A method, system and device for intelligent risk assessment of outdoor sports

Abstract

This application discloses an intelligent risk assessment method, system, and device for outdoor sports. The method includes configuring at least one checkpoint within a target activity area for outdoor sports; obtaining user information and historical exercise data of the target user in response to receiving checkpoint information indicating that the target user has arrived at a first checkpoint; obtaining a predicted result of the target user's exercise performance data between the first checkpoint and a second checkpoint based on the target user's historical exercise data; and obtaining the exercise risk between the first checkpoint and the second checkpoint based on the predicted result of the target user's exercise performance data between the first checkpoint and the second checkpoint and the user information. This method at least addresses the problem that existing assessment methods cannot perform real-time risk analysis during exercise, and that existing solutions can only perform overall risk analysis, and cannot perform individual risk analysis on the user's behavior during exercise.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, system and device for intelligently assessing risks in outdoor sports. Background Art

[0002] With people's increasing demand for health and fitness, outdoor sports have become an increasingly important part of modern life. Outdoor exercise not only strengthens physical fitness and boosts immunity, but also promotes mental health. However, the complex and ever-changing environment of outdoor sports presents numerous potential risk factors, such as weather changes, uneven terrain, traffic accidents, and excessive physical exertion. These risks are often difficult to foresee, leading to an increasing frequency and severity of outdoor sports accidents. Therefore, effectively assessing the risks of outdoor sports and ensuring the safety of participants has become a pressing technical challenge.

[0003] Currently, traditional risk assessment methods rely primarily on the athlete's experience and the coach's judgment, lacking scientific and systematic technical support. These traditional methods often produce subjective assessment results, making it difficult to provide personalized risk warnings for different individuals, environments, and sports. With the advancement of technology, particularly in artificial intelligence and big data analytics, intelligent risk assessment solutions have emerged. These utilize technologies such as sensors and wearable devices to collect real-time data from athletes and predict potential risks through data analysis. However, these existing technologies still have many shortcomings, particularly in assessing individual users' athletic performance and predicting risks, and further improvement is needed.

[0004] Although there are currently some methods for outdoor sports analysis and evaluation, most of them are based on historical data and some parameters of the current exercise before departure. They cannot perform real-time risk analysis during the exercise. In addition, existing solutions can only perform overall risk analysis and cannot perform individual risk analysis on user behavior during exercise. Summary of the Invention

[0005] The present invention provides an intelligent risk assessment method, system and equipment for outdoor sports, and provides an outdoor sports risk assessment solution with higher real-time performance and better ability to provide sports suggestions to users. It at least solves the problems that existing assessment methods cannot perform real-time risk analysis during exercise, and existing solutions can only perform overall risk analysis and cannot perform individual risk analysis on the user's behavior during exercise.

[0006] This application provides an intelligent risk assessment method for outdoor sports, including:

[0007] Configure at least one checkpoint within the target activity area for outdoor sports;

[0008] In response to receiving checkpoint information indicating that a target user has arrived at a first checkpoint, obtaining user information and historical movement data of the target user; the first checkpoint belongs to the at least one checkpoint;

[0009] Obtaining, based on the historical exercise data of the target user, an exercise performance data prediction result of the target user between a first checkpoint and a second checkpoint, wherein the second checkpoint belongs to the at least one checkpoint;

[0010] The motion risk between the first checkpoint and the second checkpoint is obtained based on the motion performance data prediction result of the target user between the first checkpoint and the second checkpoint and the user information.

[0011] Optionally, configuring at least one checkpoint within the target activity area for outdoor sports includes:

[0012] According to the target activity area of outdoor sports, partitioning the target activity area based on a preset partitioning rule to obtain a partitioning result;

[0013] According to the partitioning result, at least one area in each area whose safety factor meets the preset requirements is selected as a checkpoint.

[0014] Optionally, in response to receiving the check information indicating that the target user has arrived at the first checkpoint, obtaining the user information and historical movement data of the target user includes:

[0015] In response to receiving the check information that the target user has arrived at the first checkpoint, obtaining a target user ID of the target user;

[0016] At least part of the user information and / or at least part of the historical motion data of the target user is obtained according to the target user ID.

[0017] Optionally, the historical motion data includes first historical motion data and second historical motion data, wherein the first historical motion data is configured as motion data generated by the target user in the current motion, and the second historical motion data is configured as motion data generated by the target user in the completed motion;

[0018] Obtaining a predicted result of the target user's athletic performance data between a first checkpoint and a second checkpoint based on the target user's historical athletic data, including:

[0019] Establishing a user athletic ability model, wherein the user athletic ability model is configured to take completed environmental data, completed athletic performance data, and pending environmental data as input, and to output athletic performance data matching the pending environmental data;

[0020] obtaining, based on the second historical motion data, second environmental data and second motion performance data corresponding to the second historical motion data;

[0021] After training the user's athletic ability model using the second environment data and the second athletic performance data, a target user athletic ability model is obtained;

[0022] obtaining, based on the first historical motion data, first environmental data and first motion performance data corresponding to the first historical motion data;

[0023] According to the first checkpoint and the second checkpoint, obtaining target environment data between the first checkpoint and the second checkpoint;

[0024] The first environmental data and the first motion performance data are used as the completed environmental data and the completed motion performance data, and the target environmental data is used as the environmental data to be completed and input into the target user motion ability model to obtain the target motion performance data as the motion performance data prediction result of the target user between the first checkpoint and the second checkpoint.

[0025] Optionally, obtaining the movement risk between the first checkpoint and the second checkpoint based on the predicted result of the movement performance data of the target user between the first checkpoint and the second checkpoint and the user information includes:

[0026] obtaining target trajectory data of the target user between the first checkpoint and the second checkpoint based on the target motion performance data;

[0027] obtaining, based on the target trajectory data, at least one of weather data, lighting data, and animal activity data corresponding to the target trajectory;

[0028] A motion risk between a first checkpoint and a second checkpoint is obtained based on the target trajectory, at least one of weather data, lighting data, and animal activity data corresponding to the target trajectory, and user information.

[0029] Optionally, when the number of the second checkpoints is greater than 1, the method further includes:

[0030] Obtaining at least one predicted result of the athletic performance data of the target user between the first checkpoint and each of the second checkpoints;

[0031] Obtaining, according to at least one sports performance data prediction result, sports risks between each of the first checkpoints and each of the second checkpoints;

[0032] According to the movement risks between the first checkpoint and each of the second checkpoints, route suggestions and risk warnings are provided to the target user.

[0033] Optionally, after obtaining the motion risk between each of the first checkpoint and each of the second checkpoints based on at least one motion performance data prediction result, the method further includes:

[0034] Obtaining at least one predicted result of the sports performance data of the target user between each of the second checkpoints and a key checkpoint; wherein the key checkpoint is configured to belong to the at least one checkpoint and be a starting point or an end point;

[0035] Obtaining a motion risk between the second checkpoint and the key checkpoint according to at least one of the motion performance data prediction results of the target user between each of the second checkpoints and the key checkpoint;

[0036] Based on the movement risk between the second checkpoint and the key checkpoint, route suggestions and risk prompts are provided to the target user.

[0037] In another aspect, an outdoor sports risk intelligent assessment system includes a risk assessment platform and an intelligent terminal;

[0038] The risk assessment platform is configured to:

[0039] Configure at least one checkpoint within the target activity area for outdoor sports;

[0040] In response to receiving checkpoint information indicating that a target user has arrived at a first checkpoint, obtaining user information and historical movement data of the target user; the first checkpoint belongs to the at least one checkpoint;

[0041] Obtaining, based on the historical exercise data of the target user, an exercise performance data prediction result of the target user between a first checkpoint and a second checkpoint, wherein the second checkpoint belongs to the at least one checkpoint;

[0042] Obtaining a movement risk between the first checkpoint and the second checkpoint based on the predicted movement performance data of the target user between the first checkpoint and the second checkpoint and the user information;

[0043] The intelligent terminal is configured as follows:

[0044] Communicate with the at least one checkpoint and generate check information indicating that the target user has arrived at the first checkpoint.

[0045] Optionally, the historical motion data includes first historical motion data and second historical motion data, wherein the first historical motion data is configured as motion data generated by the target user in the current motion, and the second historical motion data is configured as motion data generated by the target user in the completed motion;

[0046] Obtaining a predicted result of the target user's athletic performance data between a first checkpoint and a second checkpoint based on the target user's historical athletic data, including:

[0047] Establishing a user athletic ability model, wherein the user athletic ability model is configured to take completed environmental data, completed athletic performance data, and pending environmental data as input, and to output athletic performance data matching the pending environmental data;

[0048] obtaining, based on the second historical motion data, second environmental data and second motion performance data corresponding to the second historical motion data;

[0049] After training the user's athletic ability model using the second environment data and the second athletic performance data, a target user athletic ability model is obtained;

[0050] obtaining, based on the first historical motion data, first environmental data and first motion performance data corresponding to the first historical motion data;

[0051] According to the first checkpoint and the second checkpoint, obtaining target environment data between the first checkpoint and the second checkpoint;

[0052] The first environmental data and the first motion performance data are used as the completed environmental data and the completed motion performance data, and the target environmental data is used as the environmental data to be completed and input into the target user motion ability model to obtain the target motion performance data as the motion performance data prediction result of the target user between the first checkpoint and the second checkpoint.

[0053] On the other hand, an embodiment of the present application further provides a device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.

[0054] On the other hand, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the above method.

[0055] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0056] The present invention provides an intelligent risk assessment method, system, and device for outdoor sports, including configuring at least one checkpoint within a target activity area for outdoor sports; obtaining user information and historical exercise data of the target user in response to receiving checkpoint information indicating that the target user has arrived at a first checkpoint; obtaining a predicted result of the target user's exercise performance data between the first checkpoint and a second checkpoint based on the target user's historical exercise data; obtaining a predicted result of the target user's exercise performance data between the first checkpoint and the second checkpoint based on the predicted result of the target user's exercise performance data between the first checkpoint and the second checkpoint and the user information. This method at least solves the problem that existing assessment methods cannot perform real-time risk analysis during exercise, and that existing solutions can only perform overall risk analysis and cannot perform individual risk analysis on the user's behavior during exercise. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] To more clearly illustrate the specific embodiments of this application or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0058] Figure 1 This is a flowchart of an intelligent risk assessment method for outdoor sports in this application;

[0059] Figure 2 A schematic structural diagram of a device in this application;

[0060] Markings in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory.

[0061] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0062] In order to enable those skilled in the art to better understand the present invention, the following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0063] 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 sequential order. 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 a sequence 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.

[0064] Example 1: Figure 1 As shown, an intelligent risk assessment method for outdoor sports includes:

[0065] S1. Configure at least one checkpoint within the target activity area of outdoor sports.

[0066] Optionally, the checkpoint is configured to send check information that the target user has arrived at the first checkpoint by interacting with a device set at the checkpoint when the target user arrives at the checkpoint; the check information can be sent by the checkpoint or by the target user's device.

[0067] S2. In response to receiving the check information indicating that the target user has arrived at the first checkpoint, obtain user information and historical movement data of the target user.

[0068] Specifically, the first checkpoint belongs to at least one checkpoint.

[0069] Optionally, in response to receiving check information that the target user has arrived at the first checkpoint;

[0070] Obtaining a user ID or other unique identifier of a target user based on the inspection information;

[0071] According to the user ID or other unique identifier of the target user, the user information and historical exercise data of the target user are obtained.

[0072] Optionally, the user information may include basic data such as the user's age, gender, height, weight, resting heart rate, or data related to athletic ability, and may also include equipment information for the user's outdoor exercise.

[0073] S3. Obtain a prediction result of the target user's exercise performance data between the first checkpoint and the second checkpoint based on the target user's historical exercise data.

[0074] Specifically, the second checkpoint belongs to at least one checkpoint. The second checkpoint is generally a checkpoint different from the first checkpoint, but there is also the possibility that the second checkpoint and the first checkpoint are the same checkpoint. For example, in a round trip route, if there is no checkpoint at the turning point, then this situation will occur at the checkpoint closest to the turning point.

[0075] Optionally, a sports ability model of the target user is established based on the historical sports data of the target user, and a prediction result of the sports performance data of the target user between the first checkpoint and the second checkpoint is obtained based on the performance of the completed part of this outdoor exercise.

[0076] S4. Obtain the motion risk between the first checkpoint and the second checkpoint based on the target user's motion performance data prediction result between the first checkpoint and the second checkpoint and the user information.

[0077] Optionally, the movement risk between the first checkpoint and the second checkpoint is obtained based on the predicted results of the target user's exercise performance data between the first checkpoint and the second checkpoint and user information, combined with weather factors, environmental factors and other factors based on a preset indicator system or other scoring methods.

[0078] This solution continuously assesses a user's risk during outdoor exercise and updates it at each checkpoint. This addresses the existing inability of existing assessment methods to analyze risk in real time during exercise, and addresses the issue of existing solutions only performing overall risk analysis, failing to analyze individual risks for specific user behaviors during exercise. This solution is suitable for group outdoor exercise activities or for use in areas with frequent outdoor activity.

[0079] Example 2: Based on Example 1, this example provides an intelligent risk assessment method for outdoor sports, including:

[0080] S1. Configure at least one checkpoint within the target activity area of outdoor sports.

[0081] Optionally, the checkpoint is configured to send check information that the target user has arrived at the first checkpoint by interacting with a device set at the checkpoint when the target user arrives at the checkpoint; the check information can be sent by the checkpoint or by the target user's device.

[0082] Optionally, at least one checkpoint is configured within the target activity area for outdoor sports, including:

[0083] According to the target activity area of outdoor sports, the target activity area is partitioned based on preset partitioning rules to obtain a partitioning result;

[0084] According to the zoning results, at least one area in each area whose safety factor meets the preset requirements is selected as a checkpoint.

[0085] S2. In response to receiving the check information indicating that the target user has arrived at the first checkpoint, obtain user information and historical movement data of the target user.

[0086] Specifically, the first checkpoint belongs to at least one checkpoint.

[0087] Optionally, in response to receiving check information that the target user has arrived at the first checkpoint;

[0088] Obtaining a user ID or other unique identifier of a target user based on the inspection information;

[0089] According to the user ID or other unique identifier of the target user, the user information and historical exercise data of the target user are obtained.

[0090] Optionally, the user information may include basic data such as the user's age, gender, height, weight, resting heart rate, or data related to athletic ability, and may also include equipment information for the user's outdoor exercise.

[0091] Optionally, in response to receiving the check information that the target user has arrived at the first checkpoint, obtaining user information and historical movement data of the target user includes:

[0092] In response to receiving the check information that the target user has arrived at the first checkpoint, obtaining a target user ID of the target user;

[0093] At least part of the target user's user information and / or at least part of the historical motion data are obtained according to the target user ID.

[0094] S3. Obtain a prediction result of the target user's exercise performance data between the first checkpoint and the second checkpoint based on the target user's historical exercise data.

[0095] Specifically, the second checkpoint belongs to at least one checkpoint. The second checkpoint is generally a checkpoint different from the first checkpoint, but there is also the possibility that the second checkpoint and the first checkpoint are the same checkpoint. For example, in a round trip route, if there is no checkpoint at the turning point, then this situation will occur at the checkpoint closest to the turning point.

[0096] Optionally, a sports ability model of the target user is established based on the historical sports data of the target user, and a prediction result of the sports performance data of the target user between the first checkpoint and the second checkpoint is obtained based on the performance of the completed part of this outdoor exercise.

[0097] Optionally, the historical motion data includes first historical motion data and second historical motion data, the first historical motion data being configured as motion data generated by the target user in the current motion, and the second historical motion data being configured as motion data generated by the target user in the completed motion;

[0098] Based on the historical exercise data of the target user, a predicted result of the target user's exercise performance data between the first checkpoint and the second checkpoint is obtained, including:

[0099] Establishing a user athletic ability model, the user athletic ability model being configured to take completed environmental data, completed athletic performance data, and pending environmental data as input, and to output athletic performance data matching the pending environmental data;

[0100] obtaining, based on the second historical motion data, second environmental data and second motion performance data corresponding to the second historical motion data;

[0101] After training the user's sports ability model using the second environment data and the second sports performance data, a target user's sports ability model is obtained;

[0102] Obtaining, based on the first historical motion data, first environmental data and first motion performance data corresponding to the first historical motion data;

[0103] According to the first checkpoint and the second checkpoint, obtaining target environment data between the first checkpoint and the second checkpoint;

[0104] The first environment data and the first motion performance data are used as the completed environment data and the completed motion performance data, and the target environment data is used as the environment data to be completed and input into the target user's motion ability model to obtain the target motion performance data as the motion performance data prediction result of the target user between the first checkpoint and the second checkpoint.

[0105] Specifically, to implement the above solution, a specific solution for constructing a sports performance model uses the environmental data and sports performance of a completed section, as well as the environmental data of the next section, as input to predict the sports 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 sports performance. In actual use, the environmental data and sports performance may also include more data.

[0106] In terms of model selection, LSTM is used here to capture the relationship between terrain and speed and make predictions;

[0107] Before building an LSTM model, the data needs to be preprocessed. For time series data, common processing steps include normalization / standardization and converting the data into a time series format.

[0108] Then we can construct a training set and a test set based on the second historical data for training and evaluating the model;

[0109] Then we can start designing the LSTM model architecture;

[0110] The LSTM model includes at least the input layer, LSTM layer, and Dense layer;

[0111] The input layer is configured to receive input data including a terrain curve and a speed curve of a completed road segment and combine it with terrain data of a next road segment;

[0112] 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 known terrain and speed data;

[0113] The Dense layer is used as the output layer to predict the speed curve of the next road section;

[0114] The following is a code example of an LSTM model based on Keras. It assumes that we already have data containing terrain curves and speed curves, and that these data have been properly preprocessed.

[0115] Finally, the LSTM model is trained using the adam optimizer and the mean_squared_error loss function.

[0116] S4. Obtain the motion risk between the first checkpoint and the second checkpoint based on the target user's motion performance data prediction result between the first checkpoint and the second checkpoint and the user information.

[0117] Optionally, the movement risk between the first checkpoint and the second checkpoint is obtained based on the predicted results of the target user's exercise performance data between the first checkpoint and the second checkpoint and user information, combined with weather factors, environmental factors and other factors based on a preset indicator system or other scoring methods.

[0118] Optionally, obtaining the movement risk between the first checkpoint and the second checkpoint based on the target user's movement performance data prediction result between the first checkpoint and the second checkpoint and the user information includes:

[0119] obtaining target trajectory data of the target user between a first checkpoint and a second checkpoint based on the target motion performance data;

[0120] obtaining, based on the target trajectory data, at least one of weather data, lighting data, and animal activity data corresponding to the target trajectory;

[0121] A motion risk between a first checkpoint and a second checkpoint is obtained based on a target trajectory, at least one of weather data, lighting data, and animal activity data corresponding to the target trajectory, and user information.

[0122] The target user's trajectory data can be used to more accurately assess the target user's movement risk between the first checkpoint and the second checkpoint.

[0123] Optionally, obtaining the motion risk between the first checkpoint and the second checkpoint based on the target trajectory, at least one of weather data, lighting data, and animal activity data corresponding to the target trajectory, and user information includes:

[0124] At least one risk event is obtained based on 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 road surface, sudden drop in temperature, light drop exceeding a threshold, the presence of wild animals with a high risk factor, etc.

[0125] Based on at least one risk event and user information, obtain the target user's experience in resolving risk events and the equipment they carry;

[0126] According to the target user's experience in solving risk events and the equipment they carry, the movement risk between the first checkpoint and the second checkpoint is obtained.

[0127] Optionally, when the number of second checkpoints is greater than 1, the method further includes:

[0128] Obtain at least one sports performance data prediction result of the target user between the first checkpoint and each second checkpoint;

[0129] Obtaining, based on at least one prediction result of sports performance data, sports risks between the first checkpoint and each second checkpoint;

[0130] Based on the movement risks between the first checkpoint and each second checkpoint, route suggestions and risk warnings are provided to the target user.

[0131] When the user has multiple second checkpoints to choose from, the motion risks between the first checkpoint and each second checkpoint are obtained respectively, so that the user can select the next checkpoint according to the risk situation.

[0132] Optionally, after obtaining the motion risk between the first checkpoint and each second checkpoint based on at least one motion performance data prediction result, the method further includes:

[0133] Obtaining at least one predicted result of the target user's sports performance data between each second checkpoint and a key checkpoint; the key checkpoint is configured to belong to at least one checkpoint and be a starting point or an end point;

[0134] Obtaining a motion risk between the second checkpoint and the key checkpoint based on at least one motion performance data prediction result of the target user between each second checkpoint and the key checkpoint;

[0135] Based on the movement risk between the second checkpoint and the key checkpoint, route suggestions and risk warnings are given to the target user.

[0136] Optionally, the method further includes: obtaining a predicted result of the target user's sports performance data between the first checkpoint and the key checkpoint;

[0137] Obtaining a motion risk between the first checkpoint and the key checkpoint based on a prediction result of the target user's motion performance data between the first checkpoint and the key checkpoint;

[0138] 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 a preset threshold, the user is prompted to go directly to the key checkpoint.

[0139] Using the above solution, in addition to real-time analysis of the risk of the user's next exercise segment, the risk of reaching the end point or returning to the starting point after the next exercise segment will also be analyzed. This avoids the situation where although the risk of each segment is low, the overall return risk exceeds the acceptable range due to excessive depth.

[0140] Example 3: This embodiment provides an outdoor sports risk intelligent assessment system, including a risk assessment platform and an intelligent terminal;

[0141] The risk assessment platform is configured to:

[0142] Configure at least one checkpoint within the target activity area for outdoor sports;

[0143] In response to receiving checkpoint information indicating that a target user has arrived at a first checkpoint, obtaining user information and historical movement data of the target user; the first checkpoint belonging to at least one checkpoint;

[0144] Obtaining a prediction result of the target user's exercise performance data between a first checkpoint and a second checkpoint based on the target user's historical exercise data; wherein the second checkpoint belongs to at least one checkpoint;

[0145] Obtaining a movement risk between the first checkpoint and the second checkpoint based on the target user's movement performance data prediction result between the first checkpoint and the second checkpoint and the user information;

[0146] The intelligent terminal is configured as:

[0147] Communicate with at least one checkpoint and generate check information that the target user arrives at the first checkpoint.

[0148] Specifically, the smart terminal can be a mobile phone, 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 smart terminal, and the communication method can be Bluetooth, WiFi, etc.

[0149] Optionally, the inspection information may be sent to the risk assessment platform by the smart terminal, or may be sent to the risk assessment platform by the checkpoint.

[0150] Optionally, at least one checkpoint is configured within the target activity area for outdoor sports, including:

[0151] According to the target activity area of outdoor sports, the target activity area is partitioned based on preset partitioning rules to obtain a partitioning result;

[0152] According to the zoning results, at least one area in each area whose safety factor meets the preset requirements is selected as a checkpoint.

[0153] Optionally, in response to receiving the check information that the target user has arrived at the first checkpoint, obtaining user information and historical movement data of the target user includes:

[0154] In response to receiving the check information that the target user has arrived at the first checkpoint, obtaining a target user ID of the target user;

[0155] At least part of the target user's user information and / or at least part of the historical motion data are obtained according to the target user ID.

[0156] Optionally, the historical motion data includes first historical motion data and second historical motion data, the first historical motion data being configured as motion data generated by the target user in the current motion, and the second historical motion data being configured as motion data generated by the target user in the completed motion;

[0157] Based on the historical exercise data of the target user, a predicted result of the target user's exercise performance data between the first checkpoint and the second checkpoint is obtained, including:

[0158] Establishing a user athletic ability model, the user athletic ability model being configured to take completed environmental data, completed athletic performance data, and pending environmental data as input, and to output athletic performance data matching the pending environmental data;

[0159] obtaining, based on the second historical motion data, second environmental data and second motion performance data corresponding to the second historical motion data;

[0160] After training the user's sports ability model using the second environment data and the second sports performance data, a target user's sports ability model is obtained;

[0161] Obtaining, based on the first historical motion data, first environmental data and first motion performance data corresponding to the first historical motion data;

[0162] According to the first checkpoint and the second checkpoint, obtaining target environment data between the first checkpoint and the second checkpoint;

[0163] The first environment data and the first motion performance data are used as the completed environment data and the completed motion performance data, and the target environment data is used as the environment data to be completed and input into the target user's motion ability model to obtain the target motion performance data as the motion performance data prediction result of the target user between the first checkpoint and the second checkpoint.

[0164] Optionally, obtaining the movement risk between the first checkpoint and the second checkpoint based on the target user's movement performance data prediction result between the first checkpoint and the second checkpoint and the user information includes:

[0165] obtaining target trajectory data of the target user between a first checkpoint and a second checkpoint based on the target motion performance data;

[0166] obtaining, based on the target trajectory data, at least one of weather data, lighting data, and animal activity data corresponding to the target trajectory;

[0167] A motion risk between a first checkpoint and a second checkpoint is obtained based on a target trajectory, at least one of weather data, lighting data, and animal activity data corresponding to the target trajectory, and user information.

[0168] Optionally, when the number of second checkpoints is greater than 1, the method further includes:

[0169] Obtain at least one sports performance data prediction result of the target user between the first checkpoint and each second checkpoint;

[0170] Obtaining, based on at least one prediction result of sports performance data, sports risks between the first checkpoint and each second checkpoint;

[0171] Based on the movement risks between the first checkpoint and each second checkpoint, route suggestions and risk warnings are provided to the target user.

[0172] Optionally, after obtaining the motion risk between the first checkpoint and each second checkpoint based on at least one motion performance data prediction result, the method further includes:

[0173] Obtaining at least one predicted result of the target user's sports performance data between each second checkpoint and a key checkpoint; the key checkpoint is configured to belong to at least one checkpoint and be a starting point or an end point;

[0174] Obtaining a motion risk between the second checkpoint and the key checkpoint based on at least one motion performance data prediction result of the target user between each second checkpoint and the key checkpoint;

[0175] Based on the movement risk between the second checkpoint and the key checkpoint, route suggestions and risk warnings are given to the target user.

[0176] Embodiment 4: This embodiment provides a device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the above methods.

[0177] Specifically, such as Figure 2 As shown, Figure 2 This 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 and 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 (Display), an input unit such as a keyboard (Keyboard), and optionally the user interface 104 may also 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 (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) memory, or a stable non-volatile memory (NVM), such as at least one disk storage. The processor 101 may be a general-purpose processor, including a central processing unit, 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 device, a discrete gate or transistor logic device, or a discrete hardware component.

[0178] Those skilled in the art will understand that Figure 2The structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0179] like Figure 2 As 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 risk assessment method for outdoor sports.

[0180] exist Figure 2 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in this application can be set in the electronic device, and the electronic device calls the application stored in the memory 105 for implementing an outdoor sports risk intelligent assessment method through the processor 101 to implement the above method.

[0181] Embodiment 5: This embodiment provides a computer-readable storage medium, on which a computer program is stored. A processor executes the computer program to implement any of the above methods.

[0182] In some embodiments, the computer-readable storage medium may be a memory device such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface mount memory, optical disk, or CD-ROM; or various devices including any one or any combination of the above memories. The computer may be various computing devices including smart terminals and servers.

[0183] In the above embodiments of the present disclosure, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0184] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as 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.

[0185] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0186] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0187] If the 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 is essentially 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, and the computer software product is stored in a non-volatile storage medium, including a number of instructions for enabling a device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present disclosure. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program code.

[0188] The above is only a preferred embodiment of the present disclosure. 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 disclosure. These improvements and modifications should also be regarded as within the scope of protection of the present disclosure.

Claims

1. An intelligent risk assessment method for outdoor sports, characterized in that: include: Configure at least one checkpoint within the target activity area for outdoor sports; In response to receiving checkpoint information indicating that a target user has arrived at a first checkpoint, obtaining user information and historical movement data of the target user; the first checkpoint belongs to the at least one checkpoint; Obtaining, based on the historical exercise data of the target user, an exercise performance data prediction result of the target user between a first checkpoint and a second checkpoint, wherein the second checkpoint belongs to the at least one checkpoint; Obtaining a movement risk between the first checkpoint and the second checkpoint based on the predicted movement performance data of the target user between the first checkpoint and the second checkpoint and the user information; When the number of the second checkpoints is greater than 1, the method further includes: Obtaining at least one predicted result of the athletic performance data of the target user between the first checkpoint and each of the second checkpoints; Obtaining, according to at least one sports performance data prediction result, sports risks between each of the first checkpoints and each of the second checkpoints; Obtaining at least one predicted result of the sports performance data of the target user between each of the second checkpoints and a key checkpoint; wherein the key checkpoint is configured to belong to the at least one checkpoint and be a starting point or an end point; Obtaining a motion risk between the second checkpoint and the key checkpoint according to at least one of the motion performance data prediction results of the target user between each of the second checkpoints and the key checkpoint; According to the movement risks between the first checkpoint and each of the second checkpoints and the movement risks between the second checkpoint and key checkpoints, route suggestions and risk prompts are provided to the target user.

2. The method for intelligent risk assessment of outdoor sports according to claim 1, characterized in that: The step of configuring at least one checkpoint within the target activity area for outdoor sports includes: According to the target activity area of outdoor sports, partitioning the target activity area based on a preset partitioning rule to obtain a partitioning result; According to the partitioning result, at least one area in each area whose safety factor meets the preset requirements is selected as a checkpoint.

3. The method for intelligent risk assessment of outdoor sports according to claim 1, characterized in that: The step of obtaining user information and historical movement data of the target user in response to receiving the checkpoint information indicating that the target user has arrived at the first checkpoint comprises: In response to receiving the check information that the target user has arrived at the first checkpoint, obtaining a target user ID of the target user; At least part of the user information and / or at least part of the historical motion data of the target user is obtained according to the target user ID.

4. The method for intelligent risk assessment of outdoor sports according to claim 1, characterized in that: The historical motion data includes first historical motion data and second historical motion data, wherein the first historical motion data is configured as motion data generated by the target user in the current motion, and the second historical motion data is configured as motion data generated by the target user in the completed motion; Obtaining a predicted result of the target user's athletic performance data between a first checkpoint and a second checkpoint based on the target user's historical athletic data, including: Establishing a user athletic ability model, wherein the user athletic ability model is configured to take completed environmental data, completed athletic performance data, and pending environmental data as input, and to output athletic performance data matching the pending environmental data; obtaining, based on the second historical motion data, second environmental data and second motion performance data corresponding to the second historical motion data; After training the user's athletic ability model using the second environment data and the second athletic performance data, a target user athletic ability model is obtained; obtaining, based on the first historical motion data, first environmental data and first motion performance data corresponding to the first historical motion data; According to the first checkpoint and the second checkpoint, obtaining target environment data between the first checkpoint and the second checkpoint; The first environmental data and the first motion performance data are used as the completed environmental data and the completed motion performance data, and the target environmental data is used as the environmental data to be completed and input into the target user motion ability model to obtain the target motion performance data as the motion performance data prediction result of the target user between the first checkpoint and the second checkpoint.

5. The method for intelligent risk assessment of outdoor sports according to claim 4, characterized in that: The step of obtaining the movement risk between the first checkpoint and the second checkpoint based on the movement performance data prediction result of the target user between the first checkpoint and the second checkpoint and the user information includes: obtaining target trajectory data of the target user between the first checkpoint and the second checkpoint based on the target motion performance data; obtaining, based on the target trajectory data, at least one of weather data, lighting data, and animal activity data corresponding to the target trajectory; A motion risk between a first checkpoint and a second checkpoint is obtained based on the target trajectory, at least one of weather data, lighting data, and animal activity data corresponding to the target trajectory, and user information.

6. An outdoor sports risk intelligent assessment system, characterized in that: Including risk assessment platform and smart terminal; The risk assessment platform is configured to: Configure at least one checkpoint within the target activity area for outdoor sports; In response to receiving checkpoint information indicating that a target user has arrived at a first checkpoint, obtaining user information and historical movement data of the target user; the first checkpoint belongs to the at least one checkpoint; Obtaining, based on the historical exercise data of the target user, an exercise performance data prediction result of the target user between a first checkpoint and a second checkpoint, wherein the second checkpoint belongs to the at least one checkpoint; Obtaining a movement risk between the first checkpoint and the second checkpoint based on the predicted movement performance data of the target user between the first checkpoint and the second checkpoint and the user information; When the number of the second checkpoints is greater than 1, the method further includes: Obtaining at least one predicted result of the athletic performance data of the target user between the first checkpoint and each of the second checkpoints; Obtaining, according to at least one sports performance data prediction result, sports risks between each of the first checkpoints and each of the second checkpoints; Obtaining at least one predicted result of the sports performance data of the target user between each of the second checkpoints and a key checkpoint; wherein the key checkpoint is configured to belong to the at least one checkpoint and be a starting point or an end point; Obtaining a motion risk between the second checkpoint and the key checkpoint according to at least one of the motion performance data prediction results of the target user between each of the second checkpoints and the key checkpoint; Providing route suggestions and risk warnings to the target user based on the movement risks between the first checkpoint and each of the second checkpoints, and the movement risks between the second checkpoint and key checkpoints; The intelligent terminal is configured as follows: Communicate with the at least one checkpoint and generate check information indicating that the target user has arrived at the first checkpoint.

7. The outdoor sports risk intelligent assessment system according to claim 6, characterized in that: The historical motion data includes first historical motion data and second historical motion data, wherein the first historical motion data is configured as motion data generated by the target user in the current motion, and the second historical motion data is configured as motion data generated by the target user in the completed motion; Obtaining a predicted result of the target user's athletic performance data between a first checkpoint and a second checkpoint based on the target user's historical athletic data, including: Establishing a user athletic ability model, wherein the user athletic ability model is configured to take completed environmental data, completed athletic performance data, and pending environmental data as input, and to output athletic performance data matching the pending environmental data; obtaining, based on the second historical motion data, second environmental data and second motion performance data corresponding to the second historical motion data; After training the user's athletic ability model using the second environment data and the second athletic performance data, a target user athletic ability model is obtained; obtaining, based on the first historical motion data, first environmental data and first motion performance data corresponding to the first historical motion data; According to the first checkpoint and the second checkpoint, obtaining target environment data between the first checkpoint and the second checkpoint; The first environmental data and the first motion performance data are used as the completed environmental data and the completed motion performance data, and the target environmental data is used as the environmental data to be completed and input into the target user motion ability model to obtain the target motion performance data as the motion performance data prediction result of the target user between the first checkpoint and the second checkpoint.

8. A device, characterized in that The device includes a memory and a processor, wherein 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 to 5.

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