A debugging method for an Internet of Things smart factory monitoring system

Abstract

The present invention relates to the technical field of smart factories, and in particular to a debugging method for a monitoring system of an Internet of Things smart factory, comprising: deploying audio sensors in a surrounding shape and at equal intervals at the camera end of a monitoring camera, and sensing the ambient noise audio of the monitoring camera installation environment in real time based on the audio sensors; capturing audio parameters in the ambient noise audio of the monitoring camera installation environment sensed by the audio sensors during operation, and constructing an ambient noise model of the monitoring camera installation environment based on the audio environment parameters; setting an operation trigger threshold for constructing the ambient noise model, and calculating the complexity of the noise audio. The present invention provides an autonomous tracking debugging logic for the monitoring camera by deploying audio sensors, thereby ensuring that the monitoring camera can operate autonomously, effectively reducing the demand for manpower in the operation and maintenance process of the monitoring camera, ensuring that the monitoring camera can be adaptively debugged, implementing effective video monitoring of the smart factory, and providing auxiliary services for the safe production management of the smart factory.

Description

Technical Field

[0001] The present invention relates to the technical field of smart factories, and in particular to a debugging method for a monitoring system of a smart factory of the Internet of Things. Background Art

[0002] Monitoring system debugging is a key step to ensure the normal operation of the system. First, check the equipment connection and power supply status, and then adjust the camera's image quality, field of view, etc. Then test the storage function and data transmission stability, and verify whether the alarm mechanism is sensitive. Through careful debugging, the monitoring system is guaranteed to play an accurate and efficient monitoring role.

[0003] The invention patent with application number 201010136171.9 discloses a debugging method for a monitoring system, which is characterized in that it includes the following steps: a. A calibrator is set up at the monitoring site, and the calibrator is connected to the system loop that needs to be debugged: b. The calibrator performs corresponding output or measurement; c. The calibrator receives the signal value in the corresponding system loop read and sent by the remote operation station: d. The calibrator compares the signal value in the system loop with its own output or measurement value. If the two signal quantities are unstable, it returns to step b. If the two signal quantities are stable, it proceeds to the next step; e. The calibrator sends a signal to the remote operation station that the debugging of the system loop is completed.

[0004] This application aims to solve the problem that "since the monitoring device of the operating station is a certain distance away from the on-site measurement and control points, at least two people are needed to check the system loop. One person is responsible for transmitting signals to the system loop on site, while the other person is at the operating station to observe the correctness of the data collected and displayed by the system. If the control system has more monitoring loops, more personnel will be needed to debug, which requires a lot of manpower costs and debugging time. In addition, if the operating station is far away from the site, it is necessary to communicate with hardware equipment such as walkie-talkies or other communication tools, which further increases the debugging and maintenance costs of the system."

[0005] However, the current monitoring system in smart factories consists of multiple surveillance cameras deployed in different roads and passages to implement real-time monitoring, but the monitoring angle of the surveillance camera is often limited. Although the monitoring angle can be adjusted remotely, it does not have intelligent monitoring angle adjustment logic.

[0006] To this end, we propose a debugging method for IoT smart factory monitoring system. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides a debugging method for an Internet of Things smart factory monitoring system, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A debugging method for an Internet of Things smart factory monitoring system, comprising:

[0010] Audio sensors are deployed equidistantly in a surrounding shape at the camera end of the surveillance camera, and the audio of the surveillance camera installation environment is perceived in real time based on the audio sensors; the audio parameters in the surveillance camera installation environment noise audio perceived by the audio sensor are captured, and the surveillance camera installation environment noise model is constructed based on the audio environment parameters; the environmental noise model construction operation trigger threshold is set, the noise audio complexity is calculated, and whether to construct the environmental noise model is determined by comparing the noise audio complexity with the trigger threshold, and the environmental noise model is constructed when the noise audio complexity meets the trigger threshold; the surveillance camera installation environment noise model is traversed, two points are captured on the environmental noise model as the surveillance camera debugging direction, and the debugging distance is determined based on the captured two points simultaneously; the surveillance camera debugging direction, debugging distance and two points captured on the environmental noise model are obtained, and the focal length debugging logic is set in combination with the debugging direction, debugging distance and capturing points; the surveillance camera is debugged according to the debugging direction, debugging distance and focal length debugging logic, and the surveillance camera is set to be silent for a period of time, so as to control the surveillance camera to no longer perform debugging operations within the time domain corresponding to the silent period after each debugging is completed.

[0011] Furthermore, the number of audio sensors deployed on the camera end of the surveillance camera is customized by the system end user, or is subject to:

[0012]

[0013] Where: m is the number of audio sensors designed to be deployed; w is the width of the road where the surveillance camera is deployed; q is the total number of trunk roads and branch roads connected to the road where the surveillance camera is deployed; χ is a constant; m′ is the final number of audio sensors to be deployed;

[0014] in, represents the correction function, which will Convert to decimal form and round up. max(4,m) means taking the maximum value in the brackets. The constant χ is defined by the user.

[0015] Furthermore, the audio parameters in the noise audio include: frequency, bandwidth, volume, and dynamic range, and the noise audio is the noise audio captured by each group of audio sensors from the same surveillance camera in the latest second;

[0016] Among them, after the environmental noise model is built, the environmental noise model is further stored, and during the storage stage, the environmental noise model is synchronously marked, and the marking content is the acquisition timestamp of the noise audio of the audio parameter source used to build the environmental noise model.

[0017] Furthermore, after the audio parameters in the noise audio are captured, the audio parameters are synchronously converted into construction parameters of the environmental noise model, and the conversion process is:

[0018]

[0019] Where: C is the complexity of the noise audio; F is the difference between the maximum and minimum values ​​of the audio frequency; B is the bandwidth; σ v is the standard deviation of the noise audio volume; V max is the maximum volume range of the audio sensor; DR is the dynamic range of the noise audio;

[0020] The noise audio complexity is calculated according to the audio parameters based on the above formula, and the noise audio complexity is used as a construction parameter of the environmental noise model.

[0021] Furthermore, after obtaining the environmental noise model construction parameters corresponding to all audio sensors on the surveillance camera, an environmental noise model construction surface is further set, and the environmental noise model construction surface is stretched using the environmental noise model construction parameters to construct the environmental noise model;

[0022] Among them, the shape and specifications of the environmental noise model construction surface are customized by the user end, the number of environmental noise model construction surfaces is equal to the number of audio sensors deployed on the health camera, and the distribution state of the environmental noise model construction surface is consistent with the deployment posture of the audio sensor relative to the monitoring camera.

[0023] Furthermore, the noise audio complexity used in deciding whether to construct an environmental noise model is the maximum value in C corresponding to each audio sensor from the same surveillance camera.

[0024] Furthermore, the two points captured on the environmental noise model are the center points of the surface with the largest distance from the construction surface of the environmental noise model and the surface with the smallest distance from the construction surface of the environmental noise model on the stretching forming surface of the environmental noise model. The two groups of center points are connected to each other, namely the debugging path. Among the two groups of endpoints of the debugging path, the direction from the point closest to the construction surface of the environmental noise model to the point farthest from the construction surface of the environmental noise model is the debugging direction.

[0025] The debugging distance of the surveillance camera is:

[0026]

[0027] Where: L is the monitoring camera debugging distance; α is the normalization factor; d max is the maximum distance from the capture point to the construction surface of the environmental noise model; d min The minimum distance from the capture point to the construction surface of the environmental noise model;

[0028] Among them, when the monitoring camera debugging distance L is greater than the remaining limit debuggable distance of the monitoring camera, the monitoring camera is adjusted to the limit position along the debugging direction.

[0029] Furthermore, the focus adjustment logic of the surveillance camera is expressed as:

[0030] During the debugging process of the surveillance camera based on the debugging direction and debugging distance, the current focal length of the surveillance camera is uniformly adjusted to the minimum focal length, and then uniformly adjusted from the minimum focal length to the original focal length;

[0031] Among them, the focus adjustment speed is set to:

[0032] Where: l now is the current focal length of the surveillance camera; l min is the minimum focal length of the surveillance camera; t is the time required for the surveillance camera to complete debugging based on the debugging distance.

[0033] Furthermore, the silence duration of the surveillance camera is customized by the user, and the surveillance camera executes the deletion operation of the noise audio perceived by the audio sensor in real time within the time domain corresponding to the silence duration after each debugging is completed.

[0034] Furthermore, the monitoring camera is installed on the factory wall through an electric universal shaft, and the monitoring camera completes the adjustment of direction and distance based on the electric universal shaft;

[0035] There are several groups of surveillance cameras, and the surveillance videos of the several groups of surveillance cameras are transmitted to the same display in real time and displayed centrally on the display. When the surveillance videos of each surveillance camera are displayed on the display in real time, priority queues are sorted based on the debugging of the surveillance cameras, so that the surveillance videos of the surveillance cameras that have recently performed debugging operations are played synchronously in the front display position on the display.

[0036] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0037] The present invention provides a debugging method for an Internet of Things smart factory monitoring system. During the execution of the method, an audio sensor is deployed to provide a monitoring camera with autonomous tracking debugging logic, thereby ensuring that the monitoring camera can operate autonomously, effectively reducing the demand for manpower in the operation and maintenance process of the monitoring camera, ensuring that the monitoring camera can be adaptively debugged, implementing effective video monitoring of the smart factory, and providing auxiliary services for the safe production management of the smart factory. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 It is a flowchart of a debugging method of an IoT smart factory monitoring system;

[0040] Figure 2 This is a schematic diagram of an example of an environmental noise model for installing a surveillance camera in the present invention. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] The present invention will be further described below in conjunction with the embodiments.

[0043] Embodiment 1:

[0044] A debugging method for an IoT smart factory monitoring system of this embodiment is as follows: Figure 1 As shown, including:

[0045] Step 1: Deploy audio sensors at equal distances around the camera end of the surveillance camera, and use the audio sensors to perceive the noise audio in the surveillance camera installation environment in real time;

[0046] The number of audio sensors deployed on the camera side of the surveillance camera is customized by the system user or follows:

[0047]

[0048] Where: m is the number of audio sensors designed to be deployed; w is the width of the road where the surveillance camera is deployed; q is the total number of trunk roads and branch roads connected to the road where the surveillance camera is deployed; χ is a constant; m′ is the final number of audio sensors to be deployed;

[0049] in, represents the correction function, which will Convert to decimal form and round up. max(4,m) means taking the maximum value in the brackets. The constant χ is defined by the user.

[0050] Step 2: Capture audio parameters in the surveillance camera installation environment noise audio perceived by the audio sensor, and build a surveillance camera installation environment noise model based on the audio environment parameters;

[0051] After the audio parameters in the noise audio are captured, the audio parameters are converted into the construction parameters of the environmental noise model synchronously. The conversion process is:

[0052]

[0053] Where: C is the complexity of the noise audio; F is the difference between the maximum and minimum values ​​of the audio frequency; B is the bandwidth; σ v is the standard deviation of the noise audio volume; V max is the maximum volume range of the audio sensor; DR is the dynamic range of the noise audio;

[0054] Wherein, the noise audio complexity is calculated according to the audio parameters based on the above formula, and the noise audio complexity is used as a construction parameter of the environmental noise model;

[0055] Step 21: setting a trigger threshold for building an environmental noise model, calculating the complexity of the noise audio, and deciding whether to build an environmental noise model based on the comparison between the complexity of the noise audio and the trigger threshold. When the complexity of the noise audio meets the trigger threshold, building the environmental noise model;

[0056] The noise audio complexity used in deciding whether to build an environmental noise model is the maximum value of C corresponding to each audio sensor from the same surveillance camera;

[0057] Step 3: Traverse the environmental noise model of the surveillance camera installation, capture two points on the environmental noise model as the surveillance camera debugging direction, and simultaneously determine the debugging distance based on the captured two points;

[0058] The two points captured on the environmental noise model are the center points of the surface with the largest distance and the surface with the smallest distance from the construction surface of the environmental noise model on the stretching forming surface of the environmental noise model. The two sets of center points are connected to each other, which is the debugging path. Among the two sets of endpoints of the debugging path, the direction from the point closest to the construction surface of the environmental noise model to the point farthest from the construction surface of the environmental noise model is the debugging direction.

[0059] The debugging distance of the surveillance camera is:

[0060]

[0061] Where: L is the monitoring camera debugging distance; α is the normalization factor; d max is the maximum distance from the capture point to the construction surface of the environmental noise model; d min The minimum distance from the capture point to the construction surface of the environmental noise model;

[0062] When the monitoring camera debugging distance L is greater than the remaining limit debugging distance of the monitoring camera, the monitoring camera is adjusted to the limit position along the debugging direction;

[0063] The focus debugging logic of the surveillance camera is expressed as:

[0064] During the debugging process of the surveillance camera based on the debugging direction and debugging distance, the current focal length of the surveillance camera is uniformly adjusted to the minimum focal length, and then uniformly adjusted from the minimum focal length to the original focal length;

[0065] Among them, the focus adjustment speed is set to:

[0066] Where: l now is the current focal length of the surveillance camera; l min is the minimum focal length of the surveillance camera; t is the time required for the surveillance camera to complete debugging based on the debugging distance;

[0067] Step 4: Obtain the monitoring camera's debugging direction, debugging distance, and two points captured on the environmental noise model, and set the focal length debugging logic based on the debugging direction, debugging distance, and capture points;

[0068] Step 5: Debug the surveillance camera according to the debugging direction, debugging distance and focal length debugging logic, set the surveillance camera silent time, and control the surveillance camera not to perform debugging operations within the time domain corresponding to the silent time after each debugging is completed.

[0069] In this embodiment, the method in the above embodiment provides an adaptive monitoring system debugging method for the smart factory, effectively realizes the intelligent control of the monitoring camera, and makes the monitoring images collected by the daily operation of the monitoring system in the smart factory more "valuable" and applied to the production safety management of the smart factory;

[0070] See also Figure 2 As shown, the figure further illustrates an example of the noise model of the environment in which the surveillance camera is installed, and further illustrates the debugging direction of the surveillance camera with the line segments and arrows on the line segments in the figure.

[0071] Embodiment 2:

[0072] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The debugging method of an IoT smart factory monitoring system in Example 1 is further described in detail:

[0073] The audio parameters of the noise audio include: frequency, bandwidth, volume, and dynamic range. The noise audio is the noise audio captured by the audio sensor of the same surveillance camera in the latest second.

[0074] After the environmental noise model is constructed, the environmental noise model is further stored, and during the storage stage, the environmental noise model is synchronously marked, and the marking content is the acquisition timestamp of the noise audio of the audio parameter source used for constructing the environmental noise model;

[0075] After obtaining the environmental noise model construction parameters corresponding to all audio sensors on the surveillance camera, further setting the environmental noise model construction surface, and applying the environmental noise model construction parameters to stretch the environmental noise model construction surface to construct the environmental noise model;

[0076] The shape and specifications of the environmental noise model construction surface are customized by the user, the number of environmental noise model construction surfaces is equal to the number of audio sensors deployed on the health camera, and the distribution state of the environmental noise model construction surface is consistent with the deployment posture of the audio sensor relative to the monitoring camera;

[0077] The silent duration of the surveillance camera is customized by the user. The surveillance camera executes the deletion operation of the noise audio perceived by the audio sensor in real time within the time domain corresponding to the silent duration after each debugging.

[0078] In this embodiment, through the above settings, the execution of the method in Example 1 is provided with logic and data support for step execution, ensuring the stable execution of the method in Example 1 and intelligently debugging the monitoring system of the smart factory.

[0079] like Figure 1 As shown, the surveillance camera is installed on the factory wall through an electric universal shaft, and the surveillance camera completes the adjustment of direction and distance based on the electric universal shaft;

[0080] There are several groups of surveillance cameras, and the surveillance videos of the several groups of surveillance cameras are transmitted to the same display in real time and displayed centrally on the display. When the surveillance videos of each surveillance camera are displayed on the display in real time, priority queues are sorted based on the debugging of the surveillance cameras, so that the surveillance videos of the surveillance cameras that have recently performed debugging operations are played synchronously at the front display position on the display.

[0081] Based on the above settings, the execution conditions of the method in Example 1 are further improved, and based on the debugging of the monitoring camera, a logical coordination control effect is brought to the display feedback of the monitoring image, further optimizing the debugging effect of this solution on the monitoring system in the smart factory.

[0082] In summary, during the execution of the method in the above embodiment, by deploying audio sensors, the surveillance camera is provided with autonomous tracking debugging logic, ensuring that the surveillance camera can operate autonomously, effectively reducing the demand for manpower in the operation and maintenance process of the surveillance camera, ensuring that the surveillance camera can be adaptively debugged, implementing effective video surveillance of smart factories, and providing auxiliary services for the safe production management of smart factories.

[0083] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A debugging method for an Internet of Things smart factory monitoring system, characterized in that: include: Step 1: Deploy audio sensors at equal distances around the camera end of the surveillance camera, and use the audio sensors to perceive the noise audio in the surveillance camera installation environment in real time; Step 2: Capture audio parameters in the surveillance camera installation environment noise audio perceived by the audio sensor, and build a surveillance camera installation environment noise model based on the audio environment parameters; Step 21: setting a trigger threshold for building an environmental noise model, calculating the complexity of the noise audio, and deciding whether to build an environmental noise model based on the comparison between the complexity of the noise audio and the trigger threshold. When the complexity of the noise audio meets the trigger threshold, building the environmental noise model; After the audio parameters in the noise audio are captured, the audio parameters are synchronously converted into construction parameters of the environmental noise model. The conversion process is as follows: Where: C is the complexity of the noise audio; F is the difference between the maximum and minimum values ​​of the audio frequency; B is the bandwidth; σ v is the standard deviation of the noise audio volume; V max is the maximum volume range of the audio sensor; DR is the dynamic range of the noise audio; Wherein, the noise audio complexity is calculated according to the audio parameters based on the above formula, and the noise audio complexity is used as a construction parameter of the environmental noise model; Step 3: Traverse the environmental noise model of the surveillance camera installation, capture two points on the environmental noise model as the surveillance camera debugging direction, and simultaneously determine the debugging distance based on the captured two points; Step 4: Obtain the monitoring camera's debugging direction, debugging distance, and two points captured on the environmental noise model, and set the focal length debugging logic based on the debugging direction, debugging distance, and capture points; Step 5: Debug the surveillance camera according to the debugging direction, debugging distance and focal length debugging logic, set the surveillance camera silent time, and control the surveillance camera not to perform debugging operations within the time domain corresponding to the silent time after each debugging is completed.

2. The debugging method of the Internet of Things smart factory monitoring system according to claim 1 is characterized in that: The number of audio sensors deployed on the camera end of the surveillance camera is customized by the system end user, or is subject to: Where: m is the number of audio sensors designed to be deployed; w is the width of the road where the surveillance camera is deployed; q is the total number of trunk roads and branch roads connected to the road where the surveillance camera is deployed; χ is a constant; m′ is the final number of audio sensors to be deployed; in, represents the correction function, which will Convert to decimal form and round up. max(4,m) means taking the maximum value in the brackets. The constant χ is defined by the user.

3. The debugging method of an Internet of Things smart factory monitoring system according to claim 1 is characterized in that: The audio parameters in the noise audio include: frequency, bandwidth, volume, and dynamic range. The noise audio is the noise audio captured by the audio sensor of each group of the same surveillance camera in the latest second; Among them, after the environmental noise model is built, the environmental noise model is further stored, and during the storage stage, the environmental noise model is synchronously marked, and the marking content is the acquisition timestamp of the noise audio of the audio parameter source used to build the environmental noise model.

4. The debugging method of the Internet of Things smart factory monitoring system according to claim 1 is characterized in that: After obtaining the environmental noise model construction parameters corresponding to all audio sensors on the surveillance camera, further setting the environmental noise model construction surface, and applying the environmental noise model construction parameters to stretch the environmental noise model construction surface to construct the environmental noise model; Among them, the shape and specifications of the environmental noise model construction surface are customized by the user end, the number of environmental noise model construction surfaces is equal to the number of audio sensors deployed on the health camera, and the distribution state of the environmental noise model construction surface is consistent with the deployment posture of the audio sensor relative to the monitoring camera.

5. The debugging method of the Internet of Things smart factory monitoring system according to claim 1 is characterized in that: The noise audio complexity used in deciding whether to build an environmental noise model is the maximum value of C corresponding to each audio sensor from the same surveillance camera.

6. The debugging method of an Internet of Things smart factory monitoring system according to claim 1 is characterized in that: The two points captured on the environmental noise model are the center points of the surface with the largest distance and the surface with the smallest distance from the construction surface of the environmental noise model on the stretching forming surface of the environmental noise model. The two sets of center points are connected to each other, which is the debugging path. Among the two sets of endpoints of the debugging path, the direction from the point closest to the construction surface of the environmental noise model to the point farthest from the construction surface of the environmental noise model is the debugging direction. The debugging distance of the surveillance camera is: Where: L is the monitoring camera debugging distance; α is the normalization factor; d max The maximum distance from the capture point to the construction surface of the environmental noise model; d min The minimum distance from the capture point to the construction surface of the environmental noise model; Among them, when the monitoring camera debugging distance L is greater than the remaining limit debuggable distance of the monitoring camera, the monitoring camera is adjusted to the limit position along the debugging direction.

7. The debugging method of an Internet of Things smart factory monitoring system according to claim 1 is characterized in that: The focus debugging logic of the surveillance camera is expressed as: During the debugging process of the surveillance camera based on the debugging direction and debugging distance, the current focal length of the surveillance camera is uniformly adjusted to the minimum focal length, and then uniformly adjusted from the minimum focal length to the original focal length; Among them, the focus adjustment speed is set to: Where: l now is the current focal length of the surveillance camera; l min is the minimum focal length of the surveillance camera; t is the time required for the surveillance camera to complete debugging based on the debugging distance.

8. The debugging method of an Internet of Things smart factory monitoring system according to claim 1 is characterized in that: The silence duration of the surveillance camera is customized by the user end. The surveillance camera executes the deletion operation of the noise audio perceived by the audio sensor in real time within the time domain corresponding to the silence duration after each debugging is completed.

9. The debugging method of an Internet of Things smart factory monitoring system according to claim 1 is characterized in that: The monitoring camera is installed on the factory wall through an electric universal shaft, and the monitoring camera completes the adjustment of direction and distance based on the electric universal shaft; There are several groups of surveillance cameras, and the surveillance videos of the several groups of surveillance cameras are transmitted to the same display in real time and displayed centrally on the display. When the surveillance videos of each surveillance camera are displayed on the display in real time, priority queues are sorted based on the debugging of the surveillance cameras, so that the surveillance videos of the surveillance cameras that have recently performed debugging operations are played synchronously in the front display position on the display.

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