CN118116157A - Safety early warning method and system for outdoor communication operation - Google Patents
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Abstract
The invention relates to a safety pre-warning method and a system for outdoor communication operation, wherein the method comprises the following steps: acquiring monitoring data of a communication operation area acquired by a sensor network, and transmitting the monitoring data to a centralized processing node; performing first fusion processing on the monitoring data through the centralized processing node to obtain a fusion image; processing the fusion image through a centralized processing node to obtain a corresponding output feature map, a gradient amplitude value and a gradient direction angle; performing second fusion processing on the output feature map, the gradient amplitude and the gradient direction angle to obtain corresponding fusion feature vectors; taking a plurality of fusion feature vectors as samples, and carrying out safety detection through a support vector machine model to obtain corresponding safety evaluation categories and safety evaluation scores; the fusion feature vectors correspond to different acquisition moments; and carrying out corresponding-level safety early warning on personnel in the communication operation area based on the safety evaluation category and the safety evaluation score. The invention can improve the accuracy of the safety pre-warning of the outdoor communication operation.
Description
Technical Field
The present invention relates to the field of communication security technologies, and in particular, to a security early warning method, system, electronic device, and non-transitory computer readable storage medium for outdoor communication operation.
Background
When outdoor communication operation is carried out, whether a temporary base station is built in the field or communication equipment is maintained and debugged in outdoor environments such as mountain areas, seasides and the like, it is important to ensure personal safety of communication staff. Currently, the safety early warning method for these outdoor communication operators mainly depends on the purchase of specialized early warning equipment, such as a portable video monitoring system, etc., to monitor the surrounding environment and send out an audible and visual alarm when abnormal conditions are found, so as to prevent potential safety threats to a certain extent, such as accidental falling of the operators, malfunction of the working equipment, and various dangerous conditions such as sudden natural disasters encountered during operation.
However, these specialized warning devices have several drawbacks, including additional purchase requirements, limited warning range, and insufficient warning accuracy. In some cases, although not illegally intrusive, it may pose a threat to the security of the communication personnel, and existing security early warning methods may not be able to timely and effectively discover and cope with these potential threats, thus causing the communication personnel to face a huge security risk.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a safety early warning method, a system, electronic equipment and a non-transitory computer readable storage medium for outdoor communication operation, which can improve the accuracy of safety early warning for the outdoor communication operation.
The technical scheme for solving the technical problems is as follows:
the invention provides a safety pre-warning method for outdoor communication operation, which comprises the following steps:
Acquiring monitoring data of a communication operation area acquired by a sensor network, and transmitting the monitoring data to a centralized processing node; the monitoring data comprises image data, sound data and temperature data;
Performing first fusion processing on the monitoring data through the centralized processing node to obtain a fusion image;
processing the fusion image through the centralized processing node to obtain a corresponding output feature image, a gradient amplitude and a gradient direction angle;
performing second fusion processing on the output feature map, the gradient amplitude and the gradient direction angle to obtain corresponding fusion feature vectors;
Taking a plurality of fusion feature vectors as samples, and carrying out safety detection through a support vector machine model to obtain corresponding safety evaluation categories and safety evaluation scores; the fusion feature vectors correspond to different acquisition moments;
And carrying out corresponding-level safety early warning on the personnel in the communication operation area based on the safety evaluation category and the safety evaluation score.
Optionally, the performing, by the centralized processing node, a first fusion process on the monitored data to obtain a fused image includes:
converting the sound data into a corresponding spectrogram, and converting the temperature data into a corresponding temperature thermodynamic diagram;
And superposing and fusing the spectrogram and the temperature thermodynamic diagram with the image data to obtain the fused image.
Optionally, the processing, by the centralized processing node, the fused image to obtain a corresponding output feature map, gradient amplitude and gradient direction angle includes:
performing feature extraction of convolution operation on the fusion image to obtain the output feature map;
And carrying out edge detection on the fusion image, and calculating corresponding gradient amplitude and gradient direction angle.
Optionally, the expression for performing convolution operation on the fused image is:
；
Wherein O is the output feature map, m and n represent the sizes of the convolution kernels in the horizontal and vertical directions, k represents the number of channels of the input fusion image, c represents the number of channels of the output feature map, I and j represent pixel positions in the output feature map O, O (I, j, c) represents the values of the output feature map O at positions (I, j) and channel c, I is the input image, and I (I-m, j-n, k) represents the pixel values of the input image I at positions (I-m, j-n) and channel k.
Optionally, the gradient magnitude is expressed as:
；
wherein G is the gradient amplitude and is used for representing the edge intensity of the fusion image; and/> Representing gradient values of the fused image in the horizontal direction and the vertical direction, respectively.
Alternatively, the gradient direction angle is expressed as:
；
wherein θ represents the gradient direction angle, and represents the direction of the edge of the fused image, i.e. the slope angle of the edge; arctan2 is an arctangent function used to calculate the gradient value And/>And returns to the angle in radians.
Optionally, the performing security detection by using a plurality of fusion feature vectors as samples and using a support vector machine model to obtain a corresponding security evaluation category and a security evaluation score includes:
Calculating the security assessment score by a decision function of the support vector machine model, and determining the security assessment category based on the security assessment score, the decision function being expressed as:
；
Wherein f (x) is the decision function for outputting the security assessment score; sign (), is a sign function, used for mapping the output value of the decision function into a class label; Is a Lagrangian multiplier obtained in the training process of the support vector machine model; /(I) Is a sample/>The value of the class label is 1 or-1; /(I)And phi (x) are samples respectivelyAnd a feature vector of the sample x to be predicted in the feature space; b is a bias term; n is the sample/>I.e. the number of samples in the support vector machine model.
Optionally, the determining the security assessment category based on the security assessment score includes:
if the value of the security evaluation score is smaller than 0, the security evaluation category is that the communication operation area is in a security state;
if the value of the security evaluation score is greater than 0 and less than M, the security evaluation category is that the communication operation area is in a potential risk state; the M is a safety early warning threshold value;
and if the value of the security evaluation score is greater than M, the security evaluation category is that the communication operation area is in a non-security state.
Optionally, the acquiring the monitoring data of the communication operation area acquired by the sensor network includes:
Disposing an infrared sensor, a sound sensor and an image sensor in the communication operation area;
And acquiring the temperature data, the sound data and the image data of the communication operation area at a plurality of moments respectively through the infrared sensor, the sound sensor and the image sensor so as to obtain the monitoring data.
The invention also provides a safety early warning system for outdoor communication operation, which comprises:
The data acquisition module is used for acquiring monitoring data of the communication operation area acquired by the sensor network and sending the monitoring data to the centralized processing node; the monitoring data comprises image data, sound data and temperature data;
The first fusion module is used for carrying out first fusion processing on the monitoring data through the centralized processing node to obtain a fusion image;
the image processing module is used for processing the fusion image through the centralized processing node to obtain a corresponding output characteristic diagram, a gradient amplitude value and a gradient direction angle;
The second fusion module is used for carrying out second fusion processing on the output feature map, the gradient amplitude and the gradient direction angle to obtain corresponding fusion feature vectors;
The safety evaluation module is used for carrying out safety detection by taking a plurality of fusion feature vectors as samples through a support vector machine model to obtain corresponding safety evaluation categories and safety evaluation scores; the fusion feature vectors correspond to different acquisition moments;
and the safety early warning module is used for carrying out corresponding-level safety early warning on the personnel in the communication operation area based on the safety evaluation category and the safety evaluation score.
In addition, to achieve the above object, the present invention also proposes an electronic device including: a memory for storing a computer software program; and the processor is used for reading and executing the computer software program so as to realize the safety early warning method of the outdoor communication operation.
In addition, in order to achieve the above object, the present invention also proposes a non-transitory computer readable storage medium having stored therein a computer software program which, when executed by a processor, implements a safety precaution method for an outdoor communication job as described above.
The beneficial effects of the invention are as follows:
(1) The invention can provide more comprehensive and rich environment perception information by fusing the image, sound and temperature data together, and can enhance the real-time monitoring and abnormality detection capability of a communication operation area by comprehensively utilizing the information of various data sources.
(2) According to the invention, the comprehensive information is analyzed and processed through the intelligent algorithm, potential safety threats and abnormal conditions can be found in time, and the possible safety risks can be more accurately identified through comprehensive analysis of sound, temperature and image data, so that the safety of communication operators is improved.
(3) When the safety threat or abnormal condition is detected, the system can trigger the early warning mechanism in time, notify related personnel in the modes of alarm, notification and the like, take necessary countermeasures, and provide support for related decisions to help the related personnel make more intelligent safety decisions.
(4) The invention can improve the monitoring and early warning efficiency and reduce the influence of human factors on the result through the automatic data acquisition, processing and analysis process, and simultaneously, the reliability and the accuracy of the system can be improved by comprehensively utilizing the data of various information sources, and the false alarm rate are reduced.
(5) The invention can effectively reduce accidents and accidents, improve the working quality and the safety level of communication operation and ensure the personal safety of communication operators and the integrity of equipment by timely finding and dealing with the safety threat.
In the invention, firstly, the monitoring data of the communication operation area acquired by the sensor network is acquired, and the monitoring data is sent to the centralized processing node for the first fusion processing, and the monitoring data comprises image data, sound data and temperature data, so that the fused image obtained by the first fusion processing is an image which integrates a plurality of monitoring data and has rich characteristics. And further, continuously extracting the characteristics such as gradient amplitude, gradient direction angle and the like from the fused image containing rich characteristic information, and fusing the characteristics into the convolved output characteristic image so as to obtain the fused characteristic vector which is richer in content and can describe the state of the communication operation area more comprehensively through the second fusion processing. Furthermore, the support vector machine model can accurately give a security assessment category and a security assessment score for reflecting the current communication operation area based on the fusion feature vector containing the richer feature information, and perform security early warning of corresponding level on personnel in the communication operation area based on the two factors.
In summary, the invention comprehensively acquires and processes the image, sound and temperature data of the communication operation area, performs secondary fusion processing on the characteristics to obtain more comprehensive and rich characteristic information than the common image, and performs analysis and processing by combining an intelligent algorithm to perform security evaluation and timely classification early warning. Compared with the existing mode of simply identifying the security threat picture through the early warning equipment, the method and the device can remarkably improve the capabilities of real-time monitoring, accurate security assessment, early warning and grading early warning of the security condition of the communication operation area, thereby effectively guaranteeing the security of communication operation personnel and improving the working efficiency and quality.
Drawings
FIG. 1 is a schematic diagram of a safety precaution method for outdoor communication operation;
FIG. 2 is a flow chart of a safety pre-warning method for outdoor communication operation provided by the invention;
FIG. 3 is a schematic diagram of a safety precaution system for outdoor communication operation according to the present invention;
fig. 4 is a schematic hardware structure of one possible electronic device according to the present invention;
fig. 5 is a schematic hardware structure of a possible computer readable storage medium according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the description of the present invention, the term "for example" is used to mean "serving as an example, instance, or illustration. Any embodiment described as "for example" in this disclosure is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the invention. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and processes have not been described in detail so as not to obscure the description of the invention with unnecessary detail. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
Referring to fig. 1, fig. 1 is a schematic diagram of a security early warning method for an outdoor communication operation according to the present invention. As shown in fig. 1, the terminal and the server are connected through a network, for example, a wired or wireless network connection. The terminal may include, but is not limited to, mobile terminals such as mobile phones and tablets, and fixed terminals such as computers, inquiry machines and advertising machines, where applications of various network platforms are installed. The server provides various business services for the user, including a service push server, a user recommendation server and the like.
It should be noted that, the scene diagram of the safety early warning method for the outdoor communication operation shown in fig. 1 is only an example, and the terminal, the server and the application scenario described in the embodiment of the present invention are for more clearly describing the technical solution of the embodiment of the present invention, and do not generate any limitation on the technical solution provided by the embodiment of the present invention, and as a person of ordinary skill in the art can know that, with the evolution of the system and the appearance of a new service scenario, the technical solution provided by the embodiment of the present invention is applicable to similar technical problems.
Wherein the terminal may be configured to:
Acquiring monitoring data of a communication operation area acquired by a sensor network, and transmitting the monitoring data to a centralized processing node; the monitoring data comprises image data, sound data and temperature data;
Performing first fusion processing on the monitoring data through the centralized processing node to obtain a fusion image;
processing the fusion image through the centralized processing node to obtain a corresponding output feature image, a gradient amplitude and a gradient direction angle;
performing second fusion processing on the output feature map, the gradient amplitude and the gradient direction angle to obtain corresponding fusion feature vectors;
Taking a plurality of fusion feature vectors as samples, and carrying out safety detection through a support vector machine model to obtain corresponding safety evaluation categories and safety evaluation scores; the fusion feature vectors correspond to different acquisition moments;
And carrying out corresponding-level safety early warning on the personnel in the communication operation area based on the safety evaluation category and the safety evaluation score.
Referring to fig. 2, a flowchart of a safety pre-warning method for outdoor communication operation of the present invention is provided, which includes the following steps:
Step 201, acquiring monitoring data of a communication operation area acquired by a sensor network, and sending the monitoring data to a centralized processing node.
The monitoring data may include image data, sound data, and temperature data, among others.
In some embodiments, multiple sensor nodes may be deployed in a communication work area, which are responsible for collecting various monitoring data in the environment. The sensor node may include an image sensor, a sound sensor, and a temperature sensor for respectively acquiring image data, sound data, and temperature data. The image sensor is responsible for capturing images of the communication work area, the sound sensor is responsible for recording sound in the environment, and the temperature sensor is responsible for measuring the temperature of the environment.
In some embodiments, the sensor nodes may send the collected monitoring data to a centralized processing node, typically via wireless or wired communication. The monitoring data may be encapsulated and transmitted over a network protocol, such as using the TCP/IP protocol. The sensor nodes send the image data, sound data and temperature data to a centralized processing node for subsequent processing and analysis.
In some embodiments, the centralized processing node may be responsible for receiving the monitoring data from the sensor network, and processing and analyzing the data. The centralized processing node may run an intelligent algorithm for performing feature extraction, anomaly detection, and the like on the received monitoring data. The image data may be fed into an image processing algorithm and the sound data and temperature data may be analyzed by the corresponding processing algorithm.
By the method, the monitoring data of the communication operation area acquired by the sensor network can be acquired, and the data is sent to the centralized processing node so as to be subjected to centralized processing and analysis. Therefore, the real-time monitoring and early warning capability of the safety condition of the communication operation area can be improved, and the safety of communication operators is effectively ensured.
In some embodiments, step 201 may include:
Disposing an infrared sensor, a sound sensor and an image sensor in the communication operation area;
And acquiring the temperature data, the sound data and the image data of the communication operation area at a plurality of moments respectively through the infrared sensor, the sound sensor and the image sensor so as to obtain the monitoring data.
In some embodiments, an infrared sensor may be used to detect a temperature condition of the communication work area. The infrared sensor is capable of sensing infrared radiation of the surrounding environment and measuring the temperature of the environment based on the intensity and frequency of the infrared radiation. By disposing the infrared sensor, the real-time monitoring and recording of the temperature of the communication operation area can be realized.
In some embodiments, a sound sensor may be used to capture sound signals of a communication work area. The sound sensor may sense sound waves in the environment and measure the intensity and frequency spectrum of the sound in the environment based on the amplitude and frequency of the sound waves. By disposing the sound sensor, the real-time monitoring and recording of the sound of the communication operation area can be realized.
In some embodiments, an image sensor may be used to capture images of the communication work area. The image sensor is capable of capturing visual information in the environment and converting it into digital image data. By arranging the image sensor, the visual monitoring of the communication operation area can be realized, and the real-time image information in the environment can be acquired.
In some embodiments, the infrared sensor, the sound sensor, and the image sensor collect temperature data, sound data, and image data, respectively, of the communication work area at a plurality of times. Each sensor periodically collects environmental data according to a preset sampling frequency and sends the data to a centralized processing node. Through continuous monitoring and data acquisition, the multi-dimensional monitoring data such as temperature, sound, images and the like of the communication operation area can be obtained in real time. Through the deployment and data acquisition processes, the comprehensive monitoring of the environment of the communication operation area can be realized, and necessary data support is provided for subsequent safety early warning and decision making.
Step 202, performing first fusion processing on the monitoring data through the centralized processing node to obtain a fusion image.
In some embodiments, step 202 may include:
converting the sound data into a corresponding spectrogram, and converting the temperature data into a corresponding temperature thermodynamic diagram;
And superposing and fusing the spectrogram and the temperature thermodynamic diagram with the image data to obtain the fused image.
In some embodiments, the centralized processing node may receive monitoring data from the sensor network, including, for example, image data, sound data, and temperature data. The sound data may be converted into a corresponding spectrogram, which is a spectral representation of the sound signal, representing the energy distribution of the sound at different frequencies. The conversion of the time-domain sound signal into a frequency-domain spectrogram is usually achieved by a signal processing technique such as fourier transform.
In some embodiments, the temperature data may be converted into a corresponding temperature thermodynamic diagram, which is a spatially distributed image of temperature, for representing temperature distribution at different locations in the environment. This is typically achieved by mapping temperature data to the image space and assigning different colors depending on the magnitude of the temperature values.
In some embodiments, the spectrogram and the thermogram may be superimposed and fused with the original image data to obtain a fused image. In the fusion process, the sound data and the temperature data can be normalized according to the requirement so as to keep the weight balance of various information. The finally obtained fusion image contains multidimensional information such as images, sounds, temperatures and the like, and a more comprehensive and rich environment-aware image is formed.
Through the mode, the centralized processing node can perform first fusion processing on the monitoring data from the sensor network to obtain a fusion image. The fusion image is beneficial to subsequent safety early warning and decision tasks, and the monitoring capability and early warning effect on the safety condition of the communication operation area are improved.
And 203, processing the fused image through the centralized processing node to obtain a corresponding output characteristic diagram, gradient amplitude and gradient direction angle.
In some embodiments, step 203 may comprise:
performing feature extraction of convolution operation on the fusion image to obtain the output feature map;
And carrying out edge detection on the fusion image, and calculating corresponding gradient amplitude and gradient direction angle.
In some embodiments, the central processing node may perform convolution operation on the fused image, and perform feature extraction by using a Convolutional Neural Network (CNN) or other method, to obtain a corresponding output feature map. In the convolution operation, feature information of different positions is extracted by sliding a convolution kernel on the fused image. The convolution operation may capture local information and global structural features in the image.
In some embodiments, the centralized processing node may perform edge detection on the fused image, and common methods include Sobel operators, canny operators, and the like. Edge information in the image can be effectively extracted by edge detection, and the contours of different objects and targets can be accurately extracted.
In the edge detection process, the gradient amplitude and the gradient direction angle of each pixel point can be calculated simultaneously. The gradient magnitude represents the gradient intensity of each pixel point in the image, reflecting the degree of variation in the image. The gradient direction angle indicates the direction of the gradient, indicating the direction of the fastest change.
Through the mode, the centralized processing node can perform feature extraction and edge detection on the fusion image to obtain the corresponding output feature map, gradient amplitude and gradient direction angle. The processed image features and information can be used for subsequent target detection, classification and decision tasks, so that the understanding and analysis capability of the communication operation area environment is improved.
And 204, performing second fusion processing on the output feature map, the gradient amplitude and the gradient direction angle to obtain corresponding fusion feature vectors.
In some embodiments, step 204 may include:
performing feature extraction of convolution operation on the fusion image to obtain the output feature map;
And carrying out edge detection on the fusion image, and calculating corresponding gradient amplitude and gradient direction angle.
In some embodiments, the expression for convolving the fused image is:
；
Wherein O is the output feature map, m and n represent the sizes of the convolution kernels in the horizontal and vertical directions, k represents the number of channels of the input fusion image, c represents the number of channels of the output feature map, I and j represent pixel positions in the output feature map O, O (I, j, c) represents the values of the output feature map O at positions (I, j) and channel c, I is the input image, and I (I-m, j-n, k) represents the pixel values of the input image I at positions (I-m, j-n) and channel k.
In a specific implementation, m and n denote the dimensions of the convolution kernel in the horizontal and vertical directions, describing the size of the convolution kernel. Typically, the convolution kernel is a two-dimensional matrix whose size is determined by m and n.
K represents the number of channels of the input image, i.e. the depth of the input image. For color images, there are typically three channels, red, green and blue, respectively. For a gray scale image, the number of channels is one.
C represents the number of channels of the output signature, i.e. how many channels there are as a result of the convolution operation. In deep learning, a plurality of convolution kernels are usually designed, and each convolution kernel corresponds to one channel, so as to obtain a plurality of feature maps.
I and j represent pixel positions in the output feature map O for describing the spatial positions of the feature map. i represents the number of rows and j represents the number of columns. In the convolution operation, the convolution kernel slides on the input image by one step at a time, and a pixel value of the output feature image is calculated. i and j are the positions of each pixel in the output feature map.
O (i, j, c) represents the value of the output profile O at position (i, j) at channel c, representing the result of the convolution operation.Representing traversing three dimensions of the input image I, i.e. summing m, n, K, respectively, where m and n represent the size of the convolution kernel K and K represents the number of channels of the image.
K (m, n, K, c) represents the weight of the convolution kernel K at position (m, n), channel K to channel c. The convolution kernel K is a filter for extracting features from an input image.
I (I-m, j-n, k) represents the pixel value of the input image I at position (I-m, j-n) at channel k. Where i-m and j-n denote the sliding of the convolution kernel over the image for calculating each pixel value of the output feature map.
In some embodiments, the gradient magnitude is expressed as:
；
wherein G is the gradient amplitude and is used for representing the edge intensity of the fusion image; and/> Representing gradient values of the fused image in the horizontal direction and the vertical direction, respectively.
In particular implementations, G is a gradient magnitude, representing the magnitude of the gradient of the image at each pixel point, for representing the edge intensity of the image,And/>The gradient values of the image in the horizontal direction and the vertical direction are respectively represented and are obtained by calculating edge detection operators (such as Sobel operators) of the image, and the operators perform convolution operation on the image to respectively obtain the gradient values of the image in the horizontal direction and the vertical direction.
In some embodiments, the gradient direction angle is expressed as:
；
wherein θ represents the gradient direction angle, and represents the direction of the edge of the fused image, i.e. the slope angle of the edge; arctan2 is an arctangent function used to calculate the gradient value And/>And returns to the angle in radians.
In particular implementations, θ represents the gradient direction angle, represents the direction of the image edge, i.e., the slope angle of the edge, arctan2 is the arctan function, which functions to calculateAnd/>And returns to the angle in radians.
And 205, taking a plurality of fusion feature vectors as samples, and carrying out security detection through a support vector machine model to obtain corresponding security assessment categories and security assessment scores.
The fusion feature vectors can correspond to different acquisition moments;
in some embodiments, step 205 may comprise:
Calculating the security assessment score by a decision function of the support vector machine model, and determining the security assessment category based on the security assessment score, the decision function being expressed as:
；
Wherein f (x) is the decision function for outputting the security assessment score; sign (), is a sign function, used for mapping the output value of the decision function into a class label; Is a Lagrangian multiplier obtained in the training process of the support vector machine model; /(I) Is a sample/>The value of the class label is 1 or-1; /(I)And phi (x) are samples respectivelyAnd a feature vector of the sample x to be predicted in the feature space; b is a bias term; n is the sample/>I.e. the number of samples in the support vector machine model.
In specific implementation, f (x) is a decision function of the support vector machine model, namely, which class the prediction sample x belongs to; sign (#) is a sign function for mapping the output value of the decision function to a class label; is a Lagrangian multiplier obtained in the training process of the support vector machine model; /(I) Is training sample/>The value of the class label is +1 or-1; /(I)And Φ (x) are respectively sample/>And a feature vector of the sample x to be predicted in the feature space; b is a bias term (or intercept term) for adjusting the translation of the decision function; n is the number of training samples, i.e. the number of samples in the support vector machine model. In the binary classification problem, f (x) greater than zero indicates that the prediction sample x belongs to a positive class, and less than zero indicates that the prediction sample x belongs to a negative class.
In some embodiments, the step of "determining the security assessment category based on the security assessment score" comprises:
if the value of the security evaluation score is smaller than 0, the security evaluation category is that the communication operation area is in a security state;
if the value of the security evaluation score is greater than 0 and less than M, the security evaluation category is that the communication operation area is in a potential risk state; the M is a safety early warning threshold value;
and if the value of the security evaluation score is greater than M, the security evaluation category is that the communication operation area is in a non-security state.
In some embodiments, if the value of the security assessment score is less than 0, it is determined that the communication operation area is in a secure state, which means that the security assessment score of the communication operation area is less than zero, that is, no obvious security risk or abnormal situation is found, and thus it may be determined as a secure state.
In some embodiments, if the value of the security assessment score is greater than 0 and less than M (security pre-warning threshold), it is determined that the communication operation area is in a potential risk state, which indicates that the security assessment score of the communication operation area is between zero and the security pre-warning threshold, and there is a certain security risk or abnormal situation, but not to the extent that immediate action is required.
In some embodiments, if the value of the security evaluation score is greater than M (security early warning threshold), it is determined that the communication operation area is in an unsafe state, which indicates that the security evaluation score of the communication operation area exceeds the security early warning threshold, and there is a serious security risk or abnormal situation, and corresponding security measures need to be immediately taken to ensure the security of the communication operation personnel.
Through the judging rule, the safety state of the communication operation area can be effectively evaluated and judged according to the safety evaluation score, so that corresponding safety measures can be timely taken, and smooth operation of the communication operation and safety of personnel are ensured.
And 206, carrying out corresponding-level security early warning on the personnel in the communication operation area based on the security assessment category and the security assessment score.
In the invention, the object of early warning is mainly the personnel in the communication operation area, namely the outdoor communication operation personnel. The early warning content covers the safety condition, the potential risk and the countermeasure of the communication operation area, for example, the early warning content can include information for prompting the current environment to have multiple aspects such as temperature abnormality, sound abnormality, image abnormality, equipment fault, natural disaster and the like.
In some embodiments, the safety pre-warning mode may include sending out an audible and visual alarm, a mobile phone short message, an email, a voice call, and so on to the communication operator, so that the pre-warning information should be clear and arrive in time, so as to ensure that the communication operator can react quickly.
It can be understood that after receiving the safety precaution, the communication operator should immediately take corresponding safety measures, such as evacuating the dangerous area, finding the safety refuge place, checking the state of the equipment, and the like, and at the same time, should cooperate with related personnel to carry out emergency rescue and treatment according to a preset emergency plan.
In some embodiments, a low level security pre-warning may be performed when the communication work area is assessed as a secure state. In this case, the warning may be based on general precautions such as keeping alert, noticing the surrounding environment, but the communication operator can continue to work in a relatively relaxed state.
In some embodiments, a mid-level security pre-warning should be performed when the communication job area is assessed as a potential risk status. For potential risks, the early warning may be more specific and clear, requiring the communication operator to be vigilant, taking additional security measures, such as increasing patrol, increasing inspection frequency of the communication device, etc.
In some embodiments, a high level of security precaution is required when the communication work area is assessed as unsafe.
For unsafe conditions, the early warning may include emergency measures such as immediately evacuating the communication operation area, finding a safe refuge place, alarming and asking for help, etc., so as to ensure the safety of communication operators.
By carrying out corresponding-level safety precaution according to the safety assessment category and the safety assessment score, the safety state and possible risks of the current environment can be effectively transmitted to communication operators, the operators are helped to make proper reaction and countermeasures, and personal safety and smooth communication operation are ensured.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a security early warning system for outdoor communication operation according to the present invention.
As shown in fig. 3, a safety precaution system for outdoor communication operation according to an embodiment of the present invention includes:
The data acquisition module 301 is configured to acquire monitoring data of a communication operation area acquired by a sensor network, and send the monitoring data to a centralized processing node; the monitoring data comprises image data, sound data and temperature data;
The first fusion module 302 is configured to perform a first fusion process on the monitored data through the centralized processing node, so as to obtain a fused image;
The image processing module 303 is configured to process the fused image through the centralized processing node, so as to obtain a corresponding output feature map, a gradient amplitude value and a gradient direction angle;
The second fusion module 304 is configured to perform a second fusion process on the output feature map, the gradient magnitude and the gradient direction angle, so as to obtain a corresponding fusion feature vector;
The security evaluation module 305 is configured to perform security detection by using the plurality of fusion feature vectors as samples and using a support vector machine model to obtain a corresponding security evaluation category and a security evaluation score; the fusion feature vectors correspond to different acquisition moments;
and the safety pre-warning module 306 is configured to perform corresponding-level safety pre-warning on the personnel in the communication operation area based on the safety evaluation category and the safety evaluation score.
Referring to fig. 4, fig. 4 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 4, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420, wherein the processor 420 executes the computer program 411 to implement the following steps:
Acquiring monitoring data of a communication operation area acquired by a sensor network, and transmitting the monitoring data to a centralized processing node; the monitoring data comprises image data, sound data and temperature data;
Performing first fusion processing on the monitoring data through the centralized processing node to obtain a fusion image;
processing the fusion image through the centralized processing node to obtain a corresponding output feature image, a gradient amplitude and a gradient direction angle;
performing second fusion processing on the output feature map, the gradient amplitude and the gradient direction angle to obtain corresponding fusion feature vectors;
Taking a plurality of fusion feature vectors as samples, and carrying out safety detection through a support vector machine model to obtain corresponding safety evaluation categories and safety evaluation scores; the fusion feature vectors correspond to different acquisition moments;
And carrying out corresponding-level safety early warning on the personnel in the communication operation area based on the safety evaluation category and the safety evaluation score.
Referring to fig. 5, fig. 5 is a schematic diagram of an embodiment of a computer readable storage medium according to an embodiment of the invention. As shown in fig. 5, the present embodiment provides a computer-readable storage medium 500 having stored thereon a computer program 411, which computer program 411, when executed by a processor, performs the steps of:
Acquiring monitoring data of a communication operation area acquired by a sensor network, and transmitting the monitoring data to a centralized processing node; the monitoring data comprises image data, sound data and temperature data;
Performing first fusion processing on the monitoring data through the centralized processing node to obtain a fusion image;
processing the fusion image through the centralized processing node to obtain a corresponding output feature image, a gradient amplitude and a gradient direction angle;
performing second fusion processing on the output feature map, the gradient amplitude and the gradient direction angle to obtain corresponding fusion feature vectors;
Taking a plurality of fusion feature vectors as samples, and carrying out safety detection through a support vector machine model to obtain corresponding safety evaluation categories and safety evaluation scores; the fusion feature vectors correspond to different acquisition moments;
And carrying out corresponding-level safety early warning on the personnel in the communication operation area based on the safety evaluation category and the safety evaluation score.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A security early warning method for outdoor communication operation, the method comprising:
Acquiring monitoring data of a communication operation area acquired by a sensor network, and transmitting the monitoring data to a centralized processing node; the monitoring data comprises image data, sound data and temperature data;
Performing first fusion processing on the monitoring data through the centralized processing node to obtain a fusion image;
processing the fusion image through the centralized processing node to obtain a corresponding output feature image, a gradient amplitude and a gradient direction angle;
performing second fusion processing on the output feature map, the gradient amplitude and the gradient direction angle to obtain corresponding fusion feature vectors;
Taking a plurality of fusion feature vectors as samples, and carrying out safety detection through a support vector machine model to obtain corresponding safety evaluation categories and safety evaluation scores; the fusion feature vectors correspond to different acquisition moments;
And carrying out corresponding-level safety early warning on the personnel in the communication operation area based on the safety evaluation category and the safety evaluation score.
2. The method for pre-warning safety of outdoor communication operation according to claim 1, wherein the performing, by the centralized processing node, the first fusion processing on the monitored data to obtain a fused image includes:
converting the sound data into a corresponding spectrogram, and converting the temperature data into a corresponding temperature thermodynamic diagram;
And superposing and fusing the spectrogram and the temperature thermodynamic diagram with the image data to obtain the fused image.
3. The method for safety precaution of outdoor communication operation according to claim 2, wherein the processing the fused image by the centralized processing node to obtain a corresponding output feature map, gradient amplitude and gradient direction angle comprises:
performing feature extraction of convolution operation on the fusion image to obtain the output feature map;
And carrying out edge detection on the fusion image, and calculating corresponding gradient amplitude and gradient direction angle.
4. The safety precaution method for outdoor communication operation according to claim 3, wherein the expression for performing convolution operation on the fused image is:
; wherein O is the output feature map, m and n represent the sizes of the convolution kernels in the horizontal and vertical directions, k represents the number of channels of the input fusion image, c represents the number of channels of the output feature map, I and j represent pixel positions in the output feature map O, O (I, j, c) represents the values of the output feature map O at positions (I, j) and channel c, I is the input image, and I (I-m, j-n, k) represents the pixel values of the input image I at positions (I-m, j-n) and channel k.
5. The method of claim 4, wherein the gradient magnitude is expressed as:
; wherein G is the gradient amplitude and is used for representing the edge intensity of the fusion image; and/> Representing gradient values of the fused image in the horizontal direction and the vertical direction, respectively.
6. The method of claim 5, wherein the gradient direction angle is expressed as:
; wherein θ represents the gradient direction angle, and represents the direction of the edge of the fused image, i.e. the slope angle of the edge; arctan2 is an arctangent function used to calculate the gradient value/> And/>And returns to the angle in radians.
7. The method for safety precaution of outdoor communication operation according to claim 1, wherein the step of taking the plurality of fusion feature vectors as samples and performing safety detection through a support vector machine model to obtain the corresponding safety evaluation category and safety evaluation score comprises the steps of:
Calculating the security assessment score by a decision function of the support vector machine model, and determining the security assessment category based on the security assessment score, the decision function being expressed as:
; wherein f (x) is the decision function for outputting the security assessment score; sign (), is a sign function, used for mapping the output value of the decision function into a class label; /(I) Is a Lagrangian multiplier obtained in the training process of the support vector machine model; /(I)Is a sample/>The value of the class label is 1 or-1; /(I)And Φ (x) are respectively sample/>And a feature vector of the sample x to be predicted in the feature space; b is a bias term; n is the sample/>I.e. the number of samples in the support vector machine model.
8. The method of claim 7, wherein the determining the security assessment category based on the security assessment score comprises:
if the value of the security evaluation score is smaller than 0, the security evaluation category is that the communication operation area is in a security state;
if the value of the security evaluation score is greater than 0 and less than M, the security evaluation category is that the communication operation area is in a potential risk state; the M is a safety early warning threshold value;
and if the value of the security evaluation score is greater than M, the security evaluation category is that the communication operation area is in a non-security state.
9. The method for pre-warning safety of outdoor communication operation according to any one of claims 1 to 7, wherein the acquiring the monitoring data of the communication operation area collected by the sensor network includes:
Disposing an infrared sensor, a sound sensor and an image sensor in the communication operation area;
And acquiring the temperature data, the sound data and the image data of the communication operation area at a plurality of moments respectively through the infrared sensor, the sound sensor and the image sensor so as to obtain the monitoring data.
10. A safety precaution system for outdoor communication operations, the system comprising:
The data acquisition module is used for acquiring monitoring data of the communication operation area acquired by the sensor network and sending the monitoring data to the centralized processing node; the monitoring data comprises image data, sound data and temperature data;
The first fusion module is used for carrying out first fusion processing on the monitoring data through the centralized processing node to obtain a fusion image;
the image processing module is used for processing the fusion image through the centralized processing node to obtain a corresponding output characteristic diagram, a gradient amplitude value and a gradient direction angle;
The second fusion module is used for carrying out second fusion processing on the output feature map, the gradient amplitude and the gradient direction angle to obtain corresponding fusion feature vectors;
The safety evaluation module is used for carrying out safety detection by taking a plurality of fusion feature vectors as samples through a support vector machine model to obtain corresponding safety evaluation categories and safety evaluation scores; the fusion feature vectors correspond to different acquisition moments;
and the safety early warning module is used for carrying out corresponding-level safety early warning on the personnel in the communication operation area based on the safety evaluation category and the safety evaluation score.
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