CN114024574A - Hidden danger state identification method and equipment for power transmission line - Google Patents
Hidden danger state identification method and equipment for power transmission line Download PDFInfo
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- CN114024574A CN114024574A CN202111680899.2A CN202111680899A CN114024574A CN 114024574 A CN114024574 A CN 114024574A CN 202111680899 A CN202111680899 A CN 202111680899A CN 114024574 A CN114024574 A CN 114024574A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B3/00—Line transmission systems
- H04B3/02—Details
- H04B3/46—Monitoring; Testing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L17/00—Speaker identification or verification
- G10L17/26—Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
Abstract
The application provides a method and equipment for identifying hidden danger states of a power transmission line, and belongs to the technical field of power transmission line detection. The method acquires sound alarm information, wherein the sound alarm information is obtained through sound emitted by hidden danger. And determining a corresponding monitoring equipment group in real time based on the position relation of the plurality of monitoring equipment obtaining the sound alarm information, and acquiring a hidden danger image set according to the monitoring equipment group. The hidden danger image set comprises hidden danger images respectively acquired by all monitoring devices in the monitoring device group. And determining the corresponding hidden danger identification of the hidden danger image set according to the hidden danger image set. And determining whether the motion state corresponding to the hidden danger identification is the transition operation state or not based on the sound alarm information and the hidden danger identification. And the transition operation state is the position movement of the hidden danger mark. And generating transition warning information under the condition that the motion state corresponding to the hidden danger identification is the transition operation state, and sending the transition warning information to the management terminal.
Description
Technical Field
The application relates to the technical field of power transmission line detection, in particular to a hidden danger state identification method and equipment for a power transmission line.
Background
The transmission line is an important component of the power grid, is influenced by artificial and natural conditions, and various potential safety hazards such as mechanical construction, various birds with prey and the like often appear in the transmission line. In order to maintain the safety of the power transmission line and ensure the normal operation of the power transmission line, a special person can check whether the potential safety hazard occurs near the power transmission line in a field patrol or network mode.
With the development of artificial intelligence technology, a specially-assigned patrol mode is gradually replaced by artificial intelligence, and at present, the images of the power transmission line monitored by the monitoring equipment can be identified through the artificial intelligence, so that potential safety hazards of the power transmission line can be identified. However, a general monitoring device is limited by hardware limitations such as battery capacity and network traffic, and cannot perform monitoring for a long time, and people set a timing monitoring function for the monitoring device. The timing monitoring inevitably leads to the occurrence of a monitoring empty window period of the power transmission line, and the hidden danger of the power transmission line cannot be monitored in real time, so that the hidden danger harms the power transmission line.
Based on this, a technical scheme capable of monitoring hidden troubles of the power transmission line in real time and guaranteeing safe operation of the power transmission line is urgently needed.
Disclosure of Invention
The embodiment of the application provides a method and equipment for identifying the hidden danger state of a power transmission line, which are used for monitoring the hidden danger of the power transmission line in real time.
On one hand, the application provides a method for identifying hidden danger states of a power transmission line, and the method comprises the following steps:
and acquiring sound alarm information. The sound alarm information is obtained by the sound emitted by the hidden danger. And determining a corresponding monitoring equipment group in real time based on the position relation of the plurality of monitoring equipment obtaining the sound alarm information, and acquiring a hidden danger image set according to the monitoring equipment group. The hidden danger image set comprises hidden danger images respectively acquired by all monitoring devices in the monitoring device group. And determining the corresponding hidden danger identification of the hidden danger image set according to the hidden danger image set. And determining whether the motion state corresponding to the hidden danger identification is the transition operation state or not based on the sound alarm information and the hidden danger identification. And the transition operation state is the position movement of the hidden danger mark. And generating transition warning information under the condition that the motion state corresponding to the hidden danger identification is the transition operation state, and sending the transition warning information to the management terminal.
In one implementation of the application, a first collection location corresponding to the audible alert information is determined. The first acquisition position is the position of the monitoring equipment meeting a first preset condition. The first preset condition is that the monitoring devices reach a preset number within a preset distance range, and the monitoring devices within the preset distance receive sound alarm information within preset time. And determining a plurality of second acquisition positions meeting a second preset condition according to the first acquisition positions and a density-based clustering algorithm. And the second preset condition is that the second acquisition position is connected with the first acquisition position at least in a density meeting manner. And determining an alarm area according to the first acquisition position and each second acquisition position. And determining corresponding monitoring equipment in the alarm area as a monitoring equipment group.
In an implementation mode of the application, external monitoring sounds of the power transmission line are acquired through a sound acquisition module in the monitoring equipment. The external monitoring sound comprises sound emitted by hidden danger. And inputting the external monitoring sound into a pre-trained sound recognition model to determine the type of the external monitoring sound. And determining sound alarm information under the condition that the type of the external monitoring sound is a hidden danger triggering type.
In one implementation of the application, the characteristics of the undetermined hidden danger in the hidden danger image are determined through a pre-trained convolutional neural network model. And determining whether the characteristics of the undetermined hidden danger are matched with the characteristics of the identified hidden danger. The hidden danger features of the identified hidden danger are obtained based on a convolutional neural network model before the undetermined hidden danger features are determined. And under the condition that the undetermined hidden danger features are matched with the hidden danger features of the identified hidden danger, taking the hidden danger identification of the identified hidden danger as the hidden danger identification of the hidden danger image.
In an implementation manner of the application, under the condition that the undetermined hidden danger features are not matched with the hidden danger features of the identified hidden danger, the hidden danger identifications of the undetermined hidden danger features are generated, and the corresponding hidden danger of the hidden danger images is updated to be the identified hidden danger.
In one implementation of the application, a positioning coordinate system is generated based on the position of the alerting device in the audible alert message. And determining a signal acquisition time delay value according to the signal acquisition time of the alarm audio data in the sound alarm information. And determining the sound source positioning coordinate of the hidden danger sound source in a positioning coordinate system according to the signal acquisition delay value. And determining whether the position of the hidden danger identification changes according to the sound source positioning coordinate and the hidden danger identification, and determining whether the motion state corresponding to the hidden danger identification is a transition operation state according to the position change of the hidden danger identification.
In one implementation of the application, a direction of movement corresponding to a transition job state is determined. And judging whether the hidden danger exists on a driving path along the moving direction or not. And generating transition warning information under the condition that the power transmission line exists on a driving path with hidden danger along the moving direction.
In one implementation of the application, a number of voice samples are input to a voice recognition model for training. Wherein the sound samples comprise at least: lightning sound, bird strike sound, mechanical construction sound. And inputting the test sound sample into the trained sound recognition model, and determining a recognition result. And determining whether the voice recognition model completes training or not according to the recognition result.
On the other hand, the embodiment of the application provides a hidden danger transition operation identification device for a power transmission line, and the device comprises:
at least one processor; and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
and acquiring sound alarm information. The sound alarm information is obtained by the sound emitted by the hidden danger. And determining a corresponding monitoring equipment group in real time based on the position relation of the plurality of monitoring equipment obtaining the sound alarm information, and acquiring a hidden danger image set according to the monitoring equipment group. The hidden danger image set comprises hidden danger images respectively acquired by all monitoring devices in the monitoring device group. And determining the corresponding hidden danger identification of the hidden danger image set according to the hidden danger image set. And determining whether the motion state corresponding to the hidden danger identification is the transition operation state or not based on the sound alarm information and the hidden danger identification. And the transition operation state is the position movement of the hidden danger mark. And generating transition warning information under the condition that the motion state corresponding to the hidden danger identification is the transition operation state, and sending the transition warning information to the management terminal.
In an implementation manner of the present application, the at least one processor may specifically be configured to: and determining a first acquisition position corresponding to the sound alarm information. The first acquisition position is the position of the monitoring equipment meeting a first preset condition. The first preset condition is that the monitoring devices reach a preset number within a preset distance range, and the monitoring devices within the preset distance receive sound alarm information within preset time. And determining a plurality of second acquisition positions meeting a second preset condition according to the first acquisition positions and a density-based clustering algorithm. And the second preset condition is that the second acquisition position is connected with the first acquisition position at least in a density meeting manner. And determining an alarm area according to the first acquisition position and each second acquisition position. And determining corresponding monitoring equipment in the alarm area as a monitoring equipment group.
Through the scheme, the hidden danger of the power transmission line can be monitored in real time by using the sound emitted by the hidden danger, the state of the hidden danger is identified according to the monitoring equipment, the damage to the power transmission line caused by the hidden danger is avoided, and the safe operation of the power transmission line is guaranteed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a hidden danger state identification method for a power transmission line in an embodiment of the present application;
fig. 2 is another schematic flow chart of a hidden danger state identification method for a power transmission line in an embodiment of the present application;
fig. 3 is a schematic diagram of a hidden danger state identification method for a power transmission line in an embodiment of the present application;
fig. 4 is another schematic diagram of a hidden danger state identification method for a power transmission line in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a hidden danger state identification device for a power transmission line in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The transmission line is an important component of the power grid, is influenced by artificial and natural conditions, and various potential safety hazards such as mechanical construction, various birds with prey and the like often appear in the transmission line. The transmission line monitoring equipment becomes a main monitoring mode. The device takes pictures regularly (30 minutes or 1 hour for one time) and transmits back to the monitoring platform, and the on-site judgment is carried out by the on-duty personnel for treatment and management. In the aspect of hidden danger identification, a mode of identifying hidden dangers by adopting a back-end cloud server or an equipment side front-end analysis mode is often adopted.
With the development of artificial intelligence technology, the identification precision of the hidden danger target based on the monitoring image of a single monitoring device reaches the large-scale popularization and application degree, and the missing report rate and the false report rate can be controlled below 5%.
The monitoring device is limited by the battery power and the network flow of the monitoring device, and the image monitoring cannot be carried out in an all-weather real-time monitoring mode, so that a monitoring blank window period is inevitably formed. Moreover, when the offline construction machine performs a transition operation, the behavior of the machine cannot be recognized by the hidden-danger target detection based on the monitoring image of the single monitoring device. In 2021, the accidents of wire scraping caused by mechanical transition construction are caused too much, and hidden troubles are caused to the safety of the line.
Based on the above, the embodiment of the application provides a method and equipment for identifying the hidden danger state of the power transmission line, which are used for monitoring the hidden danger of the power transmission line in real time and ensuring the safe operation of the power transmission line.
Various embodiments of the present application are described in detail below with reference to the accompanying drawings.
The embodiment of the application provides a method for identifying a hidden danger state of a power transmission line, and as shown in fig. 1, the method may include steps S101 to S105:
s101, the server acquires sound alarm information.
The sound alarm information is obtained by the sound emitted by the hidden danger.
In the embodiment of the application, the sound alarm information at least comprises sound alarm time, alarm equipment position and alarm audio data.
It should be noted that the server is only an exemplary execution subject used in the hidden danger state identification method for the power transmission line, and the execution subject is not limited to the server, which is not specifically limited in this application.
In this embodiment of the application, the sound alarm information may be generated by a sound collection module in the monitoring device, or the sound collection module collects sound and sends the collected sound to the server, and then the server generates the sound alarm information.
In an embodiment of the present application, if the sound alarm information is generated by the sound collection module, a sound identification model is preset in the sound collection module and is used for identifying a sound corresponding to the sound alarm information. The voice recognition model may be a pre-trained neural network model.
In an embodiment of the present application, before obtaining the sound alarm information, the server may execute the following method to determine the sound alarm information, and obtain the determined sound alarm information when performing hidden danger state identification, where the steps of determining the sound alarm information and obtaining the sound alarm information may be performed by the server at the same time, or may be performed by determining the sound alarm information first, and then obtaining the sound alarm information. Taking the example that the voice recognition model is set in the server, specifically:
firstly, the server acquires external monitoring sound of the power transmission line through a sound acquisition module in the monitoring equipment.
The external monitoring sound comprises sound emitted by hidden danger.
The monitoring and shooting device is arranged on a tower and a power transmission tower of the power transmission line, and a sound collection module of the monitoring and shooting device can collect sounds around the power transmission line in real time, namely external monitoring sounds. The monitoring equipment can be in wireless network connection with the server and transmits data such as external monitoring sound.
Then, the server inputs the external monitoring sound into a pre-trained sound recognition model to determine the type of the external monitoring sound.
In the embodiment of the application, the server may obtain the type of the external monitoring sound through a pre-trained sound recognition model, where the type of the external monitoring sound is related to a sound sample used for training the sound recognition model. The application can obtain the voice recognition model through the following embodiments:
and the server inputs a plurality of voice samples into the voice recognition model for training.
Wherein the sound samples comprise at least: lightning sound, bird strike sound, mechanical construction sound.
And the server inputs the test sound sample into the trained sound recognition model and determines a recognition result.
And the server determines whether the voice recognition model completes training according to the recognition result.
The test sound sample can be the sound emitted by various types of machines, the recognized machine type can be output according to the recognition result, and if the test sound sample is the sound of the elephant, the recognized sound can be output according to the recognition result.
The common hidden danger of the power transmission line is used as a training sample, so that a sound recognition model which is suitable for a power transmission line scene and can accurately recognize the hidden danger of the power transmission line can be obtained.
And finally, the server determines the sound alarm information under the condition that the type of the external monitoring sound is the hidden danger triggering type.
In the embodiment of the application, after the type of the external monitoring sound is output by the sound identification model, for example, "crane sound", and the server can determine that the type is a hidden danger triggering type, the server determines the sound alarm information corresponding to the external monitoring sound, where the sound alarm information includes sound alarm time, alarm device position, and alarm audio data. The sound alarm time is the time when the sound collection module of the monitoring equipment collects external monitoring sound, the alarm equipment position is the position of the monitoring equipment sending the external monitoring sound, and the alarm audio data is an audio file of the external monitoring sound.
Through the scheme, the server can detect the hidden danger near the power transmission line in real time by detecting the hidden danger sound, so that the safety of the power transmission line is guaranteed, the empty window period of the monitoring of the power transmission line is avoided, and the hidden danger is damaged to the power transmission line when the monitoring equipment is not monitored.
S102, the server determines a corresponding monitoring equipment group in real time based on the position relation of the monitoring equipment which obtains the sound alarm information, and acquires a hidden danger image set according to the monitoring equipment group.
In the embodiment of the present application, the server determines, in real time, a corresponding monitoring device group based on the position relationship of the plurality of monitoring devices that obtain the sound alarm information, as shown in fig. 2, specifically including the following steps:
s201, the server determines a first acquisition position corresponding to the sound alarm information.
The first acquisition position is the position of the monitoring equipment meeting a first preset condition, the first preset condition is that the monitoring equipment reaches a preset number within a preset distance range, and the monitoring equipment within the preset distance receives sound alarm information within preset time.
In the embodiment of the application, the sound alarm information may be acquired by a plurality of monitoring devices, and the sound corresponding to the sound alarm information may be the sound emitted by the same hidden danger or may not be the sound emitted by the same hidden danger. In the monitoring devices corresponding to the sound alarm information, whether the number of the monitoring devices which are larger than the preset threshold value exists within the distance range of the preset value of each monitoring device or not is determined, and the external monitoring sound generating the sound alarm information is collected within the preset time, after the server determines that the monitoring devices meet the conditions, the monitoring device which determines the earliest time of the sound alarm information within the preset time can be used as a first monitoring device, and the position of the first monitoring device is used as a first collection position.
The preset value can be set according to an actual use scene, and the preset threshold value and the preset time can also be set in actual use, which is not limited in the present application.
S202, the server determines a plurality of second acquisition positions meeting a second preset condition according to the first acquisition positions and a density-based clustering algorithm.
And the second preset condition is that the second acquisition position is connected with the first acquisition position at least in a density meeting manner.
In the embodiment of the present application, a Density-Based Clustering of Applications with Noise (DBSCAN) may determine the Density-connected second collection positions of the first collection positions. The specific mode is as follows:
step 1, a server traverses all corresponding monitoring equipment of sound alarm information, and finds out a set of all first acquisition positions which meet the condition that the neighborhood distance is smaller than a preset value and the time interval is smaller than preset time;
step 2, the server randomly selects a first acquisition position, finds out all second acquisition positions with accessible density and generates a cluster;
and 3, removing the second acquisition positions with the reachable density found in the step 2 from the rest first acquisition positions by the server.
The server repeats steps 2-3 from the updated set of first acquisition locations until the first acquisition locations are traversed or removed. And obtaining a device list of the cluster set, namely the monitoring equipment group.
S203, the server determines an alarm area according to the first acquisition position and each second acquisition position.
The server may determine a cluster including the first collecting location and the plurality of second collecting locations, which is the alarm region, according to the executing step of the density-based clustering algorithm in S202.
And S204, the server determines corresponding monitoring devices in the alarm area as a monitoring device group.
The server determines each monitoring device in the alarm area to generate a monitoring device group, and the same monitoring device group can simultaneously carry out monitoring work.
According to the scheme, each monitoring device in the power transmission line can form a device group for monitoring and shooting the hidden danger of power transmission at the same time, so that when the hidden danger occurs, images of the hidden danger are collected at all angles, a hidden danger image set is obtained, and the accuracy of monitoring the hidden danger of power transmission is guaranteed. In addition, according to the method and the system, time data are utilized in a DBSCAN algorithm, so that the accuracy of the time-space information during hidden danger warning is guaranteed, a hidden danger target can be accurately and timely positioned, and the working efficiency of monitoring equipment during monitoring of hidden dangers of the power transmission line is improved.
S103, the server determines the corresponding hidden danger identification of the hidden danger image set according to the hidden danger image set.
In the embodiment of the application, a server may preset a model for identifying a hidden danger identifier of a hidden danger image in a hidden danger image set, before the hidden danger identifier is obtained by identification, the embodiment of the application may also perform target identification on the hidden danger image, and identify whether a hidden danger target exists in the hidden danger image in advance, where the hidden danger target identification may identify the hidden danger target through a target identification algorithm, such as yolov 5. The target recognition algorithm can be arranged in the monitoring equipment or the server.
In an embodiment of the present application, the identifying, by the server, the hidden danger image to determine the hidden danger identifier of the hidden danger image specifically includes:
firstly, the server determines the characteristics of the undetermined hidden danger in the hidden danger image through a pre-trained convolutional neural network model.
The server can input the hidden danger images into a pre-trained convolutional neural network model, the type of the convolutional neural network model is not limited, and the convolutional neural network model can include resnet50, VGG16 and the like and is used for extracting features of the hidden danger images.
And then, the server determines whether the characteristics of the undetermined hidden danger are matched with the characteristics of the hidden danger of the identified hidden danger.
The hidden danger features of the identified hidden danger are obtained based on a convolutional neural network model before the undetermined hidden danger features are determined.
In the embodiment of the application, the server can compare the undetermined hidden danger features extracted by the convolutional neural network with the hidden danger features of the identified hidden danger, and the identified hidden danger can be obtained according to the hidden danger image sent by the monitoring equipment before the current hidden danger image is obtained. The server calculates the characteristic distance between the characteristics of the undetermined hidden danger and the characteristics of the hidden danger of the identified hidden danger, wherein the characteristic distance can be Euclidean distance or Manhattan distance, and the type of the characteristic distance is not specifically limited.
The server sets a distance threshold for the feature distance, and the server may determine the feature distance less than the distance threshold as the selected feature distance. After the server determines the selected feature distance, the server determines that the hidden danger features of the identified hidden danger corresponding to the selected distance are matched with the characteristics of the undetermined hidden danger.
And then, under the condition that the undetermined hidden danger features are matched with the hidden danger features of the identified hidden danger, the server takes the hidden danger identification of the identified hidden danger as the hidden danger identification of the hidden danger image.
In addition, the server generates a hidden danger identification of the undetermined hidden danger feature under the condition that the undetermined hidden danger feature is not matched with the hidden danger feature of the identified hidden danger, and updates the corresponding hidden danger of the hidden danger image to be the identified hidden danger.
The hidden danger identification method and the hidden danger identification device can match the current hidden danger image with the hidden danger characteristics of the identified hidden danger, and if the current hidden danger image is monitored by the monitoring equipment, the hidden danger can be re-identified, namely, the hidden danger identification of the identified hidden danger is used as the hidden danger identification of the hidden danger image; if the hidden danger image is not identified, the server recognizes the hidden danger in the hidden danger image as a new hidden danger and updates the hidden danger identification.
Through the scheme, the hidden danger at different times can be associated with the identity, the situation that the same hidden danger is identified as different hidden dangers, the server is troubled by identification is avoided, and tracking and identification of the hidden dangers cannot be carried out. Moreover, by means of the scheme, the problem that when the server stores data, the data with the same hidden danger generates a new storage sequence to cause repeated data storage and influence on the working efficiency of the server can be avoided.
And S104, the server determines whether the motion state corresponding to the hidden danger identification is the transition operation state or not based on the sound alarm information and the hidden danger identification.
And the transition operation state is the position movement of the hidden danger mark. The transition operation state indicates a state in which the potential trouble has moved.
In this embodiment of the present application, the server determines, based on the sound alarm information and the hidden danger representation, whether the motion state corresponding to the hidden danger identifier is a transition job state, specifically including:
firstly, the server generates a positioning coordinate system according to the position of the alarm equipment in the sound alarm information.
In the embodiment of the present application, the positioning coordinate system is shown in fig. 3, and the present application may use a position of the alarm device, that is, a position of the monitoring device, as an origin, or may preset a geographical position as an origin of coordinates.
And then, the server determines a signal acquisition delay value according to the signal acquisition time of the alarm audio data in the sound alarm information.
In the embodiment of the application, the sound collection module in the monitoring equipment can be provided with a plurality of microphone arrays, and the signal collection time delay value is obtained by collecting the signal collection time of the alarm audio data through each microphone array.
The signal acquisition time of the same alarm audio data can be acquired through the sound acquisition modules in the plurality of monitoring devices, and a signal acquisition time delay value is obtained.
And then, the server determines the sound source positioning coordinate of the hidden danger sound source in the positioning coordinate system according to the signal acquisition time delay value.
The server can calculate the distance and the direction between the monitoring equipment and the hidden danger sound source in the positioning coordinate system by utilizing the space triangle cosine theorem and the cosine formula according to the signal acquisition time delay value, and then calculates the sound source positioning coordinate of the hidden danger sound source through the coordinate of the monitoring equipment in the positioning coordinate system. As shown in fig. 3, U1 is the coordinates of the monitoring device, and U2 is the coordinates of the hidden danger sound source.
And finally, the server determines whether the position of the hidden danger identification changes according to the sound source positioning coordinate and the hidden danger identification, and determines whether the motion state corresponding to the hidden danger identification is a transition operation state according to the position change of the hidden danger identification.
In the embodiment of the application, whether the position of the hidden danger of the same hidden danger identifier in a positioning coordinate system moves or not can be determined according to the hidden danger identifier, and if the position of the hidden danger identifier changes, the server determines that the corresponding motion state of the hidden danger identifier is a transition operation state.
Through the scheme, the hidden danger is positioned by using a sound source positioning mode, hidden danger images do not need to be acquired by means of binocular and other three-dimensional image acquisition equipment, the hardware cost is saved, and unnecessary resource waste is reduced.
And S105, the server generates transition warning information and sends the warning information to the management terminal under the condition that the motion state corresponding to the hidden danger identification is determined to be the transition operation state.
In the embodiment of the present application, the server generates transition warning information, which specifically includes:
first, the server determines a moving direction corresponding to the transition job state.
Fig. 4 is a schematic diagram showing the moving direction of the transition operation state.
Secondly, the server judges whether the hidden danger exists on a driving path along the moving direction or not.
As shown in fig. 4, a power transmission line a exists on a traveling path of a potential risk 401 along a moving direction 402.
And thirdly, the server generates transition warning information on the condition that the power transmission line exists on the driving path of the hidden trouble along the moving direction.
In an embodiment of the application, the monitoring device does not trigger the monitoring function through the sound alarm information, the monitoring device presets a photographing time interval, and the monitoring device can photograph the power transmission line at the interval of the photographing time interval under the condition that the sound alarm information does not exist.
In the above embodiment of the application, the sound that can send hidden danger is discerned to whether judge for can cause the hidden danger of harm to transmission line, and then trigger the prison function of taking a picture of prison equipment group based on sound alarm information generation, shoot the discernment to the hidden danger. And sound source positioning is carried out through sound alarm information, whether the hidden danger is in a transition operation state or not is determined according to the positioning data, and corresponding alarm information is generated, so that the warning management terminal can manage or eliminate the hidden danger.
By the scheme, the power transmission line can be monitored in real time, the phenomenon that the monitoring of the power transmission line is in an empty window period is avoided, the hardware cost is low, battery energy of monitoring equipment cannot be wasted, safe operation of the power transmission line can be guaranteed, and the use experience of monitoring related departments of the power transmission line is improved.
Fig. 5 is a hidden danger transition operation identification device 500 for a power transmission line according to an embodiment of the present application, where the device 500 includes:
at least one processor 501; and a memory 502 communicatively coupled to the at least one processor 501. Wherein the memory 502 stores instructions executable by the at least one processor 501, the instructions being executable by the at least one processor 501 to enable the at least one processor 501 to:
and acquiring sound alarm information. The sound alarm information is obtained by the sound emitted by the hidden danger. And determining a corresponding monitoring equipment group in real time based on the position relation of the plurality of monitoring equipment obtaining the sound alarm information, and acquiring a hidden danger image set according to the monitoring equipment group. The hidden danger image set comprises hidden danger images respectively acquired by all monitoring devices in the monitoring device group. And determining the corresponding hidden danger identification of the hidden danger image set according to the hidden danger image set. And determining whether the motion state corresponding to the hidden danger identification is the transition operation state or not based on the sound alarm information and the hidden danger identification. And the transition operation state is the position movement of the hidden danger mark. And generating transition warning information under the condition that the motion state corresponding to the hidden danger identification is the transition operation state, and sending the transition warning information to the management terminal.
In this embodiment, the at least one processor 501 is further specifically capable of:
and determining a first acquisition position corresponding to the sound alarm information. The first acquisition position is the position of the monitoring equipment meeting a first preset condition. The first preset condition is that the monitoring devices reach a preset number within a preset distance range, and the monitoring devices within the preset distance receive sound alarm information within preset time. And determining a plurality of second acquisition positions meeting a second preset condition according to the first acquisition positions and a density-based clustering algorithm. And the second preset condition is that the second acquisition position is connected with the first acquisition position at least in a density meeting manner. And determining an alarm area according to the first acquisition position and each second acquisition position. And determining corresponding monitoring equipment in the alarm area as a monitoring equipment group.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The devices and the methods provided by the embodiment of the application are in one-to-one correspondence, so the devices also have beneficial technical effects similar to the corresponding methods.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for identifying hidden danger states of a power transmission line is characterized by comprising the following steps:
acquiring sound alarm information; the sound alarm information is obtained through sound emitted by hidden danger;
determining a corresponding monitoring equipment group in real time based on the position relation of the plurality of monitoring equipment obtaining the sound alarm information, and obtaining a hidden danger image set according to the monitoring equipment group; the hidden danger image set comprises hidden danger images respectively acquired by each monitoring device in the monitoring device group;
determining a hidden danger identifier corresponding to the hidden danger image set according to the hidden danger image set;
determining whether the motion state corresponding to the hidden danger identification is a transition operation state or not based on the sound alarm information and the hidden danger identification; the transition operation state is the position movement of the hidden danger mark;
and generating transition warning information under the condition that the motion state corresponding to the hidden danger identification is the transition operation state, and sending the warning information to a management terminal.
2. The method according to claim 1, wherein determining, in real time, a corresponding monitoring device group based on a positional relationship of a plurality of monitoring devices from which the sound alarm information is obtained specifically comprises:
determining a first acquisition position corresponding to the sound alarm information; the first acquisition position is the position of the monitoring equipment meeting a first preset condition; the first preset condition is that the monitoring equipment reaches a preset number within a preset distance range, and the monitoring equipment within the preset distance receives the sound alarm information within a preset time;
determining a plurality of second acquisition positions meeting a second preset condition according to the first acquisition positions and a density-based clustering algorithm; the second preset condition is that the second acquisition position is connected with the first acquisition position at least in a density meeting manner;
determining an alarm area according to the first acquisition position and each second acquisition position;
and determining each corresponding monitoring device in the alarm area as the monitoring device group.
3. The method of claim 1, wherein before obtaining the audible alarm information, the method further comprises:
acquiring external monitoring sound of the power transmission line through a sound acquisition module in the monitoring equipment; the external monitoring sound comprises sound emitted by the hidden danger;
inputting the external monitoring sound into a pre-trained sound recognition model to determine the type of the external monitoring sound;
and determining sound alarm information under the condition that the type of the external monitoring sound is a hidden danger triggering type.
4. The method according to claim 1, wherein identifying the hidden danger image to determine a hidden danger identifier of the hidden danger image specifically includes:
determining characteristics of the undetermined hidden danger in the hidden danger image through a pre-trained convolutional neural network model;
determining whether the undetermined hidden danger features are matched with the hidden danger features of the identified hidden danger; the hidden danger features of the identified hidden danger are obtained based on the convolutional neural network model before the undetermined hidden danger features are determined;
and under the condition that the undetermined hidden danger features are matched with the hidden danger features of the identified hidden danger, taking the hidden danger identification of the identified hidden danger as the hidden danger identification of the hidden danger image.
5. The method of claim 4, further comprising:
and under the condition that the characteristics of the undetermined hidden danger are not matched with the characteristics of the identified hidden danger, generating a hidden danger identification of the characteristics of the undetermined hidden danger, and updating the hidden danger corresponding to the hidden danger image to be the identified hidden danger.
6. The method according to claim 1, wherein determining whether the motion state corresponding to the hidden danger indicator is a transition operation state based on the sound alarm information and the hidden danger indicator specifically includes:
generating a positioning coordinate system according to the position of the alarm equipment in the sound alarm information;
determining a signal acquisition time delay value according to the signal acquisition time of the alarm audio data in the sound alarm information;
determining the sound source positioning coordinate of the sound source with the hidden danger in the positioning coordinate system according to the signal acquisition delay value;
and determining whether the position of the hidden danger identification changes or not according to the sound source positioning coordinate and the hidden danger identification, and determining whether the motion state corresponding to the hidden danger identification is a transition operation state or not according to the position change of the hidden danger identification.
7. The method according to claim 1, wherein generating transition warning information specifically includes:
determining a moving direction corresponding to the transition operation state;
judging whether the hidden danger exists on a driving path along the moving direction or not;
and generating the transition warning information under the condition that the power transmission line exists on a running path of the hidden danger along the moving direction.
8. The method of claim 3, further comprising:
inputting a plurality of sound samples into the sound recognition model for training; wherein the sound samples comprise at least: lightning stroke sound, bird strike sound, mechanical construction sound;
inputting a test sound sample into the trained sound recognition model, and determining a recognition result;
and determining whether the voice recognition model completes training or not according to the recognition result.
9. A hidden danger transition operation identification equipment for a power transmission line is characterized by comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring sound alarm information; the sound alarm information is obtained through sound emitted by hidden danger;
determining a corresponding monitoring equipment group in real time based on the position relation of the plurality of monitoring equipment obtaining the sound alarm information, and obtaining a hidden danger image set according to the monitoring equipment group; the hidden danger image set comprises hidden danger images respectively acquired by each monitoring device in the monitoring device group;
determining a hidden danger identifier corresponding to the hidden danger image set according to the hidden danger image set;
determining whether the motion state corresponding to the hidden danger identification is a transition operation state or not based on the sound alarm information and the hidden danger identification; the transition operation state is the position movement of the hidden danger mark;
and generating transition warning information under the condition that the motion state corresponding to the hidden danger identification is the transition operation state, and sending the warning information to a management terminal.
10. The apparatus of claim 9, wherein the at least one processor is specifically capable of:
determining a first acquisition position corresponding to the sound alarm information; the first acquisition position is the position of the monitoring equipment meeting a first preset condition; the first preset condition is that the monitoring equipment reaches a preset number within a preset distance range, and the monitoring equipment within the preset distance receives the sound alarm information within a preset time;
determining a plurality of second acquisition positions meeting a second preset condition according to the first acquisition positions and a density-based clustering algorithm; the second preset condition is that the second acquisition position is connected with the first acquisition position at least in a density meeting manner;
determining an alarm area according to the first acquisition position and each second acquisition position;
and determining each corresponding monitoring device in the alarm area as the monitoring device group.
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