CN114768158B - Intelligent fire fighting system and automatic inspection method thereof - Google Patents
Intelligent fire fighting system and automatic inspection method thereof Download PDFInfo
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- CN114768158B CN114768158B CN202210464291.4A CN202210464291A CN114768158B CN 114768158 B CN114768158 B CN 114768158B CN 202210464291 A CN202210464291 A CN 202210464291A CN 114768158 B CN114768158 B CN 114768158B
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- A—HUMAN NECESSITIES
- A62—LIFE-SAVING; FIRE-FIGHTING
- A62C—FIRE-FIGHTING
- A62C37/00—Control of fire-fighting equipment
- A62C37/04—Control of fire-fighting equipment with electrically-controlled release
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/06—Electric actuation of the alarm, e.g. using a thermally-operated switch
Abstract
The invention provides an intelligent fire fighting system and an automatic inspection method thereof, wherein the intelligent fire fighting system comprises: the system comprises a collecting end, a multi-end inspection device and a real-time warning information acquiring and processing device, wherein the collecting end is used for collecting inspection information in an inspection range based on the multi-end inspection device and receiving warning information in the inspection range, and obtaining real-time warning information based on the inspection information and the warning information; the prediction end is used for carrying out multi-dimensional analysis prediction on the routing inspection information to obtain a corresponding prediction result; the generating end is used for generating a corresponding latest disaster memory spool based on the real-time alarm information and generating a corresponding latest inspection plan based on the latest disaster memory spool and the prediction result; the inspection end is used for controlling the corresponding inspection device to perform inspection based on the latest inspection plan; the method is used for generating an efficient inspection plan based on the multi-dimensional analysis of the inspection information and the historical inspection information, and improves the automatic inspection mode of fixing the death board of the traditional intelligent fire-fighting system.
Description
Technical Field
The invention relates to the technical field of intelligent fire fighting, in particular to an intelligent fire fighting system and an automatic inspection method thereof.
Background
The fire-fighting work is consistent with the end content of urban development and industrial production, new bases and challenges are brought to various industries along with the development of the Internet and artificial intelligence technology, at present, intelligent fire fighting gradually appears in fire-fighting systems of governments and factories, and compared with the traditional fire-fighting systems, the intelligent fire-fighting systems have stronger networking advantages, but the existing intelligent fire-fighting systems still have some problems in the aspect of routing inspection application; for example:
the inspection plan of the existing intelligent fire-fighting system is mostly formed by manual setting, so that the inspection period is too rigid, the adjustment and the change are inconvenient, the inspection effect is poor, and various disasters in the inspection range cannot be effectively and comprehensively prevented;
the existing intelligent fire fighting system mostly adopts a uniform inspection mode and inspection period for large-scale inspection, but due to environmental factors and human factors in a large scale, the frequency of various disasters in each local area is possibly different, and an inspection plan cannot be generated in a targeted manner according to the disaster occurrence characteristics of the local areas in the large-scale inspection area and adjusted in real time, so that the inspection effect is poor.
Therefore, the invention provides an intelligent fire fighting system and an automatic inspection method thereof.
Disclosure of Invention
The invention provides an intelligent fire-fighting system and an automatic inspection method thereof, which are used for generating an efficient inspection plan based on multi-dimensional analysis of inspection information and historical inspection information, and improving the automatic inspection mode of fixing the traditional intelligent fire-fighting system.
The invention provides an intelligent fire fighting system, comprising:
the system comprises a collecting end, a multi-end inspection device and a real-time warning information acquiring and processing device, wherein the collecting end is used for collecting inspection information in an inspection range based on the multi-end inspection device and receiving warning information in the inspection range, and obtaining real-time warning information based on the inspection information and the warning information;
the prediction end is used for carrying out multi-dimensional analysis prediction on the routing inspection information to obtain a corresponding prediction result;
the generating end is used for generating a corresponding latest disaster memory spool based on the real-time alarm information and generating a corresponding latest inspection plan based on the latest disaster memory spool and the prediction result;
and the inspection end is used for controlling the corresponding inspection device to perform inspection based on the latest inspection plan.
Preferably, the collection end includes:
the multi-terminal polling module is used for acquiring polling information in a polling range based on a multi-terminal polling device and a corresponding preset polling period;
the alarm condition receiving module is used for receiving alarm information received by all alarm channels in the inspection range;
and the information integration module is used for analyzing the inspection information to obtain corresponding inspection warning information and integrating the inspection warning information and the warning information to obtain corresponding real-time warning information.
Preferably, the multi-terminal polling module includes:
the first inspection unit is used for acquiring an inspection video within the inspection range based on the unmanned aerial vehicle device and a first preset period;
the second inspection unit is used for acquiring satellite monitoring images in the inspection range in real time based on the satellite monitoring device and a second preset period;
the third inspection unit is used for acquiring inspection information of the fire-fighting equipment in the inspection range based on a third preset period;
wherein the routing inspection information includes: the patrol video, the danger monitoring image and the fire-fighting equipment patrol information.
Preferably, the information integration module includes:
the first judging unit is used for determining corresponding inspection condition judging conditions based on the information sources of the inspection information, identifying sub inspection alarm condition information corresponding to the inspection information of each information source in the inspection range based on the inspection condition judging conditions, summarizing all the sub inspection alarm condition information and obtaining corresponding inspection alarm condition information;
and the information duplication removing unit is used for carrying out duplication removing processing on the patrol inspection alarm condition information and the alarm information to obtain corresponding duplication removing results, sequencing the duplication removing results according to the emergency level to obtain corresponding real-time alarm condition information, and starting a corresponding rescue plan based on the real-time alarm condition information.
Preferably, the predicting end includes:
the area division module is used for dividing the inspection area in the inspection range into a plurality of sub inspection areas;
the information calling module is used for calling the historical disaster information and the historical patrol information of the sub patrol areas;
the first generation module is used for determining a disaster memory spool corresponding to each disaster in the sub-inspection area based on the historical disaster information;
the first extraction module is used for extracting sub-history information characteristics corresponding to the history routing inspection information of each information source;
the information fusion module is used for fusing all the sub-historical information characteristics with the disaster memory spools to obtain corresponding historical information characteristic spools of the corresponding types of disasters in the corresponding sub-inspection areas;
the second extraction module is used for extracting a corresponding characteristic fluctuation spool section when a corresponding type of disaster happens each time from the historical information characteristic spool and obtaining a corresponding characteristic fluctuation spool section set of the corresponding type of disaster in a corresponding sub-inspection area;
the alignment fusion module is used for performing alignment fusion on the characteristic fluctuation axis segment set to obtain corresponding distinguishing characteristic axis segments of the corresponding type of disasters in the corresponding sub-inspection areas;
the characteristic synthesis module is used for obtaining a distinguishing characteristic line axis section set corresponding to the sub-inspection area based on the corresponding distinguishing characteristic line axis sections of all disaster types in the corresponding sub-inspection area;
the third extraction module is used for determining sub-inspection information corresponding to the sub-inspection area based on the inspection information, and extracting the characteristics of the sub-inspection information to obtain the corresponding sub-inspection information characteristics;
the second generation module is used for generating a sub inspection information characteristic spool corresponding to the sub inspection area based on the sub inspection information characteristic fitting;
the local prediction module is used for judging whether a spool segment which is partially overlapped with the sub-inspection information characteristic spool exists in a distinguishing characteristic spool segment set corresponding to the sub-inspection area, if so, determining the predicted disaster type and the predicted occurrence time which are possibly generated in the sub-inspection area based on the disaster type and the overlapped position which correspond to the overlapped spool segment, and taking the predicted disaster type and the predicted occurrence time as the local prediction result corresponding to the sub-inspection area, otherwise, taking the sub-inspection area without any disaster in a preset period as the corresponding local prediction result;
and the result integration module is used for integrating the local prediction results to obtain corresponding prediction results.
Preferably, the first generating module includes:
the information classification unit is used for classifying the historical disaster information based on disaster types to obtain the historical information corresponding to each disaster;
and the information fusion unit is used for fusing the historical information and the time spool based on the occurrence time in the historical information to obtain a corresponding disaster memory spool of the corresponding type of disaster in the sub-routing inspection area.
Preferably, the generating end includes:
the spool updating module is used for updating the corresponding disaster memory spool of the corresponding type of disaster in the sub-inspection area based on the real-time alarm information to obtain the corresponding latest disaster memory spool of the corresponding type of disaster in the sub-inspection area;
and the third generation module is used for generating a corresponding latest inspection plan based on the latest disaster memory spool and the prediction result.
Preferably, the third generating module includes:
the first determining unit is used for determining predicted occurrence time corresponding to disaster types possibly occurring in the corresponding sub inspection areas in a preset period based on the prediction result;
the calling unit is used for calling out the characteristic fluctuation axis section corresponding to the sub-inspection area corresponding to the predicted occurrence time;
the second determining unit is used for determining a first routing inspection item corresponding to the predicted occurrence time based on the information source of the historical routing inspection information corresponding to the characteristic fluctuation axis segment;
a third determining unit, configured to obtain a corresponding first inspection item set based on the first inspection items corresponding to all predicted occurrence times included in the sub inspection area;
the time sequence arrangement unit is used for carrying out time sequence arrangement on all corresponding predicted occurrence times of all first inspection items in the corresponding inspection subareas, wherein the first inspection items are contained in the first inspection item set, and a corresponding predicted occurrence time sequence is obtained;
a fourth determining unit, configured to generate a first patrol inspection time gradient corresponding to the sub patrol inspection area based on the predicted occurrence time sequence;
the analysis unit is used for analyzing the corresponding latest disaster memory spool in the sub-inspection area and obtaining the average occurrence frequency of the corresponding types of disasters in the sub-inspection area;
the fifth determining unit is used for determining a corresponding second inspection item based on the information source of the historical inspection information corresponding to the characteristic fluctuation spool section corresponding to the disaster situation of the corresponding type in the corresponding sub inspection area;
a sixth determining unit, configured to determine, based on the average occurrence frequency, a second inspection time gradient corresponding to the second inspection item;
the item integration unit is used for obtaining an inspection item set corresponding to the sub inspection area based on the first inspection item and the second inspection item;
the first generation unit is used for fusing and arranging a first inspection time gradient and a second inspection item time gradient corresponding to a first inspection item contained in the inspection item set according to a time sequence to generate an inspection time gradient corresponding to each inspection item to be executed in the sub inspection area;
and the second generation unit is used for generating a corresponding latest inspection plan based on the position of each sub inspection area in the inspection range and the inspection time gradient corresponding to each inspection item to be executed in the corresponding sub inspection area.
Preferably, the inspection end includes:
the instruction generating module is used for generating a corresponding inspection control instruction based on the latest inspection plan;
and the instruction execution module is used for controlling the corresponding inspection device to automatically inspect in the inspection range based on the inspection control instruction.
The invention provides an automatic inspection method, which comprises the following steps:
s1: acquiring polling information in a polling range based on a multi-terminal polling device, receiving alarm information in the polling range, and acquiring real-time alarm information based on the polling information and the alarm information;
s2: carrying out multidimensional analysis and prediction on the routing inspection information to obtain a corresponding prediction result;
s3: generating a corresponding disaster memory spool based on the alarm information, and generating a corresponding latest inspection plan based on the disaster memory spool and the prediction result;
s4: and controlling a corresponding inspection device to perform inspection based on the latest inspection plan.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of an intelligent fire fighting system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an acquisition end according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a multi-terminal polling module according to an embodiment of the present invention;
FIG. 4 is a diagram of an information integration module according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a prediction end according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating a first generation module according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a generating end according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a third generation module in an embodiment of the invention;
FIG. 9 is a schematic diagram of a polling end according to an embodiment of the present invention;
fig. 10 is a flowchart of an automatic inspection method according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example 1:
the invention provides an intelligent fire fighting system, which is shown in a figure 1 and comprises:
the system comprises a collecting end, a multi-end inspection device and a real-time warning information acquiring and processing device, wherein the collecting end is used for collecting inspection information in an inspection range based on the multi-end inspection device and receiving warning information in the inspection range, and obtaining real-time warning information based on the inspection information and the warning information;
the prediction end is used for carrying out multi-dimensional analysis prediction on the routing inspection information to obtain a corresponding prediction result;
the generating end is used for generating a corresponding latest disaster memory spool based on the real-time alarm information and generating a corresponding latest inspection plan based on the latest disaster memory spool and the prediction result;
and the inspection end is used for controlling the inspection device to inspect based on the latest inspection plan.
In this embodiment, the multi-end inspection device includes: unmanned aerial vehicle device, satellite monitoring device, fire-fighting equipment patrol and examine information acquisition device.
In this embodiment, the patrol scope is the control scope of the intelligent fire fighting system.
In this embodiment, the patrol inspection information is information obtained by patrol inspection in the patrol inspection range based on the multi-terminal patrol inspection device.
In this embodiment, the alarm information is the information related to the disaster alarm when the alarm is in the polling range.
In this embodiment, the real-time alarm information is information of a disaster that occurs in real time within the polling range.
In this embodiment, the prediction result is information obtained by performing graph analysis prediction on the inspection information.
In this embodiment, the latest disaster memory spool is a spool representing the occurring time of a disaster in the inspection range generated based on the real-time alarm information.
In this embodiment, the latest patrol plan is a patrol plan generated based on the latest disaster memory spool and the prediction result.
In this embodiment, the inspection device is an unmanned aerial vehicle device, a satellite monitoring device and a fire-fighting equipment inspection information acquisition device.
The beneficial effects of the above technology are: based on the acquired multi-terminal inspection information, disaster prediction in an inspection range is realized, and based on a disaster prediction result and multi-dimensional analysis of historical inspection information and the multi-terminal inspection information, an efficient inspection plan can be generated, so that an automatic inspection mode of fixing a crash board of a traditional intelligent fire-fighting system is improved. The inspection plan can be adjusted and changed in real time conveniently, the inspection effect is enhanced, and various disasters in the inspection range can be prevented efficiently and comprehensively; the method and the device realize the purpose of pertinently generating the patrol plan aiming at the disaster occurrence characteristics of the local areas in the large-range patrol area, thereby further enhancing the patrol effect.
Example 2:
on the basis of the embodiment 1, the acquisition end, referring to fig. 2, includes:
the multi-terminal polling module is used for acquiring polling information in the polling range based on the multi-terminal polling device and the corresponding preset polling period;
the alarm condition receiving module is used for receiving alarm information received by all alarm channels in the inspection range;
and the information integration module is used for analyzing the inspection information to obtain corresponding inspection warning information and integrating the inspection warning information and the warning information to obtain corresponding real-time warning information.
In this embodiment, the predetermined polling period is a period in which each polling device executes polling.
In this embodiment, the alarm channels include, for example: a telephone alarm channel, a network alarm channel, a short message alarm channel and the like.
In the embodiment, the patrol alarm condition information is disaster condition related information which exists in real time in a patrol range obtained by analyzing the patrol information.
The beneficial effects of the above technology are: the inspection information is analyzed in real time to judge the disaster condition in the inspection range, and then the disaster condition is integrated with the alarm information, so that comprehensive disaster condition information in the inspection range can be acquired, the situation that the alarm cannot be given can be avoided, the disaster condition damage is continuously enlarged, and data are provided for the follow-up accurate generation of the inspection plan.
Example 3:
on the basis of embodiment 2, the multi-terminal polling module, referring to fig. 3, includes:
the first inspection unit is used for acquiring an inspection video within the inspection range based on the unmanned aerial vehicle device and a first preset period;
the second inspection unit is used for acquiring satellite monitoring images in the inspection range in real time based on the satellite monitoring device and a second preset period;
the third inspection unit is used for acquiring inspection information of the fire-fighting equipment in the inspection range based on a third preset period;
wherein, the patrol information includes: the patrol video, the danger monitoring image and the fire-fighting equipment patrol information.
In this embodiment, the unmanned aerial vehicle device is used for patrolling and examining unmanned aerial vehicle and the controlling means that patrols and examines at the within range promptly.
In this embodiment, the first cycle of predetermineeing is promptly the cycle that utilizes unmanned aerial vehicle to patrol and examine at the scope of patrolling and examining that sets up in advance.
In this embodiment, the patrol video is a video acquired by patrol acquisition in a patrol range based on the unmanned aerial vehicle device and a first preset period.
In this embodiment, the satellite monitoring device is the device that is used for monitoring the satellite meteorological condition in the scope of patrolling and examining promptly.
In this embodiment, the second preset period is a preset period for performing polling within the polling range by using the danger monitoring device.
In this embodiment, the satellite monitoring image is an image obtained by performing inspection acquisition within an inspection range based on the satellite monitoring device and a second preset period.
In this embodiment, the third preset period is a period of the preset inspection information of the tiger dune fire-fighting equipment.
In this embodiment, the fire fighting equipment inspection information is information obtained by inspecting the fire fighting equipment within the inspection and inspection range.
The beneficial effects of the above technology are: based on the patrol and examine information fire-fighting equipment patrol and examine information that unmanned aerial vehicle device and satellite monitoring device obtained, can realize patrolling and examining the disaster that probably violently takes place to patrol and examine the within range and carry out all-round control.
Example 4:
on the basis of embodiment 3, the information integration module, referring to fig. 4, includes:
the first judging unit is used for determining corresponding inspection condition judging conditions based on the information sources of the inspection information, identifying sub inspection alarm condition information corresponding to the inspection information of each information source in the inspection range based on the inspection condition judging conditions, summarizing all the sub inspection alarm condition information and obtaining corresponding inspection alarm condition information;
and the information duplication removing unit is used for carrying out duplication removing processing on the patrol inspection alarm condition information and the alarm information to obtain corresponding duplication removing results, sequencing the duplication removing results according to the emergency level to obtain corresponding real-time alarm condition information, and starting a corresponding rescue plan based on the real-time alarm condition information.
In this embodiment, the information sources include: unmanned aerial vehicle device, satellite monitoring device, fire-fighting equipment patrol information.
In this embodiment, the alert condition determining condition is a determining condition for determining whether a disaster corresponding to the category occurs, for example: and when the patrol video summarizes and monitors that a large-area fire map exists in the patrol range, judging that a fire disaster occurs in the patrol range.
In this embodiment, the sub-inspection alarm information identifies whether the corresponding alarm condition discrimination condition identifies whether the sub-inspection alarm condition occurs in the inspection range based on the corresponding alarm condition discrimination condition, if so, the corresponding inspection information is used as the corresponding sub-inspection alarm information, otherwise, the sub-inspection alarm information is no information.
In this embodiment, the patrol warning information is information obtained by collecting all the sub patrol warning information.
In this embodiment, the duplicate removal result is a result obtained after the duplicate removal processing is performed on the duplicate removal result alarm information.
The beneficial effects of the above technology are: all disasters occurring in the patrol inspection range are judged based on the judgment conditions of the occurrence alarms corresponding to each information source contained in the patrol inspection information, and the corresponding patrol inspection information and alarm information in the region where the disasters occur are subjected to duplication elimination and summarization, so that comprehensive disasters information in the patrol inspection range can be obtained, and effective monitoring of the disasters in the patrol inspection range is realized.
Example 5:
on the basis of the embodiment 4, the predicting end, referring to fig. 5, includes:
the area division module is used for dividing the patrol area in the patrol range into a plurality of sub-patrol areas;
the information calling module is used for calling the historical disaster information and the historical patrol information of the sub patrol area;
the first generation module is used for determining a disaster memory spool corresponding to each disaster in the sub-inspection area based on the historical disaster information;
the first extraction module is used for extracting sub-history information characteristics corresponding to the history routing inspection information of each information source;
the information fusion module is used for fusing all the sub-historical information characteristics with the disaster situation memory spools to obtain corresponding historical information characteristic spools of the corresponding types of disaster situations in the corresponding sub-inspection areas;
the second extraction module is used for extracting corresponding characteristic fluctuation spool segments when corresponding types of disasters occur each time from the historical information characteristic spools to obtain corresponding characteristic fluctuation spool segment sets of the corresponding types of disasters in corresponding sub-inspection areas;
the alignment fusion module is used for performing alignment fusion on the characteristic fluctuation axis segment set to obtain corresponding distinguishing characteristic axis segments of the corresponding type of disasters in the corresponding sub-inspection areas;
the characteristic synthesis module is used for obtaining a distinguishing characteristic line axis section set corresponding to the sub-inspection area based on the corresponding distinguishing characteristic line axis sections of all disaster types in the corresponding sub-inspection area;
the third extraction module is used for determining sub-inspection information corresponding to the sub-inspection area based on the inspection information, and extracting the characteristics of the sub-inspection information to obtain the corresponding sub-inspection information characteristics;
the second generation module is used for generating sub inspection information characteristic bobbins corresponding to the sub inspection areas based on the sub inspection information characteristic fitting;
the local prediction module is used for judging whether a spool section which is partially overlapped with the sub inspection information characteristic spool exists in a distinguishing characteristic spool section set corresponding to the sub inspection area or not, if so, determining the predicted disaster type and the predicted occurrence time which possibly occur in the sub inspection area based on the disaster type and the overlapping position corresponding to the overlapped spool section, and taking the predicted disaster type and the predicted occurrence time as a local prediction result corresponding to the sub inspection area, otherwise, taking the partial prediction result as a corresponding local prediction result that the sub inspection area can not generate any disaster in a preset period;
and the result integration module is used for integrating the local prediction results to obtain corresponding prediction results.
In this embodiment, the sub inspection area is a sub area obtained by dividing the inspection area within the inspection range.
In this embodiment, the historical disaster information is information of the disaster that has occurred in the corresponding sub-inspection area.
In this embodiment, the historical patrol information is patrol information that the corresponding sub-patrol area has acquired.
In the embodiment, the disaster memory spool is a time axis for representing information that disasters occur once in the sub-inspection area, wherein the information is obtained by fusing the historical information and the time spool based on occurrence time in the historical information and corresponds to the type of the disasters in the sub-inspection area.
In this embodiment, the sub-history information features are corresponding information features extracted from the history routing inspection information of each information source.
In this embodiment, the historical information characteristic spool is a time spool in which the historical information characteristics corresponding to the disaster of the corresponding type obtained by fusing all the sub-historical information characteristics with the disaster memory spool change in the corresponding sub-inspection area.
In this embodiment, the characteristic fluctuation spool segment is a spool segment extracted from the historical information characteristic spool, where the historical information characteristic occurs to determine each time a corresponding type of disaster occurs.
In this embodiment, the set of characteristic fluctuation axis segments is a set formed by corresponding characteristic fluctuation axis segments in the corresponding sub-inspection regions corresponding to the type of disaster.
In this embodiment, the distinguishing characteristic axis segment is a historical information characteristic axis segment which is used for distinguishing whether corresponding types of disasters exist in the corresponding sub inspection area after the corresponding types of disasters obtained after the characteristic fluctuation axis segment set is aligned and fused.
In this embodiment, the distinguishing characteristic line axis segment set is a set formed by distinguishing characteristic line axis segments corresponding to all disaster types in the corresponding sub-inspection areas.
In this embodiment, the sub inspection information is inspection information corresponding to the sub inspection area determined in the inspection information.
In this embodiment, the sub inspection information features are corresponding features obtained after feature extraction is performed on the sub inspection information.
In this embodiment, the sub inspection information characteristic spool is a time spool representing variation of inspection information characteristics of the corresponding sub inspection area generated based on sub inspection information characteristic fitting.
In this embodiment, based on the disaster type and the coincidence position corresponding to the overlapped spool segments, the predicted disaster type and the predicted occurrence time that are likely to occur in the sub-inspection area are determined, which include:
taking the disaster types corresponding to the corresponding overlapped bobbin sections as predicted disaster types possibly occurring in the sub-inspection area;
and taking the time interval between the overlapping position and the corresponding disaster occurrence time of the type in the corresponding distinguishing characteristic axis section as the corresponding predicted occurrence time.
In this embodiment, the local prediction result includes the predicted disaster type and the predicted occurrence time of the corresponding sub-inspection area within the preset period or no disaster occurs.
The beneficial effects of the above technology are: the method comprises the steps of carrying out classified disaster analysis on historical disaster information and historical patrol information of a local patrol area in a patrol area to obtain distinguishing characteristic spool segments corresponding to different types of disasters in corresponding sub-patrol areas, comparing and distinguishing the distinguishing characteristic spool segments with sub-patrol information characteristic spools generated latest, accurately distinguishing possible disaster types and predicted occurrence time in the corresponding sub-patrol areas based on the change characteristics of the past patrol information, further generating a patrol plan which is adjusted in real time for the local area in the patrol area for follow-up generation, and overcoming the limitation of the traditional intelligent fire-fighting patrol mode.
Example 6:
on the basis of the embodiment 5, the first generating module, referring to fig. 6, includes:
the information classification unit is used for classifying the historical disaster information based on disaster types to obtain the historical information corresponding to each disaster;
and the information fusion unit is used for fusing the historical information and the time spool based on the occurrence time in the historical information to obtain a disaster memory spool corresponding to the corresponding type of disaster in the sub-routing inspection area.
In this embodiment, the disaster types are, for example: fire, flood, etc.
In this embodiment, the historical information is information corresponding to each disaster, which is obtained after classifying the historical disaster information based on the disaster type.
In the embodiment, the disaster memory spool is a time axis for representing information that disasters occur once in the sub-inspection area, wherein the information is obtained by fusing the historical information and the time spool based on occurrence time in the historical information and corresponds to the type of the disasters in the sub-inspection area.
The beneficial effects of the above technology are: the information corresponding to each disaster in the historical disaster information is fused with the time spool to obtain the corresponding disaster memory spool of each disaster in the corresponding sub-inspection area, so that reference data is provided for accurately predicting the possible disasters in the corresponding sub-inspection area.
Example 7:
on the basis of the embodiment 6, the generating end, referring to fig. 7, includes:
the spool updating module is used for updating the corresponding disaster memory spool of the corresponding type of disaster in the sub-inspection area based on the real-time alarm information to obtain the corresponding latest disaster memory spool of the corresponding type of disaster in the sub-inspection area;
and the third generation module is used for generating a corresponding latest inspection plan based on the latest disaster memory spool and the prediction result.
In this embodiment, the latest disaster memory spool is the latest disaster memory spool corresponding to the type of disaster in the sub inspection area after the corresponding disaster memory spool corresponding to the type of disaster in the sub inspection area is updated based on the real-time alarm information.
The beneficial effects of the above technology are: and updating the corresponding disaster memory spool in the sub-inspection area corresponding to the type of the disaster based on the real-time alarm information to obtain the corresponding latest disaster memory spool, and generating a more reasonable inspection plan aiming at the disaster occurrence characteristics and the disaster prediction result in the sub-inspection area based on the latest disaster memory spool and the prediction result, thereby enhancing the inspection effect.
Example 8:
on the basis of embodiment 7, the third generation module, referring to fig. 8, includes:
the first determining unit is used for determining the predicted occurrence time corresponding to the disaster type possibly occurring in the corresponding sub inspection area in the preset period based on the prediction result;
the calling unit is used for calling out the characteristic fluctuation axis section corresponding to the sub-inspection area corresponding to the predicted occurrence time;
the second determining unit is used for determining a first routing inspection item corresponding to the predicted occurrence time based on the information source of the historical routing inspection information corresponding to the characteristic fluctuation axis segment;
a third determining unit, configured to obtain a corresponding first inspection item set based on the first inspection items corresponding to all predicted occurrence times included in the sub inspection area;
the time sequence arrangement unit is used for carrying out time sequence arrangement on all corresponding predicted occurrence times of all first inspection items in the corresponding inspection subareas in the first inspection item set to obtain corresponding predicted occurrence time sequences;
a fourth determining unit, configured to generate a first inspection time gradient corresponding to the sub inspection area based on the predicted occurrence time sequence;
the analysis unit is used for analyzing the corresponding latest disaster memory spool in the sub-inspection area to obtain the average occurrence frequency of the corresponding types of disasters in the sub-inspection area;
the fifth determining unit is used for determining a corresponding second inspection item based on the information source of the historical inspection information corresponding to the characteristic fluctuation spool section corresponding to the disaster situation of the corresponding type in the corresponding sub inspection area;
a sixth determining unit, configured to determine, based on the average occurrence frequency, a second inspection time gradient corresponding to the second inspection item;
the item integration unit is used for obtaining an inspection item set corresponding to the sub inspection area based on the first inspection item and the second inspection item;
the first generation unit is used for fusing and arranging a first inspection time gradient and a second inspection item time gradient corresponding to a first inspection item contained in the inspection item set according to a time sequence to generate an inspection time gradient corresponding to each inspection item to be executed in the sub inspection area;
and the second generation unit is used for generating a corresponding latest inspection plan based on the position of each sub inspection area in the inspection range and the inspection time gradient corresponding to each inspection item to be executed in the corresponding sub inspection area.
In this embodiment, the first polling item is a polling item corresponding to the predicted occurrence time determined based on the information source of the historical polling information corresponding to the characteristic fluctuation spool segment, where the U-shaped love item includes: the method comprises the following steps that an unmanned aerial vehicle device patrols and examines items, a satellite monitoring device patrols and examines items, and fire-fighting equipment patrols and examines information acquisition items.
In this embodiment, the first patrol item set is a set formed by the first patrol items corresponding to all the predicted occurrence times included in the sub patrol area.
In this embodiment, the predicted occurrence time sequence is a time sequence obtained by time-sequentially arranging all the predicted occurrence times corresponding to all the first inspection items in the corresponding inspection sub-area.
In this embodiment, the first patrol inspection time gradient is a patrol inspection time gradient corresponding to the sub patrol inspection area generated based on the predicted occurrence time sequence.
In this embodiment, generating the first patrol inspection time gradient corresponding to the sub patrol inspection area based on the predicted occurrence time sequence is: and setting corresponding patrol inspection time at a preset time interval before each predicted occurrence time contained in the predicted occurrence time sequence, and arranging the patrol inspection time according to a time sequence to obtain a corresponding first patrol inspection time gradient.
In this embodiment, analyzing the latest disaster memory spool corresponding to the sub-inspection area to obtain the average frequency of occurrence of the corresponding types of disasters in the sub-inspection area includes:
in the formula,. DELTA.t i The average time interval of the ith disaster in the sub-inspection area is defined, i is the ith disaster in the sub-inspection area, j is the corresponding type of disaster in the jth sub-inspection area, t ij For j th occurrence of ith seed in sub-inspection areaTime corresponding to disaster, t ij-1 The time n corresponding to the (j-1) th disaster in the sub inspection area i The total times of the ith disaster in the sub routing inspection area,
the average occurrence frequency of the ith disaster in the sub-routing inspection area (namely the total number of times of the ith disaster in a preset period) is T, which is the preset period;
for example, if the time of fire occurrence in the sub inspection area is, in order, 5/month/1 day in 2021, 5/month/31 day in 2021, and 7/month/11 day in 2021, the average time interval of fire occurrence in the sub inspection area is 35 days, and if the preset period is 70 days, the average occurrence frequency is 2 times.
In this embodiment, the second inspection item is an inspection item determined based on an information source of historical inspection information corresponding to the characteristic fluctuation spool segment corresponding to the disaster in the corresponding sub inspection area.
In this embodiment, it is determined based on the average occurrence frequency that a second inspection time gradient corresponding to the second inspection item is: and arranging polling time at the interval of the occurrence time corresponding to the average occurrence frequency from the current time, and arranging the polling time according to a time sequence to obtain a corresponding first polling time gradient.
In this embodiment, the patrol item set is a set of patrol items to be executed, which are corresponding to the sub patrol areas, and is obtained by summarizing the first patrol item and the second patrol item.
In this embodiment, the patrol inspection time gradient is a variation gradient of patrol inspection time corresponding to each patrol inspection item that needs to be executed in the generated sub patrol inspection area after the first patrol inspection time gradient and the second patrol inspection item time gradient corresponding to the first patrol inspection item included in the patrol inspection item set are arranged in a fused manner according to a time sequence.
The beneficial effects of the above technology are: and fusing a second inspection time gradient corresponding to a second inspection item determined based on the latest disaster memory spool and a first inspection time gradient corresponding to a first inspection item determined based on the prediction result to generate a corresponding latest inspection plan, wherein the generated inspection plan fully considers the disaster occurrence characteristics in the corresponding sub inspection area and also considers the prediction result of possible disasters in the corresponding sub inspection area, so that the inspection effect is enhanced, and the intelligent fire-fighting system can comprehensively and timely early warn all disasters occurring in the inspection range.
Example 9:
on the basis of the embodiment 1, the inspection terminal, referring to fig. 9, includes:
the instruction generating module is used for generating a corresponding inspection control instruction based on the latest inspection plan;
and the instruction execution module is used for controlling the corresponding inspection device to automatically inspect within the inspection range based on the inspection control instruction.
In this embodiment, the inspection control instruction is an instruction for controlling the inspection device to perform inspection, which is generated based on the latest inspection plan.
The beneficial effects of the above technology are: and generating an instruction for controlling the inspection device to inspect based on the latest inspection plan, so that automatic inspection based on the latest inspection plan is realized, and the automatic inspection function of the intelligent fire-fighting system is realized.
Example 10:
the invention provides an automatic inspection method, which comprises the following steps with reference to fig. 10:
s1: acquiring patrol information in a patrol range based on a multi-terminal patrol device, receiving alarm information in the patrol range, and acquiring real-time alarm information based on the patrol information and the alarm information;
s2: carrying out multidimensional analysis and prediction on the routing inspection information to obtain a corresponding prediction result;
s3: generating a corresponding disaster memory spool based on the alarm information, and generating a corresponding latest inspection plan based on the disaster memory spool and the prediction result;
s4: and controlling a corresponding inspection device to perform inspection based on the latest inspection plan.
The beneficial effects of the above technology are: the inspection information is analyzed in real time to judge the disaster condition in the inspection range, and then the disaster condition is integrated with the alarm information, so that comprehensive disaster condition information in the inspection range can be acquired, the situation that the alarm cannot be given can be avoided, the disaster condition damage is continuously enlarged, and data are provided for the follow-up accurate generation of the inspection plan.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (6)
1. An intelligent fire fighting system, comprising:
the system comprises a collecting end, a multi-end inspection device and a real-time warning information acquiring and processing end, wherein the collecting end is used for collecting inspection information in an inspection range based on the multi-end inspection device, receiving warning information in the inspection range and acquiring real-time warning information based on the inspection information and the warning information;
the prediction end is used for carrying out multi-dimensional analysis prediction on the routing inspection information to obtain a corresponding prediction result;
the generating end is used for generating a corresponding latest disaster memory spool based on the real-time alarm information and generating a corresponding latest inspection plan based on the latest disaster memory spool and the prediction result;
the inspection end is used for controlling the corresponding inspection device to perform inspection based on the latest inspection plan;
the collection end comprises:
the multi-terminal polling module is used for acquiring polling information in a polling range based on a multi-terminal polling device and a corresponding preset polling period;
the alarm condition receiving module is used for receiving alarm information received by all alarm channels in the inspection range;
the information integration module is used for analyzing the inspection information to obtain corresponding inspection warning information and integrating the inspection warning information and the warning information to obtain corresponding real-time warning information;
the multi-end inspection module comprises:
the first inspection unit is used for acquiring an inspection video within the inspection range based on the unmanned aerial vehicle device and a first preset period;
the second inspection unit is used for acquiring satellite monitoring images in the inspection range in real time based on the satellite monitoring device and a second preset period;
the third inspection unit is used for acquiring inspection information of the fire-fighting equipment in the inspection range based on a third preset period;
wherein, the patrol information includes: the polling video, the satellite monitoring image and the fire-fighting equipment polling information;
the information integration module comprises:
the first judging unit is used for determining corresponding warning condition judging conditions based on the information sources of the routing inspection information, identifying sub routing inspection warning condition information corresponding to the routing inspection information of each information source in the routing inspection range based on the warning condition judging conditions, and summarizing all the sub routing inspection warning condition information to obtain corresponding routing inspection warning condition information;
the information duplication removing unit is used for carrying out duplication removing processing on the routing inspection alarm condition information and the alarm information to obtain corresponding duplication removing results, sequencing the duplication removing results according to emergency levels to obtain corresponding real-time alarm condition information, and starting a corresponding rescue plan based on the real-time alarm condition information;
the prediction end comprises:
the area division module is used for dividing the inspection area in the inspection range into a plurality of sub inspection areas;
the information calling module is used for calling the historical disaster information and the historical patrol information of the sub patrol area;
the first generation module is used for determining disaster situation memory spools corresponding to each disaster situation in the sub-inspection area based on the historical disaster situation information;
the first extraction module is used for extracting sub-history information characteristics corresponding to the history routing inspection information of each information source;
the information fusion module is used for fusing all the sub-historical information characteristics with the disaster memory spools to obtain corresponding historical information characteristic spools of the corresponding types of disasters in the corresponding sub-inspection areas;
the second extraction module is used for extracting corresponding characteristic fluctuation spool segments when corresponding types of disasters occur each time from the historical information characteristic spools to obtain corresponding characteristic fluctuation spool segment sets of the corresponding types of disasters in corresponding sub-inspection areas;
the alignment fusion module is used for performing alignment fusion on the characteristic fluctuation axis segment set to obtain corresponding distinguishing characteristic axis segments of the corresponding type of disasters in the corresponding sub-inspection areas;
the characteristic synthesis module is used for obtaining a set of distinguishing characteristic bobbin sections corresponding to the sub-inspection areas based on the distinguishing characteristic bobbin sections corresponding to all disaster types in the corresponding sub-inspection areas;
the third extraction module is used for determining sub-inspection information corresponding to the sub-inspection area based on the inspection information, and extracting the characteristics of the sub-inspection information to obtain the corresponding sub-inspection information characteristics;
the second generation module is used for generating a sub inspection information characteristic spool corresponding to the sub inspection area based on the sub inspection information characteristic fitting;
the local prediction module is used for judging whether a spool segment which is partially overlapped with the sub-inspection information characteristic spool exists in a distinguishing characteristic spool segment set corresponding to the sub-inspection area, if so, determining the predicted disaster type and the predicted occurrence time which are possibly generated in the sub-inspection area based on the disaster type and the overlapped position which correspond to the overlapped spool segment, and taking the predicted disaster type and the predicted occurrence time as the local prediction result corresponding to the sub-inspection area, otherwise, taking the sub-inspection area without any disaster in a preset period as the corresponding local prediction result;
and the result integration module is used for integrating the local prediction results to obtain corresponding prediction results.
2. The intelligent fire fighting system according to claim 1, wherein the first generation module includes:
the information classification unit is used for classifying the historical disaster information based on disaster types to obtain the historical information corresponding to each disaster;
and the information fusion unit is used for fusing the historical information and the time spool based on the occurrence time in the historical information to obtain a disaster memory spool corresponding to the corresponding type of disaster in the sub-routing inspection area.
3. The intelligent fire fighting system of claim 2, wherein the generating end comprises:
the bobbin updating module is used for updating the disaster situation memory bobbins corresponding to the types of disaster situations in the sub-inspection area based on the real-time alarm situation information to obtain the latest disaster situation memory bobbins corresponding to the types of disaster situations in the sub-inspection area;
and the third generation module is used for generating a corresponding latest inspection plan based on the latest disaster memory spool and the prediction result.
4. The intelligent fire fighting system of claim 3, wherein the third generation module comprises:
the first determining unit is used for determining the predicted occurrence time corresponding to the disaster type possibly occurring in the corresponding sub inspection area in the preset period based on the prediction result;
the calling unit is used for calling out the characteristic fluctuation axis section corresponding to the sub-inspection area corresponding to the predicted occurrence time;
the second determining unit is used for determining a first routing inspection item corresponding to the predicted occurrence time based on the information source of the historical routing inspection information corresponding to the characteristic fluctuation axis segment;
a third determining unit, configured to obtain a corresponding first inspection item set based on the first inspection items corresponding to all predicted occurrence times included in the sub inspection area;
the time sequence arrangement unit is used for carrying out time sequence arrangement on all corresponding predicted occurrence times of all first inspection items in the corresponding inspection subareas, wherein the first inspection items are contained in the first inspection item set, and a corresponding predicted occurrence time sequence is obtained;
a fourth determining unit, configured to generate a first patrol inspection time gradient corresponding to the sub patrol inspection area based on the predicted occurrence time sequence;
the analysis unit is used for analyzing the corresponding latest disaster memory spool in the sub-inspection area to obtain the average occurrence frequency of the corresponding types of disasters in the sub-inspection area;
the fifth determining unit is used for determining a corresponding second inspection item based on the information source of the historical inspection information corresponding to the characteristic fluctuation spool section corresponding to the disaster situation of the corresponding type in the corresponding sub inspection area;
a sixth determining unit, configured to determine, based on the average occurrence frequency, a second inspection time gradient corresponding to the second inspection item;
the item integration unit is used for obtaining an inspection item set corresponding to the sub inspection area based on the first inspection item and the second inspection item;
the first generation unit is used for fusing and arranging a first inspection time gradient and a second inspection item time gradient corresponding to a first inspection item in the inspection item set according to a time sequence to generate an inspection time gradient corresponding to each inspection item to be executed in the sub inspection area;
and the second generation unit is used for generating a corresponding latest inspection plan based on the position of each sub inspection area in the inspection range and the inspection time gradient corresponding to each inspection item to be executed in the corresponding sub inspection area.
5. The intelligent fire fighting system according to claim 1, wherein the inspection terminal includes:
the instruction generating module is used for generating a corresponding inspection control instruction based on the latest inspection plan;
and the instruction execution module is used for controlling the corresponding inspection device to automatically inspect within the inspection range based on the inspection control instruction.
6. An automatic inspection method is characterized by comprising the following steps:
s1: acquiring polling information in a polling range based on a multi-terminal polling device, receiving alarm information in the polling range, and acquiring real-time alarm information based on the polling information and the alarm information;
s2: carrying out multidimensional analysis and prediction on the routing inspection information to obtain a corresponding prediction result;
s3: generating a corresponding disaster memory spool based on the alarm information, and generating a corresponding latest inspection plan based on the disaster memory spool and the prediction result;
s4: controlling a corresponding inspection device to perform inspection based on the latest inspection plan;
wherein, S1: based on the information of patrolling and examining of multi-end inspection device collection patrol and examine the within range and receive alarm information of patrolling and examining the within range, based on patrol and examine the information with alarm information obtains real-time alert feelings information, include:
the multi-terminal polling module is used for acquiring polling information in a polling range based on a multi-terminal polling device and a corresponding preset polling period;
the alarm condition receiving module is used for receiving alarm information received by all alarm channels in the inspection range;
the information integration module is used for analyzing the inspection information to obtain corresponding inspection warning information and integrating the inspection warning information and the warning information to obtain corresponding real-time warning information;
the multi-end inspection module comprises:
the first inspection unit is used for acquiring an inspection video within the inspection range based on the unmanned aerial vehicle device and a first preset period;
the second inspection unit is used for acquiring satellite monitoring images in the inspection range in real time based on the satellite monitoring device and a second preset period;
the third inspection unit is used for acquiring inspection information of the fire-fighting equipment in the inspection range based on a third preset period;
wherein the routing inspection information includes: the polling video, the satellite monitoring image and the fire-fighting equipment polling information;
the information integration module comprises:
the first judging unit is used for determining corresponding inspection condition judging conditions based on the information sources of the inspection information, identifying sub inspection alarm condition information corresponding to the inspection information of each information source in the inspection range based on the inspection condition judging conditions, summarizing all the sub inspection alarm condition information and obtaining corresponding inspection alarm condition information;
the information duplication removing unit is used for carrying out duplication removing processing on the routing inspection alarm condition information and the alarm information to obtain corresponding duplication removing results, sequencing the duplication removing results according to emergency levels to obtain corresponding real-time alarm condition information, and starting a corresponding rescue plan based on the real-time alarm condition information;
carrying out multidimensional analysis and prediction on the routing inspection information to obtain a corresponding prediction result, wherein the method comprises the following steps:
the area division module is used for dividing the inspection area in the inspection range into a plurality of sub inspection areas;
the information calling module is used for calling the historical disaster information and the historical patrol information of the sub patrol areas;
the first generation module is used for determining a disaster memory spool corresponding to each disaster in the sub-inspection area based on the historical disaster information;
the first extraction module is used for extracting sub-history information characteristics corresponding to the history routing inspection information of each information source;
the information fusion module is used for fusing all the sub-historical information characteristics with the disaster memory spools to obtain corresponding historical information characteristic spools of the corresponding types of disasters in the corresponding sub-inspection areas;
the second extraction module is used for extracting corresponding characteristic fluctuation spool segments when corresponding types of disasters occur each time from the historical information characteristic spools to obtain corresponding characteristic fluctuation spool segment sets of the corresponding types of disasters in corresponding sub-inspection areas;
the alignment fusion module is used for performing alignment fusion on the characteristic fluctuation axis segment set to obtain corresponding distinguishing characteristic axis segments of the corresponding type of disasters in the corresponding sub-inspection areas;
the characteristic synthesis module is used for obtaining a set of distinguishing characteristic bobbin sections corresponding to the sub-inspection areas based on the distinguishing characteristic bobbin sections corresponding to all disaster types in the corresponding sub-inspection areas;
the third extraction module is used for determining sub-inspection information corresponding to the sub-inspection areas based on the inspection information, and extracting the characteristics of the sub-inspection information to obtain corresponding sub-inspection information characteristics;
the second generation module is used for generating sub inspection information characteristic bobbins corresponding to the sub inspection areas based on the sub inspection information characteristic fitting;
the local prediction module is used for judging whether a spool segment which is partially overlapped with the sub-inspection information characteristic spool exists in a distinguishing characteristic spool segment set corresponding to the sub-inspection area, if so, determining the predicted disaster type and the predicted occurrence time which are possibly generated in the sub-inspection area based on the disaster type and the overlapped position which correspond to the overlapped spool segment, and taking the predicted disaster type and the predicted occurrence time as the local prediction result corresponding to the sub-inspection area, otherwise, taking the sub-inspection area without any disaster in a preset period as the corresponding local prediction result;
and the result integration module is used for integrating the local prediction results to obtain corresponding prediction results.
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