High-place operation real-time safety monitoring method and system

Abstract

The invention provides a high-place operation real-time safety monitoring method and system, and belongs to the technical field of monitoring management, and the method specifically comprises the steps: carrying out the determination of an identification deviation risk source in risk sources through employing the error alarm data in a similar historical monitoring scene, determining the image data of different identification deviation risk sources in different operation monitoring images, and carrying out the calculation of the image data, and determining the operation monitoring image issued to the safety management personnel in combination with the distribution data of the identification deviation risk source in the operation monitoring image, taking the operation monitoring image as an issued monitoring image, and carrying out safety risk identification processing by the safety management personnel based on the issued monitoring image and the alarm information of the monitoring device. And when the security risk exists, the alarm information is output to the operator, and the alarm data of different risk sources are utilized to determine whether the switching processing of the lowered monitoring image needs to be carried out, so that the accuracy of the security monitoring processing is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of monitoring and management, and in particular relates to a real-time safety monitoring method and system for operations at height. Background Art

[0002] Due to the special nature of the height and environment of working at height, the consequences of accidents are often very serious.

[0003] Specifically, the invention patent application CN202011197079.3, "An Intelligent Safety Monitoring System for Construction Sites," identifies various safety hazards on construction sites, such as inadequate edge protection, delayed scaffolding erection, workers not wearing safety helmets, workers not wearing safety belts when working at heights, and mixed lifting of long and short materials, thereby improving construction safety. However, analysis reveals the following technical problems: When identifying risks during high-altitude operations, due to the large number of risk sources, how safety management personnel can promptly identify and process operational risks from operation monitoring images and output warning information to operators in a timely manner becomes a technical problem that needs to be solved urgently.

[0004] In response to the above technical problems, this application specifically provides a real-time safety monitoring method and system for high-altitude operations. Summary of the Invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: According to one aspect of the present invention, a method for real-time safety monitoring of operations at heights is provided.

[0006] A real-time safety monitoring method for working at heights, specifically comprising: S1 uses multiple monitoring devices to obtain operation monitoring images of high-altitude operations, determines distribution data of risk sources in different operation monitoring images based on analysis results of the operation monitoring images, and determines matching monitoring images for the risk sources using the distribution data; S2: determining similar historical monitoring scenarios of the risk source based on the image data of the risk source in different matching monitoring images, and determining the identification deviation risk source in the risk source by using the false alarm data in the similar historical monitoring scenarios; S3 determines image data of different identification deviation risk sources in different operation monitoring images, and combines the distribution data of the identification deviation risk sources in the operation monitoring images to determine the operation monitoring images to be delegated to the safety management personnel, and uses them as the delegated monitoring images; S4 The safety manager identifies and processes safety risks based on the decentralized monitoring images and the alarm information of the monitoring device. When there is a safety risk, the alarm information is output to the operating personnel, and the alarm data of different risk sources are used to determine whether it is necessary to switch the decentralized monitoring images.

[0007] The beneficial effects of the present invention are: The image data of different identification deviation risk sources in different operation monitoring images and the distribution data of identification deviation risk sources are used to determine the operation monitoring images delegated to safety management personnel. This takes into account the differences in the image data of identification deviation risk sources in the operation monitoring images, which lead to differences in the identification accuracy of safety management personnel, and also takes into account the differences in the number of identification deviation risk sources. It realizes the determination of operation monitoring images from multiple angles, lays the foundation for safety management personnel to conduct secondary verification of operation safety, and ensures the accuracy of alarm processing.

[0008] The alarm data of different risk sources is used to determine whether it is necessary to switch the decentralized monitoring images, thereby avoiding the technical problem of insufficient reliability of the secondary verification processing of the decentralized monitoring images due to changes in the risk sources. The automatic switching processing of the decentralized monitoring images is realized, the matching of the secondary verification processing is ensured, and the accuracy and reliability of the alarm processing are improved.

[0009] A further technical solution is that the risk sources include guardrails, holes, live intervals, safety helmets, safety belts, and mixed lifting of long and short materials.

[0010] A further technical solution is that the matching monitoring image of the risk source is an operation monitoring image in which the risk source exists.

[0011] A further technical solution is that the method for determining similar historical monitoring scenarios of the risk source is: Determining image sizes and image angles of the risk source in the different matching surveillance images using image data of the risk source in the different matching surveillance images; Determining image data similarity coefficients between historical surveillance scenes and different matching surveillance images based on the image size and image angle; A comprehensive similarity coefficient of the historical monitoring scene is determined based on an average value of image data similarity coefficients with different matching monitoring images, and the comprehensive similarity coefficient is used to determine whether the historical monitoring scene is a similar historical monitoring scene.

[0012] A further technical solution is that the image data similarity coefficient is determined based on an average value of a deviation rate of an image size and a deviation rate of an image angle.

[0013] A further technical solution is that when the comprehensive similarity coefficient of the historical monitoring scene is greater than a preset similarity coefficient threshold, the historical monitoring scene is determined to be a similar historical monitoring scene.

[0014] A further technical solution is to identify and process security risks based on the decentralized monitoring images and the alarm information of the monitoring device, specifically including: When there is no alarm information in the monitoring device, the security manager is used to identify the risk source of the decentralized monitoring image to obtain a risk identification result, and determines whether it is necessary to output an alarm information to the staff according to the risk identification result; When the monitoring device generates an alarm, the security manager identifies the risk source involved in the alarm to obtain a risk identification result, and determines whether to output the alarm to the staff based on the risk identification result.

[0015] A further technical solution is to output warning information to the operator, specifically including: The safety manager transmits the alarm information to the worker's helmet through a wireless sensor device, and the helmet outputs the alarm information to the worker through a vibration device.

[0016] A further technical solution is to determine whether it is necessary to perform the switching process of the decentralized monitoring image, which specifically includes: Based on the alarm data of different risk sources, determine the risk source with the alarm data and use it as the alarm risk source, use the alarm risk source in the decentralized monitoring image as the matching risk source, and use the alarm risk sources other than the matching risk source as other risk sources; Whether the switching process of the decentralized monitoring image needs to be performed is determined based on the number of the other risk sources.

[0017] In a second aspect, the present invention provides a computer system comprising: a memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method for real-time safety monitoring of high-altitude operations when running the computer program.

[0018] Other features and advantages will be described in the following description, and in part will become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and other features and advantages of the present invention will become more apparent by describing in detail example embodiments thereof with reference to the accompanying drawings.

[0021] Figure 1 It is a flow chart of a method for real-time safety monitoring of operations at height; Figure 2 is a flow chart of a method for determining similar historical monitoring scenarios for risk sources; Figure 3 It is a flowchart of the method for identifying deviation risk sources among risk sources; Figure 4 It is a flow chart of a method for determining decentralized monitoring images. DETAILED DESCRIPTION

[0022] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. Like reference numerals in the figures represent like or similar structures, and thus their detailed description will be omitted.

[0023] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "including" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0024] Example 1 To solve the above problems, according to one aspect of the present invention, Figure 1 According to one aspect of the present invention, a method for real-time safety monitoring of high-altitude operations is provided, specifically comprising: S1 uses multiple monitoring devices to obtain operation monitoring images of high-altitude operations, determines distribution data of risk sources in different operation monitoring images based on analysis results of the operation monitoring images, and determines matching monitoring images for the risk sources using the distribution data; Furthermore, the risk sources include guardrails, holes, live intervals, safety helmets, safety belts, and mixed lifting of long and short materials.

[0025] Specifically, the matching monitoring image of the risk source is an operation monitoring image in which the risk source exists.

[0026] S2: determining similar historical monitoring scenarios of the risk source based on the image data of the risk source in different matching monitoring images, and determining the identification deviation risk source in the risk source by using the false alarm data in the similar historical monitoring scenarios; Specifically, such as Figure 2 As shown, the method for determining similar historical monitoring scenarios of the risk source is: Determining image sizes and image angles of the risk source in the different matching surveillance images using image data of the risk source in the different matching surveillance images; Determining image data similarity coefficients between historical surveillance scenes and different matching surveillance images based on the image size and image angle; A comprehensive similarity coefficient of the historical monitoring scene is determined based on an average value of image data similarity coefficients with different matching monitoring images, and the comprehensive similarity coefficient is used to determine whether the historical monitoring scene is a similar historical monitoring scene.

[0027] Furthermore, the image data similarity coefficient is determined based on an average value of a deviation rate of an image size and a deviation rate of an image angle.

[0028] It should also be noted that, when the comprehensive similarity coefficient of the historical monitoring scene is greater than a preset similarity coefficient threshold, the historical monitoring scene is determined to be a similar historical monitoring scene.

[0029] In another embodiment, the method for determining similar historical monitoring scenarios of the risk source is: Obtaining a deviation number between the historical monitoring scene and the number of the matched monitoring images, and when the deviation number does not meet a requirement, determining that the historical monitoring scene does not belong to a similar historical monitoring scene; When the number of deviations meets the requirements: Determining, using the image data of the risk source in different matching surveillance images, image sizes and image angles of the risk source in the different matching surveillance images; determining, based on the image sizes and image angles, image data similarity coefficients between the historical surveillance scene and the different matching surveillance images; and determining that the historical surveillance scene does not belong to a similar historical surveillance scene when the image data similarity coefficients between the historical surveillance scene and the different matching surveillance images are all less than a preset similarity coefficient threshold; When there is a matching surveillance image whose image data similarity coefficient is not less than the preset similarity coefficient threshold: Obtaining image data similarity coefficients between the historical monitoring scene and different matching monitoring images, when an average value of the image data similarity coefficients with different matching monitoring images is less than a preset coefficient threshold: It is determined that the historical monitoring scene does not belong to a similar historical monitoring scene; When the average value of the similarity coefficients of the image data with different matching surveillance images is not within the preset similarity coefficient interval: determining that the historical surveillance scene belongs to a similar historical surveillance scene; When the average similarity coefficients of image data with different matching surveillance images are within the preset similarity coefficient range: The matching surveillance images whose image data similarity coefficient is greater than a preset similarity coefficient threshold are regarded as similar surveillance images. When the proportion of the similar surveillance images in the matching surveillance images is greater than the proportion of the preset images, it is determined that the historical surveillance scene belongs to a similar historical surveillance scene; When the proportion of the similar surveillance images in the matching surveillance images is not greater than the proportion of the preset number of images: The comprehensive similarity coefficient of the historical monitoring scene is determined by the number of similar monitoring images, their proportion in the matching monitoring image, and the average value of the image data similarity coefficients with different matching monitoring images, and the comprehensive similarity coefficient is used to determine whether the historical monitoring scene is a similar historical monitoring scene.

[0030] Furthermore, the false alarm data includes the number of false alarms.

[0031] Specifically, such as Figure 3 As shown, the method for determining the identification deviation risk source among the risk sources is: Determining the number of false alarms in different similar historical monitoring scenarios based on the false alarm data of the risk source in different similar historical monitoring scenarios; Determine the false alarm coefficients for different similar historical monitoring scenarios based on the ratio of the number of false alarms to the monitoring processing time for different similar historical monitoring scenarios; Whether the risk source is an identification deviation risk source is determined according to the average value of the false alarm coefficients of different similar historical monitoring scenarios.

[0032] Furthermore, when the average value of the false alarm coefficients of different similar historical monitoring scenarios is greater than a preset alarm coefficient threshold, the risk source is determined to be an identification deviation risk source.

[0033] Optionally, the method for determining the identification deviation risk source among the risk sources is: Determine the number of false alarms in different similar historical monitoring scenarios based on the false alarm data of the risk source in different similar historical monitoring scenarios; if no false alarms are found in any of the similar historical monitoring scenarios, determine that the risk source does not belong to an identification bias risk source; When there are false alarms in similar historical monitoring scenarios: Similar historical monitoring scenarios with a number of false alarms are considered as false alarm scenarios. When the number of false alarm scenarios is greater than a preset scenario number threshold, it is determined that the risk source belongs to an identification bias risk source. When the number of false alarm scenarios is not greater than the preset scenario number threshold: When there is a false alarm scenario where the number of false alarms is greater than a preset number threshold, it is determined that the risk source is an identification deviation risk source; If there are no false alarms with a frequency greater than the preset threshold: When the number of the false alarm scenarios is not within the preset alarm number range: determining that the risk source does not belong to the identification deviation risk source; When the number of false alarm scenarios is within the preset alarm number range: Determining false alarm coefficients for different similar historical monitoring scenarios based on the ratio of the number of false alarms to the monitoring processing time for different similar historical monitoring scenarios; when the average value of the false alarm coefficients for different similar historical monitoring scenarios is greater than a preset alarm coefficient threshold, determining that the risk source is an identification bias risk source; When the average value of the false alarm coefficients of different similar historical monitoring scenarios is not greater than the preset alarm coefficient threshold, and when the number of similar historical monitoring scenarios with false alarm coefficients greater than the preset alarm coefficient threshold does not meet the requirement, it is determined that the risk source belongs to an identification bias risk source; When the number of similar historical monitoring scenarios whose false alarm coefficient is greater than the preset alarm coefficient threshold meets the requirements, the alarm identification deviation coefficient of the risk source is determined based on the average value of the false alarm coefficients of different similar historical monitoring scenarios and the number of false alarm scenarios, and the alarm identification deviation coefficient is used to determine whether the risk source is an identification deviation risk source.

[0034] It should be noted that the image data for identifying the deviation risk source in different operation monitoring images includes the image sizes in the different operation monitoring images.

[0035] S3 determines image data of different identification deviation risk sources in different operation monitoring images, and combines the distribution data of the identification deviation risk sources in the operation monitoring images to determine the operation monitoring images to be delegated to the safety management personnel, and uses them as the delegated monitoring images; Specifically, such as Figure 4 As shown, the method for determining the decentralized monitoring image is: determining the number of identification deviation risk sources in the operation monitoring image based on the distribution data of the identification deviation risk sources in the operation monitoring image; determining, based on image data of different identification deviation risk sources in the operation monitoring image, image sizes of different identification deviation risk sources in the operation monitoring image, and determining identification reliability coefficients of different identification deviation risk sources using preset identification reliability coefficients corresponding to the image sizes; Based on the sum of the identification reliability coefficients of different identification deviation risk sources in the operation monitoring image, a reliability coefficient sum is determined, and the reliability coefficient sum is used to determine whether the operation monitoring image is a decentralized monitoring image.

[0036] Specifically, the decentralized monitoring image is the operation monitoring image with the largest reliability coefficient.

[0037] Optionally, the method for determining the decentralized monitoring image is: S31 determines the number of identification deviation risk sources in the operation monitoring image based on the distribution data of the identification deviation risk sources in the operation monitoring image, and determines the risk source identification matching coefficient of the operation monitoring image in combination with the number of risk sources in the operation monitoring image; S32: determining the image sizes of the different risk sources in the operation monitoring image based on the image data of the different risk sources in the operation monitoring image, and determining the recognition reliability coefficients of the different risk sources using preset recognition reliability coefficients corresponding to the image sizes; S33 determines the recognition reliability coefficient based on the average value of the recognition reliability coefficients of different recognition deviation risk sources in the operation monitoring image, the average value of the recognition reliability coefficients of different risk sources, and the product of the risk source identification matching coefficient, and uses the recognition reliability coefficient to determine whether the operation monitoring image is a decentralized monitoring image.

[0038] Optionally, the above step S31 includes the following contents: S311 determines the number of risk sources in the operation monitoring image based on the distribution data of the identified deviation risk sources in the operation monitoring image. When the number of risk sources in the operation monitoring image is less than a preset number of risk sources, it is determined that the operation monitoring image does not belong to a decentralized monitoring image. When the number of risk sources in the operation monitoring image is not less than the preset number of risk sources, the process proceeds to step S312. S312 determines the number of identification deviation risk sources in the operation monitoring image. When the proportion of the identification deviation risk sources in the operation monitoring image to the total number of identification deviation risk sources is greater than the proportion of the preset number of risk sources, the process proceeds to step S313. When the proportion of the identification deviation risk sources in the operation monitoring image to the total number of identification deviation risk sources is not greater than the proportion of the preset number of risk sources, it is determined that the operation monitoring image does not belong to a decentralized monitoring image. S313 determines the risk source identification matching coefficient of the operation monitoring image based on the number of identified deviation risk sources in the operation monitoring image and the number of risk sources in the operation monitoring image. When the risk source identification matching coefficient of the operation monitoring image is greater than the preset matching coefficient threshold, the process proceeds to step S32. When the risk source identification matching coefficient of the operation monitoring image is not greater than the preset matching coefficient threshold, it is determined that the operation monitoring image does not belong to a decentralized monitoring image.

[0039] Optionally, the above step S32 includes the following contents: S321 determines the image sizes of different risk sources in the operation monitoring image based on the image data of different risk sources in the operation monitoring image, and uses preset recognition reliability coefficients corresponding to the image sizes to determine the recognition reliability coefficients of different risk sources. When the recognition reliability coefficients of different risk sources are all less than the preset recognition reliability coefficient threshold, it is determined that the operation monitoring image does not belong to the decentralized monitoring image. When there is a risk source whose recognition reliability coefficient is not less than the preset recognition reliability coefficient threshold, the process proceeds to step S322. At step S322, when the identification reliability coefficients of different identification deviation risk sources are all less than the preset identification reliability coefficient threshold, it is determined that the operation monitoring image does not belong to the decentralized monitoring image. When there is an identification deviation risk source whose identification reliability coefficient is not less than the preset identification reliability coefficient threshold, the process proceeds to step S323. S323: When the average value of the identification reliability coefficients of different identification bias risk sources is greater than the preset identification reliability coefficient threshold, the process proceeds to step S324; when the average value of the identification reliability coefficients of different identification bias risk sources is not greater than the preset identification reliability coefficient threshold, the process proceeds to step S33; S324 When the risk source identification matching coefficient of the operation monitoring image is within the preset matching coefficient range, it is determined that the operation monitoring image belongs to a decentralized monitoring image. When the risk source identification matching coefficient of the operation monitoring image is not within the preset matching coefficient range, it proceeds to step S33.

[0040] S4 The safety manager identifies and processes safety risks based on the decentralized monitoring images and the alarm information of the monitoring device. When there is a safety risk, the alarm information is output to the operating personnel, and the alarm data of different risk sources are used to determine whether it is necessary to switch the decentralized monitoring images.

[0041] Furthermore, security risk identification and processing is performed based on the decentralized monitoring images and the alarm information of the monitoring device, specifically including: When there is no alarm information in the monitoring device, the security manager is used to identify the risk source of the decentralized monitoring image to obtain a risk identification result, and determines whether it is necessary to output an alarm information to the staff according to the risk identification result; When the monitoring device generates an alarm, the security manager identifies the risk source involved in the alarm to obtain a risk identification result, and determines whether to output the alarm to the staff based on the risk identification result.

[0042] It should also be noted that the warning information output to the operator includes: The safety manager transmits the alarm information to the worker's helmet through a wireless sensor device, and the helmet outputs the alarm information to the worker through a vibration device.

[0043] Furthermore, determining whether it is necessary to perform the switching process of the decentralized monitoring image specifically includes: Based on the alarm data of different risk sources, determine the risk source with the alarm data and use it as the alarm risk source, use the alarm risk source in the decentralized monitoring image as the matching risk source, and use the alarm risk sources other than the matching risk source as other risk sources; Whether the switching process of the decentralized monitoring image needs to be performed is determined based on the number of the other risk sources.

[0044] In another embodiment, determining whether the switching process of the decentralized surveillance image needs to be performed specifically includes: Based on the alarm data of different risk sources, determine the risk source with the alarm data and use it as the alarm risk source, use the alarm risk source in the decentralized monitoring image as the matching risk source, and use the alarm risk sources other than the matching risk source as other risk sources; Determining an image matching coefficient of the lower monitoring image based on the number of matching risk sources and the number of alarms of different matching risk sources; According to the number of alarm risk sources in different operation monitoring images and the number of alarms of different alarm risk sources, the image matching coefficients of different operation monitoring images are determined, and the image matching coefficients of the lower monitoring image and different operation monitoring images are used to determine whether switching processing of the lower monitoring image is required.

[0045] Specifically, when the image matching coefficient of the lower monitoring image is not the largest, it is determined that the switching process of the lower monitoring image needs to be performed.

[0046] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method for real-time safety monitoring of high-altitude operations when running the computer program.

[0047] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0048] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0049] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A real-time safety monitoring method for high-altitude operations, characterized in that: Specifically include: Using multiple monitoring devices to acquire operation monitoring images of operations at height, determining distribution data of risk sources in different operation monitoring images based on analysis results of the operation monitoring images, and determining matching monitoring images for the risk sources using the distribution data; Determining similar historical monitoring scenarios of the risk source based on image data of the risk source in different matching monitoring images, and determining an identification deviation risk source in the risk source using false alarm data in the similar historical monitoring scenarios; Determine image data of different identification deviation risk sources in different operation monitoring images, and determine the operation monitoring image to be delegated to the safety management personnel based on the distribution data of the identification deviation risk sources in the operation monitoring images, and use the image as the delegated monitoring image; The safety management personnel identify and process safety risks based on the decentralized monitoring images and the alarm information of the monitoring device. When there is a safety risk, the alarm information is output to the operating personnel, and the alarm data of different risk sources are used to determine whether it is necessary to switch the decentralized monitoring images.

2. The real-time safety monitoring method for working at heights according to claim 1, characterized in that: The risk sources include guardrails, holes, live intervals, safety helmets, safety belts, and mixed lifting of long and short materials.

3. The real-time safety monitoring method for working at heights according to claim 1, characterized in that: The matching monitoring image of the risk source is an operation monitoring image in which the risk source exists.

4. The real-time safety monitoring method for working at heights according to claim 1, wherein: The method for determining similar historical monitoring scenarios of the risk source is: Determining image sizes and image angles of the risk source in the different matching surveillance images using image data of the risk source in the different matching surveillance images; Determining image data similarity coefficients between historical surveillance scenes and different matching surveillance images based on the image size and image angle; A comprehensive similarity coefficient of the historical monitoring scene is determined based on an average value of image data similarity coefficients with different matching monitoring images, and the comprehensive similarity coefficient is used to determine whether the historical monitoring scene is a similar historical monitoring scene.

5. The real-time safety monitoring method for working at heights according to claim 4, characterized in that: The image data similarity coefficient is determined based on an average value of a deviation rate of an image size and a deviation rate of an image angle.

6. The real-time safety monitoring method for working at heights according to claim 1, characterized in that: The method for determining the identification deviation risk source among the risk sources is: Determining the number of false alarms in different similar historical monitoring scenarios based on the false alarm data of the risk source in different similar historical monitoring scenarios; Determine the false alarm coefficients for different similar historical monitoring scenarios based on the ratio of the number of false alarms to the monitoring processing time for different similar historical monitoring scenarios; Whether the risk source is an identification deviation risk source is determined according to the average value of the false alarm coefficients of different similar historical monitoring scenarios.

7. The real-time safety monitoring method for working at heights according to claim 6, characterized in that: When the average value of the false alarm coefficients of different similar historical monitoring scenarios is greater than a preset alarm coefficient threshold, the risk source is determined to be an identification deviation risk source.

8. The real-time safety monitoring method for working at heights according to claim 1, wherein: Identifying and processing security risks based on the decentralized monitoring images and the alarm information of the monitoring device specifically includes: When there is no alarm information in the monitoring device, the security manager is used to identify the risk source of the decentralized monitoring image to obtain a risk identification result, and determines whether it is necessary to output an alarm information to the staff according to the risk identification result; When the monitoring device generates an alarm, the security manager identifies the risk source involved in the alarm to obtain a risk identification result, and determines whether to output the alarm to the staff based on the risk identification result.

9. The real-time safety monitoring method for working at heights according to claim 8, characterized in that: Output warning information to operators, including: The safety manager transmits the alarm information to the worker's helmet through a wireless sensor device, and the helmet outputs the alarm information to the worker through a vibration device.

10. A computer system comprising: A memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a real-time safety monitoring method for high-altitude operations as described in any one of claims 1-9 is executed.

Images