CN-119741663-B - Dangerous area identification method and device based on machine vision

CN119741663BCN 119741663 BCN119741663 BCN 119741663BCN-119741663-B

Abstract

The invention provides a dangerous area identification method and device based on machine vision, which relates to the dangerous area identification technical field, the invention forms a training image set through preprocessing by acquiring the monitoring image data of dangerous sources in a history monitoring area, builds a dangerous identification model, trains the dangerous identification model, carries out dangerous source identification on the acquired real-time monitoring image according to the trained dangerous identification model, and according to the identification result, the method comprises the steps of defining a basic dangerous area by setting a basic safety distance, calculating an increase early warning distance of each type of dangerous coefficient according to historical data of each type of static area, calculating a dynamic dangerous coefficient according to historical data of each type of dynamic equipment, calculating a buffer distance in a moving direction, extracting a device characteristic set by acquiring historical dangerous accident data of an area to be identified, and identifying future safety states of the sub-area by establishing a Bayesian prediction model.

Inventors

TONG RUIPENG
LI XIN
WANG LEYAO
WEI ZONGSHUAI
LU JIALE

Assignees

中国矿业大学(北京)

Dates

Publication Date: 20260508
Application Date: 20250305

Claims (7)

1. The dangerous area identification method based on machine vision is characterized by comprising the following specific steps of: Step 1, establishing a space coordinate system, dividing a region to be identified into subareas, installing a camera in the region to be identified, acquiring monitoring image data of a dangerous source in a history monitoring region, wherein the type of the dangerous source comprises a static region, dynamic equipment and staff, forming a training image set through preprocessing, constructing a dangerous identification model, and training the dangerous identification model; step 2, acquiring a real-time monitoring image, identifying a dangerous source through a trained dangerous identification model, acquiring the coordinate position and the area size of the dangerous source according to the identification result, defining a basic dangerous area through setting a basic safety distance, and simultaneously alarming dynamic equipment and staff intruding into the basic dangerous area; Step 3, according to the historical data of each type of static area, acquiring the number of times of danger occurrence of each type of static area, the area size of the area and the existence time of the dangerous area, calculating the danger coefficient of each type, and increasing the early warning distance according to the basic safety distance and the danger coefficient; Step 4, according to the historical data of each type of dynamic equipment, acquiring the historical moving speed, moving distance and area size of the dynamic equipment through continuous identification of the dynamic equipment, calculating a dynamic risk coefficient, and calculating a buffer distance in the moving direction according to the basic safety distance and the dynamic risk coefficient; step 5, acquiring historical dangerous accident data of an area to be identified, including the times of dangerous accidents and the central position coordinates of the dangerous accidents, acquiring the running time, the state and the coordinate position of dynamic equipment and the coordinate position and the working time of equipment operators identified in the historical data, forming an equipment characteristic set, and identifying the future safety state of the sub-area by establishing a Bayesian prediction model; The specific method for defining the basic dangerous area comprises the following steps of; When the dynamic equipment is identified, the basic dangerous area is defined according to the coordinate position of the dynamic equipment, the area size of the area and the state of the dynamic equipment, and when the state of the dynamic equipment is in operation and fails, the basic dangerous area is defined and marked as the dangerous area; the calculation step of increasing the early warning distance according to the basic safety distance and the danger coefficient comprises the following steps: the calculation formula for calculating the risk coefficient of each type of static region is: Wherein, the For the risk factor of each type of static area, In order to take place the number of times of the hazard, Is the first The size of the area of the secondary region, Is the first The time of existence of the secondary hazard zone; the calculation formula of the early warning distance is as follows: Wherein, the In order to pre-warn the distance, As a basis for the safety distance of the vehicle, The pre-warning distance adjustment coefficient, , For the risk factor of each type of static area.
2. The machine vision-based hazardous area identification method of claim 1, wherein said static area comprises an oil stain area, a water stain area, a service area, a heat source area; The method for preprocessing comprises the steps of selecting a dangerous source in a monitoring image through a boundary frame, wherein the boundary frame comprises a coordinate position and an area size, and then inputting label information into the boundary frame, wherein the label information comprises a dangerous source type and a dangerous source state; The dangerous source state comprises a dynamic equipment state and a working personnel state, wherein the dynamic equipment state comprises operation, faults and shutdown, and the working personnel state comprises working and non-working.
3. The machine vision-based hazardous area identification method of claim 1, wherein the hazardous identification model is based on a CNN neural network and comprises a convolution layer, an activation layer, a pooling layer and an output layer; and the convolution layer extracts features from a boundary box and a label of the input image through convolution operation, wherein a calculation formula is as follows: Wherein, the In order to input the matrix of the data, In the form of a convolution kernel, For the coordinates of the output matrix, Convolution kernel at the first Line and th Values of columns; an activation layer: Wherein, the Coordinates of an output matrix output by the convolution layer; pooling layer: Wherein, the In order to input the matrix of the data, Output for maximum pooling; Output layer: Wherein, the For the output of the pooling layer, In order to identify the result of the image, As the weight of the material to be weighed, Is a bias parameter.
4. The machine vision-based dangerous area identification method according to claim 1, wherein the historical moving speed and the moving distance are calculated in the following ways: Wherein, the Is the first The distance of the secondary movement is set to be, Is the first The speed of the secondary movement is determined by, Is the first Before the secondary movement The coordinate position of the moment in time, Is the first Before the secondary movement The coordinate position of the moment.
5. The method for identifying a dangerous area based on machine vision according to claim 4, wherein the calculating the buffer distance before the moving direction according to the basic safety distance and the dynamic dangerous coefficient comprises the following specific steps: The calculation formula of the dynamic risk coefficient is as follows: Wherein, the In order to be a dynamic risk factor, Is the first The speed of the secondary movement is determined by, Is the first The distance of the secondary movement is set to be, The area size of the area is the dynamic equipment; The calculation formula of the buffer distance is as follows: Wherein, the In order to provide a buffer distance, the buffer distance, For the dynamic distance adjustment factor to be used, 。
6. The method for identifying a dangerous area based on machine vision according to claim 1, wherein the specific steps of establishing the comprehensive identification model based on Bayesian are as follows; calculating the safety state of each sub-area according to the coordinates of the dangerous event in the historical data, namely, whether the prior probability of the dangerous event occurs or not: Wherein, the Safety state for subregion Is used to determine the prior probability of (c) for a given channel, For the number of dangerous events occurring in the historical data, As a function of the characteristics of the display, Is the first in the historical data The type of the individual security states is determined, , For the safety status of the sub-areas in the history data, Representing the risk of the occurrence of a dangerous event, Representing that no dangerous event has occurred, Is a smoothing coefficient; the formula of the oscillometric function is: ; Calculating likelihood probabilities for the device feature set in each security state: Wherein, the For a given security state The device feature set is observed below Is a function of the probability of (1), For a given security state The device characteristics are observed below Is a function of the probability of (1), The number of device feature categories; calculating posterior probability of safety state of the subarea according to Bayesian law: Wherein, the Is in a safe state Is used to determine the posterior probability of (1), Is a normalization constant; Establishing a security state decision model: Wherein, the Is the final identified security state.
7. A machine vision-based hazardous area identification device, characterized in that the machine vision-based hazardous area identification device is used for executing the machine vision-based hazardous area identification method according to any one of claims 1 to 6, and comprises the following steps; The image acquisition module is used for acquiring real-time monitoring images of the area to be identified, establishing a space coordinate system, acquiring the number of monitoring images of dangerous source types in the historical monitoring area, wherein the dangerous source types comprise static areas, dynamic equipment and staff, and forming a training image set through preprocessing; The dangerous identification module is used for constructing a dangerous identification model, training the dangerous identification model, identifying a dangerous source in the real-time monitoring image through the trained dangerous identification model, acquiring a coordinate position and an area size corresponding to the dangerous source according to an identification result, defining a basic dangerous area through setting a basic safety distance, and simultaneously alarming dynamic equipment and staff intruding into the basic dangerous area; The safety early warning module is used for acquiring the number of times of danger occurrence in each static area, the area size of the area and the existence time of the dangerous area according to the historical data of each type of static area, calculating the danger coefficient of each type, and increasing the early warning distance according to the basic safety distance and the danger coefficient; The dynamic early warning module is used for acquiring the historical moving speed and moving distance of the dynamic equipment and the area size of the area of the dynamic equipment through continuous identification of the dynamic equipment according to the historical data of each type of dynamic equipment, calculating a dynamic danger coefficient, and calculating the buffer distance of the moving direction according to the basic safety distance and the current moving speed; The area prediction module is used for acquiring historical dangerous accident data of an area to be identified, including the number of dangerous accidents and central position coordinates of the dangerous accidents, acquiring running time, running state and coordinate position of dynamic equipment identified in the historical data, and coordinate position and working time of equipment operators to form an equipment characteristic set, and identifying the area with the dangerous events in the future by establishing a Bayesian prediction model.

Description

Dangerous area identification method and device based on machine vision Technical Field The invention relates to the technical field of dangerous area identification, in particular to a dangerous area identification method and device based on machine vision. Background With the rapid development of industrial automation and intelligent manufacturing, the production process in the factory building is more and more complex and operates at high speed, various processing machines are more and more frequent, handling equipment is more and more complex, the operation environment is complex, and the like, and dangerous areas in the factory building are more and more complex, so that the risk of safety accidents is increased. Therefore, accurate identification of dangerous areas is a key factor in ensuring worker safety, efficient operation of equipment, and smooth progress of production processes. At present, dangerous area identification technology in a factory building mainly depends on traditional safety monitoring means, such as manual inspection and fixed monitoring cameras, and the problems of poor real-time performance, limited coverage range, human factor influence and the like often exist. In addition, with the development of automation and intellectualization technologies, it is highly desirable to improve the recognition ability of dangerous areas by advanced technical means. In the prior art, publication number CN 114973140A discloses a dangerous area personnel intrusion monitoring method and system based on machine vision, which comprises the steps of acquiring a monitoring image of a target construction area, inputting the monitoring image of the target construction area into a preset dangerous object detection model, judging whether a dangerous object exists in the target construction area, acquiring coordinates of the dangerous object if the dangerous object exists in the target construction area, determining the coordinates of the dangerous area according to the coordinates of the dangerous object, identifying personnel coordinates in the monitoring image, comparing the personnel coordinates with the coordinates of the dangerous area, judging whether personnel exist in the dangerous area, and sending an alarm signal to a user if the personnel exist in the dangerous area. But this solution is only aimed at stationary areas, and dynamic mobile devices tend to suffer more injury to personnel during the time course of production, while also lacking the ability to predict dangerous areas. The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art. Disclosure of Invention The invention aims to provide a dangerous area identification method and device based on machine vision, so as to solve the problems in the background technology. In order to achieve the above purpose, the present invention provides the following technical solutions: the dangerous area identification method based on machine vision comprises the following specific steps: Step 1, establishing a space coordinate system, dividing a region to be identified into subareas, installing a camera in the region to be identified, acquiring monitoring image data of a dangerous source in a history monitoring region, wherein the type of the dangerous source comprises a static region, dynamic equipment and staff, forming a training image set through preprocessing, constructing a dangerous identification model, and training the dangerous identification model; step 2, acquiring a real-time monitoring image, identifying a dangerous source through a trained dangerous identification model, acquiring the coordinate position and the area size of the dangerous source according to the identification result, defining a basic dangerous area through setting a basic safety distance, and simultaneously alarming dynamic equipment and staff intruding into the basic dangerous area; Step 3, according to the historical data of each type of static area, acquiring the number of times of danger occurrence of each type of static area, the area size of the area and the existence time of the dangerous area, calculating the danger coefficient of each type, and increasing the early warning distance according to the basic safety distance and the danger coefficient; Step 4, according to the historical data of each type of dynamic equipment, acquiring the historical moving speed, moving distance and area size of the dynamic equipment through continuous identification of the dynamic equipment, calculating a dynamic risk coefficient, and calculating a buffer distance in the moving direction according to the basic safety distance and the dynamic risk coefficient; And 5, acquiring historical dangerous accident data of the area to be identified, including the times of dangerous