CN110730327A - Method for monitoring power transmission line and acquisition front end - Google Patents
Method for monitoring power transmission line and acquisition front end Download PDFInfo
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- CN110730327A CN110730327A CN201910829150.6A CN201910829150A CN110730327A CN 110730327 A CN110730327 A CN 110730327A CN 201910829150 A CN201910829150 A CN 201910829150A CN 110730327 A CN110730327 A CN 110730327A
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Abstract
A method and collection front end to the transmission line monitoring, apply to the monitoring system of the transmission line, the monitoring system of the said transmission line includes collecting the front end used for carrying on picture and/or video acquisition analysis and carrying on the back end of decision-making management; the method comprises the following steps: after the acquisition front end determines that the acquisition conditions are met, acquiring images and/or videos of the environment where the power transmission line is located; the acquisition front end analyzes and processes at least one acquired image and/or video, and determines and records a target image and/or a target video with abnormity of the power transmission line and a corresponding analysis result; and the acquisition front end transmits the target image and/or the target video and the analysis result back to the execution rear end. The method can complete analysis and filtration on the pictures and/or videos at the acquisition front end, reduce return flow, save rear-end storage computing resources and improve the real-time performance of system monitoring.
Description
Technical Field
The application relates to the technical field of monitoring, in particular to a method for monitoring a power transmission line and an acquisition front end.
Background
The state of the transmission line is related to the safety of the power supply system. And along with the change of global atmospheric environment, extreme weather frequently happens, often leads to phenomena such as pollution flashover of transmission line insulator, windage yaw flashover, wire waving, icing and the like to take place. Therefore, serious accidents such as circuit tripping, electric arc burning, hardware and insulator damage, strand breakage and wire breakage of the lead, tower collapse and the like are caused, huge safety and hidden trouble exist, and huge economic loss can be caused. Therefore, it is particularly important to enhance the monitoring management of the power transmission line at present.
The existing transmission line monitoring system generally comprises an acquisition front end and an execution back end. The acquisition front end comprises a power transmission state monitoring device main control board deployed on a power transmission tower and a camera deployed on a power transmission line, and the monitoring device main control board acquires pictures and/or videos of the current environment of the power transmission line according to a preset position through the camera. And then, transmitting the acquired pictures and/or videos to an execution back end in a wireless public network 3G/4G or self-built wireless private network mode, analyzing and processing the acquired pictures and/or videos by the execution back end, judging whether an abnormal condition exists or not, and carrying out fault processing when the abnormal condition exists.
The above method for monitoring the transmission line mainly has the following problems: when the main control board of the monitoring device transmits the shot video and image back to the execution back end, the bandwidth required by video return is large, and the requirement on the analysis and storage capacity of the communication, power supply and execution back end is very high due to the return of a large number of images and videos, so that the whole system has very high cost, low reliability and poor monitoring effect.
Disclosure of Invention
The application provides a method and a device for monitoring a power transmission line, which are used for reducing return flow, saving back-end storage and calculation resources and improving the real-time performance of system monitoring.
In a first aspect, an embodiment of the present application provides a method for monitoring a power transmission line, which is applied to a power transmission line monitoring system, where the power transmission line monitoring system includes a collection front end for performing image and/or video collection and analysis and an execution back end for performing decision management; the method comprises the following steps: after the acquisition front end determines that the acquisition conditions are met, acquiring images and/or videos of the environment where the power transmission line is located; the acquisition front end analyzes and processes at least one acquired image and/or video, and determines and records a target image and/or a target video with abnormity of the power transmission line and a corresponding analysis result; and the acquisition front end transmits the target image and/or the target video and the analysis result back to the execution rear end.
Based on the scheme, in the embodiment of the application, in the process of monitoring the power transmission line, the pictures and/or videos can be analyzed and filtered at the front end of the acquisition, so that the return flow is reduced, the storage and calculation resources at the back end are saved, and the real-time performance of system monitoring is improved.
In a possible implementation manner, the acquisition front-end performs preprocessing on the acquired at least one image and/or video; respectively matching the preprocessed images and/or videos with preset abnormal templates, wherein the preset abnormal templates comprise image templates and/or video templates which are monitored that the power transmission line is abnormal; and determining the image and/or video with the matching similarity exceeding a set threshold value with the preset abnormal template as a target image and/or a target video.
In one possible implementation, the analysis result includes at least one of: the abnormal position coordinates of the power transmission line recorded by the target picture and/or the target video exist; the abnormal category of the abnormal power transmission line recorded by the target picture and/or the target video exists; and matching the target picture and/or the target video with a preset abnormal template to obtain the similarity exceeding a set threshold in the similarity.
In one possible implementation, the acquisition front-end determines the analysis result by: the acquisition front end determines the category corresponding to a preset abnormal template with the matching similarity of the target image and/or the target video exceeding a set threshold as the abnormal category contained in the analysis result; the acquisition front end determines the matching similarity of the target image and/or the target video and a preset abnormal template, wherein the matching similarity exceeds a set threshold value, as the similarity contained in the analysis result; and the acquisition front end determines the position coordinates of the abnormal power transmission line contained in the analysis result according to the acquisition angle and the acquisition direction of the target image and/or the target video.
In one possible implementation, the acquisition condition includes at least one of the following conditions: the time length when the acquisition front end does not acquire images and/or videos reaches a first threshold value time length; reaching the preset acquisition time; the acquisition front end receives an acquisition instruction sent by the execution back end; the acquisition front end receives an alarm event.
In one possible implementation, the method further includes: and when the acquisition front end returns the target image and/or the target video and the analysis result to the execution back end, the acquisition front end enters a dormant state.
In one possible implementation, after the acquisition front-end enters a sleep state, the method further includes waking up the acquisition front-end; the acquisition front-end wakes up the acquisition front-end by: if the acquisition front end determines that the dormancy duration reaches a second threshold duration, triggering wakeup operation; or if the acquisition front end receives a wake-up instruction sent by the execution back end, triggering wake-up operation; or if the acquisition front end receives an alarm event, triggering a wakeup operation; or if the acquisition front end determines that the acquisition condition is met, triggering the awakening operation.
In one possible implementation, the method further includes: the acquisition front end analyzes and processes at least one acquired image and/or video through an included AI module.
In a second aspect, an embodiment of the present application provides an acquisition front end for monitoring a power transmission line, where the acquisition front end may be configured to perform operations in the first aspect and any possible implementation manner of the first aspect. For example, the acquisition front-end may comprise a modular unit for performing the respective operations in the first aspect described above or any possible implementation manner of the first aspect.
In a third aspect, an embodiment of the present application provides an acquisition front end for monitoring a power transmission line, where the acquisition front end includes: a processor, a transceiver, and optionally a memory. Wherein the processor, the transceiver and the memory are in communication with each other via an internal connection path. The memory is used for storing instructions, and the processor is used for executing the instructions stored by the memory. When the processor executes the instructions stored by the memory, the execution causes the acquisition front-end to perform any of the methods of the first aspect or any possible implementation of the first aspect.
In a fourth aspect, an embodiment of the present application provides a chip system, including a processor, and optionally a memory; wherein the memory is configured to store a computer program, and the processor is configured to call and run the computer program from the memory, so that the communication device with the system on chip installed performs any one of the methods of the first aspect or any possible implementation manner of the first aspect.
In a fifth aspect, an embodiment of the present application provides a computer program product, where the computer program product includes: computer program code which, when executed by a communication unit, a processing unit or a transceiver, a processor of a communication device, causes the communication device to perform any of the methods of the first aspect or any possible implementation of the first aspect described above.
In a sixth aspect, embodiments of the present application provide a computer-readable storage medium, where a program is stored, and the program enables a communication device (e.g., a terminal device or a network device) to perform any one of the methods in the foregoing first aspect or any possible implementation manner of the first aspect.
In a seventh aspect, this application provides a computer program, which when executed on a computer, will enable the computer to implement the first aspect or any of the possible implementation manners of the first aspect.
Drawings
FIG. 1 is a schematic diagram of a conventional power transmission line image video monitoring system;
fig. 2 is a schematic diagram of a conventional transmission line image video monitoring process;
fig. 3 is a schematic diagram of a method for monitoring a power transmission line according to the present application;
fig. 4 is a schematic diagram of a power transmission line monitoring system architecture provided in the present application;
fig. 5 is a schematic diagram of a hardware connection relationship in an acquisition front end based on an AI mode according to the present application;
fig. 6 is a schematic flowchart of an image recognition process performed by an acquisition front end based on an AI mode according to the present application;
fig. 7 is a schematic diagram illustrating power consumption of an AI module at various stages during operation and sleep according to the present disclosure;
fig. 8 is a schematic flow chart of power transmission line monitoring based on the AI mode according to the present application;
fig. 9 is a schematic diagram of an acquisition front end in the first power transmission line system provided in the present application;
fig. 10 is a front-end acquisition intention in the second power transmission line system provided in the present application.
Detailed Description
At present, a power transmission line image video monitoring system is constructed as shown in fig. 1, and mainly comprises two parts, namely an image video acquisition front end and an execution rear end. The acquisition front end comprises a solar power supply system, an image video acquisition system, a communication system and the like, and specifically comprises a power transmission state monitoring host, a camera, a sensor and a solar panel which are deployed on a power transmission tower. The power supply at the front end of the acquisition mainly adopts a solar energy and storage battery mode.
In the working process, the specific flow may be as shown in fig. 2:
s200: when the set time is reached or an acquisition instruction is received, the acquisition front end starts a system;
s201: the acquisition front end acquires a field image video through a camera according to a preset position;
s202: the acquisition front end transmits the acquired image video back to the execution back end in a wireless public network 3G/4G or self-built wireless private network mode;
s203: the execution back end can analyze the received image video, and take precautionary measures for the relevant towers according to the analysis result, thereby reducing the occurrence of line accidents.
However, the above method for monitoring the power transmission line mainly has the following problems: the front end of an acquisition in the current power transmission line monitoring system lacks the functions of video image analysis and information filtering and lacks intellectualization. In the working process, the shot videos and images are transmitted back to the monitoring center by the acquisition front end without screening, and a large number of invalid images are transmitted back, so that the requirements on communication, power supply, rear end analysis and storage capacity are very high, and the whole system has very high cost, low reliability and poor monitoring effect. Meanwhile, the whole power consumption of the system is high, the power supply system is unstable, the service life of the storage battery is short, and the normal work of the system is difficult to ensure.
In order to solve the problem, the embodiment of the application provides a method for monitoring a power transmission line. The technical scheme of the embodiment of the application can be applied to various power transmission line monitoring systems, such as: the system comprises a microclimate monitoring system, an image video online monitoring system, an icing monitoring system, a tower inclination online monitoring system and the like.
Taking an image video online monitoring system as an example, the video online monitoring system is a set of online monitoring devices designed for building construction (dangerous points) around a power transmission line, external force damage, tower material theft, fire, wire galloping and wire hanging foreign matters. The method mainly transmits the collected remote video image and external force detection alarm to the execution rear end in real time through a 3G/GPRS/CDMA network, and when abnormal conditions occur, the system can send out pre-alarm information in various ways to prompt a manager to pay attention to an alarm point or take necessary preventive measures.
In order to ensure the stability of a power transmission line monitoring system in the process of monitoring the power transmission line and reduce the overall power consumption of the system, the embodiment of the application provides a method for monitoring the power transmission line, which mainly analyzes the acquired images and/or videos at the acquisition front end, and then only returns the analysis result and the abnormal target images and/or target videos of the power transmission line to the execution rear end without returning all the acquired images to the rear end, so that the return flow and bandwidth appeal are greatly reduced, and the real-time performance of monitoring is improved.
In the following, some terms referred to in the embodiments of the present application are explained for convenience of understanding.
1) AI (Artificial Intelligence), a branch of computer science, attempts to understand the essence of Intelligence and produce a new intelligent machine that can react in a manner similar to human Intelligence, and research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems.
2) And the GPIO (General-purpose input/output) has the advantages of low power consumption, low cost, small package and the like.
In addition, the term "at least one" in the embodiments of the present application means one or more, and "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein, A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. At least one of the following items or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
Unless stated to the contrary, the embodiments of the present application refer to the ordinal numbers "first", "second", etc., for distinguishing between a plurality of objects, and do not limit the sequence, timing, priority, or importance of the plurality of objects.
Furthermore, the terms "comprising" and "having" in the description of the embodiments and claims of the present application and the drawings are not intended to be exclusive. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to only those steps or modules listed, but may include other steps or modules not listed.
As shown in fig. 3, the method for monitoring a power transmission line provided in the embodiment of the present application is applied to a power transmission line monitoring system, where the power transmission line monitoring system includes a collection front end for performing image and/or video collection and analysis and an execution back end for performing decision management; wherein, the method comprises the following steps:
s300: after the acquisition front end determines that the acquisition conditions are met, acquiring images and/or videos of the environment where the power transmission line is located;
s301: the acquisition front end analyzes and processes at least one acquired image and/or video, and determines and records a target image and/or a target video with abnormity of the power transmission line and a corresponding analysis result;
s302: and the acquisition front end transmits the target image and/or the target video and the analysis result back to the execution rear end.
For example, in the embodiment of the present application, the acquisition front end may determine the target image and/or the target video by the following manners, specifically:
the acquisition front end preprocesses at least one acquired image and/or video; respectively matching the preprocessed images and/or videos with preset abnormal templates, wherein the preset abnormal templates comprise image templates and/or video templates for which the abnormality of the power transmission line is monitored; and determining the image and/or video with the matching similarity exceeding a set threshold with the preset abnormal template as a target image and/or a target video.
For example, in the embodiment of the present application, the acquisition front end may determine the analysis result in the following manner:
when the abnormal category exists in the power transmission line containing the target picture and/or the target video record in the analysis result, the acquisition front end can determine the category corresponding to a preset abnormal template of which the matching similarity with the target image and/or the target video exceeds a set threshold as the abnormal category;
when the analysis result comprises the similarity exceeding a set threshold in the similarity obtained by matching the target picture and/or the target video with a preset abnormal template, the acquisition front end determines the matching similarity of the target picture and/or the target video with the preset abnormal template, wherein the matching similarity exceeds the set threshold as the similarity;
when the abnormal position coordinates exist in the power transmission line containing the target picture and/or the target video record in the analysis result, the acquisition front end can determine the position coordinates according to the acquisition angle and the acquisition direction of the target image and/or the target video.
In the embodiment of the present application, the acquisition conditions may be various, and are not specifically limited to the following:
acquisition condition 1: the time length of the acquisition front end for not acquiring the images and/or videos reaches a first threshold time length;
acquisition condition 2: the acquisition front end reaches the preset acquisition time;
acquisition condition 3: the acquisition front end receives an acquisition instruction sent by the execution back end;
acquisition condition 4: the acquisition front end receives an alarm event.
Furthermore, in order to realize that the acquisition front end directly analyzes and processes the acquired images and/or videos, the embodiment of the application can apply the AI module to the acquisition front end in the power transmission line monitoring system, so that the acquisition front end can analyze and process the images and/or videos based on the AI module when monitoring the power transmission line.
Further, in this embodiment of the application, when the acquisition front end returns to the execution back end, only the analysis result corresponding to the target image and/or the target video with the abnormal power transmission line may be returned, and at the same time, only the target image and/or the target video may be returned.
If the return information contains the target image and/or the target video, the execution back end can more intuitively know the current state of the environment where the power transmission line is located according to the target image and/or the target video, and meanwhile, the execution back end can further perform analysis processing based on the target image and/or the target video.
To facilitate understanding of the embodiment of the present application, first, taking fig. 4 as an example, an overall architecture of the scheme when the acquisition front end performs power transmission line monitoring based on the AI mode in the embodiment of the present application is briefly introduced. As shown in fig. 4, the power transmission line monitoring system mainly includes a terminal layer 400, a network layer 401, a platform layer 402, and a service application layer 403.
The terminal layer 400 mainly performs monitoring and image and/or video acquisition through a terminal acquisition device in the terminal layer, and then performs analysis processing on the acquired image and/or video through an edge calculation device based on an AI module in the terminal layer.
The network layer 401 is configured to perform communication between the terminal layer and the service application layer mainly through a wireless public network 3G/4G or a self-established wireless private network.
A platform layer 402, which is mainly used for managing devices and data; the method is also used for reasoning training of AI, and further identifies and processes the received analysis result and the image and/or video; meanwhile, the method can be used for continuously optimizing the AI algorithm according to the acquired information, and the accuracy of the model is improved.
And a service application layer 403, which mainly analyzes the received image video, and performs prediction and early warning, trend prediction, and fault study and judgment according to the analysis result. The business application layer can also conduct command decision and operation management, and take precaution and/or remedial measures for relevant towers in a targeted manner, so that the occurrence of line accidents is reduced.
The system architecture and the service scenario for monitoring the power transmission line described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not constitute a limitation to the technical solution provided in the embodiment of the present application, and as a person of ordinary skill in the art knows that along with the evolution of the network architecture and the appearance of a new service scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems. It should be understood that fig. 4 is a simplified schematic diagram of an example for ease of understanding only, and that other layers or other devices, not shown in fig. 4, may also be included in the power transmission line monitoring system.
By the aid of the method, when the power transmission line is monitored based on the AI mode, pictures and/or videos can be analyzed and filtered at the front end of the acquisition, return flow is reduced, rear-end storage and calculation resources are saved, and system monitoring real-time performance is improved.
In the embodiment of the present application, the system structure for monitoring the power transmission line based on the AI mode is not specifically limited to the following:
system configuration 1: the AI module is arranged in the acquisition front end of the power transmission line monitoring system, so that acquired images and/or videos are analyzed and filtered on site based on the AI module, return flow is reduced, rear-end storage and calculation resources are saved, and the monitoring real-time performance of the system is improved.
System configuration 2: an AI chip is arranged in the acquisition front end, so that the acquired images and/or videos are analyzed and filtered on site based on the AI chip, the return flow is reduced, the storage and calculation resources of the rear end are saved, and the real-time performance of system monitoring is improved.
System configuration 3: the acquisition front end directly adopts an AI chip as a master control mode, so that the acquired images and/or videos are analyzed and filtered on site, the return flow is reduced, the storage and calculation resources of the rear end are saved, and the monitoring real-time performance of the system is improved.
For example, the embodiment of the present application is introduced in the manner of the system configuration 1, wherein the hardware connection relationship of the AI module built in the acquisition front end may be as shown in fig. 5. Specifically, the AI module may perform information interaction with a monitoring device main control board in the acquisition front end in an ethernet manner, and perform sleep wake-up control through a GPIO interface.
Illustratively, based on the system configuration 1 in the embodiment of the present application, a flow of image recognition by the acquisition front end is shown in fig. 6. And the monitoring device main control board in the acquisition front end pushes the acquired at least one piece of image information to the AI module, and then the AI module performs preprocessing such as decoding, color gamut conversion, adjustment and the like on the received image. And then the AI module runs an image recognition algorithm model, carries out reasoning recognition on the preprocessed image and determines a target image and/or a target video with abnormity of the power transmission line. And then, reprocessing the results of the target image and/or the target video after the target image and/or the target video are subjected to inference recognition to obtain analysis results such as attributes, similarity and the like of the target image and/or the target video. And finally, the AI module feeds back the target image and/or the target video and the analysis result to the execution back end through the main control board of the monitoring device.
In practical applications, a snapshot scene is generally processed one picture at a time.
In one embodiment, the AI module has two operation modes of working and dormant, and the AI module can normally communicate and process inference tasks in the working mode, so that the power consumption is high; most functions in the module are closed in the sleep mode, only a small amount of necessary functions are reserved, and the power consumption is extremely low. The sleep mode and the working mode can be quickly switched, so that the energy consumption is saved.
Further, in order to reduce the power consumption of the whole system, the embodiment of the application aims at providing a sleep-down function for the AI module in the acquisition front end. Namely, when the device does not work, the main control board of the monitoring device in the acquisition front end can control the AI acceleration module to enter a dormant state through the GPIO interface. When images and/or videos need to be analyzed or periodically work, the main control board of the monitoring device wakes up the AI module through the GPIO interface, the AI module is quickly started to enter a working state, and after the identification inference task is completed, the main control board enters a dormant state again.
In the embodiment of the present application, when the AI module is in the sleep mode, the AI module may be awakened in a plurality of ways, which are not specifically limited to the following:
wakeup mode 1: and awakening the AI module at regular time by setting a threshold duration.
Specifically, the main control board of the monitoring device in the acquisition front end sets a threshold duration, and when the AI module is dormant and reaches the threshold duration, the AI module is triggered to wake up; or the like, or, alternatively,
the AI module sets a threshold duration, and enters a working state when the self dormancy duration reaches the threshold duration.
In the embodiment of the application, the AI module can be started in a timed manner by setting a timer.
Awakening mode 2: and the execution back end sends a wake-up instruction to the AI module.
Specifically, when the execution back end needs to acquire image and/or video information, a wake-up instruction is sent to the acquisition front end through a network, so that a main control board of a monitoring device in the acquisition front end sends the wake-up instruction to the AI module after receiving the wake-up instruction, and the AI module is restored to a normal working state from a dormant state.
Awakening mode 3: and the main control board of the monitoring device in the acquisition front end sends a wake-up instruction to the AI module.
Specifically, when the acquisition front end initiates an image analysis task, or a sensor in the acquisition front end triggers an alarm event to start an image analysis function in a linkage manner, the main control board of the monitoring device in the acquisition front end sends a wake-up instruction to the AI module, so that the AI module is restored from a dormant state to a normal working state.
For example, in the embodiment of the present application, a power consumption situation of each stage when the AI module is running and sleeping is briefly described with reference to fig. 7. W1 in fig. 7 represents power consumption generated when a module is in sleep, and W2 represents power consumption when a module is in normal operation.
Specifically, as shown in the stage P1 in fig. 7, the stage P1 mainly represents the power consumption change condition of the AI module from the power-on start to the power-on completion; a stage P2, which mainly embodies the power consumption of the AI module in completing the work of image transmission, preprocessing, reasoning, outputting analysis results and the like in the normal working process, wherein the AI module can complete the service processing work within ms-level time in the stage P2; a stage P3, in which the AI module changes its power consumption from a normal operating state to a low power consumption sleep state, wherein this stage is also completed only in ms-class time; a stage P4, which is a power consumption condition of the AI module sleep stage, in which the AI module sleeps with low power consumption and waits for waking up, and the sleep power consumption can be realized to be not higher than 100 mw; and a stage P5, wherein the AI module is in a wake-up stage, the AI module is finished from dormancy to wake-up start, link establishment and the like, and enters a normal working state, and the stage can be quickly finished within ms-level time.
According to the method, the functions of ultra-low power consumption dormancy and quick AI module awakening are realized, so that the electric quantity overhead of the power transmission line monitoring system can be effectively saved, and the reliability of the whole system is improved. And the AI module with low power consumption can realize that the real-time performance of the line monitoring is improved from a small time level to a minute level, thereby improving the safety of the line operation.
As shown in fig. 8, the process of monitoring the power transmission line based on the AI mode in the embodiment of the present application may specifically include the following steps:
s800: when image and/or video acquisition and analysis are needed, whether the acquisition front end is in a dormant state is judged, if yes, S801 is executed, and if not, S803 is executed.
The acquisition front end comprises an AI module or a built-in AI chip, and the acquisition front end is equipment directly adopting the AI chip as a master control.
S801: and awakening the acquisition front end in the dormant state.
The acquisition front end can be awakened from a dormant state through three modes of timing awakening, local triggering and remote awakening.
S802: and the acquisition front end is recovered to a normal working state from a dormant state.
S803: and the acquisition front end acquires field images and/or videos according to a preset position.
S804: and the acquisition front end runs an image recognition algorithm to carry out reasoning recognition on the acquired image.
S805: and the acquisition front end judges whether an alarm event needs to be reported according to the reasoning result, if so, S806 is executed, and if not, S807 is executed.
S806: and the acquisition front end transmits the target image and/or the target video to be reported and the analysis result to the execution back end through a wireless public network or a private network, and S808 is executed.
S807: the acquisition front end enters a sleep mode.
S808: the acquisition front end decides whether to need dormancy according to the service requirement or the working strategy, if so, executes step S807, and if not, executes step S809.
S809: the acquisition front end maintains a working state and executes subsequent operation according to the service requirement or the working strategy.
By the method, the AI module has extremely low operation power consumption, and can provide server-level calculation power, so that the image recognition algorithm can be operated in a front-end system. And the AI module can further reduce power consumption through a sleep mode and a quick start mode, the AI module has extremely low power consumption when in the sleep state, can be awakened in millisecond (ms) level when being awakened, and immediately enters the sleep state after image recognition. The AI module greatly reduces the average power consumption of system operation by compressing the power consumption time of the equipment, so that the whole system can continuously and stably work in 20-30 days without illumination under the condition of 10-20 Ah small battery power supply. Further, in practical application, the scheme based on front-end AI identification and low-power consumption control can be further expanded to other scenes with difficult return and power supply, such as unmanned aerial vehicle inspection, robot inspection, forest fire prevention, building site safety monitoring and the like.
As shown in fig. 9, the acquisition front end for monitoring a power transmission line according to the present application includes a processor 900, a memory 901, a camera 902, and a communication interface 903.
The processor 900 is responsible for managing the bus architecture and general processing, and the memory 901 may store data used by the processor 900 in performing operations. The camera 902 is used to capture images and/or video. The communication interface 903 is used for data communication between the processor 900 and the memory 901.
The processor 900 may be a Central Processing Unit (CPU), a Network Processor (NP), or a combination of a CPU and an NP. The processor 900 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a field-programmable gate array (FPGA), a General Array Logic (GAL), or any combination thereof. The memory 901 may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The processor 900, the memory 901, the camera 902 and the communication interface 903 are interconnected. Optionally, the processor 900, the memory 901, the camera 902 and the communication interface 903 may be connected to each other through a bus 904; the bus 904 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 9, but this does not indicate only one bus or one type of bus.
Specifically, the processor 900 is configured to read the computer program in the memory 901 to execute the method flows of S300 to S302 shown in fig. 3.
In one possible implementation, the processor 900 is specifically configured to:
preprocessing at least one acquired image and/or video; respectively matching the preprocessed images and/or videos with preset abnormal templates, wherein the preset abnormal templates comprise image templates and/or video templates for which the abnormality of the power transmission line is monitored; and determining the image and/or video with the matching similarity exceeding a set threshold with the preset abnormal template as a target image and/or a target video.
In one possible implementation, the analysis result includes at least one of:
the abnormal position coordinates of the power transmission line recorded by the target picture and/or the target video exist;
the abnormal category of the abnormal power transmission line recorded by the target picture and/or the target video exists;
and matching the target picture and/or the target video with a preset abnormal template to obtain the similarity exceeding a set threshold in the similarity.
In one possible implementation, the processor 900 determines the analysis result by:
determining the category corresponding to a preset abnormal template with the matching similarity of the target image and/or the target video exceeding a set threshold as an abnormal category contained in the analysis result;
determining the matching similarity of the target image and/or the target video and a preset abnormal template, wherein the matching similarity exceeds a set threshold value, as the similarity contained in the analysis result;
and determining the position coordinates of the abnormal power transmission line contained in the analysis result according to the acquisition angle and the acquisition direction of the target image and/or the target video.
In one possible implementation, the acquisition condition includes at least one of the following conditions:
the time length of the acquisition front end for not acquiring the images and/or videos reaches a first threshold time length; reaching the preset acquisition time; the acquisition front end receives an acquisition instruction sent by the execution back end; the acquisition front end receives an alarm event.
In one possible implementation, the processor 900 is further configured to:
and entering a dormant state after the target image and/or the target video and the analysis result are returned to the execution back end.
In one possible implementation, the processor 900 is further configured to wake up the acquisition front-end after the acquisition front-end enters a sleep state;
the processor 900 wakes up the acquisition front-end by:
if the sleeping time length is determined to reach the second threshold time length, triggering wakeup operation; or the like, or, alternatively,
if receiving a wake-up instruction sent by the execution back end, triggering wake-up operation; or the like, or, alternatively,
if an alarm event is received, triggering a wakeup operation; or the like, or, alternatively,
and if the acquisition condition is determined to be met, triggering the awakening operation.
In one possible implementation, the processor 900 is further configured to:
and analyzing and processing the acquired at least one image and/or video through an included AI module.
As shown in fig. 10, the present invention provides an acquisition front end for monitoring a power transmission line, where the acquisition front end includes:
the determination module 1000: the system is used for acquiring images and/or videos of the environment where the power transmission line is located after the acquisition condition is determined to be met;
the processing module 1001: the device comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring at least one image and/or video, and determining a target image and/or a target video which record the abnormal condition of the power transmission line and a corresponding analysis result by analyzing and processing the acquired at least one image and/or video;
the backhaul module 1002: and the analysis module is used for transmitting the target image and/or the target video and the analysis result back to the execution back end.
In a possible implementation method, the processing module 1001 is specifically configured to:
preprocessing at least one acquired image and/or video; respectively matching the preprocessed images and/or videos with preset abnormal templates, wherein the preset abnormal templates comprise image templates and/or video templates for which the abnormality of the power transmission line is monitored; and determining the image and/or video with the matching similarity exceeding a set threshold with the preset abnormal template as a target image and/or a target video.
In one possible implementation, the analysis result includes at least one of:
the abnormal position coordinates of the power transmission line recorded by the target picture and/or the target video exist; the abnormal category of the abnormal power transmission line recorded by the target picture and/or the target video exists; and matching the target picture and/or the target video with a preset abnormal template to obtain the similarity exceeding a set threshold in the similarity.
In one possible implementation, the processing module 1001 determines the analysis result by:
determining the category corresponding to a preset abnormal template with the matching similarity of the target image and/or the target video exceeding a set threshold as an abnormal category contained in the analysis result;
determining the matching similarity of the target image and/or the target video and a preset abnormal template, wherein the matching similarity exceeds a set threshold value, as the similarity contained in the analysis result;
and determining the position coordinates of the abnormal power transmission line contained in the analysis result according to the acquisition angle and the acquisition direction of the target image and/or the target video.
In one possible implementation, the acquisition condition includes at least one of the following conditions:
the time length of the acquisition front end for not acquiring the images and/or videos reaches a first threshold time length; reaching the preset acquisition time; the acquisition front end receives an acquisition instruction sent by the execution back end;
the acquisition front end receives an alarm event.
In a possible implementation method, the processing module 1001 is further configured to:
and entering a dormant state after the target image and/or the target video and the analysis result are returned to the execution back end.
In a possible implementation method, after the acquisition front end enters a sleep state, the processing module 1001 is further configured to wake up the acquisition front end;
the processing module 1001 wakes up the acquisition front-end by:
if the sleeping time length is determined to reach the second threshold time length, triggering wakeup operation; or the like, or, alternatively,
if receiving a wake-up instruction sent by the execution back end, triggering wake-up operation; or the like, or, alternatively,
if an alarm event is received, triggering a wakeup operation; or the like, or, alternatively,
and if the acquisition condition is determined to be met, triggering the awakening operation.
In a possible implementation method, the processing module 1001 is further configured to:
and analyzing and processing the acquired at least one image and/or video through an included AI module.
In some possible embodiments, the various aspects of the method for monitoring a power transmission line provided by the embodiments of the present invention may also be implemented in the form of a program product, which includes program code for causing a computer device to perform the steps of the method for monitoring a power transmission line according to various exemplary embodiments of the present invention described in this specification, when the program code runs on the computer device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
According to the program product for monitoring the power transmission line, the program product can adopt a portable compact disc read only memory (CD-ROM) and comprises program codes, and can run on a server device. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an information delivery, apparatus, or device.
Readable signal media may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the periodic network action system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device.
The embodiment of the application also provides a storage medium readable by computing equipment aiming at the method for monitoring the power transmission line, namely, the content is not lost after the power failure. The storage medium stores therein a software program, which includes program code, and when the program code runs on a computing device, the software program is read and executed by one or more processors to implement any of the above schemes for terminal-side device identity authentication in embodiments of the present application.
The present application is described above with reference to block diagrams and/or flowchart illustrations of methods, apparatus (systems) and/or computer program products according to embodiments of the application. It will be understood that one block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, 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, and/or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer and/or other programmable data processing apparatus, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.
Accordingly, the subject application may also be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). Furthermore, the present application may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. In the context of this application, a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include such modifications and variations.
Claims (18)
1. A method for monitoring a power transmission line is characterized by being applied to a power transmission line monitoring system, wherein the power transmission line monitoring system comprises a collection front end for collecting and analyzing images and/or videos and an execution back end for performing decision management;
the method comprises the following steps:
after the acquisition front end determines that the acquisition conditions are met, acquiring images and/or videos of the environment where the power transmission line is located;
the acquisition front end analyzes and processes at least one acquired image and/or video, and determines and records a target image and/or a target video with abnormity of the power transmission line and a corresponding analysis result;
and the acquisition front end transmits the target image and/or the target video and the analysis result back to the execution rear end.
2. The method of claim 1, wherein the determining, by the acquisition front end, the target image and/or the target video in which the abnormality of the power transmission line is recorded by analyzing and processing the acquired at least one image and/or video comprises:
the acquisition front end preprocesses at least one acquired image and/or video;
respectively matching the preprocessed images and/or videos with preset abnormal templates, wherein the preset abnormal templates comprise image templates and/or video templates for which the abnormality of the power transmission line is monitored;
and determining the image and/or video with the matching similarity exceeding a set threshold with the preset abnormal template as a target image and/or a target video.
3. The method of claim 1 or 2, wherein the analysis results comprise at least one of:
the abnormal position coordinates of the power transmission line recorded by the target picture and/or the target video exist;
the abnormal category of the abnormal power transmission line recorded by the target picture and/or the target video exists;
and matching the target picture and/or the target video with a preset abnormal template to obtain the similarity exceeding a set threshold in the similarity.
4. The method of claim 3, wherein the acquisition front end determines the analysis result by:
the acquisition front end determines the category corresponding to a preset abnormal template with the matching similarity of the target image and/or the target video exceeding a set threshold as the abnormal category contained in the analysis result;
the acquisition front end determines the matching similarity of the target image and/or the target video and a preset abnormal template, wherein the matching similarity exceeds a set threshold value, as the similarity contained in the analysis result;
and the acquisition front end determines the position coordinates of the abnormal power transmission line contained in the analysis result according to the acquisition angle and the acquisition direction of the target image and/or the target video.
5. The method of claim 1, wherein the acquisition conditions include at least one of:
the time length of the acquisition front end for not acquiring the images and/or videos reaches a first threshold time length; reaching the preset acquisition time; the acquisition front end receives an acquisition instruction sent by the execution back end;
the acquisition front end receives an alarm event.
6. The method of any one of claims 1-5, further comprising:
and when the acquisition front end returns the target image and/or the target video and the analysis result to the execution back end, the acquisition front end enters a dormant state.
7. The method of claim 6, wherein after the acquisition front end enters a sleep state, the method further comprises waking the acquisition front end;
the acquisition front-end wakes up the acquisition front-end by:
if the acquisition front end determines that the dormancy duration reaches a second threshold duration, triggering wakeup operation; or
If the acquisition front end receives a wake-up instruction sent by the execution back end, triggering wake-up operation; or
If the acquisition front end receives an alarm event, triggering a wakeup operation; or
And if the acquisition front end determines that the acquisition condition is met, triggering an awakening operation.
8. The method of any one of claims 1-7, further comprising:
the acquisition front end analyzes and processes at least one acquired image and/or video through an included AI module.
9. The utility model provides an acquisition front end to transmission line control which characterized in that includes: the system comprises a processor, a memory, a camera and a communication interface;
the communication interface is used for receiving and sending data;
the memory for storing a computer program;
the camera is used for collecting images and/or videos;
the processor is used for reading and executing the computer program stored in the memory so as to execute the following operations:
after the acquisition condition is determined to be met, acquiring images and/or videos of the environment where the power transmission line is located through the camera; analyzing and processing at least one collected image and/or video, and determining a target image and/or a target video recorded with the abnormality of the power transmission line and a corresponding analysis result; and transmitting the target image and/or the target video and the analysis result back to an execution rear end in the power transmission line monitoring system through a communication interface.
10. The acquisition front-end of claim 9, wherein the processor, when executing the computer program stored in the memory, is specifically configured to:
preprocessing at least one acquired image and/or video; respectively matching the preprocessed images and/or videos with preset abnormal templates, wherein the preset abnormal templates comprise image templates and/or video templates for which the abnormality of the power transmission line is monitored; and determining the image and/or video with the matching similarity exceeding a set threshold with the preset abnormal template as a target image and/or a target video.
11. The acquisition front-end of claim 9 or 10, characterized in that the analysis results comprise at least one of:
the abnormal position coordinates of the power transmission line recorded by the target picture and/or the target video exist;
the abnormal category of the abnormal power transmission line recorded by the target picture and/or the target video exists;
and matching the target picture and/or the target video with a preset abnormal template to obtain the similarity exceeding a set threshold in the similarity.
12. The acquisition front end of claim 11, wherein the processor, when executing the computer program stored in the memory, determines the analysis result by:
determining the category corresponding to a preset abnormal template with the matching similarity of the target image and/or the target video exceeding a set threshold as the abnormal category contained in the analysis result;
determining the matching similarity of the target image and/or the target video and a preset abnormal template, wherein the matching similarity exceeds a set threshold value, as the similarity contained in the analysis result;
and determining the position coordinates of the abnormal power transmission line contained in the analysis result according to the acquisition angle and the acquisition direction of the target image and/or the target video.
13. The acquisition front-end of claim 9, characterized in that the acquisition conditions comprise at least one of the following conditions:
the time length of the acquisition front end for not acquiring the images and/or videos reaches a first threshold time length;
reaching the preset acquisition time;
the acquisition front end receives an acquisition instruction sent by the execution back end;
the acquisition front end receives an alarm event.
14. The acquisition front-end of any one of claims 9 to 13, wherein the processor, when executing the computer program stored in the memory, is further configured to:
and entering a dormant state after the target image and/or the target video and the analysis result are returned to the execution back end through the communication interface.
15. The acquisition front end of claim 14, wherein the processor, when executing the computer program stored in the memory, is further configured to:
when the acquisition front end enters a dormant state, awakening the acquisition front end;
the processor wakes up the acquisition front end by:
if the sleeping time length is determined to reach the second threshold time length, triggering wakeup operation; or
If receiving a wake-up instruction sent by the execution back end, triggering wake-up operation; or
If an alarm event is received, triggering a wakeup operation; or
And if the acquisition condition is determined to be met, triggering the awakening operation.
16. The acquisition front-end of any one of claims 9 to 15, wherein the processor, when executing the computer program stored in the memory, is specifically configured to:
and analyzing and processing the acquired at least one image and/or video through the AI module.
17. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of any one of claims 1 to 8.
18. A computer program product comprising computer executable instructions for causing a computer to perform the method of any one of claims 1 to 8.
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| CN114513835A (en) * | 2022-01-07 | 2022-05-17 | 国网新疆电力有限公司巴州供电公司 | Low-power-consumption control device and method for power transmission line |
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