Intelligent ski monitoring system and ski data monitoring method

Abstract

The present invention provides an intelligent ski monitoring system and a ski data monitoring method. The system includes: a smart wearable device and a desktop terminal; the smart wearable device is communicatively connected to the desktop terminal; the smart wearable device is used to collect ski data and send the ski data to the desktop terminal; the desktop terminal is used to receive the ski data sent by the smart wearable device, analyze and calculate the ski data to obtain a fall detection result, and perform monitoring and scheduling based on the fall detection result. The present invention provides a smart wearable device and a desktop terminal, connects the desktop terminal to the smart wearable device for communication, receives the ski data collected by the smart wearable device, performs analysis and calculation based on the ski data, accurately obtains the skier's movement status such as the fall detection result, and performs monitoring and scheduling based on the fall detection result, further ensuring the skier's safety.

Description

Technical Field

[0001] The present invention relates to the field of monitoring technology, and in particular to an intelligent skiing monitoring system and a skiing data monitoring method. Background Art

[0002] As people's living standards improve, winter sports are becoming increasingly popular. However, skiing is a dangerous sport, and injuries to skiers from falls, tumbles, or collisions with others are common. Monitoring abnormal skier behavior and providing timely feedback to staff and other skiers when danger occurs is particularly important.

[0003] Existing ski wearables typically only offer basic safety features, along with simple intercom and video recording capabilities. Safety features typically rely on monitoring the skier's speed. Consequently, compared to practical application needs, existing ski wearables are relatively backward and limited in functionality, lacking intelligence and convenience. Furthermore, the accuracy of monitoring the skier's speed is limited, as it only reflects the skier's speed, not whether the skier has fallen. Summary of the Invention

[0004] The present invention provides an intelligent ski monitoring system and a ski data monitoring method, which are used to solve the defect in the prior art that ski wearable equipment cannot accurately reflect the skier's sports status, and achieve the purpose of accurately reflecting the skier's sports status.

[0005] The present invention provides an intelligent ski monitoring system, comprising: an intelligent wearable device and a desktop terminal;

[0006] The smart wearable device is communicatively connected with the desktop terminal;

[0007] The smart wearable device is used to collect skiing data and send the skiing data to the desktop terminal;

[0008] The desktop terminal is used to receive the skiing data sent by the smart wearable device, analyze and calculate the skiing data to obtain a fall detection result, and perform monitoring and scheduling according to the fall detection result.

[0009] An intelligent ski monitoring system according to the present invention also includes a ski mobile terminal;

[0010] The skiing mobile terminal is communicatively connected with the smart wearable device;

[0011] The skiing mobile terminal is used to receive and record the skiing data, and select a ski trail based on the skiing data.

[0012] According to an intelligent ski monitoring system provided by the present invention, the desktop terminal includes: a back-end processing module, a front-end operation module, a communication scheduling module and a database module;

[0013] The back-end processing module is used to realize the transmission and reception of the skiing data, and analyze the skiing data according to a threshold method or a neural network model to generate a fall detection result;

[0014] The front-end operation module is used to control the back-end processing module, the communication scheduling module and the database module;

[0015] The communication scheduling module is used to perform network communication with the smart wearable device and the skiing mobile terminal, and send scheduling instructions to the smart wearable device and the skiing mobile terminal;

[0016] The database module is used to store various types of skiing data and receive control instructions sent by the front-end operation module to operate the skiing data.

[0017] According to an intelligent ski monitoring system provided by the present invention, the communication scheduling module further includes a snow path planning module;

[0018] The snow track path planning module is used to calculate the population density corresponding to each area based on the number of smart wearable devices in each area, and perform path planning based on the population density;

[0019] and,

[0020] The snow trail path planning module is also used to remind the user to select a snow trail of corresponding difficulty based on the user's skiing habits, and to provide early warning prompts based on the snow trail.

[0021] According to an intelligent ski monitoring system provided by the present invention, the back-end processing module includes a first processing module and a second processing module:

[0022] The first processing module is used to obtain acceleration data measured by a three-axis sensor, set a preset threshold according to a change in a human body's fall state, and obtain the fall detection result based on the acceleration data and the preset threshold;

[0023] The second processing module is used to obtain acceleration data measured by a three-axis sensor and input the acceleration data into a neural network model to obtain the fall detection result;

[0024] The neural network model is trained based on human fall action data and the fall detection results.

[0025] According to an intelligent ski monitoring system provided by the present invention, the intelligent wearable device includes: a camera module, a wireless module, a three-axis sensor module, an earphone module, a microprocessor module and a lithium battery module;

[0026] The camera module is used to record skiing videos and save the skiing videos to the microprocessor module;

[0027] The wireless module is used to realize communication connection with the desktop terminal and the ski mobile terminal respectively;

[0028] The three-axis sensor module is used to obtain the skiing data in real time;

[0029] The headset module is used to send voice information to the desktop terminal and receive alarm information and ski trail planning information sent by the desktop terminal;

[0030] The microprocessor module is used to send the acquired skiing video, the skiing data and the voice information to the skiing mobile terminal and the desktop terminal respectively through the wireless module;

[0031] The lithium battery module is used to power the smart wearable device.

[0032] The present invention also provides a skiing data monitoring method based on the above-mentioned intelligent skiing monitoring system, comprising:

[0033] Obtaining skiing data sent by the smart wearable device, and performing data preprocessing on the skiing data to generate standard skiing data;

[0034] Obtaining a preset threshold based on human fall motion data, and generating a first fall detection result according to the human fall motion data and the preset threshold;

[0035] Inputting the standard skiing data into a neural network model, and obtaining a second fall detection result output by the neural network model;

[0036] Based on the first fall detection result or the second fall detection result, a scheduling control instruction is sent to the smart wearable device.

[0037] According to a skiing data monitoring method provided by the present invention, obtaining a preset threshold based on human fall motion data, and generating a first fall detection result based on the human fall motion data and the preset threshold, includes:

[0038] acquiring the standard skiing data multiple times;

[0039] Obtaining the preset threshold based on the changing state of the human body falling and the acceleration change characteristics;

[0040] When the acquired skiing data is greater than the preset threshold, it is determined that the skier has fallen, and the first fall detection result is generated.

[0041] According to a skiing data monitoring method provided by the present invention, inputting the standard skiing data into a neural network model and obtaining a second fall detection result output by the neural network model includes:

[0042] Inputting the standard skiing data into a neural network model, and obtaining a second fall detection result output by the neural network model;

[0043] The neural network model extracts features from human falling motion data, makes classification decisions through a classifier, and judges the fall results of the skiing data.

[0044] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above-described skiing data monitoring methods when executing the computer program.

[0045] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any of the above-mentioned skiing data monitoring methods are implemented.

[0046] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of any one of the above-mentioned skiing data monitoring methods are implemented.

[0047] The intelligent ski monitoring system and ski data monitoring method provided by the present invention, by setting up an intelligent wearable device and a desktop terminal, communicating with the desktop terminal and the intelligent wearable device, receiving ski data collected by the intelligent wearable device, and performing analysis and calculation based on the ski data, can accurately obtain the skier's fall detection results and other movement conditions, and perform monitoring and scheduling based on the fall detection results to further ensure the safety of the skier. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 This is one of the structural diagrams of the intelligent ski monitoring system provided by the present invention;

[0050] Figure 2 This is the second structural diagram of the intelligent ski monitoring system provided by the present invention;

[0051] Figure 3 1 is a flow chart of the skiing data monitoring method provided by the present invention;

[0052] Figure 4 is a flow chart of a fall detection algorithm based on a threshold method provided by the present invention;

[0053] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0055] Figure 1 This is one of the structural diagrams of the intelligent ski monitoring system provided by the present invention. Figure 1 As shown, the present invention provides an intelligent ski monitoring system, comprising: an intelligent wearable device 101 and a desktop terminal 102;

[0056] The smart wearable device 101 is communicatively connected with the desktop terminal 102;

[0057] The smart wearable device 101 is used to collect skiing data and send the skiing data to the desktop terminal 102;

[0058] The desktop terminal 102 is used to receive the skiing data sent by the smart wearable device 101, analyze and calculate the skiing data to obtain a fall detection result, and perform monitoring and scheduling based on the fall detection result.

[0059] It can be understood that in this embodiment, the intelligent ski monitoring system includes an intelligent wearable device 101 and a desktop terminal 102. A communication connection is established between the intelligent wearable device 101 and the desktop terminal 102, and data transmission and communication can be performed between the two. At the same time, the intelligent wearable devices 101 can also communicate with each other.

[0060] The smart wearable device 101 collects the skiing data of the skier in real time during the skiing process by configuring sensors, a global positioning system (GPS) and other equipment, and sends the collected skiing data to the desktop terminal 102 through a communication connection.

[0061] After receiving the skiing data sent by the smart wearable device 101, the desktop terminal 102 analyzes and calculates the skiing data, and based on the calculation results, determines whether the skier has fallen, obtains a fall detection result, and issues instructions such as organizing rescue assistance and continuing to detect abnormal behavior based on the fall detection result. At the same time, because the desktop terminal 102 can connect to multiple smart wearable devices 101 at the same time, it can adjust the crowd density based on the multiple smart wearable devices 101 to improve the skiing experience. The desktop terminal 102 can also provide a customer question answering function through the communication connection with the smart wearable device 101, providing real-time support for skiers.

[0062] The smart wearable device 101 in the present invention can be a smart ski helmet, which can provide communication, data collection, and protection functions. The desktop terminal 102 can be a computer or a vehicle-mounted terminal.

[0063] The skiing data may include speed, acceleration, motion trajectory, longitude and latitude information and other data.

[0064] For example, a speed sensor is installed on the smart wearable helmet, which can collect the skier's movement speed in real time, analyze and calculate whether the skier is within the normal movement range, and then determine whether there is a fall.

[0065] The intelligent ski monitoring system provided by the present invention, by setting up an intelligent wearable device and a desktop terminal, communicating with the desktop terminal and the intelligent wearable device, receives skiing data collected by the intelligent wearable device, and performs analysis and calculation based on the skiing data. It can accurately obtain the skier's fall detection results and other movement conditions, and perform monitoring and scheduling based on the fall detection results to further ensure the safety of the skier.

[0066] Furthermore, it also includes a skiing mobile terminal;

[0067] The skiing mobile terminal is communicatively connected with the smart wearable device 101;

[0068] The skiing mobile terminal is used to receive and record the skiing data, and select a ski trail based on the skiing data.

[0069] It can be understood that, Figure 2 This is the second structural diagram of the intelligent ski monitoring system provided by the present invention. Figure 2As shown, the intelligent ski monitoring system also includes a ski mobile terminal, which can be connected to the smart wearable device 101 and other ski mobile terminals via a network connection or Bluetooth. By receiving the ski data sent by the smart wearable device 101, the ski slope selection is made.

[0070] Optionally, the ski mobile terminal also has the following functions:

[0071] Data recording function, showing the user's total skiing mileage, number of ski trips, average speed, maximum acceleration and other sports data, as well as the ability to view the skiing video;

[0072] The ski resort reservation function allows users to make ski resort reservations, rate and evaluate ski resorts, and view the total average score of all users for ski resorts;

[0073] Skiing instruction function, which allows users to watch skiing instruction videos, articles, etc.

[0074] The community forum function allows users to share their sports data, sports pictures and videos in the community, and leave messages for communication;

[0075] The mall function allows users to purchase or rent other ski equipment, sports necessities, etc.

[0076] The event function recommends and introduces some skiing events and skiing activities.

[0077] The present invention is also equipped with a mobile operating program that runs on an Android phone and has a visual operating interface that can be used to implement the above functions.

[0078] The present invention arranges a skiing mobile terminal in the intelligent skiing monitoring system and selects a ski trail by receiving skiing data, thereby not only improving the safety of the skiing process but also further improving the convenience of the intelligent skiing monitoring system.

[0079] Furthermore, the desktop terminal 102 includes: a backend processing module, a frontend operation module, a communication scheduling module and a database module;

[0080] The back-end processing module is used to realize the transmission and reception of the skiing data, and analyze the skiing data according to a threshold method or a neural network model to generate a fall detection result;

[0081] The front-end operation module is used to control the back-end processing module, the communication scheduling module and the database module;

[0082] The communication scheduling module is used to perform network communication with the smart wearable device 101 and the skiing mobile terminal, and send scheduling instructions to the smart wearable device 101 and the skiing mobile terminal;

[0083] The database module is used to store various types of skiing data and receive control instructions sent by the front-end operation module to operate the skiing data.

[0084] It can be understood that the desktop terminal 102 includes the following modules: a back-end processing module, a front-end operation module, a communication scheduling module and a database module.

[0085] The back-end processing module is installed in the server of the desktop terminal 102 to realize the sending and receiving of various types of data, perform calculations and processing on various types of data, and store the various types of data in the database system.

[0086] The front-end operation module is installed in the server of the desktop terminal 102, and provides a visual desktop application interface for staff to complete specific operations on the back-end processing module, communication scheduling module, and database module.

[0087] The communication scheduling module can conduct communication between systems, between intranets and between intranets and extranets, plan routes for skiers, and dispatch staff to complete assigned tasks.

[0088] The database module realizes the storage of various types of data, and can complete specific operations such as adding, deleting, modifying, and using data through the front-end operation module.

[0089] Optionally, the front-end operating system includes a visual operating system that can run on a computer, has an intuitive operating interface and humanized operating logic, and can implement the processing steps of the above-mentioned back-end processing module, front-end operation module, communication scheduling module and database module.

[0090] The present invention forms a desktop terminal by combining a back-end processing module, a front-end operation module, a communication scheduling module and a database module, which can accurately receive skiing data and perform corresponding processing, such as path planning and personnel mobilization, which is beneficial to monitoring the movement status of skiers and better ensuring the safety of skiers.

[0091] Furthermore, the communication scheduling module also includes a snow path planning module;

[0092] The snow track path planning module is used to calculate the population density corresponding to each area based on the number of the smart wearable devices 101 in each area, and perform path planning based on the population density;

[0093] and,

[0094] The snow trail path planning module is also used to remind the user to select a snow trail of corresponding difficulty based on the user's skiing habits, and to provide early warning prompts based on the snow trail.

[0095] It can be understood that, in this embodiment, the communication scheduling module also includes a snow track path planning module. The snow track path planning module provides two algorithms, one of which is a personnel density monitoring algorithm. The desktop terminal 102 is connected to multiple smart wearable devices 101. The desktop terminal 102 can determine the position of each smart wearable device 101 through the positioning device of the smart wearable device 101, and then the personnel density in different areas can be obtained. According to the personnel density, users in crowded areas are reminded to choose non-crowded areas for skiing as much as possible, and perform path planning at the same time.

[0096] The other is a snow trail planning algorithm, which can intelligently remind users to choose snow trails of corresponding difficulty based on the skiing habits of different users. At the same time, it can divide different sections into dangerous sections and non-dangerous sections according to the frequency of abnormal behavior, and intelligently remind users to pay attention to risks.

[0097] The present invention sets a snow path planning module in the communication scheduling module and provides a personnel density monitoring algorithm and a snow path planning algorithm to perform snow path planning and ski risk reminders, thereby further improving the skiing experience and safety of skiers.

[0098] The back-end processing module includes a first processing module and a second processing module:

[0099] The first processing module is used to obtain acceleration data measured by a three-axis sensor, set a preset threshold according to a change in a human body's fall state, and obtain the fall detection result based on the acceleration data and the preset threshold;

[0100] The second processing module is used to obtain acceleration data measured by a three-axis sensor and input the acceleration data into a neural network model to obtain the fall detection result;

[0101] The neural network model is trained based on human fall motion data and the fall detection results. It can be understood that in this embodiment, the back-end processing module includes a first processing module and a second processing module. When the system is operating, the first processing module receives skiing data measured by a three-axis sensor on the smart wearable device 101, which is an acceleration sensor, and obtains acceleration data measured by the three-axis sensor.

[0102] The first processor module sets a preset threshold according to the changes in the state of the human body falling. The acceleration of the human body when falling will show great differences, which can be mainly divided into four stages: before the fall, the acceleration fluctuations in the three-axis directions of the head and chest of the human body are basically stable; from the fall to the impact on the ground, the acceleration in the three-axis directions of the head and chest of the human body will drop significantly; when the fall occurs and hits the ground, the acceleration in the three-axis directions of the head and chest of the human body will fluctuate violently; after the fall until standing up, the acceleration in the three-axis directions of the head and chest of the human body will remain stable for a period of time.

[0103] Therefore, the acceleration data measured along each axis using a triaxial sensor can serve as a key physical quantity for abnormal behavior monitoring. By analyzing the four states of a person falling and their acceleration variations, the system can effectively identify daily human behavior. Using this data, specific indicators and preset thresholds are developed. The thresholds for each indicator are calculated and optimized through extensive statistical analysis and experimentation. When the acquired acceleration data exceeds the preset threshold, a fall is detected and a fall detection result is output.

[0104] After obtaining the acceleration data measured by the three-axis sensor, the second processing module inputs the acceleration data into a pre-built neural network model to obtain a fall detection result.

[0105] The neural network model uses deep learning technology and data processing technology to input complex human fall action data into the original neural network model, train the original neural network model, and obtain the neural network model.

[0106] The present invention obtains acceleration data measured by a three-axis sensor, compares the acceleration data with a preset threshold, and inputs the acceleration data into a neural network model to accurately obtain fall detection results, which is beneficial to improving the safety of skiers.

[0107] Furthermore, the smart wearable device 101 includes: a camera module, a wireless module, a three-axis sensor module, an earphone module, a microprocessor module and a lithium battery module;

[0108] The camera module is used to record skiing videos and save the skiing videos to the microprocessor module;

[0109] The wireless module is used to realize communication connection with the desktop terminal 102 and the ski mobile terminal respectively;

[0110] The three-axis sensor module is used to obtain the skiing data in real time;

[0111] The headset module is used to send voice information to the desktop terminal 102 and receive alarm information and ski trail planning information sent by the desktop terminal 102;

[0112] The microprocessor module is used to send the acquired skiing video, the skiing data and the voice information to the skiing mobile terminal and the desktop terminal 102 respectively through the wireless module;

[0113] The lithium battery module is used to power the smart wearable device 101.

[0114] It can be understood that, in this embodiment, the hardware structure of the smart ski wear may include: a camera module, a wireless module, a three-axis sensor module, an earphone module, a microprocessor module, a lithium battery module and a helmet body.

[0115] Among them, the main functions of the microprocessor module are to process instructions, execute operations, require actions, control time, and process data; the main function of the camera module is to record skiing videos and save them to the local memory card, which skiers can transfer to the skiing mobile terminal as needed.

[0116] The wireless module can be a WiFi Bluetooth module, and the main function of the wireless module is to communicate with the desktop terminal 102 and the skiing mobile terminal respectively.

[0117] The sensor module is a three-axis acceleration sensor. The main function of the sensor is to measure linear acceleration and upload the skiing data to the desktop terminal 102 for staff to use, and upload it to the skiing mobile terminal for users to view the motion data.

[0118] The main functions of the headset module are to contact the dispatch center, broadcast alarm information and snow path planning information, play music, etc.

[0119] The lithium battery module can be a low-temperature resistant lithium battery to power the smart wearable device 101.

[0120] For example, the smart wearable device uses the WiFi Bluetooth module to realize communication with the dispatch center equipped with the desktop terminal 102, communication between various smart wearable devices, and communication between the smart ski wear and the ski mobile terminal, completing the reception, storage, and calculation of various types of data to realize the skier's motion data and video recording, ski slope path planning, abnormal behavior detection, and on-site personnel scheduling.

[0121] By arranging a camera module, a three-axis sensor module and an earphone module in the smart wearable device, the present invention can accurately obtain the skier's motion data, facilitate real-time communication with the skier, and further ensure the skier's safety.

[0122] Figure 3 FIG. 1 is a flow chart of the skiing data monitoring method provided by the present invention, as shown in FIG. Figure 3 As shown, the present invention provides a skiing data monitoring method, the method comprising:

[0123] Step 301: Obtain skiing data sent by the smart wearable device, and perform data preprocessing on the skiing data to generate standard skiing data.

[0124] It can be understood that after receiving the skiing data sent by the smart wearable device, the desktop terminal will pre-process the skiing data and generate processed standard skiing data.

[0125] The preprocessing includes filtering and synthetic acceleration processing. The synthetic acceleration processing can be obtained by the following formula, which includes:

[0126]

[0127] Where a r represents the resultant acceleration, which is the vector sum of the three-axis linear accelerations, a x represents the linear acceleration of the x-axis, a y represents the linear acceleration of the y-axis, a z Indicates the linear acceleration along the z-axis.

[0128] Step 302: Obtain a preset threshold based on human fall motion data, and generate a first fall detection result according to the human fall motion data and the preset threshold.

[0129] It can be understood that the acceleration of a human body when falling will be significantly different from that in normal times. A threshold can be set based on the acceleration when falling. After obtaining the preset threshold, the first fall result is generated by comparing it with the acceleration data collected by the three-axis sensor.

[0130] Step 303: Input the standard skiing data into the neural network model to obtain a second fall detection result output by the neural network model.

[0131] It can be understood that after obtaining the standard skiing data, the data is input into a pre-built neural network model to obtain a second fall detection result output by the neural network model.

[0132] Step 304: Send a scheduling control instruction to the smart wearable device based on the first fall detection result or the second fall detection result.

[0133] It can be understood that after obtaining the first fall detection result and the second fall detection result, it is possible to determine whether the skier has fallen based on any fall detection result, and then send a scheduling control instruction to the smart wearable device, which can issue an alarm and rescue the skier.

[0134] The intelligent skiing monitoring method provided by the present invention connects a desktop terminal to a smart wearable device for communication, receives skiing data collected by the smart wearable device, and performs analysis and calculation based on the skiing data. It can accurately obtain the skier's fall detection results and other movement conditions, and perform monitoring and scheduling based on the fall detection results to further ensure the safety of the skier.

[0135] Furthermore, the obtaining of a preset threshold based on human fall motion data, and generating a first fall detection result according to the human fall motion data and the preset threshold, includes:

[0136] acquiring the standard skiing data multiple times;

[0137] Obtaining the preset threshold based on the changing state of the human body falling and the acceleration change characteristics;

[0138] When the acquired skiing data is greater than the preset threshold, it is determined that the skier has fallen, and the first fall detection result is generated.

[0139] It can be understood that, Figure 4 This is a flow chart of the fall detection algorithm based on the threshold method provided by the present invention, such as Figure 4 As shown, after obtaining the skiing data collected by the acceleration sensor, data preprocessing is performed to obtain standard skiing data.

[0140] The acceleration of the human body when falling will show great differences, which can be mainly divided into four stages: before the fall, the acceleration fluctuations in the three-axis directions of the head and chest of the human body are basically stable; from the fall to the impact of the ground, the acceleration in the three-axis directions of the head and chest of the human body will drop significantly; when falling and hitting the ground, the acceleration in the three-axis directions of the head and chest of the human body will fluctuate violently; after the fall until standing up, the acceleration in the three-axis directions of the head and chest of the human body will remain stable for a period of time.

[0141] Therefore, the acceleration data of each axis measured by the three-axis sensor can be used as an important physical quantity for abnormal behavior monitoring. It can analyze the four states of the human body during a fall and its acceleration change characteristics, effectively identify the human body's daily behavior, and use the changing state data of the human body's fall to formulate specific indicators. According to the changing state of the human body's fall and the acceleration change characteristics, a preset threshold value is set for it. The threshold of each indicator can be obtained through multiple experiments and statistics to obtain the optimal value. When these indicators set by obtaining multiple acceleration data exceed the specific threshold, it is judged as a fall and the fall detection result is output.

[0142] The present invention can accurately obtain a fall detection result by acquiring the standard skiing data multiple times and comparing it with a preset threshold value obtained based on the changing state of a human body falling and the acceleration change characteristics.

[0143] Furthermore, inputting the standard skiing data into a neural network model and obtaining a second fall detection result output by the neural network model includes:

[0144] Inputting the standard skiing data into a neural network model, and obtaining a second fall detection result output by the neural network model;

[0145] The neural network model extracts features from human falling motion data, makes classification decisions through a classifier, and judges the fall results of the skiing data.

[0146] It can be understood that the present invention uses deep learning technology and data processing technology to input complex human fall action data into the original neural network model, and the original neural network model performs wavelet transform feature extraction and discrete feature extraction to generate training samples. Then, classifiers such as support vector machines (SVM) and artificial neural networks (ANN) are used to make classification decisions and train the neural network model.

[0147] After training a neural network model based on complex human fall action data, the standard skiing data that has undergone data preprocessing is input into the neural network model to obtain the fall detection results.

[0148] By inputting the acceleration data into a neural network model, the present invention can accurately obtain fall detection results, which is beneficial to improving the safety of skiers.

[0149] Figure 5 Schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 5As shown, the electronic device may include: a processor (processor) 501, a communication interface (Communications Interface) 502, a memory (memory) 503 and a communication bus 504, wherein the processor 501, the communication interface 502, and the memory 503 communicate with each other via the communication bus 504. The processor 501 may call the logic instructions in the memory 503 to execute the skiing data monitoring method provided by the above-mentioned method embodiments, which method, for example, includes: obtaining skiing data sent by the smart wearable device, and performing data preprocessing on the skiing data to generate standard skiing data; obtaining a preset threshold based on human fall action data, and generating a first fall detection result based on the human fall action data and the preset threshold; inputting the standard skiing data into a neural network model to obtain a second fall detection result output by the neural network model; and sending a scheduling control instruction to the smart wearable device based on the first fall detection result or the second fall detection result.

[0150] In addition, the logic instructions in the above-mentioned memory 503 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0151] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the skiing data monitoring method provided by the above-mentioned method embodiments. The method, for example, includes: obtaining skiing data sent by the smart wearable device, and performing data preprocessing on the skiing data to generate standard skiing data; obtaining a preset threshold based on human fall action data, and generating a first fall detection result based on the human fall action data and the preset threshold; inputting the standard skiing data into a neural network model to obtain a second fall detection result output by the neural network model; and sending a scheduling control instruction to the smart wearable device based on the first fall detection result or the second fall detection result.

[0152] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the skiing data monitoring method provided by the above-mentioned method embodiments. The method, for example, includes: obtaining skiing data sent by a smart wearable device, and performing data preprocessing on the skiing data to generate standard skiing data; obtaining a preset threshold based on human fall action data, and generating a first fall detection result based on the human fall action data and the preset threshold; inputting the standard skiing data into a neural network model to obtain a second fall detection result output by the neural network model; and sending a scheduling control instruction to the smart wearable device based on the first fall detection result or the second fall detection result.

[0153] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0154] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent ski monitoring system, characterized in that: include: Smart wearable devices, desktop terminals and ski mobile terminals; The smart wearable device is communicatively connected with the desktop terminal; The smart wearable device is used to collect skiing data and send the skiing data to the desktop terminal; The desktop terminal is used to receive the skiing data sent by the smart wearable device, analyze and calculate the skiing data to obtain a fall detection result, and perform monitoring and scheduling according to the fall detection result; The skiing mobile terminal is communicatively connected with the smart wearable device; The skiing mobile terminal is used to receive and record the skiing data, and select a ski trail based on the skiing data; The communication scheduling module also includes a snow path planning module; The snow track path planning module is used to calculate the population density corresponding to each area based on the number of smart wearable devices in the area, and perform path planning based on the population density; and, The snow trail path planning module is also used to remind the user to select a snow trail of corresponding difficulty based on the user's skiing habits, and to provide early warning prompts based on the snow trail; The smart wearable device includes: a camera module, a wireless module, a three-axis sensor module, a headset module, a microprocessor module and a lithium battery module; The camera module is used to record skiing videos and save the skiing videos to the microprocessor module; The wireless module is used to realize communication connection with the desktop terminal and the ski mobile terminal respectively; The three-axis sensor module is used to obtain the skiing data in real time; The headset module is used to send voice information to the desktop terminal and receive alarm information and ski trail planning information sent by the desktop terminal; The microprocessor module is used to send the acquired skiing video, the skiing data and the voice information to the skiing mobile terminal and the desktop terminal respectively through the wireless module; The lithium battery module is used to power the smart wearable device.

2. The intelligent ski monitoring system according to claim 1, characterized in that: The desktop terminal includes: a back-end processing module, a front-end operation module, a communication scheduling module and a database module; The back-end processing module is used to realize the transmission and reception of the skiing data, and analyze the skiing data according to a threshold method or a neural network model to generate a fall detection result; The front-end operation module is used to control the back-end processing module, the communication scheduling module and the database module; The communication scheduling module is used to perform network communication with the smart wearable device and the skiing mobile terminal, and send scheduling instructions to the smart wearable device and the skiing mobile terminal; The database module is used to store various types of skiing data and receive control instructions sent by the front-end operation module to operate the skiing data.

3. The intelligent ski monitoring system according to claim 2, characterized in that: The back-end processing module includes a first processing module and a second processing module: The first processing module is used to obtain acceleration data measured by a three-axis sensor, set a preset threshold according to a change in a human body's fall state, and obtain the fall detection result based on the acceleration data and the preset threshold; The second processing module is used to obtain acceleration data measured by a three-axis sensor and input the acceleration data into a neural network model to obtain the fall detection result; The neural network model is trained based on human fall action data and the fall detection results.

4. A skiing data monitoring method based on the intelligent skiing monitoring system according to any one of claims 1 to 3, characterized in that: include: Obtaining skiing data sent by the smart wearable device, and performing data preprocessing on the skiing data to generate standard skiing data; Obtaining a preset threshold based on human fall motion data, and generating a first fall detection result according to the human fall motion data and the preset threshold; Inputting the standard skiing data into a neural network model, and obtaining a second fall detection result output by the neural network model; Based on the first fall detection result or the second fall detection result, a scheduling control instruction is sent to the smart wearable device.

5. The ski data monitoring method according to claim 4, characterized in that: The step of obtaining a preset threshold based on human fall motion data and generating a first fall detection result according to the human fall motion data and the preset threshold includes: acquiring the standard skiing data multiple times; Obtaining the preset threshold based on the changing state of the human body falling and the acceleration change characteristics; When the acquired skiing data is greater than the preset threshold, it is determined that the skier has fallen, and the first fall detection result is generated.

6. The ski data monitoring method according to claim 4, characterized in that: Inputting the standard skiing data into a neural network model and obtaining a second fall detection result output by the neural network model includes: Inputting the standard skiing data into a neural network model, and obtaining a second fall detection result output by the neural network model; The neural network model extracts features from human falling motion data, makes classification decisions through a classifier, and judges the fall results of the skiing data.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the ski data monitoring method according to any one of claims 4 to 6 are implemented.

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