity verification result shows that the reassembled data packet is complete, the complete return dataset is obtained.

[0214] Furthermore, the data parsing module 206 is also used for:

[0215] For the complete returned dataset, extract key feature vectors to obtain a feature vector set;

[0216] The feature vector set is grouped into risk regions to obtain the region division results;

[0217] Calculate the regional risk assessment value based on the regional division results;

[0218] Based on the regional risk assessment values, the trajectory adjustment parameters are optimized to obtain a preliminary set of adjustment parameters;

[0219] Based on the initial adjustment of the parameter set, the trajectory display order was calibrated to obtain the synchronous trajectory parameters;

[0220] Based on the synchronized trajectory parameters, the adjusted trajectory display parameters are generated.

[0221] Furthermore, the data parsing module 206 is also used for:

[0222] Obtain geographic information and risk-related data for regional division to obtain a structured dataset;

[0223] Based on the structured dataset, regional features are extracted, and the initial risk value for each region is calculated using the following formula:

[0224]

[0225] in, Indicates the first Risk score for each region This indicates the total number of risk assessment indicators. Indicates the first The weight values ​​of each risk indicator, Indicates the first The region in the first Standardized values ​​for each risk indicator;

[0226] Based on the initial risk value and the area boundary, the adjusted risk value is obtained;

[0227] By using the adjusted risk values, the region is divided into risk levels, resulting in a risk level distribution.

[0228] Based on the risk level distribution, the regional risk assessment value is obtained.

[0229] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of an emergency rescue remote control method as described above.

[0230] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0231] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A remote control method for emergency rescue, characterized in that, The method includes: Acquire environmental signal strength and initial positioning data; Based on the initial positioning coordinates, enhanced multi-source positioning data is obtained; The movement speed and altitude information are extracted from the enhanced multi-source positioning data to determine the real-time dynamic trajectory path; An encryption key sequence is generated based on the real-time dynamic trajectory path to obtain an encrypted transmission packet; When the encrypted transmission packet exceeds the channel capacity, it is divided into a sub-packet sequence and a check code is added. The integrity of the sub-packet is determined based on the check code and the missing part is retransmitted to obtain the complete return dataset. The complete returned dataset is parsed to analyze potential danger zones and determine the adjusted trajectory display parameters; Based on the adjusted trajectory display parameters, dynamic rescue guidance information is obtained; the dynamic rescue guidance information is used to indicate the movement trajectory and surrounding environment information of the person being rescued.

2. The method according to claim 1, characterized in that, The acquisition of environmental signal strength and preliminary location data includes: The raw dataset is obtained by collecting environmental signal strength and positioning data through multi-source sensors; The initial positioning coordinates are obtained by integrating global navigation system base station positioning data and communication network positioning data; When the initial positioning coordinate deviation exceeds a preset threshold, the initial positioning coordinates are smoothed to obtain smoothed coordinates. The optimized coordinates are obtained based on the smoothed coordinates and the environmental signal strength. When the deviation between the optimized coordinates and historical positioning data exceeds a preset threshold, the optimized coordinates are recalibrated using the environmental signal strength to obtain the calibrated coordinates. The positioning data is updated based on the calibration coordinates and the real-time signal strength collected by the sensor to obtain the updated coordinates; Based on the updated coordinates and the time synchronization data of the global navigation system, the time deviation is corrected to obtain the initial positioning coordinates.

3. The method according to claim 1, characterized in that, The step of obtaining enhanced multi-source positioning data based on the initial positioning coordinates includes: The environmental signal strength and the initial positioning data are weighted and fused to obtain enhanced positioning coordinates; The enhanced positioning coordinates are smoothed to obtain smooth positioning coordinates; Based on the environmental signal strength, the smoothed positioning coordinates are optimized to obtain optimized positioning coordinates; The optimized positioning coordinates are calibrated to correct the coordinate deviation, thus obtaining the calibrated positioning coordinates; The enhanced multi-source positioning data is obtained based on the calibrated positioning coordinates.

4. The method according to claim 1, characterized in that, The step of extracting movement speed and altitude information from the enhanced multi-source positioning data to determine the real-time dynamic trajectory path includes: Velocity and altitude data are obtained from the enhanced multi-source positioning data to generate an initial trajectory path; The initial trajectory path is smoothed to obtain a smoothed trajectory path; Based on the smoothed trajectory path and timestamp data, the corrected trajectory path is obtained; Based on environmental signals, abnormal points are identified on the corrected trajectory path to obtain abnormal point markers. When the number of abnormal point markers exceeds a preset threshold, the correction trajectory path is readjusted to obtain an adjusted trajectory path; Based on real-time data, the adjusted trajectory path is updated to obtain the updated trajectory path; Based on the updated trajectory path, the real-time dynamic trajectory path is obtained.

5. The method according to claim 1, characterized in that, When the encrypted transmission packet exceeds the channel capacity, it is segmented into a sub-packet sequence and a checksum is added. The integrity of the sub-packets is determined based on the checksum, and the missing parts are retransmitted to obtain the complete return dataset, including: When the data volume of the encrypted transmission packet exceeds the channel capacity, the encrypted transmission packet is decomposed into a sub-packet sequence to obtain a sub-packet sequence dataset; Based on the Cyclic Redundancy Check (CRC) code, the integrity of the sub-packet sequence dataset is checked to obtain the sub-packet integrity check result; the sub-packet integrity check result is used to determine whether the sub-packet is complete. When the sub-packet integrity check result is that the sub-packet is incomplete, the incomplete sub-packet is resent to obtain the updated sub-packet sequence; Based on the updated sub-packet sequence, the sub-packet transmission order is adjusted to obtain the synchronized sub-packet dataset; Based on the aforementioned synchronization sub-packet dataset, the reassembled data packet is obtained; The integrity of the reassembled data packet is verified to obtain a data integrity verification result; the data integrity verification result is used to indicate whether the reassembled data packet is complete. When the data integrity verification result indicates that the reconstructed data packet is complete, the complete return dataset is obtained.

6. The method according to claim 1, characterized in that, The step of parsing the complete returned dataset, analyzing potential danger zones, and determining the adjusted trajectory display parameters includes: From the complete returned dataset, key feature vectors are extracted to obtain a feature vector set; The feature vector set is grouped into risk regions to obtain the region division results; Based on the regional division results, calculate the regional risk assessment value; Based on the regional risk assessment values, the trajectory adjustment parameters are optimized to obtain a preliminary set of adjustment parameters; Based on the aforementioned preliminary adjustment parameter set, the trajectory display order is calibrated to obtain the synchronous trajectory parameters; The adjusted trajectory display parameters are generated based on the synchronized trajectory parameters.

7. The method according to claim 6, characterized in that, The step of calculating the regional risk assessment value based on the regional division results includes: Obtain geographic information and risk-related data for regional division to obtain a structured dataset; Based on the structured dataset, regional features are extracted, and the initial risk value for each region is calculated using the following formula: in, Indicates the first Risk score for each region This indicates the total number of risk assessment indicators. Indicates the first The weight values ​​of each risk indicator, Indicates the first The region in the first Standardized values ​​for each risk indicator; Based on the initial risk value and the region boundary, the adjusted risk value is obtained; The risk level of the region is divided based on the adjusted risk value to obtain the risk level distribution. Based on the risk level distribution, the regional risk assessment value is obtained.

8. An emergency rescue remote control system, characterized in that, The system includes: The data acquisition module is used to acquire environmental signal strength and initial positioning data; The positioning data optimization module is used to obtain enhanced multi-source positioning data based on the initial positioning coordinates; The dynamic trajectory path generation module is used to extract movement speed and altitude information from the enhanced multi-source positioning data to determine the real-time dynamic trajectory path. The data encryption module is used to generate an encryption key sequence based on the real-time dynamic trajectory path to obtain an encrypted transmission packet; The data transmission module is used to, when the encrypted transmission packet exceeds the channel capacity, divide it into a sub-packet sequence and add a check code, determine the integrity of the sub-packet based on the check code and retransmit the missing part to obtain the complete return dataset; The data parsing module is used to parse the complete returned dataset, analyze potential danger areas, and determine the adjusted trajectory display parameters; The rescue guidance module is used to obtain dynamic rescue guidance information based on the adjusted trajectory display parameters; the dynamic rescue guidance information is used to indicate the movement trajectory and surrounding environment information of the person being rescued.

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