CN117133110B

CN117133110B - Gymnasium safety risk early warning method and system based on machine vision

Info

Publication number: CN117133110B
Application number: CN202311403075.XA
Authority: CN
Inventors: 王先亮; 李延涛; 邓依然; 焦守锴; 白晶; 王翰; 宗可豪; 张宪亮
Original assignee: Shandong University
Current assignee: Shandong University
Priority date: 2023-10-27
Filing date: 2023-10-27
Publication date: 2024-02-02
Anticipated expiration: 2043-10-27
Also published as: CN117133110A

Abstract

The invention discloses a gym safety risk early warning method and system based on machine vision, wherein a gym monitoring module dynamically tracks personnel entering a gym area and acquires gesture data of the gym personnel, a walk monitoring module monitors a main road and each branch road of the gym area and acquires the walk data of the main road and each branch road, an assessment module analyzes the gesture data and the walk data based on a risk early warning model after acquiring the gesture data and the walk data to assess whether the gym is safe or not, and an alarm module sends a second warning signal to an administrator when the gym is assessed to be safe, and the administrator needs to intervene in time to manage the gym when receiving the second warning signal. The early warning system can comprehensively evaluate whether the gym is used or not based on the machine vision after acquiring multiple items of data inside the gym in the use process of the gym, analysis is more comprehensive, and management of the gym by an administrator is facilitated.

Description

Gymnasium safety risk early warning method and system based on machine vision

Technical Field

The invention relates to the technical field of gymnasium safety precaution, in particular to a gymnasium safety risk precaution method and system based on machine vision.

Background

Gymnasiums are places where a large number of people gather, and users and staff may face potential risks, such as equipment faults, when using and managing gymnasiums, so that improving safety awareness and taking corresponding precautions are important for guaranteeing the safety of the gymnasiums;

the gym safety risk early warning system is a technical solution aiming at improving the safety of gym users and staff, and aims at identifying and relieving potential safety risks so as to ensure the normal operation of the gym and the health and safety of the users.

The prior art has the following defects:

the prior early warning system is only used for monitoring the safety use risk of the gymnastic equipment in the use process, and when the gymnastic equipment has the safety use risk, the early warning system sends out an early warning prompt, however, in practical application, the risk factors influencing the safety use of the gymnastic equipment are many, and whether the gymnastic equipment has the safety use risk or not is only analyzed by a single analysis, so that the analysis is not comprehensive enough, the safety use of the gymnastic equipment cannot be guaranteed, and the manager cannot know the whole safety condition of the gymnastic equipment, so that the management is inconvenient.

Disclosure of Invention

The invention aims to provide a gymnasium safety risk early warning method and system based on machine vision, which are used for solving the defects in the background technology.

In order to achieve the above object, the present invention provides the following technical solutions: a gymnasium safety risk early warning system based on machine vision comprises a region monitoring module, a device state acquisition module, a control warning module, a gymnasium monitoring module, an image processing module, a pavement monitoring module, an evaluation module and an alarm module;

and the area monitoring module is used for: the system comprises a body-building area, a wake-up equipment state acquisition module, a body-building monitoring module and a walkway monitoring module, wherein the body-building area is used for monitoring whether a person walks into the body-building area or not, and when the person walks into the body-building area, the wake-up equipment state acquisition module, the body-building monitoring module and the walkway monitoring module are awakened;

the device state acquisition module: the system log is used for acquiring the running state of the exercise equipment through the system log arranged in the exercise equipment when the exercise equipment is started;

and the control warning module is used for: when the running state of the body-building equipment is poor, firstly warning a person using the body-building equipment, then controlling the body-building equipment to stop running, and sending a first warning signal to an administrator;

body-building monitoring module: the system comprises a plurality of dynamic tracking monitoring cameras, a plurality of monitoring cameras and a plurality of monitoring cameras, wherein the dynamic tracking monitoring cameras are used for dynamically tracking personnel entering a body-building area and acquiring gesture data of the body-building personnel;

an image processing module: carrying out real-time image processing on gesture data captured by a camera;

the pavement monitoring module: the system comprises a main road and each branch road used for monitoring a body-building area, and acquiring pavement data of the main road and each branch road;

and an evaluation module: after the attitude data and the pavement data are acquired, analyzing the attitude data and the pavement data based on a risk early warning model, and evaluating whether the gymnasium has safety risks or not;

an alarm module: and when the safety risk of the gym is evaluated, sending a second warning signal to the administrator.

Preferably, the establishing of the risk early warning model includes the following steps:

calculating risk early warning coefficientThe computational expression is:

；

in the method, in the process of the invention,for body-building posture similarity, add>Is the main road occupancy rate->For branch passage index>、/>、/>The body-building posture similarity, the main road occupancy rate and the proportional coefficient of the branch passage index are respectively +.>、/>、/>Are all greater than 0;

acquiring risk early warning coefficientsAfter the value, the risk early warning coefficient is +.>And comparing the value with a preset early warning threshold value to finish the establishment of a risk early warning model.

Preferably, the evaluation module obtains the similarity of the body-building posture, after the walk data includes the main road occupancy rate and the branch road traffic index, marks the body-building posture similarity, the main road occupancy rate and the branch road traffic index as JSZ, ZLY, ZTX respectively, and substitutes the risk early-warning coefficientThe risk early warning coefficient is calculated according to the calculation formula of (2)>Value and early-warning risk coefficientComparing the value with an early warning threshold value;

if risk early warning coefficientThe value is less than the early warning threshold value, and the safety risk of the gym is estimated;

if risk early warning coefficientThe value is more than or equal to the early warning threshold value, and the gymnasium is assessed to have no safety risk.

Preferably, the calculation expression of the fitness posture similarity is:

；

in the method, in the process of the invention,for body-building posture similarity, add>For the learner to use the inner product of the real-time posture vector of the exercise device and the standard posture vector, +.>The real-time attitude vector norm and the standard attitude vector norm are respectively.

Preferably, the real-time attitude vector obtaining step includes:

installing a sensor on the fitness equipment, wherein the sensor comprises a gyroscope, an accelerometer and an Inertial Measurement Unit (IMU) and is used for monitoring the movement of a student in real time and measuring the body direction, the joint angle and the movement track of the student;

the camera system is used for capturing the movement of a student, gesture information comprising the position, angle and movement track of joints is extracted through a computer vision technology, and real-time gesture vectors can be obtained through analyzing images and video streams.

Preferably, the standard posture vector obtaining step includes:

a database is created containing standard poses including various exercise actions, standard pose vectors are recorded, and the exercise trainer standard poses are measured using a sensor system.

Preferably, the calculation expression of the main road occupancy rate is:

；

in the method, in the process of the invention,is the main road occupancy rate->M is the number of obstacles on the main road,represents the floor area of the kth obstacle, < > and->Representing the main road area.

Preferably, the logic for obtaining the branch passage index is:

calculating the discrete degree ZQ of the occupancy rate of all the branches and the average occupancy rate of the obstacle；

If the average occupancy rate of the obstacle is larger than the occupancy threshold value and the discrete degree of the occupancy rate is smaller than or equal to the discrete threshold value, the branch passage index ZTX=1.8;

if the average occupancy rate of the obstacle is larger than the occupancy threshold value and the discrete degree of the occupancy rate is larger than the discrete threshold value, the branch passage index ZTX=1.5;

if the average occupancy rate of the obstacle is less than or equal to the occupancy threshold value and the discrete degree of the occupancy rate is more than the discrete threshold value, the branch passage index ZTX=1.2;

if the average occupancy rate of the obstacle is less than or equal to the occupancy threshold value and the discrete degree of the occupancy rate is less than or equal to the discrete threshold value, the branch passage index ztx=0.6.

Preferably, the calculation expression of the occupancy rate discrete degree ZQ is:

；

in the method, in the process of the invention,，/>indicating the number of branches in the gym +.>Is a positive integer>Representing the occupancy of the obstacle on the ith branch,/->Representing the average occupancy of the obstacle for all branches.

The invention also provides a gymnasium safety risk early warning method based on machine vision, which comprises the following steps:

s1: the monitoring end monitors whether a person walks into the body-building area, and when the person walks into the body-building area, the early warning system is started;

s2: when the exercise equipment is started, the running state of the exercise equipment is obtained through a system log arranged in the exercise equipment, when the running state of the exercise equipment is poor, a person using the exercise equipment is warned, then the exercise equipment is controlled to stop running, and a first warning signal is sent to an administrator;

s3: dynamically tracking personnel entering a body-building area through a dynamic tracking monitoring camera, acquiring gesture data of the body-building personnel, monitoring a main road and each branch road of the body-building area, and acquiring pavement data of the main road and each branch road;

s4: after acquiring gesture data and pavement data, the processing end analyzes the gesture data and the pavement data based on a risk early warning model and evaluates whether the gymnasium has safety risk or not;

s5: and when the safety risk of the gym is evaluated, sending a second warning signal to the administrator.

In the technical scheme, the invention has the technical effects and advantages that:

according to the invention, personnel entering a body-building area is dynamically tracked through the body-building monitoring module, gesture data of the body-building personnel are acquired, the image processing module carries out real-time image processing on the gesture data captured by the camera, the walk monitoring module monitors a main road and each branch road of the body-building area, after the walk data of the main road and each branch road are acquired, the assessment module analyzes the gesture data and the walk data based on the risk early-warning model after acquiring the gesture data and the walk data, and assesses whether the body-building house has safety risk or not, and when the body-building house is assessed to have the safety risk, the alarm module sends a second warning signal to an administrator, and when the administrator receives the second warning signal, the administrator needs to intervene in time to manage the body-building house. The early warning system can comprehensively evaluate whether the gym is used or not based on the machine vision after acquiring multiple items of data inside the gym in the use process of the gym, analysis is more comprehensive, and management of the gym by an administrator is facilitated.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.

FIG. 1 is a block diagram of a system according to the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Example 1: referring to fig. 1, the machine vision-based gymnasium security risk early warning system of the present embodiment includes a region monitoring module, a device status acquisition module, a control warning module, a fitness monitoring module, an image processing module, a walk monitoring module, an evaluation module, and an alarm module;

and the area monitoring module is used for: the system is used for monitoring whether a person walks into the body-building area, and when the person walks into the body-building area, the system wakes up the equipment state acquisition module, the body-building monitoring module and the pavement monitoring module, and comprises the following steps:

and (2) sensor installation: firstly, installing motion or personnel detection sensors around an entrance of a gymnasium and a gymnasium area; these sensors may be infrared sensors, cameras, ultrasonic sensors, or other types of sensors for monitoring the ingress and egress of personnel;

and (3) personnel detection: when a person walks into the body-building area, the sensor detects the existence of the person and triggers a corresponding signal; this signal will be passed to the area monitoring module.

The device state acquisition module: when the exercise equipment is started, the system log arranged in the exercise equipment is used for acquiring the running state of the exercise equipment, and the running state data of the exercise equipment is sent to the control warning module, and the method comprises the following steps of:

and (3) equipment starting detection: the module monitors the starting state of the body-building equipment firstly; this may be accomplished by detecting a power up, start button press, or other related signal of the device;

system log acquisition: upon detecting the start-up of the device, the module will access a system log internal to the device; the system log is data for recording the running condition of equipment in the equipment and comprises operation, fault and warning information of the equipment;

and (3) data extraction: the module extracts data related to the running state of the equipment from the system log; this may include a record of the current operating mode of the device, time of use, speed, inclination, load and any abnormal events;

data analysis: analyzing the extracted data to determine whether the device is in a normal operating state; the module may check whether the device is within specified parameters, whether there are any fault codes or warnings, and whether there is a record of an abnormal event;

abnormality detection: if the module detects that the running state of the equipment has abnormal conditions, the module marks the abnormal conditions and records corresponding information; the abnormal conditions may include equipment failure, abnormal operation, equipment downtime, etc.

And the control warning module is used for: when the running state of the body-building equipment is bad, firstly warning the personnel using the body-building equipment, wherein the warning comprises a voice warning, then controlling the body-building equipment to stop running, sending a first warning signal to an administrator, when the administrator receives the first warning signal, hanging a forbidden mark on the body-building equipment, and sending overhaul information to the overhaul personnel of the body-building equipment, wherein the steps of:

abnormality detection: the module analyzes the running state data to detect whether an abnormal condition exists; this may include equipment downtime, operation outside of safety parameters, fault codes, or other warning signs;

voice warning: if the module detects the running state difference, the module can trigger a voice warning system to warn a person using the exercise equipment through built-in sound equipment; for example, the system may play a warning message that alerts the user to stop moving and leave the device;

and (3) stopping the equipment: meanwhile, the module can send an instruction to the body-building equipment to stop the operation of the body-building equipment; this may be achieved through the control interface of the device to ensure that the device no longer constitutes a potential hazard to the user;

first warning signal: once the equipment is stopped, the module generates a first warning signal and sends the first warning signal to a gym manager or a monitoring system; this signal includes detailed information about the equipment and operating conditions so that the administrator knows the situation;

forbidden use of the identification: after receiving the first warning signal, the administrator should go to the affected exercise equipment immediately and hang the forbidden mark to prevent other users from using the equipment until the problem is solved;

and sending overhaul information: the administrator should also send overhaul information to the overhaul personnel or maintenance team of the equipment to request them to solve the problem as soon as possible; the service information should include detailed description about the problem, equipment model number and location, etc.

Body-building monitoring module: the system comprises a plurality of dynamic tracking monitoring cameras, a camera module and an image processing module, wherein the dynamic tracking monitoring cameras are used for dynamically tracking personnel entering a body-building area and acquiring gesture data of the body-building personnel, and the gesture data are sent to the image processing module and comprise the following steps:

camera configuration: firstly, ensuring that the camera is properly configured to cover a significant portion of the exercise area; the cameras should be installed at various locations to capture panoramic and critical angles for effective dynamic tracking and data acquisition;

and (3) personnel detection: the camera performs dynamic monitoring to detect personnel entering the body-building area; this may involve motion sensors, face recognition, or other techniques to determine the location and identity of the person;

dynamic tracking: once a person is detected, the camera should start to track their motion dynamically; this includes tracking the position, posture and motion trajectory of the person;

and (3) attitude data acquisition: the camera captures gesture data of a user, including joint angles, body positions, action frequencies and the like; these data may be extracted by computer vision techniques.

An image processing module: carrying out real-time image processing on gesture data captured by a camera, and sending the processed gesture data to an evaluation early warning module, wherein the method comprises the following steps of:

and (3) attitude data receiving: the image processing module receives gesture data from the body-building monitoring module, wherein the gesture data comprise joint angles, body positions, movement tracks and the like of a user;

data preprocessing: the module may need to perform some pre-processing on the data, such as denoising, filtering, or coordinate conversion, before processing to ensure accuracy and consistency of the data.

The pavement monitoring module: the method for monitoring the main road and each branch road of the body-building area, acquiring the pavement data of the main road and each branch road, and transmitting the pavement data to the evaluation module comprises the following steps:

camera configuration: firstly, the camera should be correctly configured to cover the main channel and the walkway of the body-building area; the cameras should be mounted in different positions to ensure that the activity of the whole area is captured;

and (3) detecting a pavement: the camera performs dynamic monitoring to detect the activities of personnel on the walkway; this may involve motion sensors, image analysis, or other techniques to determine whether the walkway is being used or not and the use case;

and (3) obtaining pavement data: once personnel activity is detected, the module obtains walkway data including the number of people, direction of movement, speed, and time to occupy the walkway.

And an evaluation module: after the gesture data and the pavement data are acquired, the gesture data and the pavement data are analyzed based on the risk early warning model, whether the gymnasium has safety risk or not is evaluated, and an evaluation result is sent to the alarm module.

An alarm module: when evaluating that the gymnasium has the security risk, send the second warning signal to the administrator, when the administrator received the second warning signal, need in time intervene to manage the gymnasium, the management includes to the management of body-building personnel, includes following steps to the pavement management:

generating a second warning signal: if the assessment module determines that the safety risk exists, the assessment module generates a second warning signal; this signal includes detailed information and describes the situation regarding the risk;

sending an alarm: the second warning signal is sent to an administrator or related staff of the gym; this may be accomplished by email, text messaging, cell phone applications, or other communication means;

administrator intervention: once the administrators receive the alert, they need to quickly intervene in managing the gym; the management measures may comprise the steps of:

a. and (3) managing fitness personnel: the administrator can go to the affected exercise area, interact with the exercise personnel and provide necessary guidance and warnings; they may prompt personnel to stop unsafe activities, provide correct posture demonstration, or assist the victim if necessary;

b. and (3) pavement management: an administrator can go to the pavement area to solve the problem of congestion, guide personnel to safely use the pavement, and ensure that the channel is unobstructed; they can adjust the layout of the facilities and add signs or warnings to improve the safety of the walkway;

dangerous condition treatment: if the administrator finds a dangerous situation, they should immediately take the necessary actions, such as removing unsafe devices, assisting wounded, calling for emergency, or taking other emergency measures;

recording and feedback: the administrator should also record information about the event for subsequent analysis and improvement; they may also provide feedback to gym users to improve their behavior and safety.

According to the method, personnel entering a body-building area are dynamically tracked through the body-building monitoring module, gesture data of body-building personnel are acquired, the image processing module carries out real-time image processing on the gesture data captured by the camera, the walk monitoring module monitors a main road and all branches of the body-building area, after the walk data of the main road and all branches are acquired, the assessment module analyzes the gesture data and the walk data based on the risk early warning model after acquiring the gesture data and the walk data, and whether the gymnasium is safe or not is assessed, when the gymnasium is assessed to be safe, the alarm module sends a second warning signal to an administrator, and when the administrator receives the second warning signal, the administrator needs to intervene in time to manage the gymnasium. The early warning system can comprehensively evaluate whether the gym is used or not based on the machine vision after acquiring multiple items of data inside the gym in the use process of the gym, analysis is more comprehensive, and management of the gym by an administrator is facilitated.

Example 2: after acquiring the attitude data and the pavement data, the assessment module analyzes the attitude data and the pavement data based on the risk early warning model and assesses whether the gymnasium has safety risk or not;

the assessment module acquires gesture data and pavement data, wherein the gesture data comprises body-building gesture similarity, and the pavement data comprises main road occupancy rate and branch road traffic indexes;

the establishment of the risk early warning model comprises the following steps:

calculating risk early warning coefficientThe computational expression is:

；

in the method, in the process of the invention,for body-building posture similarity, add>Is the main road occupancy rate->For branch passage index>、/>、/>The body-building posture similarity, the main road occupancy rate and the proportional coefficient of the branch passage index are respectively +.>、/>、/>Are all greater than 0>Is a base number of natural logarithm, and takes a value of 2.72;

After the evaluation module acquires the body-building posture similarity and the walk data comprise the main road occupancy rate and the branch road traffic index, marking the body-building posture similarity, the main road occupancy rate and the branch road traffic index as JSZ, ZLY, ZTX respectively, and substituting the body-building posture similarity, the main road occupancy rate and the branch road traffic index into the risk early-warning coefficientThe risk early warning coefficient is calculated according to the calculation formula of (2)>Value and risk early warning coefficient +.>Comparing the value with an early warning threshold value;

The risk early warning coefficient is obtained by comprehensively analyzing the body-building posture similarity, the main road occupancy rate and the branch passage indexAfter the value, the risk early warning coefficient is about to be added>The value is compared with a preset early warning threshold value, whether the gymnasium has safety risk or not is estimated according to the comparison result, analysis is more comprehensive, and data processing efficiency is effectively improved.

The calculation expression of the body-building posture similarity is as follows:

；

in the method, in the process of the invention,for body-building posture similarity, add>For the learner to use the inner product of the real-time posture vector of the exercise device and the standard posture vector, +.>The real-time posture vector norm and the standard posture vector norm respectively show that the smaller the similarity of the body-building posture is, the less the action of a student using body-building equipment is, the easier the safety accident is, and the specific expression is as follows:

excessive weightlifting: attempting to lift excessive weight can lead to muscle strain, tendon injury, or joint problems, and furthermore, use of improper loads can lead to imbalance and poor control;

incorrect posture: misalignments in performing actions, such as bending the spine or unstable posture, may increase the risk of injury to the back, neck and joints;

rapid, uncontrolled movement: performing too rapid or uncontrolled movements may lead to falls, collisions with equipment or other persons, increasing the risk of injury;

incorrect balancing: in balance training or using unstable equipment, incorrect balancing may lead to falls or sprains;

beyond the self-capability range: attempting to perform movements beyond its own capabilities or using unfamiliar equipment may lead to runaway and unsafe conditions;

excessive force: overexertion and too frequent training may lead to muscle fatigue, overtraining syndrome and injury;

improper rest and recovery: failure to give the body sufficient rest and recovery time may lead to excessive fatigue, increasing the risk of injury;

pain and discomfort are ignored: ignoring pain, discomfort or warning signals of the body may lead to continued training, further exacerbating the injury;

the real-time attitude vector and the standard attitude vector are obtained as follows:

real-time attitude vector acquisition:

a. a sensor: mounting sensors, such as gyroscopes, accelerometers, inertial Measurement Units (IMUs), etc., on the exercise equipment for monitoring the movements of the trainee in real time, the sensors being capable of measuring the body direction, joint angle and trajectory of the trainee, the real-time attitude vectors being constructed from the data obtained from the sensors;

b. a camera head: capturing the movement of a student by using a camera system, extracting gesture information by using a computer vision technology, wherein the gesture information can comprise the position, the angle and the movement track of a joint, and acquiring a real-time gesture vector by analyzing an image and a video stream;

standard attitude vector acquisition:

a. a library of predefined gestures: creating a database or library containing standard poses, the library comprising standard poses of various exercise actions, which can be defined by professional coaches or professionals in the field of exercise, the standard pose vectors being usually calculated and recorded in advance;

b. sensor measurement: the sensor system may be used to measure a standard posture performed by a professional fitness trainer or expert, and this data may be stored as a standard posture vector for use in subsequent comparison and analysis.

The calculation expression of the main road occupancy rate is as follows:

；

in the method, in the process of the invention,is the main road occupancy rate->M is the number of obstacles on the main road,represents the floor area of the kth obstacle, < > and->Representing a main road area;

the larger the main road occupancy rate is, the more obstacles are on the main road of the gymnasium, the larger the occupied area is, the more escape of students is not facilitated when the safety accident occurs, and the following is expressed:

impeding passage: the obstacle can obstruct the passing path of students, and limit the freedom of movement of the students in the gym; this may lead to congestion, especially during peak hours;

risk of fall: students may fall by tripping or hitting obstacles, especially when they are moving fast or performing high intensity training; this increases the risk of injury;

escape from fire or emergency is difficult: in emergency situations, such as fire or other emergency situations, a main road with a large number of obstacles may prevent a trainee from escaping quickly; the obstruction obstructs access to the emergency exit, increasing the likelihood of injury or entrapment;

ventilation and safety vent are blocked: the obstruction may interfere with the proper functioning of the ventilation system and with accessibility to the safety vent; this can pose a threat to the health and safety of the learner;

difficulty of emergency rescue: in emergency situations, rescue workers may find it difficult to access areas with a large number of obstacles, which may delay the time of emergency rescue.

The acquisition logic of the branch passage index is as follows:

calculating the discrete degree ZQ of the occupancy rate of all the branches, wherein the expression is as follows:

；

in the method, in the process of the invention,，/>indicating the number of branches in the gym +.>Is a positive integer>Representing the occupancy of the obstacle on the ith branch,/->Representing the average occupancy rate of the obstacles of all the branches;

if the average occupancy rate of the obstacle is larger than the occupancy threshold value and the discrete degree of the occupancy rate is smaller than or equal to the discrete threshold value, analyzing that the occupancy rate of the obstacle on all branches in the gymnasium is large, wherein the branch passage index ZTX=1.8;

if the average occupancy rate of the obstacle is larger than the occupancy threshold value and the discrete degree of the occupancy rate is larger than the discrete threshold value, analyzing that the occupancy rate of the obstacle on a branch in the gymnasium is large, but the occupancy rate of the obstacle with part of branches is small, wherein the branch passage index ztx=1.5;

if the average occupancy rate of the obstacle is less than or equal to the occupancy threshold value and the discrete degree of the occupancy rate is more than the discrete threshold value, analyzing that the occupancy rate of the obstacle on a branch in the gymnasium is small, but the occupancy rate of the obstacle with part of branches is large, and the branch passage index ztx=1.2;

if the average occupancy rate of the obstacle is less than or equal to the occupancy threshold value and the discrete degree of the occupancy rate is less than or equal to the discrete threshold value, analyzing that the occupancy rate of the obstacle on all branches in the gymnasium is small, and the branch passage index ztx=0.6.

As can be seen from the logic for obtaining the branch passage index, the greater the branch passage index value, the less the branches are used for passage in the gymnasium, the following problems may be caused:

traffic congestion: if the branches are not sufficiently spacious or confusing, the trainees may obstruct each other while using the device or training, resulting in traffic congestion, which may affect their training efficiency, cause dissatisfaction, and may even result in collisions,

increased risk of injury: branch congestion increases the risk of injury, which may occur to students from collisions, tripping, or other careless actions, especially during peak hours or crowded times,

user satisfaction is reduced: the inconvenience of passing can reduce the satisfaction of the learner, who goes to the gym for exercise, may feel dissatisfied if they encounter difficulty in passing, may even choose to leave,

escape difficulty: in emergency situations, such as fire or other emergency situations, a crowded branch may make it difficult for students to escape quickly, which may threaten their safety,

inconvenience and dissatisfaction: the trainees may be inconvenienced in that they cannot easily use the equipment or area within the gym, which may result in an unsatisfactory and negative experience.

Example 3: the gymnasium safety risk early warning method based on machine vision in the embodiment comprises the following steps:

the monitoring end monitors whether personnel walk into the body-building area or not, when personnel walk into the body-building area, the early warning system is started, when the body-building equipment is started, the running state of the body-building equipment is acquired through a system log arranged in the body-building equipment, when the running state of the body-building equipment is poor, personnel using the body-building equipment are warned, the warning comprises voice warning, then the body-building equipment is controlled to stop running, a first warning signal is sent to an administrator, when the administrator receives the first warning signal, an identification for prohibiting use is hung on the body-building equipment, and overhaul information is sent to an overhaul personnel of the body-building equipment, personnel entering the body-building area are dynamically tracked through a dynamic tracking monitoring camera, gesture data of the body-building personnel are acquired, real-time image processing is carried out on gesture data captured by the camera, a main road and various branches of the body-building area are monitored, and the walk path data of the main road and various branches are acquired, after the gesture data and the walk path data are acquired, whether the body-building house has risks exist or not is estimated based on a risk model analysis gesture data and walk path data, when the exercise house has risks, a second warning signal is sent to the administrator, and the exercise house is required to be managed, and the exercise house intervention risk is warner is received.

The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.

The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.

It should be understood that the term "and/or" is merely an association relationship describing the associated object, and means that three relationships may exist, for example, a and/or B may mean: there are three cases, a alone, a and B together, and B alone, wherein a, B may be singular or plural. In addition, the character "/" herein generally indicates that the associated object is an "or" relationship, but may also indicate an "and/or" relationship, and may be understood by referring to the context.

In the present application, "at least one" means one or more, and "a plurality" means two or more. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b, or c may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.

It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. Gymnasium safety risk early warning system based on machine vision, its characterized in that: the system comprises a region monitoring module, an equipment state acquisition module, a control warning module, a body building monitoring module, an image processing module, a pavement monitoring module, an evaluation module and an alarm module;

an alarm module: when the safety risk of the gym is evaluated, a second warning signal is sent to an administrator;

calculating risk early warning coefficientThe computational expression is: />The method comprises the steps of carrying out a first treatment on the surface of the In (1) the->For body-building posture similarity, add>Is the main road occupancy rate->For branch passage index>The body-building posture similarity, the main road occupancy rate and the proportional coefficient of the branch passage index are respectively +.>Are all greater than 0>Is a base number of natural logarithm, and takes a value of 2.72; acquiring risk early warning coefficient->After the value, the risk early warning coefficient is +.>And comparing the value with a preset early warning threshold value to finish the establishment of a risk early warning model.

2. The machine vision-based gym safety risk early warning system of claim 1, wherein: after the evaluation module acquires the body-building posture similarity and the walk data comprise the main road occupancy rate and the branch road traffic index, marking the body-building posture similarity, the main road occupancy rate and the branch road traffic index as JSZ, ZLY, ZTX respectively, and substituting the body-building posture similarity, the main road occupancy rate and the branch road traffic index into the risk early-warning coefficientThe risk early warning coefficient is calculated according to the calculation formula of (2)>Value and risk early warning coefficient +.>Comparing the value with an early warning threshold value; if the risk early warning coefficient value->An early warning threshold value is less than the early warning threshold value, and the safety risk of the gym is estimated; if the risk early warning coefficient value->And (5) the safety risk of the gymnasium is estimated to be not existed.

3. The machine vision-based gym safety risk early warning system of claim 2, wherein: the calculation expression of the fitness posture similarity is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->For body-building posture similarity, add>For the learner to use the inner product of the real-time posture vector of the exercise device and the standard posture vector, +.>The real-time attitude vector norm and the standard attitude vector norm are respectively.

4. The machine vision-based gym safety risk warning system of claim 3, wherein: the real-time attitude vector obtaining step includes:

5. The machine vision-based gym safety risk warning system of claim 4, wherein: the standard attitude vector obtaining step includes:

6. The machine vision-based gym safety risk early warning system of claim 1, wherein: the calculation expression of the main road occupancy rate is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->As the occupancy rate of the main road,m is the number of obstacles on the main road, < ->Represents the floor area of the kth obstacle, < > and->Representing the main road area.

7. The machine vision-based gym safety risk warning system of claim 5, wherein: the acquisition logic of the branch passage index is as follows:

8. The machine vision-based gym safety risk warning system of claim 7, wherein: the calculation expression of the occupancy rate discrete degree ZQ is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the invention,，/>indicating the number of branches in the gym +.>Is a positive integer>Representing the occupancy of the obstacle on the ith branch,/->Representing the average occupancy of the obstacle for all branches.

9. A gymnasium safety risk early warning method based on machine vision, realized by the early warning system of any one of claims 1-8, characterized in that: the early warning method comprises the following steps: