Building fire-fighting facility intelligent maintenance method and system based on fire-fighting internet of things

Abstract

The application discloses a building fire-fighting facility intelligent maintenance method and system based on a fire-fighting Internet of Things, which comprises a maintenance information collection module, an inspection height analysis module and a monitoring analysis module, the maintenance information collection module is used for collecting relevant information of unmanned aerial vehicle full-automatic inspection maintenance, the inspection height analysis module is used for real-time analysis of the best inspection height of the unmanned aerial vehicle in the unmanned aerial vehicle inspection process, and the monitoring analysis module is used for intelligent monitoring and analysis of the erosion and aging influence on the building fire-fighting facility during inspection, and the maintenance information collection module, the inspection height analysis module and the monitoring analysis module are connected with each other, the application realizes remote, efficient and intelligent inspection and maintenance of the building external fire-fighting facility, improves the precision and efficiency of the maintenance through the intelligent identification and monitoring function, and has the characteristics of high efficiency and intelligence.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things fire protection, in particular to a building fire-fighting facility intelligent maintenance method and system based on fire-fighting Internet of Things. BACKGROUND

[0002] With the rapid advancement of urbanization, one after another towering high-rise buildings and grand commercial complexes have emerged like mushrooms after a spring rain, outlining a brand-new skyline for the city and injecting vitality into urban life. However, behind this bustling scene, fire safety is like an impregnable defense line, silently guarding the peace of the city and the safety of the people, and its importance is increasingly important. Building fire-fighting facilities, as the core element of this defense line, are directly related to the safety of the life and property of the general public, and are the solid cornerstone of maintaining social stability and harmony.

[0003] However, when the traditional building fire-fighting facility maintenance mode encounters many challenges of the new era, its limitations begin to gradually appear. In particular, for those fire-fighting facilities installed on the outside of the building, which are originally difficult to access and actually have many difficulties, such as high-position fire water tank, fire smoke exhaust fan and lightning protection facility, their maintenance work is facing unprecedented severe test. These facilities, although not completely hidden or inaccessible, are located in special locations, such as on the top of high-rise buildings, on the edge of external walls, or even hidden in the wall gaps of special-shaped buildings, making manual inspection extremely difficult. When working at high altitudes, workers not only have to overcome their fear, but also have to carry heavy tools, work in adverse weather conditions and complex working environments, which undoubtedly greatly increases the difficulty and danger of the work. Secondly, external fire-fighting facilities are exposed to the outdoor environment for a long time, and are easily affected by factors such as wind and rain erosion and dust accumulation, resulting in performance degradation and shortened service life. The traditional maintenance method can only perform periodic and superficial cleaning and maintenance, and cannot timely discover and handle the damage or rust of the facilities. In addition, the traditional maintenance method lacks intelligent means, and the maintenance effect of the facilities cannot be monitored and evaluated in real time. Therefore, the maintenance work often has blindness and lag, and it is difficult to timely discover and handle potential safety hazards. Therefore, it is urgent to develop a building fire-fighting facility intelligent maintenance method and system based on fire-fighting Internet of Things to realize remote, efficient and intelligent inspection and maintenance of external fire-fighting facilities. SUMMARY

[0004] The present application relates to the technical field of Internet of Things fire protection, in particular to a building fire-fighting facility intelligent maintenance method and system based on fire-fighting Internet of Things.

[0005] In order to solve the above technical problems, the present application provides the following technical solutions: the building fire-fighting facility intelligent maintenance system based on the fire-fighting Internet of Things, comprising a maintenance information collection module, a patrol height analysis module and a monitoring analysis module, the maintenance information collection module is used for collecting relevant information of unmanned aerial vehicle full-automatic patrol maintenance, the patrol height analysis module is used for real-time analysis of the best patrol height of the unmanned aerial vehicle in the unmanned aerial vehicle patrol process, and the monitoring analysis module is used for intelligent monitoring and analysis of the erosion and aging influence of the building fire-fighting facility during patrol.

[0006] According to the above technical solutions, the maintenance information collection module comprises a patrol route database and a laser radar scanning unit, the patrol route database is used for pre-storing unmanned aerial vehicle maintenance patrol flight routes, and the laser radar scanning unit is used for emitting laser beams to detect surrounding target objects during patrol.

[0007] According to the above technical solutions, the patrol height analysis module comprises an environmental wind speed acquisition module, a scanning signal calculation module, a safety radius calculation module and a patrol height acquisition module, the environmental wind speed acquisition module is used for acquiring the wind speed of the environment during patrol, the scanning signal calculation module is electrically connected with the laser radar scanning unit, the scanning signal calculation module analyzes the surrounding unmanned aerial vehicle flight environment during patrol of the building fire-fighting facility according to the scanning signal of the laser radar, and the environmental wind speed acquisition module and the scanning signal calculation module are electrically connected with the safety radius calculation module, and the safety radius calculation module is used for calculating the minimum safety flight radius required during unmanned aerial vehicle patrol.

[0008] According to the above technical solutions, the monitoring analysis module comprises a monitoring image acquisition module, a chromatic aberration recognition module, a maintenance feature database and a feature recognition and judgment module, the monitoring image acquisition module is used for acquiring image pictures of the patrol building fire-fighting facility, the chromatic aberration recognition module is used for recognizing and analyzing dust accumulation according to chromatic aberration, the maintenance feature database is used for recording and storing related features that have a necessary influence on the target fire-fighting facility, and the feature recognition and judgment module is used for recognizing the features of the collected pictures and judging whether manual intervention is required for maintenance of the target fire-fighting facility according to the features.

[0009] According to the above technical solutions, the scanning signal calculation module further comprises a space structure analysis submodule and an obstacle point analysis submodule, the space structure analysis submodule is used for analyzing and judging the complexity of the space structure around the unmanned aerial vehicle according to the scanning signal, and the obstacle point analysis submodule is used for analyzing and judging the space obstacle points on the unmanned aerial vehicle patrol path according to the scanning signal.

[0010] A building fire-fighting facility intelligent maintenance method based on the fire-fighting Internet of Things, the building fire-fighting facility intelligent maintenance method comprises the following steps:

[0011] Step S1: Establish a building fire-fighting facility inspection database, and store the pre-set unmanned aerial vehicle inspection route for fire-fighting facilities in the fire-fighting facility inspection database;

[0012] Step S2: According to the inspection route, the unmanned aerial vehicle performs automatic inspection on the connected system fire-fighting facilities, and when the target fire-fighting facility is reached based on the inspection route, the laser radar scanning unit is started to monitor the flight environment around the target fire-fighting facility;

[0013] Step S3: The inspection height analysis module is started synchronously, and the lowest safe flight height H min is comprehensively analyzed when the target fire-fighting facility is inspected according to the inspection path, and then the inspection height H min is used to approach the fire-fighting facility to remove the interference of the relative maximum wind brought by the unmanned aerial vehicle wing on the target fire-fighting facility surface.

[0014] Step S4: Finally, the monitoring and analysis module is started, the target fire-fighting facility after preliminary cleaning by the airflow brought by the unmanned aerial vehicle wing is monitored and fed back, and the target fire-fighting facility is analyzed to determine whether manual intervention is needed for maintenance of the target fire-fighting facility.

[0015] According to the above technical solution, the step S2 further comprises:

[0016] Step S21: The laser radar scanning unit takes the position of the unmanned aerial vehicle as the center, and respectively emits diffused laser beams to the "front", "rear", "left", "right" and "down" directions of the unmanned aerial vehicle;

[0017] Step S22: The laser beams move to the target direction at the speed of light, and reflect when encountering obstacles;

[0018] Step S23: The laser radar scanning unit captures the reflected laser beam signals in real time, and generates a scanning signal data set and sends it to the system, so as to realize monitoring the obstacle distribution around the target fire-fighting facility based on the scanning signal collection when the target fire-fighting facility is reached based on the inspection route, and providing data basis for subsequent analysis of the lowest safe flight height.

[0019] According to the above technical solution, the step S3 further comprises:

[0020] Step S31: Obtain the scanning signal data set Q={q1, q2, …, q n}, wherein q i is the i-th reflected laser beam signal value after one round of detection scanning of the diffused laser beams emitted to the "front", "rear", "left", "right" and "down" directions of the unmanned aerial vehicle;

[0021] Step S32: calculating the discrete degree of each reflected laser beam signal value of the scanning signal data set after one round of detection scanning wherein is the average reflected laser beam signal value of the scanning signal data set, and n is the number of captured reflected laser beams after one round of detection scanning;

[0022] Step S33: calculating the spatial structure complexity value of the surrounding of the UAV obtained in the next round of detection scanning by the formula F = a·S 2 and outputting, wherein a is a coefficient;

[0023] Step S34: collecting the environmental wind speed v during detection scanning, and finally calculating the minimum safe flight radius R of the UAV in the current flight environment of the UAV by the formula:

[0024] R = b·log a (Fv+1) + r

[0025] wherein b is a coefficient, and is a constant greater than 0, a is a system preset value, and a > 1, (Fv+1) > 1, and r is the minimum safe flight radius value in the best flight working condition of the experiment;

[0026] wherein b is a coefficient, and is a constant greater than 0, a is a system preset value, and a > 1, (Fv+1) > 1, and r is the minimum safe flight radius value in the best flight working condition of the experiment;

[0027] In the formula, the minimum safe flight radius of the UAV during the patrol inspection is related to the environmental wind speed and the spatial structure complexity of the surrounding, and is enhanced with the increase of the environmental wind speed and the spatial structure complexity of the surrounding, but the increasing amplitude value presents a gradually slowing down trend;

[0028] Step S35: constructing a spatial structure model of the surrounding based on the spatial distance determined according to the reflected laser beam signal value and the direction vector of the laser beam with the UAV as the center; selecting the point closest to the position of the UAV in the spatial structure model as the spatial obstacle point on the patrol inspection path of the UAV, and measuring the distance l from the current position of the UAV to the spatial obstacle point z When l z > r, the UAV is controlled to slowly descend relative to the flight height of the patrol inspection target fire-fighting facility, the spatial obstacle point is updated based on the spatial structure model, and the numerical relationship between l z and r is constantly compared until l z = R, the descending is stopped, the current flight height relative to the patrol inspection target fire-fighting facility is measured, and the flight height is output as the minimum safe flight height H min .

[0029] According to the above technical solution, the step S4 further comprises:

[0030] Step S41: reaching the minimum safe flight height H minAfterwards, the monitoring image acquisition module is started to acquire the image picture of the target building fire-fighting facility;

[0031] Step S42: when the system preset calibration region exists in the currently acquired image picture, three vertices of the edge-inscribed equilateral triangle of the calibration region are directly selected, and the maximum color difference value E is recognized by using the color difference recognition module;

[0032] Step S43: when E < 75% ΔE, it is judged that the dust on the surface of the target fire-fighting facility is not effectively cleaned by the airflow caused by the wings of the unmanned aerial vehicle, and then the monitoring analysis module directly outputs the analysis result, prompting that manual intervention is needed for the maintenance of the target fire-fighting facility, wherein ΔE is the maximum color difference value between different points recorded in the system under the clean state of the surface of the target fire-fighting facility;

[0033] When E ≥ 75% ΔE, the features of the acquired picture are recognized, the recognized features are fitted, and similarity matching is performed with the necessary influence-related features recorded and stored for the target fire-fighting facility. When the similarity matching result is greater than 80%, the result of judging that manual intervention is needed for the maintenance of the target fire-fighting facility is output, and the required maintenance reason of the maximum similarity feature displayed in the maintenance feature database is also given.

[0034] Compared with the prior art, the beneficial effects achieved by the present application are: the present application, by means of the maintenance information collection module, the inspection height analysis module and the monitoring analysis module, realizes remote, efficient and intelligent inspection and maintenance of the external fire-fighting facilities of the building. In addition, through the intelligent recognition and monitoring function, the accuracy and efficiency of the maintenance are improved, and in the process of inspection and maintenance, the unmanned aerial vehicle fan airflow can be used to remove as much dust, fallen leaves and other sundries on the fire-fighting facilities as possible, providing better conditions for subsequent recognition and monitoring, and reducing the maintenance cost of some situations where the cleaning difficulty is relatively low but the equipment aging speed and equipment performance are affected if the cleaning is not timely. BRIEF DESCRIPTION OF DRAWINGS

[0035] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation of the present application.

[0036] In the drawings:

[0037] Figure 1 is a schematic diagram of the system module of the present application;

[0038] Figure 2 is a schematic diagram of the method flow of the present application. DETAILED DESCRIPTION

[0039] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0040] Please refer to Figure 1 The present application provides a technical solution: a building fire-fighting facility intelligent maintenance system based on a fire-fighting Internet of Things, which comprises a maintenance information collection module, a patrol height analysis module and a monitoring analysis module. The maintenance information collection module is used to collect relevant information of unmanned aerial vehicle full-automatic patrol maintenance. The patrol height analysis module is used to analyze the best patrol height of the unmanned aerial vehicle in real time during the unmanned aerial vehicle patrol process. The monitoring analysis module is used to intelligently monitor and analyze the erosion and aging impact on the building fire-fighting facility during the patrol. The maintenance information collection module, the patrol height analysis module and the monitoring analysis module are connected with each other.

[0041] The maintenance information collection module comprises a patrol route database and a laser radar scanning unit. The patrol route database is used to pre-store the unmanned aerial vehicle maintenance patrol flight route. The laser radar scanning unit is used to emit a laser beam to detect the surrounding target objects during the patrol process.

[0042] The patrol height analysis module comprises an environmental wind speed acquisition module, a scanning signal calculation module, a safety radius calculation module and a patrol height acquisition module. The environmental wind speed acquisition module is used to acquire the wind speed of the environment during the patrol. The scanning signal calculation module is electrically connected with the laser radar scanning unit. The scanning signal calculation module analyzes the surrounding unmanned aerial vehicle flight environment during the patrol of the building fire-fighting facility according to the scanning signal of the laser radar. The environmental wind speed acquisition module and the scanning signal calculation module are both electrically connected with the safety radius calculation module. The safety radius calculation module is used to calculate the minimum safety flight radius required by the unmanned aerial vehicle during the patrol.

[0043] The monitoring analysis module comprises a monitoring image acquisition module, a chromatic aberration recognition module, a maintenance feature database and a feature recognition and judgment module. The monitoring image acquisition module is used to acquire the image picture of the patrol building fire-fighting facility. The chromatic aberration recognition module is used to recognize and analyze and judge the dust accumulation condition according to the chromatic aberration. The maintenance feature database is used to record and store the related features that have a necessary impact on the target fire-fighting facility. The feature recognition and judgment module is used to recognize the features of the acquired picture and judge whether human intervention is required for the maintenance of the target fire-fighting facility according to the features.

[0044] The scanning signal calculation module further comprises a space structure analysis submodule and an obstacle point analysis submodule, the space structure analysis submodule is used for judging the complexity of the space structure around the unmanned aerial vehicle according to the scanning signal analysis, and the obstacle point analysis submodule is used for judging the space obstacle points on the unmanned aerial vehicle inspection path according to the scanning signal analysis.

[0045] Please refer to Figure 2 A building fire-fighting facility intelligent maintenance method based on a fire-fighting Internet of Things, the building fire-fighting facility intelligent maintenance method comprises the following steps:

[0046] Step S1: establishing a building fire-fighting facility inspection database, storing a pre-set unmanned aerial vehicle inspection fire-fighting facility inspection route in the fire-fighting facility inspection database;

[0047] Step S2: according to the inspection route, the unmanned aerial vehicle performs full-automatic inspection on the fire-fighting facility connected to the system, when reaching the target fire-fighting facility based on the inspection route, starting a laser radar scanning unit to monitor the flight environment around the target fire-fighting facility;

[0048] Step S3: a simultaneous starting of an inspection height analysis module, comprehensively analyzing the lowest safe flight height H min of the unmanned aerial vehicle when inspecting the target fire-fighting facility according to the inspection path, and then performing inspection on the fire-fighting facility at an inspection height of H min , so as to remove the interference of the relative maximum wind brought by the wings of the unmanned aerial vehicle on the surface of the target fire-fighting facility;

[0049] Step S4: finally starting a monitoring analysis module to monitor and feed back the target fire-fighting facility after the preliminary cleaning by the air flow brought by the wings of the unmanned aerial vehicle, and analyzing the target fire-fighting facility to determine whether manual intervention is needed for the maintenance of the target fire-fighting facility.

[0050] Step S2 further comprises:

[0051] Step S21: the laser radar scanning unit takes the position of the unmanned aerial vehicle as the center, respectively emits diffused laser beams to the "front", "rear", "left", "right" and "down" directions of the unmanned aerial vehicle;

[0052] Step S22: the laser beams move to the target direction at the speed of light, and reflect when encountering obstacle objects;

[0053] Step S23: the laser radar scanning unit captures the reflected laser beam signals in real time, and generates a scanning signal data set and sends it to the system, so as to realize the monitoring of the obstacle distribution around the target fire-fighting facility based on the scanning signal collection when reaching the target fire-fighting facility based on the inspection route, and providing a data basis for the subsequent analysis of the lowest safe flight height.

[0054] Step S3 further comprises:

[0055] Step S31: Obtain the scanning signal data set Q={q1, q2, …, qn}, wherein q n i is the ith reflected laser beam signal value after a round of detection scanning by emitting diffuse laser beams to the "front", "back", "left", "right" and "down" directions of the UAV;

[0056] Step S32: Calculate the dispersion degree of each reflected laser beam signal value of the scanning signal data set after a round of detection scanning , wherein

[0057] is the average reflected laser beam signal value of the scanning signal data set, and n is the number of captured reflected laser beams after a round of detection scanning;

[0058] Step S33: Calculate and output the value of the complexity of the spatial structure around the UAV obtained by the next round of detection scanning by the formula F=α·S 2 , wherein α is a coefficient;

[0059] Step S34: Collect the environmental wind speed v during detection scanning, and finally calculate the minimum safe flight radius required by the UAV in the current UAV flight environment by the formula: a R=β·log(Fv+1)+r

[0060]

[0061] , wherein β is a coefficient, a is a system preset value, a>1, (Fv+1)>1, and r is the minimum safe flight radius value under the best flight working condition in the experiment;

[0062] In the formula, the minimum safe flight radius of the UAV during inspection is related to the environmental wind speed and the complexity of the spatial structure around the UAV, and increases with the increase of the environmental wind speed and the complexity of the spatial structure around the UAV, but the increasing amplitude value presents a gradually slowing down trend. As can be seen from the above formula, the smaller the environmental wind speed is, the greater the value of β·log a ​​The smaller the value of (Fv+1) is, the less the unmanned aerial vehicle flight interference is, thereby approaching the best flight condition, and thus the redundancy space of the minimum safe flight radius additionally provided due to the aircraft condition can be lower, and conversely, if the environmental wind speed is larger, a larger safe flight radius is required to effectively overcome the interference of the airflow on the accuracy of the flight path, thereby causing the unmanned aerial vehicle to collide, damage, and the like; meanwhile, the minimum safe flight radius is also in direct proportion to the complexity of the space structure, and if the surrounding space structure is more complex, there is a greater possibility that an obstacle that is not accurately detected or some state-unstable interference object interferes with the flight condition, and thus the minimum safe flight radius is further provided by the formula, thereby effectively avoiding the damage of the unmanned aerial vehicle wing caused by the complex structure accumulation, shaking, and floating objects.

[0063] Step S35: determining the spatial distance according to the reflected laser beam signal value and the direction vector of the laser beam with the unmanned aerial vehicle as the center, and constructing a surrounding space structure model; selecting the nearest point to the unmanned aerial vehicle position in the space structure model as the spatial obstacle point on the unmanned aerial vehicle inspection path, and measuring the distance l from the current position of the unmanned aerial vehicle to the spatial obstacle point z When l z >R, the unmanned aerial vehicle is controlled to slowly descend relative to the flight height of the inspection target fire-fighting facility, and the spatial obstacle point is updated based on the space structure model, and the numerical relationship between l z and R is continuously compared until l z =R, the descent is stopped, and the current flight height relative to the inspection target fire-fighting facility is measured, and the flight height is output as the minimum safe flight height H min .

[0064] The minimum safe flight height H min is intelligently set, which is determined by the environmental wind speed, the complexity of the surrounding space, and the minimum safe radius and the like, to ensure the safety and accuracy of the unmanned aerial vehicle in the inspection process, and when the unmanned aerial vehicle meets these conditions, the flight height of the unmanned aerial vehicle is gradually reduced until the low safe flight height H min calculated and analyzed, thereby the relative maximum wind force caused by the rotation of the unmanned aerial vehicle wing under the safe premise can be used to remove the dust, leaves, and other interference objects on the fire-fighting facility, and the unmanned aerial vehicle inspection can automatically perform simple maintenance on the target fire-fighting facility, thereby improving the maintenance frequency, effectively avoiding the interference objects that are accumulated for a long time and cannot be cleaned by the airflow of the unmanned aerial vehicle, thereby greatly reducing the need for manual intervention in maintenance, reducing the manpower and material resources required for high-altitude work, and also achieving the effect of providing a clear field of view for subsequent visual inspection and monitoring.

[0065] Step S4 further comprises:

[0066] Step S41: reaching the minimum safe flight height H min After that, the monitoring image acquisition module is started to acquire the image of the target building fire-fighting facilities;

[0067] Step S42: when the system preset calibration region exists in the current acquired image, the three vertices of the edge-inscribed equilateral triangle of the calibration region are directly selected, and the maximum color difference value E is identified by using the color difference identification module;

[0068] Step S43: when E < 75% ΔE, it is judged that the target fire-fighting facility surface dust is not effectively cleaned by the airflow caused by the unmanned aerial vehicle wing, and the monitoring analysis module directly outputs the analysis result, prompting the need for manual intervention to maintain the target fire-fighting facility, wherein ΔE is the maximum color difference value between different points recorded in the system under the clean state of the target fire-fighting facility surface;

[0069] When E ≥ 75% ΔE, the feature of the acquired image is identified, the identified feature is fitted, and the necessary influence-related features of the target fire-fighting facility recorded and stored are matched in similarity, when the similarity matching result is greater than 80%, the result of judging the need for manual intervention to maintain the target fire-fighting facility is output, and the required maintenance reason of the maximum similarity feature displayed in the maintenance feature database is given, such as the maximum similarity feature is fallen leaves or mud or bird droppings or rust area in the database, then the fallen leaves, mud, droppings and rust on the fire-fighting facility need to be cleaned are output respectively. In the inspection process, the system captures the image of the target building fire-fighting facility through the monitoring image acquisition module, and analyzes the color difference value of the preset region by using the color difference identification module. If the color difference value does not reach the preset cleaning standard, the system will judge that the target fire-fighting facility surface dust is not effectively cleaned, and directly prompt the need for manual intervention for maintenance, without the need for subsequent feature recognition. Because the surface dust is not effectively cleaned, the captured image is blocked by dust, which will inevitably affect the accuracy of subsequent feature recognition, and then through hierarchical judgment, the feature recognition program with low recognition accuracy can be omitted, the system calculation is effectively reduced, and the speed of unmanned aerial vehicle inspection can be appropriately improved, without long loading analysis.

[0070] If the color difference value reaches or exceeds the cleaning standard, the system will further use the feature recognition judgment module to fit the identified feature and match the record in the maintenance feature database in similarity to judge whether the fire-fighting facility has problems such as corrosion, stains, and debris, and give the corresponding maintenance suggestion.

[0071] The application realizes remote, efficient and intelligent inspection and maintenance of the building external fire-fighting facilities through the maintenance information collection module, the inspection height analysis module and the monitoring analysis module, improves the accuracy and efficiency of the maintenance through the intelligent identification monitoring function, and can use the unmanned aerial vehicle fan airflow to remove dust, fallen leaves and other sundries on the fire-fighting facilities as much as possible in the inspection and maintenance process, thereby providing better conditions for subsequent identification monitoring, and reducing the maintenance cost of some conditions that have low cleaning difficulty but will affect the aging speed and performance of the equipment if not cleaned in time.

[0072] It should be noted that, in this document, the terms such as first and second are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between such entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or inherent to such a process, method, article or apparatus.

[0073] Finally, it should be noted that: the above only describes the preferred embodiments of the application, and does not limit the application, although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A fire-fighting Internet of Things based building fire-fighting facility intelligent maintenance method, characterized in that: The building fire-fighting facility intelligent maintenance method comprises the following steps: Step S1: establishing a building fire-fighting facility inspection database, and storing a pre-set inspection route of the unmanned aerial vehicle for inspecting the fire-fighting facility in the fire-fighting facility inspection database; Step S2: based on the inspection route, the unmanned aerial vehicle performs full-automatic inspection on the fire-fighting facility connected to the system, and when the unmanned aerial vehicle reaches the target fire-fighting facility based on the inspection route, a laser radar scanning unit is started to monitor the flight environment around the target fire-fighting facility; Step S3: The inspection height analysis module is activated simultaneously to comprehensively analyze the minimum safe flight altitude when inspecting target fire protection facilities based on the inspection path. , and then with The inspection is carried out at a height close to the fire protection facilities, using the relative maximum wind force brought by the drone's wings to remove interference that may affect the surface of the target fire protection facilities; Step S4: finally, a monitoring and analyzing module is started to monitor the situation of the target fire-fighting facility after the airflow brought by the unmanned aerial vehicle wing is cleaned, and the target fire-fighting facility is analyzed to determine whether manual intervention is needed for the maintenance of the target fire-fighting facility; The step S3 further comprises: Step S31: Obtain a scanning signal data set wherein is the i-th reflected laser beam signal value after a round of detection scanning for emitting a diffuse laser beam to the "front", "rear", "left", "right" and "down" directions of the unmanned aerial vehicle. Step S32: calculating the dispersion degree of each reflected laser beam signal value of the scanning signal data set after one round of detection scanning wherein is the average reflected laser beam signal value of the scanning signal data set, and n is the number of captured reflected laser beams after one round of detection scanning. Step S33: Using the formula Calculate and output the complexity value of the spatial structure around the UAV obtained from the second round of detection and scanning, where For coefficients; Step S34: the environment wind speed v is collected during the detection and scanning, and finally the minimum safe flight radius required by the unmanned aerial vehicle under the current flight environment is calculated through the formula: The minimum safe flight radius of the unmanned aerial vehicle during the inspection is related to the size of the environment wind speed and the complexity of the surrounding space structure, and is enhanced with the increase of the size of the environment wind speed and the complexity of the surrounding space structure, but the increasing amplitude value presents a gradually slowing down trend; wherein, is a constant with a coefficient greater than 0, is a system preset value, and , r is a minimum safe flight radius value under the optimal flight condition of the experiment. The step S2 further comprises: Step S35: centering on the unmanned aerial vehicle, determining the spatial distance according to the reflected laser beam signal value, and constructing a spatial structure model of the periphery according to the direction vector of the laser beam; selecting the point closest to the unmanned aerial vehicle in the spatial structure model as the spatial obstacle point on the unmanned aerial vehicle inspection path, and measuring the distance from the current position of the unmanned aerial vehicle to the spatial obstacle point When , the unmanned aerial vehicle is controlled to slowly descend relative to the flight height of the inspection target fire-fighting facility, and the spatial obstacle point is updated based on the spatial structure model, and the numerical relationship between and is continuously compared, until , the descent is stopped, and the current flight height relative to the inspection target fire-fighting facility is measured, and the flight height is output as the lowest safe flight height .

2. The fire-fighting Internet of Things based building fire facility intelligent maintenance method according to claim 1, characterized in that: Step S21: the laser radar scanning unit takes the position of the unmanned aerial vehicle as the center, and respectively emits diffused laser beams to the "front", "rear", "left", "right" and "down" directions of the unmanned aerial vehicle; Step S22: the laser beams move to the target direction at the speed of light, and reflect when encountering obstacles; Step S23: the laser radar scanning unit captures the reflected laser beam signals in real time, and generates a scanning signal data set and sends it to the system, so as to realize the monitoring of the obstacle distribution around the target fire-fighting facility based on the scanning signal collection when the unmanned aerial vehicle reaches the target fire-fighting facility based on the inspection route, and to provide a data basis for the subsequent analysis of the minimum safe flight height. The step S4 further comprises: 3.The fire-fighting Internet of Things based building fire-fighting facility intelligent maintenance method according to claim 1, characterized in that: Step S42: when the system pre-set calibration area exists in the current collected image, the three vertices of the edge-inscribed equilateral triangle of the calibration area are directly selected, and the maximum chromatic aberration value E is identified by using the chromatic aberration identification module. Step S41: reaching the minimum safe flight height Afterwards, the monitoring image acquisition module is started to acquire the image picture of the target building fire-fighting facilities. The building fire-fighting facility intelligent maintenance system comprises a maintenance information collection module, an inspection height analysis module and a monitoring and analyzing module, the maintenance information collection module is used for collecting relevant information of the unmanned aerial vehicle full-automatic inspection and maintenance, the inspection height analysis module is used for analyzing the best inspection height of the unmanned aerial vehicle in real time during the unmanned aerial vehicle inspection process, and the monitoring and analyzing module is used for intelligently monitoring and analyzing the erosion and aging influence on the building fire-fighting facility during the inspection. Step S43: When the target fire-fighting facility surface is not effectively cleaned by the airflow caused by the UAV wing, the monitoring and analysis module directly outputs the analysis result, prompting the need for manual intervention to maintain the target fire-fighting facility, wherein the maximum color difference value between different points entered into the system under the target fire-fighting facility surface cleaning state is different. When the identification feature is fitted, and a similarity matching is performed with the recorded and stored impact-related features of the target fire-fighting facility. When the similarity matching result is greater than 80%, a result is output that indicates that manual intervention is required for maintenance of the target fire-fighting facility, and the required maintenance reason is displayed in the maintenance feature database with the maximum similarity feature.

4. A fire-fighting Internet of Things based intelligent maintenance system for building fire-fighting facilities, which is used to implement the method according to any one of claims 1 to 3, characterized in that: The maintenance information collection module comprises an inspection route database and a laser radar scanning unit, the inspection route database is used for pre-storing the unmanned aerial vehicle maintenance inspection flight route, and the laser radar scanning unit is used for emitting laser beams to detect the surrounding target objects during the inspection.

5. The fire-fighting Internet of Things based building fire facility intelligent maintenance system according to claim 4, characterized in that: ​ 6. The fire-fighting Internet of Things based building fire facility intelligent maintenance system according to claim 4, characterized in that: The patrol height analysis module comprises an ambient wind speed acquisition module, a scanning signal calculation module, a safety radius calculation module and a patrol height acquisition module, the ambient wind speed acquisition module is used for collecting the wind speed of the environment during patrol, the scanning signal calculation module is electrically connected with the laser radar scanning unit, the scanning signal calculation module analyzes the surrounding unmanned aerial vehicle flight environment during the patrol of the building fire protection according to the scanning signal of the laser radar, the ambient wind speed acquisition module and the scanning signal calculation module are electrically connected with the safety radius calculation module, and the safety radius calculation module is used for calculating the minimum safety flight radius required during the patrol of the unmanned aerial vehicle.

7. The fire-fighting Internet of Things based building fire facility intelligent maintenance system according to claim 4, characterized in that: The monitoring analysis module comprises a monitoring image acquisition module, a chromatic aberration identification module, a maintenance feature database and a feature identification and judgment module, the monitoring image acquisition module is used for collecting the image picture of the building fire protection facility during patrol, the chromatic aberration identification module is used for identifying and analyzing the dust accumulation condition according to chromatic aberration, the maintenance feature database is used for recording and storing the related features that affect the target fire protection facility, and the feature identification and judgment module is used for identifying the features of the collected picture and judging whether manual intervention is required for the maintenance of the target fire protection facility according to the features.

8. The fire-fighting Internet of Things based building fire facility intelligent maintenance system according to claim 6, characterized in that: The scanning signal calculation module further comprises a space structure analysis submodule and an obstacle point analysis submodule, the space structure analysis submodule is used for analyzing and judging the complexity of the space structure around the unmanned aerial vehicle according to the scanning signal, and the obstacle point analysis submodule is used for analyzing and judging the space obstacle points on the patrol path of the unmanned aerial vehicle according to the scanning signal.

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