CN117288388A - Intelligent monitoring method for gas leakage and related equipment - Google Patents
Intelligent monitoring method for gas leakage and related equipment Download PDFInfo
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Abstract
The application discloses a method and related equipment for intelligent monitoring of gas leakage, and relates to the field of gas leakage monitoring, wherein the method comprises the following steps: acquiring a plurality of pieces of image information based on a robot array; acquiring current scene information based on all the image information, wherein the scene information comprises an urban center scene, an urban suburban scene and a wild scene; determining an arrangement array type and a sweeping mode of the robot array based on the scene information; acquiring smell concentration information and wind speed information acquired by each robot in the robot array in the sweeping process; and determining gas leakage position information based on all the odor concentration information and the wind speed information.
Description
Technical Field
The present disclosure relates to the field of gas leakage monitoring, and more particularly, to a method and related apparatus for intelligent monitoring of gas leakage.
Background
In the gas leakage monitoring process in the prior art, a worker usually controls a robot through a terminal device to acquire relevant gas concentration information, determines a leakage position based on the gas concentration information, and sends position coordinates to the terminal of the worker. However, in some places with poor GPS positioning, the position information is often inaccurate, and by adopting a terminal to perform control, staff needs to be trained in advance to perform effective monitoring by controlling the monitoring device.
Disclosure of Invention
In the summary, a series of concepts in a simplified form are introduced, which will be further described in detail in the detailed description. The summary of the present application is not intended to define the key features and essential features of the claimed subject matter, nor is it intended to be used to determine the scope of the claimed subject matter.
In a first aspect, the present application proposes a method for intelligently monitoring gas leakage, where the method includes:
acquiring a plurality of pieces of image information based on a robot array;
acquiring current scene information based on all the image information, wherein the scene information comprises an urban center scene, an urban suburban scene and a wild scene;
determining an arrangement array type and a sweeping mode of the robot array based on the scene information;
acquiring smell concentration information and wind speed information acquired by each robot in the robot array in the sweeping process;
and determining gas leakage position information based on all the odor concentration information and the wind speed information.
In one embodiment, the acquiring the current scene information based on all the image information includes:
inputting all the image information into a scene recognition model, wherein the scene recognition model comprises a convolution module, at least two feature extraction modules, an average pooling layer, a full-connection layer and an output layer;
and outputting the current scene information according to the scene recognition model.
In one embodiment, determining the array pattern and the sweep pattern of the robot array based on the scene information includes:
controlling the arrangement mode of the robot array to be an annular arrangement array mode and controlling the sweeping mode to be unidirectional linear sweeping when the scene information is the urban center scene; and/or the number of the groups of groups,
controlling the arrangement mode of the robot array to be a double-arc arrangement array mode under the condition that the scene information is the urban suburban scene, wherein the sweeping mode is a zigzag line sweeping mode; and/or
When the scene information is the outdoor scene, the arrangement mode of the robot array is controlled to be a double rectangular arrangement mode, and the sweep mode is bidirectional straight line sweep.
In one embodiment, the method further comprises:
and when the odor concentration information measured by all robots in the robot array is larger than the preset concentration and/or the difference value of the odor concentration information measured by any two robots is larger than the preset concentration difference, adjusting the arrangement mode into an annular arrangement mode and adjusting the sweeping mode into square wave-shaped sweeping.
In one embodiment, the determining the gas leakage position information based on all of the odor concentration information and the wind speed information includes:
all the odor concentration information and the wind speed information are reverse fitted based on a gaussian diffusion model to obtain the gas leakage position information.
In one embodiment, the method further comprises:
determining leakage position distance information based on the leakage position information and monitoring task start position information when the leakage position information is determined;
determining robot pitch information based on the leakage position distance information and the number information of the robot arrays;
and controlling all robots of the robot array to move based on the robot pitch information to form a robot landmark with the robot pitch information as a space.
In one embodiment, the method further comprises:
and controlling the flickering frequency and/or the color information of the prompting lamplight of each robot by the distance information of the robot road sign and the leakage position.
In a second aspect, the present application further provides an intelligent monitoring device for gas leakage, including:
a first acquisition unit configured to acquire a plurality of pieces of image information based on a robot array;
the second acquisition unit is used for acquiring current scene information based on all the image information, wherein the scene information comprises a city center scene, a city suburb scene and a field scene;
a first determining unit configured to determine an arrangement pattern and a sweep pattern of the robot array based on the scene information;
a third obtaining unit, configured to obtain smell concentration information and wind speed information obtained by each robot in the robot array during a sweeping process;
and a second determining unit configured to determine gas leakage position information based on all of the odor concentration information and the wind speed information.
In a third aspect, an electronic device, comprising: a memory, a processor and a computer program stored in and executable on the processor for performing the steps of the method for intelligent monitoring of gas leakage according to any of the first aspects described above when the computer program stored in the memory is executed.
In a fourth aspect, the present application also proposes a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of intelligent monitoring of gas leakage of any of the above aspects.
In summary, the method for intelligently monitoring gas leakage in the embodiment of the application comprises the following steps: acquiring a plurality of pieces of image information based on a robot array; acquiring current scene information based on all the image information, wherein the scene information comprises an urban center scene, an urban suburban scene and a wild scene; determining an arrangement array type and a sweeping mode of the robot array based on the scene information; acquiring smell concentration information and wind speed information acquired by each robot in the robot array in the sweeping process; and determining gas leakage position information based on all the odor concentration information and the wind speed information. According to the gas leakage monitoring method, the arrangement mode and the scanning mode of the robot can be flexibly selected according to different scene information, so that the effect of gas leakage monitoring can be optimized, potential gas leakage sources can be timely found, and potential environmental and safety risks are reduced. Selecting an appropriate strategy helps achieve better monitoring results in various scenarios.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the specification. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a schematic flow chart of a method for intelligent monitoring of gas leakage according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a scene recognition model according to an embodiment of the present application;
fig. 3 is a schematic view of a scenario for monitoring a city center according to an embodiment of the present disclosure;
FIG. 4 is a schematic view of a suburban monitoring scenario provided in an embodiment of the present application;
fig. 5 is a schematic view of a field monitoring scenario provided in an embodiment of the present application;
fig. 6 is a schematic diagram of a gas leakage accurate detection scenario provided in an embodiment of the present application;
fig. 7 is a schematic diagram of robot guidepost guidance provided in an embodiment of the present application;
fig. 8 is a schematic structural diagram of a device for intelligent monitoring of gas leakage according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device for intelligent monitoring of gas leakage according to an embodiment of the present application.
Detailed Description
The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application.
Referring to fig. 1, a schematic flow chart of a method for intelligently monitoring gas leakage provided in an embodiment of the present application may specifically include:
s110, acquiring a plurality of pieces of image information based on a robot array;
illustratively, the robots carry an image sensor, a gas concentration sensor and a wind velocity sensor, and a plurality of robots form a robot array, each robot capable of acquiring image information.
S120, acquiring current scene information based on all the image information, wherein the scene information comprises an urban center scene, an urban suburban scene and a wild scene;
illustratively, inputting image information into the value image recognition model can automatically recognize work scenes, such as urban center scenes, suburban scenes, outdoor scenes, and the like.
S130, determining an array type and a sweep mode of the robot array based on the scene information;
illustratively, the arrangement array type of the robot array is determined according to different scenes, the arrangement array type is the formation of the robots in the robot array, and the sweeping mode is the scanning route of the robot array.
S140, acquiring smell concentration information and wind speed information acquired by each robot in the robot array in the sweeping process;
for example, the odor concentration information and the wind speed information may be acquired by a gas concentration sensor and a wind speed sensor carried by the robot.
And S150, determining gas leakage position information based on all the smell concentration information and the wind speed information.
For example, the gas leakage position information may be calculated by the scent concentration information and the wind speed information acquired by the robot.
In summary, the gas leakage monitoring method provided by the application flexibly selects the arrangement mode and the scanning mode of the robot according to different scene information, so that the gas leakage monitoring effect can be optimized, the potential gas leakage source can be timely found, and the potential environment and safety risk can be reduced. Selecting an appropriate strategy helps achieve better monitoring results in various scenarios.
In one embodiment, the acquiring the current scene information based on all the image information includes:
inputting all the image information into a scene recognition model, wherein the scene recognition model comprises a convolution module, at least two feature extraction modules, an average pooling layer, a full-connection layer and an output layer;
and outputting the current scene information according to the scene recognition model.
Illustratively, as shown in FIG. 2, the network structure of the scene recognition model includes a conventional convolution module, 11 feature extraction modules, a global averaging pooling layer, a full connection layer, and an output layer. The feature extraction module consists of a multi-scale residual error module and an attention mechanism module. The improved network model is divided into three stages, namely front stage, middle stage and rear stage, and each stage has different characteristics.
The first stage: and inputting a training set image sample, and extracting conventional convolution characteristics. The feature map is then input to feature extraction modules a1 and b1 and downsampled by max pooling (feature map size becomes 1/2 of the original).
And a second stage: and 5 feature extraction modules (a 2-e 2) are adopted to further learn the feature images, and the feature image size is changed into 1/4 of the original image through maximum pooling.
And a third stage: comprising modules a3 and b3. And finally, obtaining a final classification result through global average pooling and a full connection layer. The jump connection is used to merge the multi-scale feature maps to avoid information loss caused by deep networks. The attention mechanism is used to learn the interdependencies between feature map channels and emphasize useful features.
The scene recognition model type adopted by the method adopts a multi-scale residual error module, and can effectively capture image features of different scales. This helps the model to better understand the details and overall information in the image, improving the performance of image classification. The jump connection merges the multi-scale characteristic diagrams, and the problem of information loss in a deep network is avoided. Through jump connection, the model can better understand image semantics by utilizing low-level and high-level characteristic information, and classification accuracy is improved. The introduction of the attention mechanism helps the model learn the interdependencies between the feature map channels, emphasizing useful features. The method can improve the attention degree of the model to the key features, reduce the interference to the noise features and further improve the classification performance.
In one embodiment, determining the array pattern and the sweep pattern of the robot array based on the scene information includes:
controlling the arrangement mode of the robot array to be an annular arrangement array mode and controlling the sweeping mode to be unidirectional linear sweeping when the scene information is the urban center scene; and/or the number of the groups of groups,
controlling the arrangement mode of the robot array to be a double-arc arrangement array mode under the condition that the scene information is the urban suburban scene, wherein the sweeping mode is a zigzag line sweeping mode; and/or
When the scene information is the outdoor scene, the arrangement mode of the robot array is controlled to be a double rectangular arrangement mode, and the sweep mode is bidirectional straight line sweep.
Exemplary, as shown in fig. 3, is a schematic view of a scenario monitored by a city center. The robot array adopts an annular array type, and six robots, namely 1, 2, 3, 4, 5 and 6, are annularly arranged, and are narrow in roads and dense in buildings in the urban center, so that the whole area can be covered to the greatest extent by adopting the annular array type, the robots can reach narrow areas and corners, and the monitoring coverage rate is improved. The scanning mode adopts unidirectional linear scanning, the M direction is the scanning direction, the unidirectional linear scanning can ensure that the robot covers the whole urban central area in a consistent direction, and the method is favorable for rapidly detecting a gas leakage source and taking timely measures.
As shown in fig. 4, a schematic view of a suburban monitoring scenario is shown. The arrangement mode adopts a double-arc arrangement array mode, namely 1, 6 and 5 form an arc arrangement, 2, 3 and 4 form an arc arrangement, the urban suburban area usually has a larger space, the double-arc arrangement array mode can effectively cover a wide area, and meanwhile, the interference between robots is reduced. The scanning mode adopts the broken line scanning 1, 6 and 5 to move in the left side of the broken line along the M1 direction, and the scanning mode adopts the M2 to move in the right side of the broken line along the 2, so that the broken line scanning can adapt to different terrains, for example, special terrains such as hills or water areas possibly exist in suburbs, and the like, and the mode is helpful for fully covering the areas.
As shown in fig. 5, a scene diagram of field monitoring is shown. The arrangement mode adopts a double-rectangular arrangement matrix type, namely 1, 2, 3 and 4 form a first rectangle, 5, 6, 7 and 8 form a second rectangle, the field scene generally comprises wide open areas, and the double-rectangular arrangement matrix type can flexibly cover the open areas and simultaneously maintain uniform distribution among robots. The scanning mode adopts bi-directional linear scanning, namely M1 and M2 directions are adopted in a distribution mode, in the field, the robot can perform linear scanning in the two directions, more ground is covered, and monitoring efficiency is improved.
In summary, the intelligent monitoring method for gas leakage provided by the embodiment of the application flexibly selects the arrangement mode and the scanning mode of the robot according to different scene information, so that the effect of gas leakage monitoring can be optimized, the potential gas leakage source can be ensured to be found in time, and the potential environment and safety risk are reduced. Selecting an appropriate strategy helps achieve better monitoring results in various scenarios.
In one embodiment, the method further comprises:
and when the odor concentration information measured by all robots in the robot array is larger than the preset concentration and/or the difference value of the odor concentration information measured by any two robots is larger than the preset concentration difference, adjusting the arrangement mode into an annular arrangement mode and adjusting the sweeping mode into square wave-shaped sweeping.
For example, in the case where the odor concentration information measured by the robots is greater than the preset concentration and/or the difference between the odor concentration information measured by any two robots is greater than the preset concentration difference, the possibility of leakage in the area is proved to be very high, in this case, the robot array is arranged in a circular manner as shown in fig. 6, and the sweeping manner is adjusted to be a square wave-shaped sweep, i.e., an M direction, in either the urban center, the suburban area or the field environment. Square wave scanning methods typically involve the robot moving back and forth in two directions, which can more frequently traverse potential sources of gas leakage, ensuring higher detection accuracy. When the difference value between the smell concentration information is larger than the preset concentration difference, the scanning mode can better position the gas leakage source.
In one embodiment, the determining the gas leakage position information based on all of the odor concentration information and the wind speed information includes:
all the odor concentration information and the wind speed information are reverse fitted based on a gaussian diffusion model to obtain the gas leakage position information.
For example, odor concentration information and wind velocity information measured by each robot in the array of robots are obtained, which should include gas concentration, wind velocity components, and position information in order to train and reverse fit the model. The effect of gas leakage was modeled using the formula of the gaussian diffusion model. The parameters of the model include gas leakage efficiency Q, wind speed components u, v, w, vertical and horizontal diffusion coefficients sigma y Sum sigma z And a leakage source depth H. The training process involves fitting the collected scent concentration information and wind velocity information to known leak source location information to determine model parameters. Once model training is complete, the fitted model may be used for reverse fitting. I.e. the possible gas leakage location information is deduced back from the known odour concentration information and wind speed information. The method is realized by solving the inverse problem of the Gaussian diffusion model, namely, given concentration information and wind speed information, and solving the leakage source position. By reverse fitting, an estimate of the gas leak location can be obtained, which location estimate can be used to guide further emergency response measures.
In one embodiment, the method further comprises:
determining leakage position distance information based on the leakage position information and monitoring task start position information when the leakage position information is determined;
determining robot pitch information based on the leakage position distance information and the number information of the robot arrays;
and controlling all robots of the robot array to move based on the robot pitch information to form a robot landmark with the robot pitch information as a space.
Illustratively, as shown in fig. 7, the star-shaped positions are leakage positions, the pentagon-shaped positions are monitoring task start positions, and 1, 2, 3, 4, 5, and 6 are arranged at equal intervals based on the leakage positions and the monitoring task start positions. And under the condition that the leakage position is determined, determining distance information of the leakage position and the starting position of the monitoring task, determining distance information of robots based on the distance information of the leakage position and the number information of the robot arrays, controlling the robots to move, keeping the distance information between the adjacent robots to be the same, and taking the robots as robot signposts. Therefore, an operator only needs to wait in situ after the monitoring task starts, and after the leakage position is detected, the detection personnel can find the leakage position and perform corresponding treatment according to the robot road signs rearranged by the array robots. The road sign formed by the robot does not need to carry a terminal to inquire specific coordinate information, the operation is simpler, and meanwhile, the method can also play a good role in the area with poor GPS positioning.
In one embodiment, the method further comprises:
and controlling the flickering frequency and/or the color information of the prompting lamplight of each robot by the distance information of the robot road sign and the leakage position.
For example, on robotic roadways, cue lights may be provided that may flash or change color to provide more visual information. The cue lights of robots closer to the source of the leak may blink at a higher frequency or display a particular color to indicate that the leak is closer. The use of the prompting light enables the information about the leakage source to be informed to operators more intuitively and in real time, and is beneficial to improving the efficiency and safety of emergency response.
Referring to fig. 8, an embodiment of an apparatus for intelligent monitoring of gas leakage in an embodiment of the present application may include:
a first acquisition unit 21 for acquiring a plurality of pieces of image information based on the robot array;
a second acquiring unit 22 for acquiring current scene information based on all the image information, wherein the scene information includes a city center scene, a suburban scene, and a field scene;
a first determining unit 23 configured to determine an arrangement pattern and a sweep pattern of the robot array based on the scene information;
a third acquiring unit 24, configured to acquire smell concentration information and wind speed information acquired by each robot in the robot array during the sweeping process;
a second determining unit 25 for determining gas leakage position information based on all the above odor concentration information and the above wind speed information.
As shown in fig. 9, the embodiment of the present application further provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and capable of running on the processor, where the processor 320 implements any one of the steps of the method for intelligent monitoring of gas leakage described above when the processor 320 executes the computer program 311.
Since the electronic device described in this embodiment is a device for implementing an apparatus for intelligent monitoring of gas leakage in this embodiment, based on the method described in this embodiment, those skilled in the art can understand the specific implementation of the electronic device in this embodiment and various modifications thereof, so how to implement the method in this embodiment in this electronic device will not be described in detail herein, and only those devices for implementing the method in this embodiment by those skilled in the art are within the scope of protection intended in this application.
In a specific implementation, the computer program 311 may implement any of the embodiments corresponding to fig. 1 when executed by a processor.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Embodiments of the present application also provide a computer program product comprising computer software instructions that, when run on a processing device, cause the processing device to perform the flow of intelligent monitoring of gas leakage in the corresponding embodiments
The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriberline, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer readable storage media can be any available media that can be stored by a computer or data storage devices such as servers, data centers, etc. that contain an integration of one or more available media. Usable media may be magnetic media (e.g., floppy disks, hard disks, magnetic tape), optical media (e.g., DVD), or semiconductor media (e.g., solid State Disk (SSD)), or the like.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RandomAccessMemory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. The intelligent monitoring method for gas leakage is characterized by comprising the following steps:
acquiring a plurality of pieces of image information based on a robot array;
acquiring current scene information based on all the image information, wherein the scene information comprises an urban center scene, an urban suburban scene and a wild scene;
determining an arrangement array type and a sweeping mode of the robot array based on the scene information;
acquiring smell concentration information and wind speed information acquired by each robot in the robot array in the sweeping process;
and determining gas leakage position information based on all the smell concentration information and the wind speed information.
2. The intelligent monitoring method for gas leakage according to claim 1, wherein the acquiring current scene information based on all the image information comprises:
inputting all the image information into a scene recognition model, wherein the scene recognition model comprises a convolution module, at least two feature extraction modules, an average pooling layer, a full-connection layer and an output layer;
and outputting the current scene information according to the scene recognition model.
3. The method for intelligent monitoring of gas leakage according to claim 1, wherein determining an arrangement pattern and a sweep pattern of the robot array based on the scene information comprises:
controlling the arrangement mode of the robot array to be annular arrangement array mode under the condition that the scene information is the urban center scene, wherein the sweeping mode is unidirectional linear sweeping; and/or the number of the groups of groups,
controlling the arrangement mode of the robot array to be a double-arc arrangement array under the condition that the scene information is the suburban scene, wherein the sweeping mode is a zigzag line sweeping; and/or
And controlling the arrangement mode of the robot array to be a double rectangular arrangement array mode under the condition that the scene information is the field scene, and controlling the sweeping mode to be bidirectional linear sweeping.
4. The intelligent monitoring method for gas leakage according to claim 3, further comprising:
and when the odor concentration information measured by all robots in the robot array is larger than the preset concentration and/or the difference value of the odor concentration information measured by any two robots is larger than the preset concentration difference, adjusting the arrangement mode into an annular arrangement mode and adjusting the sweeping mode into square wave-shaped sweeping.
5. The gas leakage intelligent monitoring method according to claim 1, wherein the determining gas leakage position information based on all the odor concentration information and the wind speed information includes:
all the odor concentration information and the wind speed information are reversely fitted based on a Gaussian diffusion model to obtain the gas leakage position information.
6. The intelligent monitoring method of gas leakage according to claim 1, further comprising:
determining leakage position distance information based on the leakage position information and monitoring task start position information under the condition that the leakage position information is determined;
determining robot pitch information based on the leakage position distance information and the number information of the robot arrays;
and controlling all robots of the robot array to move based on the robot pitch information to form a robot landmark with the robot pitch information as a space.
7. The intelligent monitoring method of gas leakage according to claim 6, further comprising:
and controlling the flickering frequency and/or the color information of the prompting lamplight of each robot by the distance information of the robot road sign and the leakage position.
8. An intelligent monitoring device is revealed to gas, its characterized in that includes:
a first acquisition unit configured to acquire a plurality of pieces of image information based on a robot array;
the second acquisition unit is used for acquiring current scene information based on all the image information, wherein the scene information comprises a city center scene, a city suburb scene and a field scene;
the first determining unit is used for determining an arrangement array type and a sweeping mode of the robot array based on the scene information;
the third acquisition unit is used for acquiring smell concentration information and wind speed information acquired by each robot in the robot array in the sweeping process;
and a second determining unit configured to determine gas leakage position information based on all of the odor concentration information and the wind speed information.
9. An electronic device, comprising: memory and processor, characterized in that the processor is adapted to carry out the steps of the method for intelligent monitoring of gas leakage according to any of claims 1-7 when executing a computer program stored in the memory.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements a method for intelligent monitoring of gas leakage according to any of claims 1-7.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311271959.4A CN117288388A (en) | 2023-09-28 | 2023-09-28 | Intelligent monitoring method for gas leakage and related equipment |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311271959.4A CN117288388A (en) | 2023-09-28 | 2023-09-28 | Intelligent monitoring method for gas leakage and related equipment |
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| CN117288388A true CN117288388A (en) | 2023-12-26 |
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| CN202311271959.4A Pending CN117288388A (en) | 2023-09-28 | 2023-09-28 | Intelligent monitoring method for gas leakage and related equipment |
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