Object security detection method and device, storage medium and electronic equipment

Abstract

The invention discloses an object security detection method and device, a storage medium and electronic equipment. The method comprises the steps that N position coordinates of N positions passed by a target object in a first time period are acquired based on an ultra wide band (UWB) positioning tag worn by the target object, the UWB positioning tag is used for determining position information of the target object through a positioning signal sent by a UWB base station, and N is a positive integer; determining a moving track of the target object in the first time period based on the N position coordinates, and determining a track type of the moving track; and sending early warning prompt information to the UWB positioning tag under the condition that the target object will enter the dangerous area after the second time period is predicted according to the track type of the moving track and the moving speed of the target object in the first time period. According to the method and the device, the technical problem of relatively low safety of a safety detection mode of a construction object provided in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of computers, and in particular to a method and device for detecting the safety of an object, a storage medium, and an electronic device. Background Art

[0002] The construction areas of hydropower stations are mainly concentrated in high-altitude and cold regions, where the climate and environment are severe, the infrastructure is weak, geological disasters occur frequently, and there are many dangerous sections of roads, which pose great challenges to construction safety. In order to ensure the safety of the construction object, the relevant technology usually sends an alarm to the construction object when the construction object is located in a dangerous area, so as to prompt the construction object to leave the dangerous area.

[0003] However, the construction object safety detection method provided in the above-mentioned related art is used to remind the construction object only when the construction object enters the dangerous area. Therefore, the safety of the construction object cannot be guaranteed before the alarm is issued to the construction object. In other words, the construction object safety detection method provided in the above-mentioned related art still has the technical problem of low safety.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a method and device for safety detection of an object, a storage medium, and an electronic device, so as to at least solve the technical problem of low safety existing in the safety detection method of construction objects provided in the related art.

[0006] According to one aspect of an embodiment of the present application, a method for safety detection of an object is provided, comprising: obtaining N position coordinates of N positions passed by the target object within a first time period based on an ultra-wideband (UWB) positioning tag worn by the target object, wherein the UWB positioning tag is used to determine the position information of the target object through a positioning signal sent by a UWB base station, and N is a positive integer; determining a moving trajectory of the target object within the first time period based on the N position coordinates, and determining a trajectory type of the moving trajectory; and sending a warning prompt message to the UWB positioning tag when it is predicted that the target object will enter a dangerous area after a second time period based on the trajectory type of the moving trajectory and the moving speed of the target object within the first time period.

[0007] According to another aspect of an embodiment of the present application, a safety detection device for an object is also provided, including: an acquisition unit, used to acquire N position coordinates of N positions passed by the target object within a first time period based on an ultra-wideband UWB positioning tag worn by the target object, wherein the UWB positioning tag is used to determine the position information of the target object through a positioning signal sent by a UWB base station, and N is a positive integer; a determination unit, used to determine a moving trajectory of the target object within the first time period based on the N position coordinates, and determine a trajectory type of the moving trajectory; a prompt unit, used to send an early warning prompt message to the UWB positioning tag when it is predicted that the target object will enter a dangerous area after a second time period based on the trajectory type of the moving trajectory and the moving speed of the target object in the first time period.

[0008] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the security detection method of the above-mentioned object when running.

[0009] According to another aspect of the embodiments of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the security detection method of the above object.

[0010] According to another aspect of the embodiments of the present application, there is also provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the security detection method of the object through the computer program.

[0011] In an embodiment of the present application, the N position coordinates of the N positions passed by the target object in the first time period are obtained based on the ultra-wideband UWB positioning tag worn by the target object, wherein the UWB positioning tag is used to determine the position information of the target object through the positioning signal sent by the UWB base station, and N is a positive integer. Then, the moving trajectory of the target object in the first time period is determined based on the N position coordinates, and the trajectory type of the moving trajectory is determined. Then, according to the trajectory type of the moving trajectory and the moving speed of the target object in the first time period, when it is predicted that the target object will enter the dangerous area after the second time period, an early warning prompt information is sent to the UWB positioning tag. In other words, using an embodiment of the present application, by predicting future behavior based on the historical behavior pattern of the target object, the system can issue an early warning in time before the target object approaches the dangerous area, so that the target object (such as a person or vehicle) has the opportunity to adjust the path according to the early warning information to avoid entering the potential dangerous area, significantly improving the safety of the hydropower project construction site, and solving the technical problem of low safety in the safety prediction method of the object provided by the relevant technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0013] Figure 1 is a flow chart of an optional object security detection method according to an embodiment of the present application;

[0014] Figure 2 is a schematic diagram of an optional object security detection method according to an embodiment of the present application;

[0015] Figure 3 is a schematic diagram of an optional object security detection method according to an embodiment of the present application;

[0016] Figure 4 is a schematic diagram of an optional object security detection method according to an embodiment of the present application;

[0017] Figure 5 is a schematic structural diagram of an optional object safety detection device according to an embodiment of the present application;

[0018] Figure 6 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] Alternatively, as an alternative, Figure 1 As shown, the security detection method of the above object includes:

[0022] S102, obtaining N position coordinates of N positions passed by the target object within a first time period based on an ultra-wideband UWB positioning tag worn by the target object, wherein the UWB positioning tag is used to determine the position information of the target object through a positioning signal sent by a UWB base station, and N is a positive integer.

[0023] It should be noted that the safety detection method for the above-mentioned object can be but is not limited to being applied to construction safety detection scenarios for construction personnel or vehicles. By predicting whether personnel or vehicles will move to a dangerous area within a certain period of time, and when it is predicted that the personnel or vehicle may move to a dangerous area within a certain period of time, early warning information is sent to the personnel or vehicle, so that the personnel or vehicle can adjust the direction of movement in time to avoid entering a dangerous area, thereby ensuring the safety of personnel and vehicles during the construction process.

[0024] Optionally, the target object may be, but is not limited to, an object moving in a construction environment, such as a construction vehicle, a construction worker, a project inspector, etc., and this is not limited in the present embodiment.

[0025] Furthermore, the above-mentioned ultra-wideband UWB tag can be used, but not limited to, to indicate a UWB tag associated with an ultra-wideband (UWB) positioning system worn on a target object, and the UWB tag can be configured on the target object in the form of a work badge. Among them, the UWB positioning system consists of three parts: a base station (that is, the above-mentioned UWB base station is similar to a wifi router and is used to transmit signals), a UWB tag (used to use the received UWB signal for positioning), and software (used to display the real-time position of the target object on an electronic map). On the one hand, the position coordinates of the personnel are obtained through sensors, and on the other hand, a software platform is equipped to display the specific position of the target object in space through an electronic map.

[0026] It should be noted that the above-mentioned UWB positioning system can be used, but is not limited to, to indicate the UWB smart hydropower station high-precision positioning system. It adopts the patented LocalSense wireless pulse technology, starting from the two aspects of "safety" and "efficiency" in the three core elements of hydropower station development, that is, "wisdom = safety + efficiency". Through reasonable scheme design, it greatly optimizes the management status of complex personnel composition at the production site of the power plant, vacuum in the supervision of working conditions and environment, difficult to implement security protection in dangerous areas, and difficult to monitor the working conditions of personnel and equipment, and helps the intelligent transformation and upgrading of power companies. The UWB smart power plant high-precision positioning system introduces the concept of spatiotemporal big data analysis. By deploying a reasonable number of LocalSense micro base stations in the plant area, the position of LocalSense micro tags on employees, vehicles, equipment, and tools can be accurately located in real time, and the position information of people, vehicles, and objects can be displayed on the software system platform of the power plant control center with zero delay, simulating an intuitive three-dimensional dynamic scene, and based on big data algorithms, greatly optimizing the management level of the power plant. In addition, for high-risk operation scenarios in power plants, functions such as safety warning, emergency rescue, and post-event evidence collection have been realized, and intelligent and precise control of power plant regional operation management, high-risk operation monitoring, on-the-job management, inspection process management, tool management, and real-time vehicle trajectory monitoring has been realized. In addition, the UWB smart power plant high-precision positioning system is organically combined with the power plant's video acquisition equipment, access control card, vehicle access, safety warning, operation process management and other systems to help the power plant achieve integrated linkage of the entire operation process, greatly improving the overall visualization, coordination, decision-making support and safety prevention capabilities of the power plant system.

[0027] S104: Determine a movement trajectory of the target object within a first time period based on the N position coordinates, and determine a trajectory type of the movement trajectory.

[0028] Optionally, in some embodiments, the above-mentioned determination of the moving trajectory of the target object within the first time period based on the N position coordinates, and determination of the trajectory type of the moving trajectory may include but is not limited to: when the direction of the connection line corresponding to the connection line between each two adjacent position coordinates in the N position coordinates matches the target direction, the moving trajectory is determined to be a linear trajectory; when the moving trajectory includes M target sub-moving trajectories, the moving trajectory is determined to be a periodic type trajectory, wherein M is an integer and M is greater than 1; when the direction of the connection line corresponding to the connection line between each two adjacent position coordinates in the N position coordinates does not match the target direction, and the moving trajectory does not include M target sub-moving trajectories, the moving trajectory is determined to be an irregular type trajectory; wherein, the target sub-moving trajectory matches the reference moving trajectory, and the difference between the acquisition time of the position coordinate corresponding to the starting point of the target sub-moving trajectory and the acquisition time of the position coordinate corresponding to the end point of the target sub-moving trajectory matches the target time difference area, and the N position coordinates include the position coordinate corresponding to the starting point of the target sub-moving trajectory and the position coordinate corresponding to the end point of the target sub-moving trajectory.

[0029] S106: When it is predicted that the target object will enter a dangerous area after a second time period according to the trajectory type of the moving trajectory and the moving speed of the target object in the first time period, a warning prompt message is sent to the UWB positioning tag.

[0030] It should be noted that the above-mentioned dangerous areas can be, but are not limited to, areas marked by electronic fences, which are dangerous areas during construction. Electronic fences are a type of security boundary or area demarcated in virtual geographic space using wireless technologies such as radio frequency identification (RFID), global positioning system (GPS), wireless local area network (WLAN), ultra-wideband (UWB), etc., combined with software algorithms. Its function is similar to that of a physical fence, but it does not rely on physical obstacles, but instead uses electronic or digital means to monitor and control the location of people or objects to ensure that they do not cross the boundary into unauthorized areas.

[0031] Furthermore, in the above-mentioned case where, based on the trajectory type of the moving trajectory and the moving speed of the target object in the first time period, it is predicted that the target object will enter the dangerous area after the second time period, before sending the warning prompt information to the UWB positioning tag, it may be but is not limited to also include: in the case where the moving trajectory is a linear trajectory, the following steps are performed: determining the moving direction of the moving trajectory; extending the end point of the moving trajectory along the moving direction until it coincides with the target boundary point on the area boundary of the dangerous area; determining the target distance between the target boundary point and the end point of the moving trajectory; in the case where it is predicted based on the target distance and the moving speed that the position of the target object after the second time period is within the area boundary, determining that the target object will enter the dangerous area after the second time period; in the case where the moving trajectory is a periodic trajectory, the following steps are performed: the target time difference, N position coordinates, N acquisition timestamps corresponding to the N position coordinates, and the moving speed Input into a first prediction model to obtain a first prediction result, wherein the first prediction model is a time series model; when the first prediction result indicates that the position of the target object after the second time period is within the regional boundary, it is determined that the target object will enter a dangerous area after the second time period; when the moving trajectory is an irregular trajectory, perform the following steps: input the moving trajectory, N position coordinates, N acquisition timestamps corresponding to the N position coordinates, and the moving speed, and the environmental characteristics of the target object into a second prediction model to obtain a second prediction result, wherein the second prediction model is a deep learning model; when the second prediction result indicates that the position of the target object after the second time period is within the regional boundary, it is determined that the target object will enter a dangerous area after the second time period; wherein, the i-th acquisition timestamp in the N acquisition timestamps is used to represent the acquisition time of the i-th position coordinate in the N position coordinates, i is an integer and i is greater than or equal to 1.

[0032] It should be noted that the above-mentioned warning prompt information may include but is not limited to at least one of the following: audio warning prompt information, text warning prompt information, and no limitation is made to this in this embodiment. Further, after sending the warning prompt information to the UWB positioning tag, the above-mentioned UWB positioning tag will play the above-mentioned warning prompt information to prompt the above-mentioned target object to adjust the route planning.

[0033] Optionally, as an optional example, the above steps can be illustrated by, but not limited to, the following example: read the position of the person every minute within 15 minutes, and the line connecting the positions every minute is the movement trajectory of the person within that minute. If it is detected that the speed and direction trend of the person has changed significantly within a certain minute, the time interval is adjusted to 1 minute to 15 seconds to read the person's position once according to the distance from the dangerous area. When the trend shows that the person has entered a dangerous area, the UWB system automatically sends an alarm message to the corresponding badge for the badge user to understand and avoid.

[0034] In an embodiment of the present application, the N position coordinates of the N positions passed by the target object in the first time period are obtained based on the ultra-wideband UWB positioning tag worn by the target object, wherein the UWB positioning tag is used to determine the position information of the target object through the positioning signal sent by the UWB base station, and N is a positive integer. Then, the moving trajectory of the target object in the first time period is determined based on the N position coordinates, and the trajectory type of the moving trajectory is determined. Then, according to the trajectory type of the moving trajectory and the moving speed of the target object in the first time period, when it is predicted that the target object will enter the dangerous area after the second time period, an early warning prompt information is sent to the UWB positioning tag. In other words, using an embodiment of the present application, by predicting future behavior based on the historical behavior pattern of the target object, the system can issue an early warning in time before the target object approaches the dangerous area, so that the target object (such as a person or vehicle) has the opportunity to adjust the path according to the early warning information to avoid entering the potential dangerous area, significantly improving the safety of the hydropower project construction site, and solving the technical problem of low safety in the safety prediction method of the object provided by the relevant technology.

[0035] Optionally, as an optional solution, determining the movement trajectory of the target object within the first time period based on the N position coordinates includes:

[0036] Add a connecting line between every two adjacent position coordinates among the N position coordinates to obtain the moving trajectory.

[0037] Optionally, as an optional solution, determining the trajectory type of the moving trajectory includes:

[0038] When the connection direction corresponding to the connection line between each two adjacent position coordinates in the N position coordinates matches the target direction, it is determined that the moving trajectory is a linear trajectory.

[0039] Optionally, when the connection direction corresponding to the connection line between each two adjacent position coordinates in the N position coordinates matches the target direction, determining that the moving trajectory is a linear trajectory can be achieved by, but is not limited to, the following three methods:

[0040] Method 1, using the direction consistency calculation and comparison method, includes the following steps:

[0041] S1, calculate the connection direction: use the vector analysis method to calculate the connection vector from the i-th position coordinate to the i+1-th position coordinate. For each pair of adjacent coordinates, calculate its vector direction (i.e., the unit vector of the connection).

[0042] S2, calculate target direction: set a target direction vector, which can be the line vector calculated for the first time, or a predefined vector that is consistent with the expected direction of the moving trajectory.

[0043] S3, direction comparison and matching: Compare the line vector calculated from each pair of adjacent coordinates with the target direction vector, and determine the consistency of the direction by calculating the angle or dot product between the two vectors. If the consistency of the direction meets the preset threshold (for example, the angle is less than a certain angle or the dot product is close to 1), the line direction is considered to match the target direction.

[0044] S4, straight line trajectory judgment: If the directions of the lines between all adjacent coordinates match the target direction, and the distances between the lines change smoothly (without large jumps), it can be determined that the moving trajectory is a straight line trajectory.

[0045] Method 2, using the least squares method to fit the straight line, includes the following steps:

[0046] S1, data collection: collect N position coordinate data of the target object.

[0047] S2, Line Fitting: Use the least squares method to fit these position coordinate data to obtain the parameters (slope and intercept) of the best fitting line.

[0048] S3, residual analysis: Calculate the vertical distance (residual) from each position coordinate point to the fitted line. If the mean or standard deviation of all residuals is less than a preset threshold, the movement trajectory can be considered to be a straight line.

[0049] S4, direction consistency verification: further verify whether the direction of the fitted straight line is consistent with the set or calculated target direction. If they are consistent, it is confirmed that the moving trajectory is a linear trajectory.

[0050] Method 3: Using the regression algorithm to identify straight line segments, including the following steps:

[0051] S1, initialization: select the first and second coordinate points among the N position coordinates as the starting point and current point of the straight line segment.

[0052] S2, straight line segment fitting: fit a straight line segment based on the current point and the starting point.

[0053] S3, check the next point: check whether the third position coordinate point is within the preset straight line segment error range. If so, take the point as the new current point and update the straight line segment; if not, take the current point as the new starting point and start fitting a new straight line segment.

[0054] S4, recursive iteration: repeat step S3 until all position coordinate points have been checked.

[0055] S5, straight line trajectory judgment: If only one straight line segment is fitted in the whole process, or the direction change of all straight line segments is less than a certain threshold, the trajectory is determined to be a straight line.

[0056] It should be noted that in this embodiment, other implementation modes may be selected to implement the above steps, and this is not limited in this embodiment.

[0057] In the case where the moving trajectory includes M target sub-moving trajectories, the moving trajectory is determined to be a periodic type trajectory, where M is an integer and M is greater than 1.

[0058] Optionally, in the case where the moving trajectory includes M target sub-moving trajectories, determining that the moving trajectory is a periodic type trajectory may be implemented by, but is not limited to, the following three methods:

[0059] Method 1, periodic trajectory recognition based on time difference and position coordinate matching, includes the following steps:

[0060] S1, extract sub-trajectories: From the N position coordinate sequences, select M possible sub-trajectories for analysis, each sub-trajectory consists of a set of continuous position coordinates.

[0061] S2, define reference movement trajectory: select a sub-trajectory as the reference movement trajectory, usually the first identified periodic pattern.

[0062] S3, matching sub-trajectories: matching the position coordinates of each target sub-trajectory with the reference trajectory to determine whether their starting and ending positions are similar, and whether the shape (path) of the sub-trajectory matches the reference trajectory.

[0063] S4, analyzing the time difference: Calculate the time difference between the starting point and the end point of each target sub-trajectory, and then compare it with the target time difference to confirm whether the time difference is within an allowable error range of the target time difference. If the time differences of the M sub-trajectories all conform to periodicity, then it can be preliminarily determined that the trajectory is periodic.

[0064] S5, periodicity verification: further verify whether all sub-trajectories follow a roughly fixed time interval, that is, calculate the difference in starting time between consecutive sub-trajectories, and ensure that the deviation between these differences and the target time difference is within the threshold, so as to confirm the periodicity of the M target sub-trajectories.

[0065] S6, trajectory type determination: If all target sub-movement trajectories match the reference movement trajectory, and the time difference conforms to the periodicity, then the movement trajectory can be determined to be a periodic type trajectory.

[0066] Method 2 uses machine learning algorithms to identify periodic trajectories, including the following steps:

[0067] S1, data preprocessing: collect N location coordinates and their corresponding acquisition time, clean the data, and remove outliers and noise.

[0068] S2, feature extraction: extract the features of periodic movement from the data, such as cycle time, path shape within each cycle, movement speed, etc.

[0069] S3, training model: Use historical data to train machine learning models (such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), etc.). The models should be able to recognize and learn the characteristics of periodic trajectories.

[0070] S3, trajectory prediction and classification: The current mobile trajectory data is input into the model. The model predicts the trajectory type based on the learned periodic features. If the model predicts a periodic type, the next step of verification is performed.

[0071] S4, periodicity verification: by comparing the predicted cycle time with the time interval of the M target sub-movement trajectories actually collected, confirm whether they match, and check the similarity of the path morphology.

[0072] S5, result output: If the verification result is true, the determination that the moving trajectory is a periodic type trajectory is output.

[0073] Method 3: Periodicity detection based on autocorrelation function, including the following steps:

[0074] S1, trajectory data preparation: collect the complete N position coordinates and their corresponding acquisition time.

[0075] S2, position coordinate serialization: convert the position coordinate into a time series, where each time point corresponds to a position coordinate.

[0076] S3, calculate the autocorrelation function: Use the autocorrelation function (ACF) to analyze time series data. The autocorrelation function can reveal the similarity of time series under different time lags.

[0077] S4, Periodicity Detection: Periodicity is detected by observing the peak of ACF. If obvious peaks appear at multiple time lag points, it indicates that the time series data has a periodic pattern.

[0078] S5, target sub-movement trajectory identification: Based on the results of periodic detection, M target sub-movement trajectories are identified, corresponding to the paths in each periodic pattern.

[0079] S6, confirmation of periodic type trajectory: If the M target sub-movement trajectories show periodic characteristics in both space and time, then it can be determined that the movement trajectory belongs to the periodic type.

[0080] It should be noted that in this embodiment, other implementation modes may be selected to implement the above steps, and this is not limited in this embodiment.

[0081] When the connection direction corresponding to the connection line between each two adjacent position coordinates in the N position coordinates does not match the target direction, and the movement trajectory does not include M target sub-movement trajectories, the movement trajectory is determined to be an irregular trajectory.

[0082] Optionally, when the connection directions corresponding to the connection lines between each two adjacent position coordinates in the N position coordinates do not match the target direction, and the moving trajectory does not include M target sub-moving trajectories, the moving trajectory is determined to be an irregular trajectory. For the specific implementation method, please refer to the implementation method given above, and it will not be repeated in this embodiment.

[0083] Among them, the target sub-moving trajectory matches the reference moving trajectory, and the difference between the acquisition time of the position coordinate corresponding to the starting point of the target sub-moving trajectory and the acquisition time of the position coordinate corresponding to the end point of the target sub-moving trajectory matches the target time difference area, and the N position coordinates include the position coordinate corresponding to the starting point of the target sub-moving trajectory and the position coordinate corresponding to the end point of the target sub-moving trajectory.

[0084] For example, the linear trajectory can be but is not limited to referring to Figure 2 The above periodic trajectory can be but not limited to the reference trajectory shown in Figure 3 The above irregular trajectory can be but not limited to the following: Figure 4 The trajectory shown.

[0085] It should be noted that the above example is an optional example provided to facilitate explanation of the security detection method of the above object, and there is no limitation on the specific implementation of the security detection method of the above object.

[0086] Optionally, as an optional solution, after determining the moving trajectory of the target object in the first time period based on the N position coordinates and determining the trajectory type of the moving trajectory, the method further includes:

[0087] In the case where the moving trajectory is a linear trajectory, the following steps are performed: determining the moving direction of the moving trajectory; extending the end point of the moving trajectory along the moving direction until it coincides with the target boundary point on the area boundary of the dangerous area; determining the target distance between the target boundary point and the end point of the moving trajectory; and in the case where it is predicted based on the target distance and the moving speed that the position of the target object after the second time period is within the area boundary, determining that the target object will enter the dangerous area after the second time period.

[0088] Optionally, in this embodiment, when it is determined that the position where the target object will be located after the second time period is within the area boundary based on the target distance and the moving speed, determining that the target object will enter the dangerous area after the second time period may include, but is not limited to: determining a first distance of the target object from the end position after the second time period by using the above-mentioned moving speed and the second time period, and when the first distance is greater than the above-mentioned target distance, determining that the target object will enter the dangerous area after the second time period.

[0089] For example, the above steps may be explained and illustrated by the following examples, but are not limited to:

[0090] S1. Determine the moving direction of the moving trajectory: Assume that the moving trajectory of the construction vehicle is linear, and the UWB positioning system records the moving trajectory of the vehicle from point A to point B. By calculating the connection vector from point A to point B, the moving direction of the vehicle can be determined as 30° east of north.

[0091] S2. Extend the end point of the moving trajectory along the moving direction until it coincides with the target boundary point on the area boundary of the dangerous area: After point B, the vehicle continues to move in the direction of 30° east of north. The dangerous area is an unmarked and unsafe high-drop area in the construction area. By extending point B in this direction until it intersects with the boundary line of the dangerous area, assume the intersection point is point C. Point C is the boundary point that the vehicle will reach if it continues to move in a straight line in the current direction.

[0092] S3. Determine the target distance between the target boundary point and the end point of the moving trajectory: Using the known coordinate data (or obtained in real time through the UWB positioning system), calculate the straight-line distance between point B and point C. Assume this distance is D meters. This distance D is the target distance, representing the distance from the current position of the vehicle to the boundary of the dangerous area.

[0093] S4. When it is determined that the position where the target object will be located after the second time period is within the area boundary based on the target distance and the moving speed, determine that the target object will enter the dangerous area after the second time period: Assume that the average moving speed of the construction vehicle is V meters per minute. If the distance of D meters is less than the product of V meters per minute and the second time period T minutes (i.e., V*T < D), then after T minutes, the vehicle will be within the boundary of the dangerous area, meaning the vehicle will enter the dangerous area. For example, if V = 10 meters per minute, D = 50 meters, and T is set to 6 minutes, then the predicted position of the vehicle after 6 minutes will be within the dangerous area because it has moved at least 60 meters.

[0094] It should be noted that the above example is an optional example provided for the convenience of explaining the above-mentioned safety detection method of the object, and does not limit the specific implementation manner of the above-mentioned safety detection method of the object.

[0095] When the moving trajectory is a periodic trajectory, the following steps are performed: the target time difference, N position coordinates, N acquisition timestamps corresponding to the N position coordinates, and the moving speed are input into a first prediction model to obtain a first prediction result, wherein the first prediction model is a time series model; when the first prediction result indicates that the position of the target object after the second time period is within the regional boundary, it is determined that the target object will enter the dangerous area after the second time period.

[0096] It should be noted that when processing periodic trajectory prediction, the first prediction model can be, but is not limited to, a time series prediction model, which can predict the position of the target object within a certain time period in the future based on historical data. In this embodiment, the model needs to process the periodic movement trajectory of the target object, especially a time series model that can handle periodicity and sequence dependence, such as ARIMA (autoregressive integrated moving average model), Prophet model (time series prediction model developed by Facebook) or LSTM (long short-term memory) model in recursive neural network (RNN), etc., or other time series models, which are not limited in this embodiment.

[0097] Optionally, in some embodiments, the first prediction model may be trained based on but not limited to the following steps:

[0098] S1, data collection and preprocessing: collect time series data including N location coordinates, N acquisition timestamps corresponding to the N location coordinates, target time difference and moving speed. Clean the data, process missing values ​​and outliers, and ensure data quality.

[0099] S2, feature engineering: Convert the timestamp into a form that can be understood by the model, such as the hour, minute, day of the week, etc. For periodic trajectories, time features are particularly important. At the same time, calculate the movement speed and standardize or normalize the location coordinates for easy model processing.

[0100] S3, training data segmentation: divide the data into training set and test set. The training set is used to train the model, and the test set is used to verify the prediction performance of the model.

[0101] S4, Model selection: Select an appropriate time series prediction model based on the characteristics of the trajectory data (such as whether it contains obvious trends, seasonality or periodicity, etc.). If the data shows obvious periodicity, you can consider using the LSTM model, because LSTM can capture long-term dependencies in the data and is very suitable for processing periodic sequences.

[0102] S5, model training: Use the training set data to train the selected time series prediction model. During the model training process, it may be necessary to adjust the parameters of the model (such as the number of neurons in LSTM, the depth of the hidden layer, etc.), and use techniques such as cross-validation to avoid overfitting.

[0103] S6, model evaluation: Evaluate the prediction performance of the model on the test set. Common evaluation indicators include mean square error (MSE), root mean square error (RMSE) or mean absolute error (MAE). If the model performance is poor, it may be necessary to return to the model selection and parameter adjustment steps for further optimization.

[0104] S7, prediction: Use the trained model to predict the location after the second time period. This is based on the model's learning of historical data. The model predicts the location of the target object after the second time period based on the target time difference, location coordinates, collection timestamp, and movement speed.

[0105] S8, result analysis: If the first prediction result indicates that the position of the target object after the second time period is within the area boundary, that is, the distance between the predicted position and the boundary of the dangerous area is less than the edge of the dangerous area, then it can be determined that the target object will enter the dangerous area after the second time period.

[0106] When the moving trajectory is an irregular trajectory, the following steps are performed: the moving trajectory, N position coordinates, N acquisition timestamps corresponding to the N position coordinates, the moving speed, and the characteristics of the environment where the target object is located are input into a second prediction model to obtain a second prediction result, wherein the second prediction model is a deep learning model; when the second prediction result indicates that the position of the target object after the second time period is within the regional boundary, it is determined that the target object will enter the dangerous area after the second time period.

[0107] It should be noted that when predicting irregular trajectories, the second prediction model can use a deep learning model, which can handle complex data relationships and nonlinear patterns and is very suitable for processing irregular features that may exist in trajectory data. Specifically, a long short-term memory network (LSTM), a convolutional neural network (CNN), or a hybrid network that combines the characteristics of the two (such as CNN-LSTM) can be used as the second prediction model. In the scenario of irregular mobile trajectory prediction, LSTM is often selected for its ability to process sequence data. It can capture long-term and short-term dependencies in time series and is particularly effective for predicting future positions.

[0108] Optionally, in some embodiments, the second prediction model may be trained based on, but not limited to, the following steps:

[0109] S1, data collection and preprocessing: collect the movement trajectory data of the target object, including N position coordinates, corresponding N acquisition timestamps, movement speed, and characteristic information of the environment where the target object is located (such as terrain, climate conditions, obstacle locations, etc.). Data preprocessing includes cleaning, standardization (such as normalization of position coordinates and speed), feature extraction (such as extracting time period information from timestamps), etc., to ensure that the data is suitable for model training.

[0110] S2, feature engineering and data format conversion: Convert environmental features and mobile trajectory data into a format readable by the model. It may be necessary to discretize continuous features, one-hot encode category features, and build sliding windows to form time series input.

[0111] S3, data set division: the processed data set is divided into training set, validation set and test set. The training set is used for model training, the validation set is used to adjust model parameters and avoid overfitting, and the test set is used to finally evaluate the model performance.

[0112] S4, model architecture design: Building an LSTM network model architecture may include an input layer, one or more LSTM layers, a fully connected layer, and an output layer. The input layer receives N location coordinates, N acquisition timestamps, movement speed, and environmental feature information; the output layer predicts the future location of the target object after the second time period.

[0113] S5, model training: Use the training set data to train the LSTM model and optimize the network weights to minimize the prediction error. During the training process, you may need to adjust parameters such as the network structure (such as the number of LSTM units and the depth of the hidden layer), learning rate, batch size, etc.

[0114] S6, Model Validation: Evaluate the prediction performance of the model on the validation set, using evaluation metrics such as mean square error (MSE), root mean square error (RMSE), or mean absolute error (MAE) to ensure that the model can accurately predict irregular trajectories.

[0115] S7, model testing and adjustment: Finally, the model performance is tested on the test set. If the model performs poorly, it may be necessary to return to the model architecture design and model training steps to adjust parameters and optimize the model.

[0116] S8, prediction and result analysis: Once the training is completed and the model performance is verified, the model can be used to predict the future position. The current trajectory, speed and environmental feature information of the target object are input into the trained LSTM model to obtain the second prediction result, that is, the predicted position of the target object after the second time period. If the predicted position shows that the target object may enter the dangerous area after the second time period (that is, the predicted position is within the boundary of the area), it can be determined that the target object is at risk of entering the dangerous area, thereby triggering the early warning mechanism.

[0117] The i-th acquisition timestamp among the N acquisition timestamps is used to indicate the acquisition time of the i-th position coordinate among the N position coordinates, and i is an integer and i is greater than or equal to 1.

[0118] Optionally, as an optional solution, before obtaining the N position coordinates of the N positions passed by the target object within the first time period based on the ultra-wideband UWB positioning tag worn by the target object, the method further includes:

[0119] S1, determines the transmission speed of the positioning signal.

[0120] S2, determining the distance information between the target object and the UWB base station at the second time according to the first time when the UWB base station sends the positioning signal to the UWB positioning tag, the second time when the UWB positioning tag receives the positioning signal, and the transmission speed of the positioning signal;

[0121] S3, determining the object position coordinates corresponding to the target object at the second time based on the distance information, wherein the N position coordinates include the object position coordinates.

[0122] It should be noted that the above positioning signal is a UWB signal, which can be transmitted in the air in the form of electromagnetic waves, and its transmission speed is known. In this embodiment, the distance information between the target object and the UWB base station at the second time can be determined by the transmission speed of the positioning signal and the time it takes for the positioning signal to be sent by the UWB base station to the UWB positioning tag (that is, the time difference between the first time when the UWB base station sends the positioning signal to the UWB positioning tag and the second time when the UWB positioning tag receives the positioning signal).

[0123] Optionally, the above-mentioned determination of the object position coordinates corresponding to the target object at the second time based on the distance information may include but is not limited to:

[0124] S1, distance information acquisition: In step S2, the distance information between the target object and the UWB base station at the second time has been calculated based on the first time when the UWB base station sends the positioning signal to the UWB positioning tag, the second time when the UWB positioning tag receives the positioning signal, and the transmission speed of the positioning signal.

[0125] S2, the base station location is known: In the UWB system, the location of each base station needs to be determined and recorded in advance. These locations are usually known reference points used to calculate the relative position of the target object.

[0126] S3, trilateration or multilateration: In the case of at least three base stations, trilateration or more complex multilateration methods can be used. For each base station, using the distance information between it and the target object, the possible location of the target object can be found on a circle with the base station as the center and the distance as the radius. If there are three base stations, these circles will intersect at one or two points, and further calculations can eliminate impossible solutions and determine a unique location.

[0127] S4, Positioning Solution: Input the distance information and base station location into the positioning solution algorithm. Common positioning algorithms include algorithms based on time of arrival (TOA), algorithms based on time difference of arrival (TDOA), angle of arrival (AOA) algorithms, or a combination of these. The solution algorithm will calculate the location coordinates of the target object.

[0128] S5, if an algorithm based on time difference of arrival (TDOA) is used, it is necessary to first synchronize the timestamps of all base stations, and then construct a hyperbola based on the TDOA values ​​of two or more base stations, and determine the location of the target object by intersecting the hyperbolas of at least three base stations.

[0129] S6, if the algorithm based on time of arrival (TOA) is used, time synchronization between the UWB positioning tag and the base station is required to calculate the distance between the target object and each base station, and then determine the position through trilateral measurement.

[0130] S7, Data Processing and Error Correction: During the positioning solution process, it may be necessary to deal with the multipath problem of signal propagation and correct the positioning error to improve positioning accuracy. This may include the use of various filtering techniques (such as Kalman filtering) or error compensation based on historical position data.

[0131] S8, result output: the solution algorithm will output the position coordinates of the target object at the second time, and this coordinate will become a point in the N position coordinate sequence for subsequent trajectory analysis and prediction.

[0132] Further, as an optional example, the above steps may be explained by, but not limited to, the following examples:

[0133] S1, determine the transmission speed of the positioning signal: The transmission speed of the positioning signal is mainly determined by the propagation speed of electromagnetic waves in the air. Under normal circumstances, the speed of electromagnetic waves in the air is close to the speed of light, about 300,000,000 meters per second. In the UWB positioning system, the signal transmission speed can be regarded as a known constant, so step S1 has been completed.

[0134] S2, according to the first time when the UWB base station sends the positioning signal to the UWB positioning tag, the second time when the UWB positioning tag receives the positioning signal, and the transmission speed of the positioning signal, determine the distance information between the target object and the UWB base station at the second time; in step S2, taking a specific UWB base station as an example, assuming that the base station is identified as "B1", the time when the positioning signal is sent to the UWB positioning tag worn by the engineer is 16:15:00 (first time), and the time when the engineer's UWB tag receives the signal and responds is 16:15:00.000001 seconds (second time). The signal propagation time difference is calculated to be 1 microsecond (0.000001 seconds), and then the distance is calculated based on the signal propagation speed. Signal propagation time difference = second time - first time = 1 microsecond; Positioning signal transmission speed = 300,000,000 m / s; Distance information (i.e. the distance between the engineer and the B1 base station) = Signal propagation time difference × Positioning signal transmission speed / 2 = 1 microsecond × 300,000,000 m / s / 2 = 150 m; The reason for dividing by 2 here is that the signal goes from the base station to the tag, and then from the tag back to the base station, so the actual distance is half of the round-trip time difference.

[0135] S3, determine the object position coordinates corresponding to the target object at the second time based on the distance information: In order to determine the position coordinates of the engineer at 16:15:00.000001 seconds (the second time), the distance information of at least three UWB base stations (B1, B2, B3) is used for positioning and solving. Assume that the distances between the B2 and B3 base stations and the engineer are 120 meters and 200 meters respectively. Through trilateration, the precise position coordinates of the engineer can be calculated using these distance information and the known base station position coordinates. It is known that the three-dimensional coordinates of the B1, B2 and B3 base stations are (100, 100, 0), (300, 100, 0) and (200, 300, 0) (unit: meter). Using the distance information and base station coordinates, three spheres with B1, B2 and B3 as the center and the distance information as the radius are constructed. By calculating the intersection of the three spheres, the position coordinates of the engineer can be obtained. Ideally, the three spheres should intersect at one point, but in practice, due to measurement errors, the spheres may not intersect exactly, so an algorithm that minimizes the error (such as the least squares method) may be needed to determine the most likely position.

[0136] It should be noted that the above example is an optional example provided to facilitate explanation of the security detection method of the above object, and there is no limitation on the specific implementation of the security detection method of the above object.

[0137] Optionally, as an optional solution, when sending the warning prompt information to the UWB positioning tag, it also includes:

[0138] Send warning information to the mobile device used by the target object.

[0139] It should be noted that in this embodiment, for people or vehicles that may enter the dangerous area within a period of time, the system will send an intrusion alarm to the UWB tag on the vehicle based on the calculation time of the electronic map of the construction area that may enter the dangerous area within 5 minutes, and send a corresponding alarm to the driver's mobile user APP based on the driver information read by the construction management platform.

[0140] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0141] According to another aspect of the embodiments of the present application, a device for detecting a safety of an object for implementing the above-mentioned method for detecting a safety of an object is also provided. Figure 5 As shown, the device comprises:

[0142] For specific embodiments, reference may be made to the examples shown in the above-mentioned object security detection method, and this embodiment will not be described in detail here.

[0143] An acquisition unit 502 is used to acquire N position coordinates of N positions passed by the target object within a first time period based on an ultra-wideband UWB positioning tag worn by the target object, wherein the UWB positioning tag is used to determine the position information of the target object through a positioning signal sent by a UWB base station, and N is a positive integer;

[0144] A determining unit 504 is used to determine a moving trajectory of the target object within a first time period based on the N position coordinates, and determine a trajectory type of the moving trajectory;

[0145] The prompt unit 506 is used to send warning prompt information to the UWB positioning tag when it is predicted that the target object will enter the dangerous area after the second time period according to the trajectory type of the moving trajectory and the moving speed of the target object in the first time period.

[0146] Optionally, in this embodiment, the determination unit includes: an adding module, configured to add a connecting line between every two adjacent position coordinates in the N position coordinates to obtain a movement trajectory.

[0147] Optionally, in this embodiment, the above-mentioned determination unit also includes: a first determination module, which is used to determine that the moving trajectory is a straight-line trajectory when the connection direction corresponding to the connection line between each two adjacent position coordinates in the N position coordinates matches the target direction; a second determination module, which is used to determine that the moving trajectory is a periodic type trajectory when the moving trajectory includes M target sub-moving trajectories, wherein M is an integer and M is greater than 1; a third determination module, which is used to determine that the moving trajectory is an irregular type trajectory when the connection direction corresponding to the connection line between each two adjacent position coordinates in the N position coordinates does not match the target direction and the moving trajectory does not include M target sub-moving trajectories; wherein the target sub-moving trajectory matches the reference moving trajectory, and the difference between the acquisition time of the position coordinate corresponding to the starting point of the target sub-moving trajectory and the acquisition time of the position coordinate corresponding to the end point of the target sub-moving trajectory matches the target time difference area, and the N position coordinates include the position coordinate corresponding to the starting point of the target sub-moving trajectory and the position coordinate corresponding to the end point of the target sub-moving trajectory.

[0148] Optionally, in this embodiment, the above-mentioned device also includes: a first determination unit, which is used to perform the following steps when the moving trajectory is a linear trajectory: determine the moving direction of the moving trajectory; extend the end point of the moving trajectory along the moving direction until it coincides with the target boundary point on the area boundary of the dangerous area; determine the target distance between the target boundary point and the end point of the moving trajectory; when it is predicted based on the target distance and the moving speed that the position of the target object after the second time period is within the area boundary, determine that the target object will enter the dangerous area after the second time period; a second determination unit, which is used to perform the following steps when the moving trajectory is a periodic trajectory: input the target time difference, N position coordinates, N acquisition timestamps corresponding to the N position coordinates, and the moving speed into the first prediction model to obtain a first prediction result, wherein the first prediction model is a time series A column model; when the first prediction result indicates that the position of the target object after the second time period is within the regional boundary, it is determined that the target object will enter the dangerous area after the second time period; a third determination unit is used to perform the following steps when the moving trajectory is an irregular trajectory: input the moving trajectory, N position coordinates, N acquisition timestamps corresponding to the N position coordinates, and the moving speed, and the environmental characteristics of the target object into the second prediction model to obtain a second prediction result, wherein the second prediction model is a deep learning model; when the second prediction result indicates that the position of the target object after the second time period is within the regional boundary, it is determined that the target object will enter the dangerous area after the second time period; wherein the i-th acquisition timestamp in the N acquisition timestamps is used to represent the acquisition time of the i-th position coordinate in the N position coordinates, i is an integer and i is greater than or equal to 1.

[0149] Optionally, in this embodiment, the above-mentioned device also includes: a third determination unit, used to determine the transmission speed of the positioning signal; a fourth determination unit, used to determine the distance information between the target object and the UWB base station at the second time according to the first time when the UWB base station sends the positioning signal to the UWB positioning tag, the second time when the UWB positioning tag receives the positioning signal, and the transmission speed of the positioning signal; a fifth determination unit, used to determine the object position coordinates corresponding to the target object at the second time based on the distance information, wherein the N position coordinates include the object position coordinates.

[0150] Optionally, in this embodiment, the prompt unit is further used to: send warning prompt information to a mobile device used by the target object.

[0151] According to another aspect of the embodiment of the present application, an electronic device for implementing the above-mentioned object security detection method is also provided. This embodiment is described by taking the electronic device as a server as an example. Figure 6As shown, the electronic device includes a memory 602 and a processor 604. The memory 602 stores a computer program, and the processor 604 is configured to execute the steps in any of the above method embodiments through the computer program.

[0152] Optionally, in this embodiment, the electronic device may be located in at least one network device among a plurality of network devices of a computer network.

[0153] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0154] S1, obtaining N position coordinates of N positions passed by the target object within a first time period based on an ultra-wideband UWB positioning tag worn by the target object, wherein the UWB positioning tag is used to determine the position information of the target object through a positioning signal sent by a UWB base station, and N is a positive integer;

[0155] S2, determining a moving trajectory of the target object within a first time period based on the N position coordinates, and determining a trajectory type of the moving trajectory;

[0156] S3, when it is predicted that the target object will enter a dangerous area after a second time period according to the trajectory type of the moving trajectory and the moving speed of the target object in the first time period, sending a warning prompt information to the UWB positioning tag.

[0157] Alternatively, a person skilled in the art may understand that: Figure 6 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 6 The structure of the electronic device is not limited. Figure 6 More or fewer components (such as network interfaces, etc.) as shown in, or with Figure 6 Different configurations are shown.

[0158] Among them, the memory 602 can be used to store software programs and modules, such as the program instructions / modules corresponding to the object security detection method and device in the embodiments of the present application. The processor 604 executes various functional applications and data processing by running the software programs and modules stored in the memory 602, that is, realizing the above-mentioned object security detection method. The memory 602 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 602 may further include a memory remotely located relative to the processor 604, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. As an example, Figure 6 As shown, the memory 602 may include but is not limited to the acquisition unit 502, the determination unit 504 and the prompt unit 506 in the object safety detection device. In addition, it may also include but is not limited to other module units in the object safety detection device, which will not be repeated in this example.

[0159] Optionally, the transmission device 606 is used to receive or send data via a network. Specific examples of the network may include a wired network and a wireless network. In one example, the transmission device 606 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 606 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0160] In addition, the electronic device further includes: a display 608, and a connection bus 610 for connecting various module components in the electronic device.

[0161] In other embodiments, the terminal device or server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the multiple nodes through network communication. The nodes may form a point-to-point network, and any form of computing device, such as a server, terminal or other electronic device, may become a node in the blockchain system by joining the point-to-point network.

[0162] According to one aspect of the present application, a computer program product is provided, the computer program product comprising a computer program / instruction, the computer program / instruction comprising a program code for executing the above method. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions provided by the embodiments of the present application are executed.

[0163] According to one aspect of the present application, another computer program product is also provided, including a non-volatile computer-readable storage medium, the non-volatile computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the method in each embodiment of the present application are implemented.

[0164] According to one aspect of the present application, a computer-readable storage medium is provided, and a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above method.

[0165] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0166] S1, obtaining N position coordinates of N positions passed by the target object within a first time period based on an ultra-wideband UWB positioning tag worn by the target object, wherein the UWB positioning tag is used to determine the position information of the target object through a positioning signal sent by a UWB base station, and N is a positive integer;

[0167] S2, determining a moving trajectory of the target object within a first time period based on the N position coordinates, and determining a trajectory type of the moving trajectory;

[0168] S3, when it is predicted that the target object will enter a dangerous area after a second time period according to the trajectory type of the moving trajectory and the moving speed of the target object in the first time period, sending a warning prompt information to the UWB positioning tag.

[0169] Optionally, in the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0170] Optionally, in this embodiment, a person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing hardware related to the terminal device through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0171] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.

[0172] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0173] In the several embodiments provided in the present application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0174] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0175] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0176] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for detecting safety of an object, characterized in that: include: Acquire N position coordinates of N positions passed by the target object within a first time period based on an ultra-wideband (UWB) positioning tag worn by the target object, wherein the UWB positioning tag is used to determine the position information of the target object through a positioning signal sent by a UWB base station, and N is a positive integer; Determine a movement trajectory of the target object within the first time period based on the N position coordinates, and determine a trajectory type of the movement trajectory; When it is predicted that the target object will enter a dangerous area after a second time period according to the trajectory type of the moving trajectory and the moving speed of the target object in the first time period, early warning information is sent to the UWB positioning tag.

2. The method according to claim 1, characterized in that The determining the moving trajectory of the target object within the first time period based on the N position coordinates includes: A connecting line is added between every two adjacent position coordinates in the N position coordinates to obtain the moving track.

3. The method according to claim 1, characterized in that The determining of the trajectory type of the moving trajectory includes: When the connection direction corresponding to the connection line between each two adjacent position coordinates in the N position coordinates matches the target direction, determining that the moving trajectory is a linear trajectory; In a case where the movement trajectory includes M target sub-movement trajectories, determining that the movement trajectory is a periodic type trajectory, wherein M is an integer and M is greater than 1; When the connection direction corresponding to the connection line between each two adjacent position coordinates in the N position coordinates does not match the target direction, and the movement trajectory does not include M target sub-movement trajectories, the movement trajectory is determined to be an irregular trajectory; Among them, the target sub-moving trajectory matches the reference moving trajectory, and the difference between the acquisition time of the position coordinates corresponding to the starting point of the target sub-moving trajectory and the acquisition time of the position coordinates corresponding to the end point of the target sub-moving trajectory matches the target time difference area, and the N position coordinates include the position coordinates corresponding to the starting point of the target sub-moving trajectory and the position coordinates corresponding to the end point of the target sub-moving trajectory.

4. The method according to claim 3, characterized in that After determining the moving trajectory of the target object in the first time period based on the N position coordinates and determining the trajectory type of the moving trajectory, the method further includes: In the case where the moving trajectory is a straight-line trajectory, the following steps are performed: determining the moving direction of the moving trajectory; extending the end point of the moving trajectory along the moving direction until it coincides with a target boundary point on the area boundary of the dangerous area; determining a target distance between the target boundary point and the end point of the moving trajectory; and determining that the target object will enter the dangerous area after the second time period when it is predicted based on the target distance and the moving speed that the position of the target object after the second time period is within the area boundary; In the case where the moving trajectory is a periodic trajectory, the following steps are performed: the target time difference, the N position coordinates, the N acquisition time stamps corresponding to the N position coordinates, and the moving speed are input into a first prediction model to obtain a first prediction result, wherein the first prediction model is a time series model; in the case where the first prediction result indicates that the position of the target object after the second time period is within the boundary of the area, it is determined that the target object will enter the dangerous area after the second time period; In the case where the moving trajectory is an irregular trajectory, the following steps are performed: the moving trajectory, the N position coordinates, the N acquisition time stamps corresponding to the N position coordinates, the moving speed, and the environmental characteristics of the target object are input into a second prediction model to obtain a second prediction result, wherein the second prediction model is a deep learning model; in the case where the second prediction result indicates that the position of the target object after the second time period is within the boundary of the area, it is determined that the target object will enter the dangerous area after the second time period; The i-th acquisition timestamp among the N acquisition timestamps is used to indicate the acquisition time of the i-th position coordinate among the N position coordinates, and i is an integer and i is greater than or equal to 1.

5. The method according to any one of claims 1 to 4, characterized in that Before acquiring the N position coordinates of the N positions passed by the target object within the first time period based on the ultra-wideband UWB positioning tag worn by the target object, the method further includes: determining a transmission speed of the positioning signal; Determine the distance information between the target object and the UWB base station at the second time according to the first time when the UWB base station sends the positioning signal to the UWB positioning tag, the second time when the UWB positioning tag receives the positioning signal, and the transmission speed of the positioning signal; The object position coordinates corresponding to the target object at the second time are determined based on the distance information, wherein the N position coordinates include the object position coordinates.

6. The method according to any one of claims 1 to 4, characterized in that When sending the warning prompt information to the UWB positioning tag, the method further includes: The early warning prompt information is sent to the mobile device used by the target object.

7. A safety detection device for an object, characterized in that: include: An acquisition unit, configured to acquire N position coordinates of N positions passed by the target object within a first time period based on an ultra-wideband (UWB) positioning tag worn by the target object, wherein the UWB positioning tag is used to determine the position information of the target object through a positioning signal sent by a UWB base station, and N is a positive integer; a determining unit, configured to determine a moving trajectory of the target object within the first time period based on the N position coordinates, and determine a trajectory type of the moving trajectory; The prompt unit is used to send early warning prompt information to the UWB positioning tag when it is predicted that the target object will enter a dangerous area after a second time period based on the trajectory type of the moving trajectory and the moving speed of the target object in the first time period.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program is executed by a processor to perform the method described in any one of claims 1 to 6.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 6 through the computer program.

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