CN111111074B - Fire extinguishing scheduling method and system for power tunnel fire-fighting robot - Google Patents
Fire extinguishing scheduling method and system for power tunnel fire-fighting robot Download PDFInfo
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Abstract
The utility model provides a fire extinguishing dispatching method and a fire extinguishing dispatching system for a fire-fighting robot in an electric power tunnel, wherein the dispatching method comprises the following steps: acquiring tunnel environment state data, and pre-judging the position and the intensity of a suspected fire source by adopting a transverse data fitting and longitudinal data fitting method according to the tunnel environment state data; scheduling the video robot to be close to the suspected fire source position within a safe distance, and judging the development stage of the fire; according to the development stage of the fire, the fire-fighting robots around are dispatched in a unified way according to the intensity of the fire, and the fire-fighting robots are dispatched to the fire occurrence site to carry out fire-fighting and fire-extinguishing treatment; and (4) evacuating the fire-fighting robot after the completion of the scheduling and spraying from the fire source site, acquiring the fire condition continuous monitoring data of the video robot in real time, and carrying out secondary fire extinguishing treatment once after re-burning. The fire monitoring and extinguishing device can realize automatic fire monitoring and extinguishing, ensures the safe operation of power equipment, does not need personnel to watch, does not need human intervention, greatly reduces the labor cost and improves the working safety of personnel.
Description
Technical Field
The disclosure relates to the technical field related to robot scheduling control, in particular to a fire extinguishing scheduling method and system for a fire-fighting robot in an electric power tunnel.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The electric tunnel is just like a main artery of a city, and continuously transmits electric energy to thousands of households. The safety of the electric power tunnel is a prerequisite guarantee for normal operation of the whole city.
In recent years, due to reasons such as line aging and substandard construction process, fire occurs inside the tunnel. Due to the characteristics of being limited by the internal structure of the tunnel, closed space and the like, once a fire disaster happens, personnel are difficult to enter for rescue. Although fire extinguishing devices are installed in part of tunnels, the fire extinguishing devices are fixedly installed, and accurate fire extinguishing cannot be realized when a fire disaster occurs. In order to solve the problem, the property loss is reduced to the minimum, the electric power tunnel fire-fighting robot is generated at the same time, and how to rapidly and efficiently dispatch the fire-fighting robot to perform fire extinguishing work is a problem to be solved.
Disclosure of Invention
In order to solve the problems, the fire extinguishing scheduling method and system for the fire-fighting robot in the electric power tunnel are provided, the fire source in the electric power tunnel can be accurately judged, the fire can be quickly and efficiently extinguished, personal injury is avoided, property loss is reduced to the minimum, and therefore safe operation of electric power equipment is guaranteed.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
one or more embodiments provide a fire extinguishing scheduling method for a power tunnel fire-fighting robot, comprising the following steps:
acquiring tunnel environment state data, and pre-judging the position and the intensity of a suspected fire source by adopting a transverse data fitting and longitudinal data fitting method according to the tunnel environment state data;
the video robot is dispatched to be close to the suspected fire source position within a safe distance, the development stage of the fire is judged according to the data collected by the video robot, and whether fire fighting treatment needs to be carried out or not is judged;
according to the development stage of the fire, uniformly scheduling the surrounding fire-fighting robots according to the intensity of the fire, generating a planned path corresponding to the fire-fighting robots, and scheduling the fire-fighting robots to a fire occurrence site for fire-fighting and fire-extinguishing treatment;
and (4) evacuating the fire-fighting robot after the completion of the scheduling and spraying from the fire source site, acquiring the fire condition continuous monitoring data of the video robot in real time, judging whether the fire is reignited, and if so, performing secondary fire extinguishing treatment.
One or more embodiments provide an electric power tunnel fire-fighting robot fire-extinguishing dispatching system, which is characterized in that: the electric power tunnel fire-fighting robot fire-fighting scheduling method comprises a monitoring platform and various robots arranged on an electric power tunnel at fixed points, wherein the robots comprise an inspection robot, a video robot and a fire-fighting robot, and further comprise an environmental information monitoring unit arranged in the electric power tunnel at the fixed points, the monitoring platform is connected with the environmental information monitoring unit and the robots respectively, and the monitoring platform executes the electric power tunnel fire-fighting robot fire-fighting scheduling method.
One or more embodiments provide a power tunnel fire-fighting robot fire-fighting scheduling system, including:
a suspected fire source prejudgment module: the system is used for acquiring tunnel environment state data and pre-judging the position and the intensity of a suspected fire source by adopting a transverse data fitting and longitudinal data fitting method according to the tunnel environment state data;
the fire development stage judgment module: the video robot is used for dispatching the video robot to be close to the suspected fire source position within a safe distance, judging the development stage of the fire through data collected by the video robot, judging whether fire fighting treatment needs to be carried out, if so, informing relevant personnel to process, sending alarm information, executing the next step, and if not, sending the alarm information to inform the relevant personnel to process;
fire-fighting robot scheduling module: the fire fighting robot is used for uniformly scheduling the surrounding fire fighting robots according to the development stage of the fire and the intensity of the fire, generating a planned path corresponding to the fire fighting robots, and scheduling the fire fighting robots to a fire occurrence site for fire fighting and extinguishing;
an evacuation monitoring scheduling module: the fire-fighting robot is used for dispatching the fire-fighting robot after the spraying is finished to evacuate the fire source site, acquiring the fire condition continuous monitoring data of the video robot in real time and carrying out secondary fire-fighting treatment once after the fire is reignited.
An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions, when executed by the processor, performing the steps of the above method.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the above method.
Compared with the prior art, the beneficial effect of this disclosure is:
(1) the dispatching system and the dispatching method can realize automatic fire monitoring and fire extinguishing, and realize full-automatic execution by setting various robots to monitor and rescue the fire, thereby ensuring the safe operation of power equipment, needing no personnel on duty and no human intervention, greatly reducing the labor cost and improving the working safety of the personnel.
(2) According to the method, the transverse fitting of the data and the longitudinal fitting of the data are combined, pre-judgment is performed in advance according to the data to realize early prevention, loss caused by fire is reduced, the position and the strength of a suspected fire source can be accurately judged by combining the two fitting methods, misjudgment is reduced, timeliness of the system is improved, and timely fire extinguishment can be realized.
(3) The fire-fighting robot that this disclosure set up is provided with passive injection apparatus on, can explode automatically at the temperature and surpass the scope of settlement, has improved the stability of putting out a fire, avoids the intensity of a fire out of control, reduces the loss of fire, improves power equipment's operating stability.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure.
FIG. 1 is a flow chart of a method of example 1 of the present disclosure;
FIG. 2 is a flowchart of a suspected fire source determination method in the method according to embodiment 1 of the disclosure;
FIG. 3 is a flowchart of a method for determining the stage of fire development in the method according to embodiment 1 of the present disclosure;
FIG. 4 is a flowchart of a method of scheduling a fire fighting robot to extinguish a fire in the method of embodiment 1 of the present disclosure;
fig. 5 is a flowchart of a method of evacuation monitoring in the method of embodiment 1 of the present disclosure;
fig. 6 is a schematic structural view of a fire-fighting robot passive spraying device according to embodiment 1 of the disclosure;
the fire extinguishing device comprises a fire extinguishing tank 1, a fire extinguishing tank 2, a hanging device 3, a pressure sensor 4 and a temperature sensing glass bulb.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments in the present disclosure may be combined with each other. The embodiments will be described in detail below with reference to the accompanying drawings.
In one or more embodiments, as shown in fig. 1, an electric power tunnel fire-fighting robot fire-fighting scheduling system at least includes a monitoring platform and various robots disposed at fixed points in an electric power tunnel, and may include an inspection robot, a video robot, and a fire-fighting robot, and may further include an environmental information monitoring unit disposed at fixed points in the electric power tunnel.
The collection of environmental information in the electric power tunnel can be patrolled and examined the collection through patrolling and examining the robot motion in the electric power tunnel, also can carry out the fixed point through the environmental information monitoring unit of fixed point setting in the electric power tunnel and detect.
The environmental information monitoring unit can include a plurality of temperature sensors, smoke sensors and harmful gas sensors that are fixed-point arranged in the electric power tunnel.
The tunnel environment monitoring system comprises a tunnel, a timing scheduling inspection robot, a sensor, a temperature sensor, a smoke sensor and a harmful gas sensor, wherein the tunnel environment information monitoring unit is not installed inside the tunnel, the inspection robot can be arranged, the timing scheduling inspection robot is used for inspecting and detecting in the tunnel, the sensor carried by the inspection robot is used for judging a heat source, and the inspection robot at least comprises the temperature sensor, the smoke sensor and the harmful gas sensor.
The video robot is used for confirming the scene fire, judging the spreading condition or the development stage of the fire, can carry detection devices such as a visible light tester, an infrared thermal imager, an audio collector, a temperature and humidity collector, a harmful gas sensor, an air oxygen content sensor and a laser ranging sensor, and can confirm the development stage of the scene fire.
The fire-fighting robot is in a standby state all the time, the shell of the fire-fighting robot is made of refractory materials, internal devices and circuits are protected at high temperature, and the fire-fighting robot can stay in a high-temperature environment to work. Still set up superfine dry powder fire control fire extinguishing tank 1 and infrared temperature measurement range finder on the fire-extinguishing robot, fire control fire extinguishing tank 1 is fixed on the fire-extinguishing robot through hooking device 2, fire control fire extinguishing tank 1 still is provided with passive injection apparatus, and passive injection apparatus is connected in superfine dry powder fire control fire extinguishing tank exit, can realize initiative and two kinds of passive fire-extinguishing mode.
Optionally, as a structure that can realize, as shown in fig. 6, passive injection apparatus includes the fixed body with fire extinguishing jar exit linkage, set up passive jet, pressure sensor 3 and temperature sensing glass bubble 4 on the fixed body, temperature sensing glass bubble 4 sets up in passive jet department, and pressure sensor 3 is used for transmitting pressure signal to the fire-fighting robot, and the fire-fighting robot detects whether there is the dry powder of usefulness of putting out a fire in judging fire extinguishing jar 1 according to the pressure value, and temperature sensing glass bubble 4 is automatic blasting after its tolerance is exceeded to the temperature.
The principle of realizing passive injection is as follows:
hooking device is connected with the robot host computer, and the storage has the superfine dry powder of certain pressure in the fire control extinguishing tank, when ambient temperature surpassed the tolerant numerical value of temperature sensing glass bubble, will explode automatically, and the fire control extinguishing tank after the blasting will switch on with the external world, sprays out superfine dry powder through self pressure, through the contact of dispersion mode separation burning things which may cause a fire disaster and air, realizes the operation of putting out a fire.
A fire extinguishing scheduling method for a fire-fighting robot in an electric power tunnel can be executed on a monitoring platform and comprises the following steps:
and S1, acquiring tunnel environment state data, and pre-judging the position and the intensity of a suspected fire source by adopting a transverse data fitting method and a longitudinal data fitting method according to the tunnel environment state data.
S2, dispatching the video robot to be close to the suspected fire source position as much as possible in a safe distance, judging the development stage of the fire through data collected by the video robot, and judging whether fire fighting treatment needs to be carried out or not; if yes, informing relevant personnel to process, sending alarm information, and executing the next step, otherwise, sending the alarm information to inform the relevant personnel to process;
s3, according to the development stage of the fire, uniformly scheduling surrounding fire-fighting robots according to the intensity of the fire, generating a planned path corresponding to the fire-fighting robots, and scheduling the fire-fighting robots to a fire occurrence site for fire-fighting and fire-extinguishing treatment;
s4, the fire-fighting robot after the scheduling and spraying is removed from the fire source site, the video robot records the whole fire-fighting and fire-extinguishing treatment, continuously monitors the fire after fire extinguishing, judges whether to re-fire, and carries out secondary fire-extinguishing treatment if the re-fire is carried out.
The method for acquiring the tunnel environment state data in step S1 may include fixed-point monitoring or cruise monitoring.
Fixed point monitoring can be realized for the environmental information monitoring unit through setting up in electric power tunnel, sets up temperature sensor, smoke transducer and harmful gas sensor in important equipment or important position department, temperature, smog concentration and harmful gas's content in the real-time supervision electric power tunnel to transmit the data of monitoring to monitor platform in real time.
Cruise monitoring can patrol and examine the robot for setting up and patrol and examine in electric power tunnel and listen, and patrol and examine the sensor monitoring data that the robot carried through self, including temperature, smog concentration and harmful gas content in gathering electric power tunnel and patrol and examine data such as position that the robot gathered data at least, will gather data transmission in the electric power tunnel and judge to monitor platform.
In step S1, as shown in fig. 2, the method for predicting the location and intensity of the suspected fire according to the tunnel environment status data may include the following steps:
s11 transverse comparison is carried out on the tunnel environment state data to obtain the position where the suspected fire happens, namely the position of the suspected fire source: the data of monitoring points inside the tunnel are subjected to fitting analysis by adopting a curve fitting algorithm, the position of data acquisition can be specifically used as an abscissa, the size of the acquired data is used as an ordinate, the size difference value of the data acquired at each position can be transversely compared, and points obviously higher than other positions are used as positions where fire is likely to occur.
S12, longitudinally comparing the monitoring data in the position of the heat source point, and judging that the intensity of the suspected fire is the intensity of the heat source: and (3) for the collector equipment in the pre-judging position obtained by transverse comparison, calling recent historical state data, performing data fitting on the historical data to form a longitudinal comparison fitting curve, and preliminarily judging the size intensity of the suspected fire by analyzing the trend of the curve and comparing the time with the temperature.
The longitudinal comparison can set empirical data, for example, a threshold range corresponding to the fire intensity can be set, and the duration and the size of the data deviating from the average position in the longitudinal comparison fitting curve are checked according to the empirical data, so that the intensity of the fire at the current position is obtained.
The longitudinal comparison can compare the curve of the fire at the current position with the curve of the historical fire according to the curve of the historical fire, and judge the stage or intensity of the fire.
In step S2, the monitoring platform determines a safe working range suitable for the robot to work according to the transversely compared data fitting curve, and approaches the suspected fire source as close as possible while ensuring the safety of the robot itself.
The monitoring platform analyzes the conclusion data and realizes data sharing with the robot in the tunnel, and preferentially calls the video robot to execute heat source discrimination work aiming at the type and position distribution of the existing robot in the tunnel.
The fire is judged as shown in fig. 3, the video robot can collect visible light video, infrared thermal imaging video, audio, temperature and humidity, harmful gas content, air oxygen content, laser ranging data and the like, and the fire on site can be confirmed.
The monitoring platform generates a corresponding planning path according to the current position of the video robot, the suspected fire source position and the safe working distance of the video robot, sends the planning path to the corresponding video robot, rapidly dispatches the robot to a fire distinguishing site, and executes a distinguishing processing flow, wherein the specific flow can be as follows:
and S21, starting the infrared thermal imager, scanning the surrounding environment, locking the heating source object, and acquiring the temperature data of the heating source and the specific direction and angle of the heating source.
S22, starting a visible light tester, and collecting visible light image data according to the specific direction and angle of a heat source; analyzing the collected visible light image to judge whether flame and smoke exist on the spot; the visible light image analysis can be carried out by combining AI intelligent analysis, and the specific method comprises the following steps:
constructing a training set, wherein the training set comprises a scene visible light picture of a fire source and a scene visible light picture of normal visible light;
extracting the characteristics of the scene pictures of the fire source in the training set and training a characteristic set;
constructing a convolutional neural network, inputting the data of the training feature set into the convolutional neural network for training, and obtaining a convolutional neural network identification model;
and acquiring a field visible light image in real time, inputting the field visible light image into the convolutional neural network identification model, and identifying whether flames and smoke exist.
S23, starting a temperature and humidity collector, a harmful gas sensor, an air oxygen content sensor and a laser ranging sensor, acquiring temperature and humidity data, air oxygen content data and harmful gas data of the surrounding environment of the video robot, and obtaining the development stage of the fire according to proportion weighted calculation: the generating position of the heat source is accurately calibrated through the laser distance measuring instrument.
Alternatively, the calculation formula of the weighted specific gravity calculation may be as follows:
wherein k is1、k2……knRespectively the acquired temperature and humidity data, air oxygen content data or harmful gas data, p1、p2……pnThe specific gravity parameters are respectively corresponding specific gravity parameters, and the specific gravity parameters can be determined according to the empirical values and the accuracy of fire judgment.
And after the data acquisition is judged, continuously observing the heating source. If the data output by the judging module through the steps 22-23 is only high-temperature heating and has no open fire or smoke, the judging module informs the user of intervention solution at the first time, and if the open fire or smoke occurs, the judging module informs the user and automatically enters a fire-fighting and fire-extinguishing processing flow at the same time.
Step S4 is to dispatch the fire-fighting robot to perform the fire-fighting and extinguishing treatment, the fire-fighting robot is in a standby state for 24 hours, the fire-fighting robot shell is processed by special refractory materials, and the special refractory materials are as follows: fire-proof paint, fire-proof cotton, aluminum silicate ceramic fiber cloth and the like. The internal devices and the circuits are protected at high temperature and can stay in high-temperature environment to work.
The fire-fighting robot carries the superfine dry powder fire-fighting tank and the infrared temperature and distance measuring detector, the outlet of the superfine dry powder fire-fighting tank is provided with the passive injection device, and the passive fire-fighting mode can be realized through the passive injection device.
After the fire is determined through heat source judgment, the monitoring platform can uniformly schedule the surrounding fire-fighting robots according to the intensity of the fire. The dispatched fire-fighting robot can be driven to the fire scene at full speed for rescue.
As shown in fig. 4, the specific flow of the rescue process is as follows:
1. after the fire-fighting robot is scheduled to a preset position, the infrared temperature and distance measuring equipment is started, and the fire-fighting robot is quickly positioned according to the heat source azimuth information provided by the video robot and is aimed at a fire source to spray superfine dry powder. The sprayed superfine dry powder can quickly diffuse around the fire source to separate the fire source from air, so that the fire can be gradually reduced and extinguished, and the fire extinguishing mode is an active fire extinguishing mode.
2. The tolerance temperature range of the fire-fighting robot is set, the ambient temperature is obtained, and when the temperature is higher than the highest tolerance temperature, the passive injection device of the fire-fighting robot can automatically explode to inject superfine dry powder. The spraying out of control phenomenon caused by high temperature can be effectively prevented, and a passive fire extinguishing mode is realized through a passive spraying device designed by the fire-fighting robot.
Step S4 is an evacuation monitoring step, as shown in fig. 5: the fire-fighting robot after spraying is dispatched to leave the fire source site quickly from the self safety consideration. The video robot monitored at a remote place records the whole fire extinguishing process, continuously monitors the fire condition after fire extinguishing, and prepares for secondary fire extinguishing treatment once re-ignition is carried out.
Example 2
The embodiment provides a power tunnel fire-fighting robot scheduling system that puts out a fire, includes:
a suspected fire source prejudgment module: the system is used for acquiring tunnel environment state data and pre-judging the position and the intensity of a suspected fire source by adopting a transverse data fitting and longitudinal data fitting method according to the tunnel environment state data;
the fire development stage judgment module: the video robot is used for dispatching the video robot to be close to the suspected fire source position within a safe distance, judging the development stage of the fire through data collected by the video robot, judging whether fire fighting treatment needs to be carried out, if so, informing relevant personnel to process, sending alarm information, executing the next step, and if not, sending the alarm information to inform the relevant personnel to process;
fire-fighting robot scheduling module: the fire fighting robot is used for uniformly scheduling the surrounding fire fighting robots according to the development stage of the fire and the intensity of the fire, generating a planned path corresponding to the fire fighting robots, and scheduling the fire fighting robots to a fire occurrence site for fire fighting and extinguishing;
an evacuation monitoring scheduling module: the fire-fighting robot is used for dispatching the fire-fighting robot after the spraying is finished to evacuate the fire source site, acquiring the fire condition continuous monitoring data of the video robot in real time and carrying out secondary fire-fighting treatment once after the fire is reignited.
Example 3
The present embodiment provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein the computer instructions, when executed by the processor, perform the steps of the method of embodiment 1.
Example 4
The present embodiment provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the method of embodiment 1.
The electronic device provided by the present disclosure may be a mobile terminal and a non-mobile terminal, where the non-mobile terminal includes a desktop computer, and the mobile terminal includes a Smart Phone (such as an Android Phone and an IOS Phone), Smart glasses, a Smart watch, a Smart bracelet, a tablet computer, a notebook computer, a personal digital assistant, and other mobile internet devices capable of performing wireless communication.
It should be understood that in the present disclosure, the processor may be a central processing unit CPU, but may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The steps of a method disclosed in connection with the present disclosure may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here. Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present disclosure, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is merely a division of one logic function, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some interfaces, and may be in an electrical, mechanical or other form.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present disclosure. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. A fire extinguishing dispatching method for a fire-fighting robot in an electric power tunnel is characterized by comprising the following steps:
acquiring tunnel environment state data, and pre-judging the position and the intensity of a suspected fire source by adopting a transverse data fitting and longitudinal data fitting method according to the tunnel environment state data;
fitting the transverse data into fitting analysis of data of monitoring points in the tunnel by adopting a curve fitting algorithm, specifically, taking the position of data acquisition as an abscissa and the size of the acquired tunnel environment state data as an ordinate, transversely comparing the size of the data acquired at each position, and taking points obviously higher than other positions as positions where fire is likely to occur;
the longitudinal data fitting is specifically to call recent historical state data of the collector equipment aiming at the collector equipment in a pre-judging position obtained by transverse comparison, perform data fitting aiming at the historical data to form a longitudinal comparison fitting curve, and preliminarily judge the intensity of the suspected fire through analyzing the trend of the curve and comparing time with temperature; the video robot is dispatched to be close to the suspected fire source position within a safe distance, the development stage of the fire is judged according to the data collected by the video robot, and whether fire fighting treatment needs to be carried out or not is judged; according to the development stage of the fire, uniformly scheduling the surrounding fire-fighting robots according to the intensity of the fire, generating a planned path corresponding to the fire-fighting robots, and scheduling the fire-fighting robots to a fire occurrence site for fire-fighting and fire-extinguishing treatment; and (4) evacuating the fire-fighting robot after the completion of the scheduling and spraying from the fire source site, acquiring the fire condition continuous monitoring data of the video robot in real time, judging whether the fire is reignited, and if so, performing secondary fire extinguishing treatment.
2. The fire extinguishing scheduling method for the fire-fighting robot of the electric power tunnel as claimed in claim 1, which is characterized in that: the method for acquiring the tunnel environment state data comprises fixed-point monitoring or cruise monitoring;
the fixed-point monitoring is realized by an environmental information monitoring unit arranged in the electric power tunnel, the temperature, the smoke concentration and the content of harmful gas in the electric power tunnel are monitored in real time, and the monitored data are transmitted to a monitoring platform in real time;
the cruise monitoring patrols and examines the robot through setting up and patrols and examines in electric power tunnel, patrols and examines the sensor monitoring data that the robot carried through self, and data transmission to the monitoring platform in will gathering electric power tunnel.
3. The fire extinguishing scheduling method for the fire-fighting robot of the electric power tunnel as claimed in claim 1, which is characterized in that: the method for dispatching the video robot to be as close to the suspected fire source position as possible in the safe distance comprises the following steps:
and generating a corresponding planning path according to the current position of the video robot, the suspected fire source position and the safe working distance of the video robot, sending the planning path to the corresponding video robot, and rapidly dispatching the robot to the site of the suspected fire source position.
4. The fire extinguishing scheduling method for the fire-fighting robot of the electric power tunnel as claimed in claim 1, which is characterized in that: the video robot is provided with an infrared thermal imager, a visible light tester, a temperature and humidity collector, a harmful gas sensor, an air oxygen content sensor and a laser distance measuring sensor.
5. The fire extinguishing scheduling method for the fire-fighting robot of the electric power tunnel as claimed in claim 1, which is characterized in that: the method for judging whether fire fighting needs to be carried out or not by judging the development stage of the fire through data collected by the video robot comprises the following steps:
acquiring data acquired by an infrared thermal imager of a video robot, locking a heating source object, and acquiring heat source temperature data and specific direction and angle of a heat source;
collecting visible light image data according to the specific direction and angle of a heat source, and analyzing the collected visible light image to judge whether flame and smoke exist on the site;
acquiring temperature and humidity data, air oxygen content data and harmful gas data of the surrounding environment of the video robot, performing weighted calculation according to proportion, and determining the development stage of the fire by combining the obtained visible light image analysis and judgment results;
and according to the specific direction and angle of the heat source, the generating position of the heat source is accurately calibrated through the laser range finder.
6. The fire extinguishing scheduling method for the fire-fighting robot of the electric power tunnel as claimed in claim 1, which is characterized in that: the fire-fighting fire-extinguishing treatment method specifically comprises the following steps:
after the fire-fighting robot is scheduled to a preset position, the infrared temperature and distance measuring equipment is started, and the fire-fighting robot is quickly positioned according to the heat source azimuth information provided by the video robot and is aimed at a fire source to spray superfine dry powder;
and setting the tolerance temperature range of the fire-fighting robot, acquiring the ambient temperature, and automatically blasting the passive injection device of the fire-fighting robot to inject superfine dry powder when the ambient temperature is higher than the highest tolerance temperature.
7. The utility model provides an electric power tunnel fire-fighting robot scheduling system that puts out a fire, characterized by: the electric power tunnel fire-fighting robot fire-fighting scheduling method comprises a monitoring platform, various robots arranged on an electric power tunnel at fixed points, and an environment information monitoring unit, wherein the robots comprise an inspection robot, a video robot and a fire-fighting robot, the environment information monitoring unit is arranged in the electric power tunnel at fixed points, the monitoring platform is respectively connected with the environment information monitoring unit and the robots, and the monitoring platform executes the electric power tunnel fire-fighting robot fire-fighting scheduling method according to any one of claims 1-6.
8. The utility model provides an electric power tunnel fire-fighting robot scheduling system that puts out a fire, characterized by includes:
a suspected fire source prejudgment module: the system is used for acquiring tunnel environment state data and pre-judging the position and the intensity of a suspected fire source by adopting a transverse data fitting and longitudinal data fitting method according to the tunnel environment state data;
fitting the transverse data into fitting analysis of data of monitoring points in the tunnel by adopting a curve fitting algorithm, specifically, taking the position of data acquisition as an abscissa and the size of the acquired tunnel environment state data as an ordinate, transversely comparing the size of the data acquired at each position, and taking points obviously higher than other positions as positions where fire is likely to occur;
the longitudinal data fitting is specifically to call recent historical state data of the collector equipment aiming at the collector equipment in a pre-judging position obtained by transverse comparison, perform data fitting aiming at the historical data to form a longitudinal comparison fitting curve, and preliminarily judge the intensity of the suspected fire through analyzing the trend of the curve and comparing time with temperature;
longitudinally comparing and setting empirical data, and checking the time length and the size of the data deviating from the average position in the longitudinally comparing and fitting curve according to the empirical data so as to obtain the intensity of the fire at the current position;
the fire development stage judgment module: the video robot is used for dispatching the video robot to be close to the suspected fire source position within a safe distance, judging the development stage of the fire through data collected by the video robot, judging whether fire fighting treatment needs to be carried out, if so, informing relevant personnel to process, sending alarm information, executing the next step, and if not, sending the alarm information to inform the relevant personnel to process;
fire-fighting robot scheduling module: the fire fighting robot is used for uniformly scheduling the surrounding fire fighting robots according to the development stage of the fire and the intensity of the fire, generating a planned path corresponding to the fire fighting robots, and scheduling the fire fighting robots to a fire occurrence site for fire fighting and extinguishing;
an evacuation monitoring scheduling module: the fire-fighting robot is used for dispatching the fire-fighting robot with finished spraying to evacuate the fire source site, acquiring the fire condition continuous monitoring data of the video robot in real time, judging whether to re-fire or not, and if so, carrying out secondary fire extinguishing treatment.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executable on the processor, the computer instructions when executed by the processor performing the steps of the method of any of claims 1 to 6.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the method of any one of claims 1 to 6.
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