Outdoor exercise risk intelligent assessment method, system and equipment

Abstract

The invention discloses an outdoor exercise risk intelligent assessment method, system and equipment, and the method comprises the steps: configuring at least one check point in a target activity region of outdoor exercise; in response to received check information that the target user reaches the first check point, obtaining user information and historical motion data of the target user; obtaining an exercise performance data prediction result of the target user between the first check point and the second check point according to the historical exercise data of the target user; and obtaining the exercise risk between the first check point and the second check point according to the exercise performance data prediction result of the target user between the first check point and the second check point and the user information. The problems that an existing evaluation method cannot carry out risk analysis in real time in the exercise process, and an existing scheme can only carry out overall risk analysis and cannot carry out independent risk analysis on behaviors of a user in the exercise process are at least solved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to an intelligent method, system, and device for assessing outdoor sports risks. Background Art

[0002] With the continuous improvement of people's health and fitness needs, outdoor sports have gradually become an important part of modern people's lives. Outdoor sports can not only enhance physical fitness, improve immunity, but also promote mental health. However, the outdoor sports environment is complex and changeable, with many potential risk factors, such as weather changes, uneven terrain, traffic accidents, excessive physical energy, etc. These risks are often difficult to predict, leading to an increasing frequency and severity of outdoor sports accidents. Therefore, how to effectively evaluate the risks of outdoor sports and ensure the safety of athletes has become an urgent technical problem to be solved.

[0003] Currently, traditional risk assessment methods mainly rely on the experience of athletes and the judgment of coaches, lacking scientific and systematic technical support. Through these traditional methods, the assessment results are often subjective and difficult to provide personalized risk warnings for different individuals, different environments, and different sports events. With the development of technology, especially the progress in the fields of artificial intelligence and big data analysis, some intelligent risk assessment solutions have gradually emerged. These solutions use technologies such as sensors and wearable devices to collect real-time data of athletes and predict possible risks through data analysis. However, these existing technologies still have many deficiencies, especially in evaluating the sports performance of individual users and predicting risks, which still need to be further improved.

[0004] Currently, although there are some methods for analyzing and evaluating outdoor sports, most of them are based on historical data and some parameters of this sports event to make predictions before departure, unable to conduct real-time risk analysis during the sports process, and the existing solutions can only conduct overall risk analysis and cannot conduct separate risk analysis on the behavior of users during sports. Summary of the Invention

[0005] The present invention provides an intelligent method, system, and device for assessing outdoor sports risks, providing an outdoor sports risk assessment solution with higher real-time performance and more capable of providing sports suggestions for users, at least solving the problems that the existing assessment methods cannot conduct real-time risk analysis during the sports process, and the existing solutions can only conduct overall risk analysis and cannot conduct separate risk analysis on the behavior of users during sports.

[0006] This application provides an intelligent method for assessing outdoor sports risks, including: Configuring at least one checkpoint within the target activity area of outdoor sports; In response to receiving the inspection information that the target user has reached the first checkpoint, obtain the user information and historical movement data of the target user; the first checkpoint belongs to the at least one checkpoint; According to the historical movement data of the target user, obtain the prediction result of the movement performance data between the first checkpoint and the second checkpoint of the target user; the second checkpoint belongs to the at least one checkpoint; According to the prediction result of the movement performance data between the first checkpoint and the second checkpoint of the target user and the user information, obtain the movement risk between the first checkpoint and the second checkpoint.

[0007] Optionally, configuring at least one checkpoint in the target activity area for outdoor sports includes: According to the target activity area for outdoor sports, partition the target activity area based on a preset zoning rule to obtain a zoning result; According to the zoning result, screen at least one area with a safety factor meeting the preset requirements in each area as a checkpoint.

[0008] Optionally, the obtaining the user information and historical movement data of the target user in response to receiving the inspection information that the target user has reached the first checkpoint includes; In response to receiving the inspection information that the target user has reached the first checkpoint, obtain the target user ID of the target user; According to the target user ID, obtain at least part of the user information and / or at least part of the historical movement data of the target user.

[0009] Optionally, the historical movement data includes first historical movement data and second historical movement data. The first historical movement data is configured as the movement data generated by the target user in this movement, and the second historical movement data is configured as the movement data generated by the target user in the completed movement; According to the historical movement data of the target user, obtaining the prediction result of the movement performance data between the first checkpoint and the second checkpoint of the target user includes: Establish a user movement ability model, which is configured to take the completed environmental data, completed movement performance data, and to-be-completed environmental data as inputs, and the movement performance data matching the to-be-completed environmental data as outputs; According to the second historical movement data, obtain the second environmental data and second movement performance data corresponding to the second historical movement data; After training the user movement ability model with the second environmental data and second movement performance data, obtain the target user movement ability model; Obtain the first environmental data and the first exercise performance data corresponding to the first historical exercise data according to the first historical exercise data; Obtain the target environmental data between the first checkpoint and the second checkpoint according to the first checkpoint and the second checkpoint; Use the first environmental data and the first exercise performance data as the completed environmental data and the completed exercise performance data, and use the target environmental data as the to-be-completed environmental data to input into the target user exercise ability model, and obtain the target exercise performance data as the prediction result of the exercise performance data of the target user between the first checkpoint and the second checkpoint.

[0010] Optionally, obtaining the exercise risk between the first checkpoint and the second checkpoint according to the prediction result of the exercise performance data of the target user between the first checkpoint and the second checkpoint and the user information includes: Obtain the target trajectory data of the target user between the first inspection and the second checkpoint according to the target exercise performance data; Obtain at least one of the weather data, light data, and animal activity data corresponding to the target trajectory according to the target trajectory data; Obtain the exercise risk between the first checkpoint and the second checkpoint according to the target trajectory, at least one of the weather data, light data, and animal activity data corresponding to the target trajectory, and the user information.

[0011] Optionally, when the number of the second checkpoints is greater than 1, it further includes: Obtain at least one of the prediction results of the exercise performance data of the target user between the first checkpoint and each of the second checkpoints; Obtain the exercise risk between the first checkpoint and each of the second checkpoints according to at least one prediction result of the exercise performance data; Give route suggestions and risk warnings to the target user according to the exercise risk between the first checkpoint and each of the second checkpoints.

[0012] Optionally, after the step of obtaining the exercise risk between the first checkpoint and each of the second checkpoints according to at least one prediction result of the exercise performance data, it further includes: Obtain at least one of the prediction results of the exercise performance data of the target user between each of the second checkpoints and the key checkpoint; the key checkpoint is configured to belong to the at least one checkpoint and be the starting point or the ending point; Obtain the movement risk between the second checkpoint and the critical checkpoint according to at least one prediction result of the movement performance data of the target user between each of the second checkpoints and the critical checkpoint; Provide route suggestions and risk warnings to the target user according to the movement risk between the second checkpoint and the critical checkpoint.

[0013] On the other hand, an intelligent outdoor movement risk assessment system includes a risk assessment platform and an intelligent terminal; The risk assessment platform is configured to: Configure at least one checkpoint within the target activity area of the outdoor movement; In response to receiving the inspection information that the target user arrives at the first checkpoint, obtain the user information and historical movement data of the target user; the first checkpoint belongs to the at least one checkpoint; According to the historical movement data of the target user, obtain the prediction result of the movement performance data of the target user between the first checkpoint and the second checkpoint; the second checkpoint belongs to the at least one checkpoint; According to the prediction result of the movement performance data of the target user between the first checkpoint and the second checkpoint and the user information, obtain the movement risk between the first checkpoint and the second checkpoint; The intelligent terminal is configured to: Communicate with the at least one checkpoint to generate the inspection information that the target user arrives at the first checkpoint.

[0014] Optionally, the historical movement data includes first historical movement data and second historical movement data. The first historical movement data is configured as the movement data generated by the target user during the current movement, and the second historical movement data is configured as the movement data generated by the target user during the completed movements; Obtaining the prediction result of the movement performance data of the target user between the first checkpoint and the second checkpoint according to the historical movement data of the target user includes: Establish a user movement ability model, which is configured to take the completed environmental data, completed movement performance data, and to-be-completed environmental data as inputs, and take the movement performance data matching the to-be-completed environmental data as the output; According to the second historical movement data, obtain the corresponding second environmental data and second movement performance data of the second historical movement data; After training the user movement ability model with the second environmental data and second movement performance data, obtain the target user movement ability model; Based on the first historical motion data, obtain the first environmental data and the first motion performance data corresponding to the first historical motion data; Based on the first checkpoint and the second checkpoint, obtain the target environmental data between the first checkpoint and the second checkpoint; Use the first environmental data and the first motion performance data as the completed environmental data and the completed motion performance data, and use the target environmental data as the to-be-completed environmental data to input into the target user motion ability model, and obtain the target motion performance data as the prediction result of the motion performance data of the target user between the first checkpoint and the second checkpoint.

[0015] On the other hand, an embodiment of the present application further provides a device, which includes a memory and a processor. A computer program is stored in the memory, and the processor executes the computer program to implement the above method.

[0016] On the other hand, an embodiment of the present application further provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and the processor executes the computer program to implement the above method.

[0017] Compared with the prior art, the present invention has the following advantages and beneficial effects: An intelligent outdoor sports risk assessment method, system and device of the present invention includes configuring at least one checkpoint in a target activity area of outdoor sports; in response to receiving the check information that the target user arrives at the first checkpoint, obtaining the user information and historical motion data of the target user; the first checkpoint belongs to the at least one checkpoint; based on the historical motion data of the target user, obtaining the prediction result of the motion performance data of the target user between the first checkpoint and the second checkpoint; the second checkpoint belongs to the at least one checkpoint; based on the prediction result of the motion performance data of the target user between the first checkpoint and the second checkpoint and the user information, obtaining the motion risk between the first checkpoint and the second checkpoint. At least solves the problems that the existing assessment methods cannot perform real-time risk analysis during the movement, and the existing solutions can only perform overall risk analysis and cannot perform separate risk analysis on the behavior of the user during the movement. Description of the Drawings

[0018] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts do not necessarily draw according to the actual scale.

[0019] Figure 1It is a schematic flowchart of an intelligent outdoor sports risk assessment method in this application; Figure 2 It is a schematic structural diagram of a device in this application; Markings in the figure: 101 - Processor, 102 - Communication bus, 103 - Network interface, 104 - User interface, 105 - Memory.

[0020] The realization of the purpose of this application, functional features and advantages will be further described in conjunction with embodiments with reference to the accompanying drawings. Specific embodiments

[0021] In order to enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0023] Embodiment 1: As Figure 1 shown, an intelligent outdoor sports risk assessment method includes: S1. Configure at least one checkpoint in the target activity area of outdoor sports.

[0024] Optionally, the checkpoint is configured such that when the target user arrives at the checkpoint, the inspection information that the target user arrives at the first checkpoint can be sent by interacting with the device set at the checkpoint; the inspection information can be sent by the checkpoint or by the device of the target user.

[0025] S2. In response to receiving the inspection information that the target user arrives at the first checkpoint, obtain the user information and historical movement data of the target user.

[0026] Specifically, the first checkpoint belongs to at least one checkpoint.

[0027] Optionally, in response to receiving the inspection information that the target user arrives at the first checkpoint; Obtain the user ID or other unique identifier of the target user according to the inspection information; Obtain the user information and historical movement data of the target user according to the user ID or other unique identifier of the target user.

[0028] Optionally, the user information may include basic data such as user age, gender, height, weight, resting heart rate, etc. or data related to exercise ability, and may also include the equipment information of the user's current outdoor exercise.

[0029] S3. Obtain the prediction result of the exercise performance data of the target user between the first checkpoint and the second checkpoint according to the historical movement data of the target user.

[0030] Specifically, the second checkpoint belongs to at least one checkpoint. Generally, the second detection point is a checkpoint different from the first checkpoint, but there is also a possibility that the second checkpoint is the same as the first checkpoint. For example, in a round-trip route, if there is no checkpoint at the turning point, this situation will occur at the checkpoint closest to the turning point.

[0031] Optionally, establish an exercise ability model of the user according to the historical movement data of the target user, and obtain the prediction result of the exercise performance data of the target user between the first checkpoint and the second checkpoint according to the performance of the completed part of the current outdoor exercise.

[0032] S4. Obtain the exercise risk between the first checkpoint and the second checkpoint according to the prediction result of the exercise performance data of the target user between the first checkpoint and the second checkpoint and the user information.

[0033] Optionally, according to the prediction result of the exercise performance data of the target user between the first checkpoint and the second checkpoint and the user information, combined with factors such as weather factors and environmental factors, based on a preset index system or other scoring methods, obtain the exercise risk between the first checkpoint and the second checkpoint.

[0034] Adopting the above solution, the exercise risk of the user is continuously evaluated during the outdoor exercise of the user and updated at each detection point. It solves at least the problem that the existing evaluation methods cannot perform real-time risk analysis during the exercise, and the existing solutions can only perform overall risk analysis and cannot perform separate risk analysis on the behavior of the user during the exercise. It is applicable to group outdoor exercise activities or areas with frequent outdoor exercise activities.

[0035] Embodiment 2: Based on Embodiment 1, an intelligent evaluation method for outdoor exercise risk includes: S1. Configure at least one checkpoint within the target activity area of outdoor sports.

[0036] Optionally, the checkpoint is configured such that when the target user arrives at the checkpoint, the checkpoint information indicating that the target user has reached the first checkpoint can be sent by interacting with the device set at the checkpoint; the checkpoint information can be sent by the checkpoint or by the device of the target user.

[0037] Optionally, configuring at least one checkpoint within the target activity area of outdoor sports includes: Based on the target activity area of outdoor sports, partition the target activity area according to a preset partitioning rule to obtain a partitioning result; According to the partitioning result, screen at least one area with a safety factor meeting the preset requirements in each area as the checkpoint.

[0038] S2. In response to receiving the checkpoint information indicating that the target user has reached the first checkpoint, obtain the user information and historical movement data of the target user.

[0039] Specifically, the first checkpoint belongs to at least one checkpoint.

[0040] Optionally, in response to receiving the checkpoint information indicating that the target user has reached the first checkpoint; Obtain the user ID or other unique identifier of the target user according to the checkpoint information; According to the user ID or other unique identifier of the target user, obtain the user information and historical movement data of the target user.

[0041] Optionally, the user information can include basic data such as user age, gender, height, weight, resting heart rate, etc. or data related to sports ability, and can also include the equipment information of the target user's current outdoor sports.

[0042] Optionally, in response to receiving the checkpoint information indicating that the target user has reached the first checkpoint, obtaining the user information and historical movement data of the target user includes: In response to receiving the checkpoint information indicating that the target user has reached the first checkpoint, obtain the target user ID of the target user; According to the target user ID, obtain at least part of the user information and / or at least part of the historical movement data of the target user.

[0043] S3. According to the historical movement data of the target user, obtain the prediction result of the movement performance data between the first checkpoint and the second checkpoint of the target user.

[0044] Specifically, the second checkpoint belongs to at least one checkpoint. Generally, the second detection point is a checkpoint different from the first checkpoint, but there is also a possibility that the second checkpoint is the same as the first checkpoint. For example, in a round-trip route, if there is no checkpoint at the turning point, this situation will occur for the checkpoint closest to the turning point.

[0045] Optionally, a user's exercise ability model is established based on the historical exercise data of the target user, and a prediction result of the exercise performance data of the target user between the first checkpoint and the second checkpoint is obtained according to the performance of the completed part of the current outdoor exercise.

[0046] Optionally, the historical exercise data includes first historical exercise data and second historical exercise data. The first historical exercise data is configured as the exercise data generated by the target user during the current exercise, and the second historical exercise data is configured as the exercise data generated by the target user during the completed exercise; Obtaining a prediction result of the exercise performance data of the target user between the first checkpoint and the second checkpoint according to the historical exercise data of the target user includes: Establishing a user exercise ability model, which is configured to take the completed environmental data, completed exercise performance data, and to-be-completed environmental data as inputs, and the exercise performance data matching the to-be-completed environmental data as outputs; According to the second historical exercise data, obtain the second environmental data and the second exercise performance data corresponding to the second historical exercise data; After training the user exercise ability model with the second environmental data and the second exercise performance data, obtain the target user exercise ability model; According to the first historical exercise data, obtain the first environmental data and the first exercise performance data corresponding to the first historical exercise data; According to the first checkpoint and the second checkpoint, obtain the target environmental data between the first checkpoint and the second checkpoint; Take the first environmental data and the first exercise performance data as the completed environmental data and completed exercise performance data, and take the target environmental data as the to-be-completed environmental data and input them into the target user exercise ability model to obtain the target exercise performance data as the prediction result of the exercise performance data of the target user between the first checkpoint and the second checkpoint.

[0047] Specifically, to implement the above solution, a specific solution for constructing an exercise ability model uses the environmental data and exercise performance of the completed section and the environmental data of the next section as inputs to predict the exercise performance of the next section; in this example, the terrain curve is used as the environmental data, and the speed curve is used as the exercise performance. In actual use, the environmental data and exercise performance can also include more data; In terms of model selection, LSTM is used here to capture the relationship between terrain and speed and make predictions; Before building the LSTM model, it is first necessary to preprocess the data; for time series data, common processing steps include: normalization / standardization, and converting the data into a time series format; Then we can construct a training set and a test set based on the second historical data for training and evaluating the model; Then we can start designing the LSTM model architecture; The LSTM model includes at least an input layer, an LSTM layer, and a Dense layer; The input layer is configured to receive input data, including the terrain curve and speed curve of the completed section of the road, and combine with the terrain data of the next section of the road; The LSTM layer can capture the dynamic relationship between terrain and speed from the time series and learn how to predict future speed based on the known terrain and speed data; The Dense layer is used as the output layer to predict the speed curve of the next section of the road; The following is a code example of an LSTM model based on Keras. Assume that we already have data containing terrain curves and speed curves, and these data have been properly preprocessed.

[0048] Finally, use the adam optimizer and the mean_squared_error loss function to train the LSTM model.

[0049] S4. Obtain the motion risk between the first checkpoint and the second checkpoint based on the prediction result of the motion performance data and the user information of the target user between the first checkpoint and the second checkpoint.

[0050] Optionally, based on the prediction result of the motion performance data and the user information of the target user between the first checkpoint and the second checkpoint, combined with factors such as weather factors and environmental factors, obtain the motion risk between the first checkpoint and the second checkpoint based on a preset index system or other scoring methods.

[0051] Optionally, obtain the motion risk between the first checkpoint and the second checkpoint based on the prediction result of the motion performance data and the user information of the target user between the first checkpoint and the second checkpoint, including: Obtain the target trajectory data of the target user between the first checkpoint and the second checkpoint according to the target motion performance data; Obtain at least one of the weather data, light data, and animal activity data corresponding to the target trajectory according to the target trajectory data; Obtain the exercise risk between the first checkpoint and the second checkpoint based on at least one of the target trajectory, weather data corresponding to the target trajectory, lighting data, and animal activity data, and user information.

[0052] Based on the trajectory data of the target user, the exercise risk of the target user between the first checkpoint and the second checkpoint can be evaluated more accurately.

[0053] Optionally, obtaining the exercise risk between the first checkpoint and the second checkpoint based on at least one of the target trajectory, weather data corresponding to the target trajectory, lighting data, and animal activity data, and user information includes: Obtain at least one risk event based on at least one of the target trajectory, weather data corresponding to the target trajectory, lighting data, and animal activity data; the risk event is configured to characterize the risks that the target user may encounter between the first checkpoint and the second checkpoint, such as rain, snow, icy roads, sudden temperature drop, lighting dropping below a threshold, presence of wild animals with a high danger coefficient, etc.

[0054] Based on at least one risk event and user information, obtain the experience of the target user in solving risk events and the equipment carrying situation; Based on the experience of the target user in solving risk events and the equipment carrying situation, obtain the exercise risk between the first checkpoint and the second checkpoint Optionally, when the number of second checkpoints is greater than 1, it further includes: Obtain at least one predicted result of exercise performance data of the target user between the first checkpoint and each second checkpoint; Based on at least one predicted result of exercise performance data, obtain the exercise risk between the first checkpoint and each second checkpoint; Based on the exercise risk between the first checkpoint and each second checkpoint, give route suggestions and risk warnings to the target user.

[0055] When the user has multiple second checkpoints to choose from, obtain the exercise risk between the first checkpoint and each second checkpoint respectively, so that the user can select the next checkpoint according to the risk situation.

[0056] Optionally, after the step of obtaining the exercise risk between the first checkpoint and each second checkpoint based on at least one predicted result of exercise performance data, it further includes: Obtain at least one predicted result of exercise performance data of the target user between each second checkpoint and the key checkpoint; the key checkpoint is configured to belong to at least one checkpoint and be the starting point or the ending point; Obtain the movement risk between the second checkpoint and the key checkpoint according to the prediction result of at least one movement performance data of the target user between each second checkpoint and the key checkpoint; Give route suggestions and risk warnings to the target user according to the movement risk between the second checkpoint and the key checkpoint.

[0057] Optionally, it further includes: obtaining the prediction result of the movement performance data of the target user between the first checkpoint and the key checkpoint; Obtain the movement risk between the first checkpoint and the key checkpoint according to the prediction result of the movement performance data of the target user between the first checkpoint and the key checkpoint; When the movement risk between the second checkpoint and the key checkpoint is greater than the risk between the first checkpoint and the key checkpoint, and the movement risk between the second checkpoint and the key checkpoint is greater than the preset threshold, prompt the user to go directly to the key checkpoint.

[0058] Adopting the above solution, in addition to analyzing the risk of the user's next movement in real time, it also analyzes the risk of reaching the end point or returning to the starting point after the next movement, avoiding the situation where although the risk of each segment is relatively low, due to going too deep, the overall return risk exceeds the acceptable range.

[0059] Embodiment 3: An intelligent outdoor movement risk assessment system in this embodiment includes a risk assessment platform and an intelligent terminal; The risk assessment platform is configured to: Configure at least one checkpoint within the target activity area of the outdoor movement; In response to receiving the inspection information that the target user arrives at the first checkpoint, obtain the user information and historical movement data of the target user; the first checkpoint belongs to at least one checkpoint; Obtain the prediction result of the movement performance data of the target user between the first checkpoint and the second checkpoint according to the historical movement data of the target user; the second checkpoint belongs to at least one checkpoint; Obtain the movement risk between the first checkpoint and the second checkpoint according to the prediction result of the movement performance data of the target user between the first checkpoint and the second checkpoint and the user information; The intelligent terminal is configured to: Communicate with at least one checkpoint to generate the inspection information that the target user arrives at the first checkpoint.

[0060] Specifically, the intelligent terminal can be a mobile phone, a PDA, etc., or other electronic devices that can store the user ID or user information of the target user; the checkpoint is configured as an electronic device that can communicate with the intelligent terminal, and the communication method can be Bluetooth, WiFi, etc.

[0061] Optionally, the inspection information can be sent by the smart terminal to the risk assessment platform, or can be sent by the checkpoint to the risk assessment platform.

[0062] Optionally, at least one checkpoint is configured within the target activity area of the outdoor sport, including: Based on the target activity area of the outdoor sport, the target activity area is partitioned according to a preset partitioning rule to obtain a partitioning result; According to the partitioning result, at least one area with a safety factor meeting the preset requirements is selected from each area as the checkpoint.

[0063] Optionally, in response to receiving the inspection information that the target user arrives at the first checkpoint, the user information and historical movement data of the target user are obtained, including: In response to receiving the inspection information that the target user arrives at the first checkpoint, the target user ID of the target user is obtained; According to the target user ID, at least part of the user information and / or at least part of the historical movement data of the target user are obtained.

[0064] Optionally, the historical movement data includes first historical movement data and second historical movement data. The first historical movement data is configured as the movement data generated by the target user during this movement, and the second historical movement data is configured as the movement data generated by the target user during the completed movement; According to the historical movement data of the target user, a prediction result of the movement performance data between the first checkpoint and the second checkpoint of the target user is obtained, including: A user movement ability model is established. The user movement ability model is configured to take the completed environmental data, the completed movement performance data, and the to-be-completed environmental data as inputs, and take the movement performance data matching the to-be-completed environmental data as outputs; According to the second historical movement data, the second environmental data and the second movement performance data corresponding to the second historical movement data are obtained; After training the user movement ability model with the second environmental data and the second movement performance data, the target user movement ability model is obtained; According to the first historical movement data, the first environmental data and the first movement performance data corresponding to the first historical movement data are obtained; According to the first checkpoint and the second checkpoint, the target environmental data between the first checkpoint and the second checkpoint is obtained; Taking the first environmental data and the first movement performance data as the completed environmental data and the completed movement performance data, and taking the target environmental data as the to-be-completed environmental data and inputting them into the target user movement ability model, the target movement performance data is obtained as the prediction result of the movement performance data between the first checkpoint and the second checkpoint of the target user.

[0065] Optionally, based on the prediction result of the target user's movement performance data between the first checkpoint and the second checkpoint and the user information, obtain the movement risk between the first checkpoint and the second checkpoint, including: Obtain the target trajectory data of the target user between the first inspection and the second checkpoint according to the target movement performance data; Obtain at least one of the weather data, light data, and animal activity data corresponding to the target trajectory according to the target trajectory data; Obtain the movement risk between the first checkpoint and the second checkpoint according to the target trajectory, at least one of the weather data, light data, and animal activity data corresponding to the target trajectory, and the user information.

[0066] Optionally, when the number of second checkpoints is greater than 1, it further includes: Obtain at least one prediction result of the target user's movement performance data between the first checkpoint and each second checkpoint; Obtain the movement risk between the first checkpoint and each second checkpoint according to at least one prediction result of the movement performance data; Give route suggestions and risk warnings to the target user according to the movement risks between the first checkpoint and each second checkpoint.

[0067] Optionally, after the step of obtaining the movement risk between the first checkpoint and each second checkpoint according to at least one prediction result of the movement performance data, it further includes: Obtain at least one prediction result of the target user's movement performance data between each second checkpoint and the key checkpoint; the key checkpoint is configured to belong to at least one checkpoint and be the starting point or the ending point; Obtain the movement risk between the second checkpoint and the key checkpoint according to at least one prediction result of the target user's movement performance data between each second checkpoint and the key checkpoint; Give route suggestions and risk warnings to the target user according to the movement risk between the second checkpoint and the key checkpoint.

[0068] Embodiment 4: This embodiment provides a device, which includes a memory and a processor. A computer program is stored in the memory, and the processor executes the computer program to implement any of the above methods.

[0069] Specifically, as Figure 2 shown, Figure 2The figure is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present application. The device is an electronic device, which may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 104 may further include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 105 may optionally be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as at least one disk memory. The processor 101 may be a general-purpose processor, including a central processor, a network processor, etc., or may be a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0070] Those skilled in the art can understand that the structure shown in the appendix Figure 2 does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0071] As Figure 2 shown, the memory 105, as a storage medium, may include an operating system, a network communication module, a user interface module, and an application program for implementing an intelligent outdoor sports risk assessment method.

[0072] In Figure 2 the shown electronic device, the network interface 103 is mainly used for data communication with a network server; the user interface 104 is mainly used for data interaction with a user; in the present application, the processor 101 and the memory 105 may be disposed in the electronic device, and the electronic device calls the application program stored in the memory 105 for implementing an intelligent outdoor sports risk assessment method through the processor 101 to implement the above method.

[0073] Embodiment 5: This embodiment provides a computer-readable storage medium, on which a computer program is stored, and the processor executes the computer program to implement any of the above methods.

[0074] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disc, or CD-ROM; or may be various devices including one or any combination of the above memories. The computer may be various computing devices including smart terminals and servers.

[0075] In the above embodiments of the present disclosure, the descriptions of the respective embodiments each have their own emphasis. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0076] In the several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods. 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 displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0077] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, in each embodiment of the present disclosure, the functional units may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0079] When an integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present disclosure, in essence, 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. This computer software product is stored in a non-volatile storage medium and includes several instructions for causing a device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present disclosure. The aforementioned non-volatile storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs.

[0080] The above are only the preferred embodiments of the present disclosure. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present disclosure, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present disclosure.

Claims

1. An intelligent risk assessment method for outdoor sports, characterized in that, Including: At least one checkpoint is configured within the target activity area for outdoor sports; In response to receiving the inspection information that the target user arrives at the first checkpoint, obtain the user information and historical movement data of the target user; the first checkpoint belongs to the at least one checkpoint; According to the historical movement data of the target user, obtain the predicted result of the movement performance data between the first checkpoint and the second checkpoint; the second checkpoint belongs to the at least one checkpoint; According to the predicted result of the movement performance data between the first checkpoint and the second checkpoint of the target user and the user information, obtain the movement risk between the first checkpoint and the second checkpoint.

2. The intelligent outdoor sports risk assessment method according to claim 1, wherein The configuration of at least one checkpoint within the target activity area for outdoor sports includes: According to the target activity area for outdoor sports, partition the target activity area based on a preset partitioning rule to obtain a partitioning result; According to the partitioning result, screen at least one area with a safety factor meeting the preset requirements in each area as a checkpoint.

3. The intelligent outdoor sports risk assessment method according to claim 1, wherein The obtaining of the user information and historical movement data of the target user in response to receiving the inspection information that the target user arrives at the first checkpoint includes: In response to receiving the inspection information that the target user arrives at the first checkpoint, obtain the target user ID of the target user; According to the target user ID, obtain at least part of the user information and / or at least part of the historical movement data of the target user.

4. The intelligent risk assessment method for outdoor sports according to claim 1, wherein The historical movement data includes first historical movement data and second historical movement data. The first historical movement data is configured as the movement data generated by the target user in this movement, and the second historical movement data is configured as the movement data generated by the target user in the completed movement; The obtaining of the predicted result of the movement performance data between the first checkpoint and the second checkpoint according to the historical movement data of the target user includes: Establish a user movement ability model, which is configured to take the completed environmental data, completed movement performance data, and to-be-completed environmental data as inputs, and take the movement performance data matching the to-be-completed environmental data as outputs; According to the second historical movement data, obtain the second environmental data and second movement performance data corresponding to the second historical movement data; After training the user movement ability model with the second environmental data and second movement performance data, obtain the target user movement ability model; According to the first historical movement data, obtain the first environmental data and first movement performance data corresponding to the first historical movement data; According to the first checkpoint and the second checkpoint, obtain the target environmental data between the first checkpoint and the second checkpoint; Take the first environmental data and the first movement performance data as the completed environmental data and completed movement performance data, and take the target environmental data as the to-be-completed environmental data and input them into the target user movement ability model to obtain the target movement performance data as the predicted result of the movement performance data between the first checkpoint and the second checkpoint of the target user.

5. The intelligent outdoor sports risk assessment method according to claim 4, characterized in that Obtaining the movement risk between the first checkpoint and the second checkpoint according to the prediction result of the movement performance data of the target user between the first checkpoint and the second checkpoint and the user information includes: Obtaining the target trajectory data of the target user between the first inspection and the second checkpoint according to the target movement performance data; Obtaining at least one of weather data, light data, and animal activity data corresponding to the target trajectory according to the target trajectory data; Obtaining the movement risk between the first checkpoint and the second checkpoint according to the target trajectory, at least one of the weather data, light data, and animal activity data corresponding to the target trajectory, and the user information.

6. The intelligent outdoor sports risk assessment method according to claim 1, characterized in that When the number of the second checkpoints is greater than 1, it further includes: Obtaining at least one prediction result of the movement performance data of the target user between the first checkpoint and each of the second checkpoints; Obtaining the movement risk between the first checkpoint and each of the second checkpoints according to at least one prediction result of the movement performance data; Giving route suggestions and risk warnings to the target user according to the movement risks between the first checkpoint and each of the second checkpoints.

7. The intelligent outdoor sports risk assessment method according to claim 6, wherein, After the step of obtaining the movement risk between the first checkpoint and each of the second checkpoints according to at least one prediction result of the movement performance data, it further includes: Obtaining at least one prediction result of the movement performance data of the target user between each of the second checkpoints and the key checkpoint; the key checkpoint is configured to belong to the at least one checkpoint and be a starting point or an ending point; Obtaining the movement risk between the second checkpoint and the key checkpoint according to at least one prediction result of the movement performance data of the target user between each of the second checkpoints and the key checkpoint; Giving route suggestions and risk warnings to the target user according to the movement risk between the second checkpoint and the key checkpoint.

8. An intelligent risk assessment system for outdoor sports, characterized in that, Including a risk assessment platform and an intelligent terminal; The risk assessment platform is configured to: Configure at least one checkpoint within the target activity area of outdoor sports; Upon receiving the inspection information that the target user arrives at the first checkpoint, obtaining the user information and historical movement data of the target user; the first checkpoint belongs to the at least one checkpoint; Obtaining the prediction result of the movement performance data of the target user between the first checkpoint and the second checkpoint according to the historical movement data of the target user; the second checkpoint belongs to the at least one checkpoint; Obtaining the movement risk between the first checkpoint and the second checkpoint according to the prediction result of the movement performance data of the target user between the first checkpoint and the second checkpoint and the user information; The intelligent terminal is configured to: Communicate with the at least one checkpoint to generate the inspection information that the target user arrives at the first checkpoint.

9. An intelligent outdoor sports risk assessment system according to claim 8, characterized in that The historical motion data includes first historical motion data and second historical motion data. The first historical motion data is configured to be the motion data generated by the target user in the current motion, and the second historical motion data is configured to be the motion data generated by the target user in the completed motion; Based on the historical motion data of the target user, obtaining a prediction result of the motion performance data between the first checkpoint and the second checkpoint, including: Establishing a user motion ability model, which is configured to take the completed environmental data, the completed motion performance data, and the to-be-completed environmental data as inputs, and output the motion performance data matching the to-be-completed environmental data; Based on the second historical motion data, obtaining the second environmental data and the second motion performance data corresponding to the second historical motion data; After training the user motion ability model with the second environmental data and the second motion performance data, obtaining the target user motion ability model; Based on the first historical motion data, obtaining the first environmental data and the first motion performance data corresponding to the first historical motion data; Based on the first checkpoint and the second checkpoint, obtaining the target environmental data between the first checkpoint and the second checkpoint; Taking the first environmental data and the first motion performance data as the completed environmental data and the completed motion performance data, and taking the target environmental data as the to-be-completed environmental data and inputting them into the target user motion ability model, obtaining the target motion performance data as the prediction result of the motion performance data between the first checkpoint and the second checkpoint of the target user.

10. A device, characterized in that, The device includes a memory and a processor. A computer program is stored in the memory, and the processor executes the computer program to implement the method according to any one of claims 1-7.

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