Production workshop fire control method and system based on image recognition technology
By using high-definition surveillance cameras and behavior recognition algorithms in the production workshop, the behavioral characteristics of production personnel are monitored and analyzed in real time, and the fire identification delay problem caused by failure of traditional temperature monitoring equipment is solved, achieving more accurate and timely fire response.
Patent Information
- Application Number
- CN202411827325.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-12
- Publication Date
- 2025-04-22
AI Technical Summary
Existing temperature monitoring equipment may be at risk of failure in production workshops, resulting in delays or failure in fire identification, resulting in property damage and casualties.
Using an image recognition technology method, by laying a high-definition surveillance camera in the production workshop, monitoring the videos in the workshop in real time, extracting the behavioral characteristics of production personnel, using a preset behavior recognition algorithm to determine whether there is any behavior of evading or extinguishing the fire, generating a fire alarm signal and triggering the fire protection system.
It effectively makes up for the shortcomings of traditional temperature monitoring equipment, improves the accuracy and timeliness of fire response, and ensures timely and reliable identification of fire conditions in the production workshop.
Smart Images
Figure CN119863884A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of fire monitoring, and in particular to a method and system for controlling fire in a production workshop based on image recognition technology. Background Art
[0002] In modern industrial production, the production workshop is a core operating area, and its safety is directly related to the production efficiency of the enterprise and the life safety of employees. In order to prevent the occurrence of fire accidents, temperature monitoring equipment, such as infrared sensors, are usually deployed in the production workshop to monitor the temperature changes in the workshop in real time, and output alarm information when abnormal temperature is detected, so as to trigger the fire protection system to spray and extinguish the fire in time. However, these temperature monitoring devices may have the risk of failure in actual application. Once the equipment fails, it will not be able to accurately identify the fire situation, resulting in delayed or failed alarms, which will cause serious property losses and casualties.
[0003] How to ensure timely and reliable identification of fire conditions in production workshops is a technical problem that urgently needs to be solved. Summary of the invention
[0004] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a production workshop fire control method, system, electronic equipment, computer storage medium and computer program product based on image recognition technology.
[0005] The present invention provides a method for controlling fire in a production workshop based on image recognition technology, the method comprising the following steps: At least one high-definition surveillance camera is deployed in the production workshop to capture real-time high-definition video in the production workshop; Extracting behavioral characteristics of production personnel from the real-time high-definition video, wherein the behavioral characteristics include but are not limited to the personnel's moving speed, moving direction, posture changes, and specific actions; Based on the extracted behavior features, a preset behavior recognition algorithm is used to determine whether the production personnel are avoiding the fire or extinguishing the fire; When the judgment result is yes, a fire alarm signal is generated and sent to the fire protection system control unit. After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection system to sprinkle and extinguish the fire.
[0006] Optionally, before extracting the behavioral features of the production personnel from the real-time high-definition video, the method further includes: Obtain the total number of fire sensors installed in the production workshop and the number of types of installations, and determine the patrol cycle according to the total number of installations and the number of types of installations; Sending a test signal to each fire sensor in the production workshop according to the patrol cycle, evaluating the failure risk of each fire sensor according to a feedback signal corresponding to the test signal, and integrating the failure risks obtained from the evaluation into a comprehensive failure risk; When the comprehensive failure risk is higher than a failure risk threshold, the extraction of behavior characteristics of production personnel from the real-time high-definition video is controlled.
[0007] Optionally, judging whether the production personnel are avoiding fire or extinguishing fire by using a preset behavior recognition algorithm based on the extracted behavior features includes: Preprocessing the extracted behavior features, including denoising, smoothing and normalization, to eliminate the influence of environmental factors and monitoring equipment performance differences on the feature data, and standardizing the feature data into a unified format and range to facilitate subsequent behavior recognition algorithm processing; A behavior recognition algorithm based on deep learning is used to match and recognize the pre-processed behavior features, and a conclusion is drawn on the behavior of production personnel in avoiding or extinguishing fires; The behavior recognition algorithm learns the correlation and distinction between different behavior features by training a large amount of sample data containing fire avoidance and fire extinguishing behaviors.
[0008] Optionally, the behavior recognition algorithm based on deep learning is used to match and recognize the preprocessed behavior features to obtain the behavior probability of the production personnel avoiding the fire or extinguishing the fire, including: A behavior recognition algorithm based on deep learning is used to match and recognize the preprocessed behavior features, and the first behavior probability of the production personnel in avoiding or extinguishing the fire is obtained; Determine an adjustment coefficient value according to the comprehensive fault risk, and calculate a second behavior probability according to the adjustment coefficient value and the first behavior probability; When the second behavior probability is higher than the probability threshold, it is determined that the production personnel have avoided the fire or extinguished the fire; when the second behavior probability is not higher than the probability threshold, it is determined that the production personnel have not avoided the fire or extinguished the fire.
[0009] Optionally, the behavior recognition algorithm is a behavior recognition model built based on a convolutional neural network (CNN).
[0010] Optionally, when the judgment result is yes, a fire alarm signal is generated and sent to a fire protection system control unit. After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection system to spray and extinguish the fire, including: When the judgment result is yes, a fire alarm signal is generated and sent to the fire protection system control unit; wherein the fire alarm signal also includes the fire location; After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection facilities corresponding to the fire location to spray and extinguish the fire.
[0011] The present invention also provides a production workshop fire control system based on image recognition technology, the system includes a processing device and a storage device, the processing device calls and runs a computer program in the storage device to implement the following steps; At least one high-definition surveillance camera is deployed in the production workshop to capture real-time high-definition video in the production workshop; Extracting behavioral characteristics of production personnel from the real-time high-definition video, wherein the behavioral characteristics include but are not limited to the personnel's moving speed, moving direction, posture changes, and specific actions; Based on the extracted behavior features, the preset behavior recognition algorithm is used to determine whether the production personnel are avoiding the fire or extinguishing the fire; When the judgment result is yes, a fire alarm signal is generated and sent to the fire protection system control unit. After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection system to sprinkle and extinguish the fire.
[0012] The present invention also provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute any of the methods described above.
[0013] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, any of the above methods is executed.
[0014] The present invention also provides a computer program product, which includes a computer program stored in a computer storage medium, and when the computer program is executed by a processor of an electronic device, it implements any of the methods described above.
[0015] The beneficial effects of the present invention are at least: The present invention utilizes high-definition video surveillance cameras in combination with behavioral feature analysis technology to achieve real-time monitoring and analysis of personnel behavior in a production workshop. When it is detected that personnel exhibit specific behaviors such as avoiding fire or extinguishing fire, a fire alarm signal is automatically generated and the fire-fighting system is triggered to spray and extinguish the fire, thereby effectively making up for the shortcomings of traditional temperature monitoring equipment and improving the accuracy and timeliness of fire response. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.
[0017] Figure 1 It is a schematic diagram of a method for controlling fire in a production workshop based on image recognition technology disclosed in an embodiment of the present invention.
[0018] Figure 2 It is a schematic diagram of a production workshop fire control system based on image recognition technology disclosed in an embodiment of the present invention. DETAILED DESCRIPTION
[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.
[0020] In order to prevent fire accidents, production workshops are usually equipped with temperature monitoring equipment, such as infrared sensors, to monitor temperature changes in the workshop in real time and output alarm information when abnormal temperature is detected, thereby triggering the fire protection system to spray and extinguish the fire in time. However, these temperature monitoring devices may have the risk of failure in actual application. Once the equipment fails, it will not be able to accurately identify the fire situation, resulting in delayed or ineffective alarms, which in turn causes serious property losses and casualties.
[0021] How to ensure timely and reliable identification of fire conditions in production workshops is a technical problem that urgently needs to be solved.
[0022] For the above technical issues, please refer to Figure 1 The embodiment of the present invention discloses a method for controlling fire in a production workshop based on image recognition technology, the method comprising the following steps: At least one high-definition surveillance camera is deployed in the production workshop to capture real-time high-definition video in the production workshop; Extracting behavioral characteristics of production personnel from the real-time high-definition video, wherein the behavioral characteristics include but are not limited to the personnel's moving speed, moving direction, posture changes, and specific actions; Based on the extracted behavior features, a preset behavior recognition algorithm is used to determine whether the production personnel are avoiding the fire or extinguishing the fire; When the judgment result is yes, a fire alarm signal is generated and sent to the fire protection system control unit. After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection system to sprinkle and extinguish the fire.
[0023] The present invention utilizes high-definition video surveillance cameras in combination with behavioral feature analysis technology to achieve real-time monitoring and analysis of personnel behavior in a production workshop. When it is detected that personnel exhibit specific behaviors such as avoiding fire or extinguishing fire, a fire alarm signal is automatically generated and the fire-fighting system is triggered to spray and extinguish the fire, thereby effectively making up for the shortcomings of traditional temperature monitoring equipment and improving the accuracy and timeliness of fire response.
[0024] It should be noted that the high-definition surveillance camera deployed in the production workshop can be a high-definition surveillance camera in the video surveillance system configured in the production workshop, that is, the existing video surveillance system in the production workshop is fully utilized to monitor the fire. Of course, the high-definition surveillance camera can also be other cameras independent of the video surveillance system, and the present invention does not specifically limit this.
[0025] Optionally, before extracting the behavioral features of the production personnel from the real-time high-definition video, the method further includes: Obtain the total number of fire sensors installed in the production workshop and the number of types of installations, and determine the patrol cycle according to the total number of installations and the number of types of installations; Sending a test signal to each fire sensor in the production workshop according to the patrol cycle, evaluating the failure risk of each fire sensor according to a feedback signal corresponding to the test signal, and integrating the failure risks obtained from the evaluation into a comprehensive failure risk; When the comprehensive failure risk is higher than a failure risk threshold, the extraction of behavior characteristics of production personnel from the real-time high-definition video is controlled.
[0026] In this embodiment, the high-definition surveillance camera in the present invention is in a standby state for monitoring and identifying fire conditions, that is, the fire conditions are usually monitored by fire sensors in the production workshop, and the high-definition surveillance camera is enabled for fire monitoring only when there is a greater risk of failure of the fire sensor.
[0027] Specifically, the total number of fire sensors installed in the production workshop and the number of types of installations are obtained, and the patrol cycle is determined based on the total number of installations and the number of types of installations. Among them, the types of fire sensors include smoke fire detectors, temperature fire detectors, gas fire detectors, composite fire detectors, etc. The duration of the patrol cycle is positively correlated with the total number of installations and the number of types of installations, that is, the more the number and types of fire sensors installed in the production workshop, even if individual fire sensors fail, other fire sensors in the fire sensor system will also detect the fire, and the patrol cycle is set to be longer at this time; conversely, the fewer the number and types of fire sensors installed in the production workshop, the greater the probability of failure of the fire sensor system, and the patrol cycle is set to be shorter at this time to update the comprehensive failure risk in a timely manner.
[0028] In each patrol cycle, a test signal is sent to each fire sensor in the production workshop, and the failure risk of each fire sensor is evaluated according to the feedback signal corresponding to the test signal, and each failure risk obtained by the evaluation is integrated into a comprehensive failure risk. The failure risk assessment can be a rule-based assessment or an assessment based on a preset model (such as a pre-built assessment model based on a deep learning algorithm), which is not specifically limited in the present invention.
[0029] When the comprehensive failure risk is higher than the failure risk threshold, it indicates that the fire monitoring system based on the fire sensor is at risk of failure and may identify the fire in time. At this time, it triggers the extraction of the behavioral characteristics of the production personnel from the real-time high-definition video captured, and then monitors the fire.
[0030] Optionally, judging whether the production personnel are avoiding fire or extinguishing fire by using a preset behavior recognition algorithm based on the extracted behavior features includes: Preprocessing the extracted behavior features, including denoising, smoothing and normalization, to eliminate the influence of environmental factors and monitoring equipment performance differences on the feature data, and standardizing the feature data into a unified format and range to facilitate subsequent behavior recognition algorithm processing; A behavior recognition algorithm based on deep learning is used to match and recognize the pre-processed behavior features, and a conclusion is drawn on the behavior of production personnel in avoiding or extinguishing fires; The behavior recognition algorithm learns the correlation and distinction between different behavior features by training a large amount of sample data containing fire avoidance and fire extinguishing behaviors.
[0031] In this embodiment, the behavior features are preprocessed first. The preprocessing and standardization steps are the key to ensure that the behavior recognition algorithm can run accurately and stably. De-noising and smoothing can reduce the impact of noise and jitter in the surveillance video on the feature data; normalization ensures that the feature data extracted from different surveillance devices and different environments are comparable.
[0032] Then, a behavior recognition algorithm based on deep learning is used to match and identify the preprocessed behavior features to draw conclusions on the production personnel's behavior in avoiding or extinguishing fires. Before the deep learning algorithm is used, it is necessary to train a large amount of sample data containing behaviors of avoiding and extinguishing fires in order to learn the correlation and distinction between different behavior features, and to be able to quickly and accurately judge the current behavior status of production personnel based on the newly extracted feature data.
[0033] Optionally, the behavior recognition algorithm based on deep learning is used to match and recognize the preprocessed behavior features to obtain the behavior probability of the production personnel avoiding the fire or extinguishing the fire, including: A behavior recognition algorithm based on deep learning is used to match and recognize the preprocessed behavior features, and the first behavior probability of the production personnel in avoiding or extinguishing the fire is obtained; Determine an adjustment coefficient value according to the comprehensive fault risk, and calculate a second behavior probability according to the adjustment coefficient value and the first behavior probability; When the second behavior probability is higher than the probability threshold, it is determined that the production personnel have avoided the fire or extinguished the fire; when the second behavior probability is not higher than the probability threshold, it is determined that the production personnel have not avoided the fire or extinguished the fire.
[0034] In this embodiment, the present invention includes two steps to determine whether the production personnel have the behavior of avoiding or extinguishing the fire, namely, firstly using the behavior recognition algorithm to match and identify the pre-processed behavior features to obtain the first behavior probability of the production personnel in avoiding or extinguishing the fire. Then, the adjustment coefficient value is determined based on the comprehensive fault risk, and the adjustment coefficient value is used to calculate the first behavior probability to obtain the second behavior probability. At this point, it can be determined whether the production personnel have the behavior of avoiding or extinguishing the fire based on whether the second behavior probability is higher than the probability threshold.
[0035] Among them, there is a positive correlation between the adjustment coefficient value and the comprehensive fault risk. This setting is that: when the fault risk is greater, the probability of fire sensor failure is greater, and the probability that the fire extinguishing system does not extinguish the fire is greater. Correspondingly, the production personnel at the fire scene will avoid or extinguish the fire more obviously, such as using a fire extinguisher to actively extinguish the fire; conversely, when the fault risk is smaller, the probability of fire sensor failure is smaller, and the probability that the fire extinguishing system does not extinguish the fire is smaller. Correspondingly, due to the intervention of the fire extinguishing system, the production personnel at the fire scene will avoid or extinguish the fire less obviously, such as just holding a fire extinguisher and watching from the side.
[0036] Optionally, the behavior recognition algorithm is a behavior recognition model built based on a convolutional neural network (CNN).
[0037] Optionally, when the judgment result is yes, a fire alarm signal is generated and sent to a fire protection system control unit. After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection system to spray and extinguish the fire, including: When the judgment result is yes, a fire alarm signal is generated and sent to the fire protection system control unit; wherein the fire alarm signal also includes the fire location; After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection facilities corresponding to the fire location to spray and extinguish the fire.
[0038] In this embodiment, when it is determined that the production personnel have not taken any action to avoid or extinguish the fire, a fire alarm signal including the fire location is generated so that the fire protection system control unit controls the fire protection facilities corresponding to the fire location to spray and extinguish the fire.
[0039] See also Figure 2 As shown, an embodiment of the present invention further provides a production workshop fire control system based on image recognition technology, the system comprising a processing device and a storage device, the processing device calls and runs a computer program in the storage device to implement the following steps; At least one high-definition surveillance camera is deployed in the production workshop to capture real-time high-definition video in the production workshop; Extracting behavioral characteristics of production personnel from the real-time high-definition video, wherein the behavioral characteristics include but are not limited to the personnel's moving speed, moving direction, posture changes, and specific actions; Based on the extracted behavior features, the preset behavior recognition algorithm is used to determine whether the production personnel are avoiding the fire or extinguishing the fire; When the judgment result is yes, a fire alarm signal is generated and sent to the fire protection system control unit. After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection system to sprinkle and extinguish the fire.
[0040] An embodiment of the present invention further provides an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the method described in any of the above embodiments.
[0041] An embodiment of the present invention further provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any of the above embodiments is executed.
[0042] An embodiment of the present invention further provides a computer program product, which includes a computer program stored in a computer storage medium, and when the computer program is executed by a processor of an electronic device, the method described in any of the above embodiments is implemented.
[0043] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0044] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0045] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.
[0046] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.
Claims
1. A method for controlling fire in a production workshop based on image recognition technology, characterized in that: The method comprises the following steps: At least one high-definition surveillance camera is deployed in the production workshop to capture real-time high-definition video in the production workshop; Extracting behavioral characteristics of production personnel from the real-time high-definition video, wherein the behavioral characteristics include but are not limited to the personnel's moving speed, moving direction, posture changes, and specific actions; Based on the extracted behavior features, a preset behavior recognition algorithm is used to determine whether the production personnel are avoiding the fire or extinguishing the fire; When the judgment result is yes, a fire alarm signal is generated and sent to the fire protection system control unit. After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection system to sprinkle and extinguish the fire.
2. The method for controlling fire in a production workshop based on image recognition technology according to claim 1, characterized in that: Before extracting the behavioral features of the production personnel from the real-time high-definition video, the method further includes: Obtain the total number of fire sensors installed in the production workshop and the number of types of installations, and determine the patrol cycle according to the total number of installations and the number of types of installations; Sending a test signal to each fire sensor in the production workshop according to the patrol cycle, evaluating the failure risk of each fire sensor according to a feedback signal corresponding to the test signal, and integrating the failure risks obtained from the evaluation into a comprehensive failure risk; When the comprehensive failure risk is higher than a failure risk threshold, the extraction of behavior characteristics of production personnel from the real-time high-definition video is controlled.
3. The method for controlling fire in a production workshop based on image recognition technology according to claim 2, characterized in that: The method of determining whether the production personnel are avoiding fire or extinguishing fire by using a preset behavior recognition algorithm based on the extracted behavior features includes: Preprocessing the extracted behavior features, including denoising, smoothing and normalization, to eliminate the influence of environmental factors and monitoring equipment performance differences on the feature data, and standardizing the feature data into a unified format and range to facilitate subsequent behavior recognition algorithm processing; A behavior recognition algorithm based on deep learning is used to match and recognize the pre-processed behavior features, and a conclusion is drawn on the behavior of production personnel in avoiding or extinguishing fires; The behavior recognition algorithm learns the correlation and distinction between different behavior features by training a large amount of sample data containing fire avoidance and fire extinguishing behaviors.
4. The method for controlling fire in a production workshop based on image recognition technology according to claim 3 is characterized in that: The behavior recognition algorithm based on deep learning is used to match and recognize the pre-processed behavior features to obtain the behavior probability of production personnel avoiding fire or extinguishing fire, including: A behavior recognition algorithm based on deep learning is used to match and recognize the preprocessed behavior features, and the first behavior probability of the production personnel in avoiding or extinguishing the fire is obtained; Determine an adjustment coefficient value according to the comprehensive fault risk, and calculate a second behavior probability according to the adjustment coefficient value and the first behavior probability; When the second behavior probability is higher than the probability threshold, it is determined that the production personnel have avoided the fire or extinguished the fire; when the second behavior probability is not higher than the probability threshold, it is determined that the production personnel have not avoided the fire or extinguished the fire.
5. The method for controlling fire in a production workshop based on image recognition technology according to claim 4, characterized in that: The behavior recognition algorithm is a behavior recognition model built based on a convolutional neural network (CNN).
6. The method for controlling fire in a production workshop based on image recognition technology according to claim 5, characterized in that: When the judgment result is yes, a fire alarm signal is generated and sent to the fire protection system control unit. After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection system to spray and extinguish the fire, including: When the judgment result is yes, a fire alarm signal is generated and sent to the fire protection system control unit; wherein the fire alarm signal also includes the fire location; After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection facilities corresponding to the fire location to spray and extinguish the fire.
7. A production workshop fire control system based on image recognition technology, the system comprising a processing device and a storage device, characterized in that: The processing device calls and runs the computer program in the storage device to implement the following steps: At least one high-definition surveillance camera is deployed in the production workshop to capture real-time high-definition video in the production workshop; Extracting behavioral characteristics of production personnel from the real-time high-definition video, wherein the behavioral characteristics include but are not limited to the personnel's moving speed, moving direction, posture changes, and specific actions; Based on the extracted behavior features, the preset behavior recognition algorithm is used to determine whether the production personnel are avoiding the fire or extinguishing the fire; When the judgment result is yes, a fire alarm signal is generated and sent to the fire protection system control unit. After receiving the fire alarm signal, the fire protection system control unit immediately triggers the fire protection system to sprinkle and extinguish the fire.
8. An electronic device comprising: A memory storing executable program code; A processor coupled to the memory; characterized in that: the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1-6.
9. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is executed.
10. A computer program product, comprising a computer program stored in a computer storage medium, characterized in that: When the computer program is executed by a processor of an electronic device, the method according to any one of claims 1 to 6 is implemented.