Intelligent unmanned aerial vehicle inspection system

Abstract

The embodiment of the invention relates to the technical field of automation, in particular to an intelligent unmanned aerial vehicle inspection system. The invention discloses an intelligent unmanned aerial vehicle inspection system. The system executes the following steps: reading a to-be-inspected target area range and a historical inspection record; generating an event thermodynamic diagram of the target area range according to historical inspection records, wherein the event thermodynamic diagram records probability distribution of events occurring at each position in the target area range; dividing the target area range into a plurality of sub-areas according to the event thermodynamic diagram, wherein the sub-areas are areas in which the difference value of event occurrence probabilities is smaller than a preset threshold value; generating an inspection route set and an inspection frequency for each sub-region, wherein the inspection route set comprises a plurality of inspection routes; and generating an airport position according to the inspection route set, and controlling the unmanned aerial vehicle to sequentially and circularly execute the inspection routes in the inspection route set according to the inspection frequency.

Description

Technical Field

[0001] Multiple embodiments of this specification relate to the field of automation technology, and specifically to an intelligent drone inspection system. Background Art

[0002] The intelligent drone inspection system is an advanced integrated management system that integrates modern drone technology, sensor technology, and data processing and analysis tools. It is specially designed for efficient and accurate monitoring and inspection of specific areas or facilities. This system uses the latest flight control technology to enable drones to fly autonomously on a preset path and flexibly adjust flight modes according to mission requirements. Equipped with advanced sensor equipment such as high-definition cameras, infrared thermal imagers, and lidar, intelligent drones can capture information about the target area. During the mission, the drone transmits the collected data back to the ground control center in real time through wireless communication technology. Due to its efficient and convenient characteristics, drone inspections are widely used in various fields, including urban inspections. It is used to detect illegal parking of vehicles, illegal garbage stacking, etc., to help promptly and quickly deal with problems that arise. Summary of the invention

[0003] Multiple embodiments of this specification describe an intelligent drone inspection system.

[0004] In a first aspect, an embodiment of this specification provides an intelligent drone inspection system, the system performing the following steps: Read the target area to be inspected and historical inspection records; Generate an event heat map of the target area based on historical inspection records, where the event heat map records the probability distribution of events occurring at each location within the target area; Dividing the target area into a plurality of sub-areas according to the event heat map, wherein the sub-areas are areas where the difference in probability of an event occurring is less than a preset threshold; Generate an inspection route set and an inspection frequency for each sub-area, wherein the inspection route set includes a plurality of inspection routes; The airport location is generated according to the inspection route set, and the UAV is controlled to cyclically execute the inspection routes in the inspection route set in sequence according to the inspection frequency.

[0005] In a second aspect, the embodiments of this specification provide another intelligent drone inspection system, including: Reading module, reads the target area to be inspected and historical inspection records; A reading module generates an event heat map of the target area based on historical inspection records, wherein the event heat map records the probability distribution of events occurring at each location within the target area; A division module, which divides the target area into a plurality of sub-areas according to the event heat map, wherein the sub-areas are areas where the difference in the probability of an event occurring is less than a preset threshold; A generation module generates an inspection route set and an inspection frequency for each sub-area, wherein the inspection route set includes a plurality of inspection routes; The execution module generates the airport location according to the inspection route set, and controls the UAV to cyclically execute the inspection routes in the inspection route set in sequence according to the inspection frequency.

[0006] In a third aspect, an embodiment of this specification provides an electronic device, including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method described in any one of the above aspects.

[0007] In a fourth aspect, an embodiment of the present specification provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the above aspects.

[0008] In a fifth aspect, an embodiment of this specification provides a computer program product, including a computer program, which implements the method described in any of the above aspects when executed by a processor.

[0009] The beneficial effects brought by the technical solutions provided by some embodiments of this specification include at least: In multiple embodiments of the present specification, the provided intelligent drone inspection system uses event heat maps and inspection frequency control to achieve differentiated inspections of different event probability areas, which helps to detect events early during inspections and helps improve event handling efficiency; through grid division, it is possible to quickly obtain event heat maps based on historical inspection records, and compensate for the randomness of historical inspection records through equivalent values.

[0010] Other features and advantages of the various embodiments of the present specification will be further disclosed in the following detailed description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 A schematic diagram of an event heat map provided in an embodiment of this specification.

[0013] Figure 2 This is a schematic diagram of the system architecture of the embodiments of this specification.

[0014] Figure 3 This is a schematic diagram of the interactive interface of the embodiment of this specification.

[0015] Figure 4 A schematic diagram of the flow chart of the drone inspection method provided in the embodiments of this specification.

[0016] Figure 5 This is a schematic diagram of events provided in accordance with an embodiment of this specification.

[0017] Figure 6 A schematic diagram of the event heat map generation process provided in the embodiments of this specification.

[0018] Figure 7 An approximate surrounding grid diagram is obtained for the embodiments of this specification.

[0019] Figure 8 A schematic diagram of the process of generating an inspection route set according to an embodiment of this specification.

[0020] Fig. 9 Schematic diagram of the drone inspection system according to an embodiment of this specification.

[0021] Fig.10 This is an example screen of an airport of a drone according to an embodiment of this specification.

[0022] Fig.11 A schematic diagram of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0023] The following is an explanation and description of the technical solutions of the embodiments of this specification in conjunction with the drawings of the embodiments of this specification, but the following embodiments are only preferred embodiments of this specification, not all. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of this specification.

[0024] The terms "first", "second", "third", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0025] In the following description, terms such as "inside", "outside", "up", "down", "left", "right", etc. that indicate directions or positional relationships are only used to facilitate the description of the embodiments and simplify the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation of this specification.

[0026] The data involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection of relevant data complies with the relevant laws, regulations and standards of relevant countries and regions.

[0027] Before describing the technical solution in this specification, an introduction is given to the application scenarios and related technologies of the technical solution.

[0028] The application of intelligent drone inspection system in urban appearance management can greatly improve the efficiency of urban management, reduce the cost of manual inspection, and quickly respond to various emergencies. For example, by using drones 10 equipped with high-definition cameras, it is possible to patrol various blocks in the city regularly or irregularly, and monitor the cleanliness of the streets in real time, including garbage dumping, illegal advertising and other phenomena. Once a problem is found, the relevant departments can be notified quickly for processing. Drones 10 can fly over parks, green belts and other areas, using multispectral cameras to monitor the health of vegetation, identify early signs of pests and diseases, and assess green coverage. This helps to take timely measures to protect and improve the urban greening environment.

[0029] By taking advantage of the high-altitude photography of the drone 10, the exterior of high-rise buildings can be inspected in detail to find possible safety hazards such as cracks and falling objects; at the same time, unauthorized building expansion or renovation can be discovered to assist urban planning and management departments in law enforcement. During peak hours or large-scale events, the drone 10 can be used to monitor traffic flow on major roads and transportation hubs, help optimize traffic signal settings, and alleviate congestion. In addition, when a traffic accident occurs, the drone 10 can quickly arrive at the scene for aerial reconnaissance and provide real-time video support to the command center so that emergency decisions can be made more quickly. After a natural disaster (such as a flood or earthquake) occurs, the drone 10 can quickly enter a dangerous area for search and rescue work, providing key information support for ground rescue teams. The drone 10 can also be used for night patrols to enhance the security of public places.

[0030] At present, drones 10 have become an important auxiliary tool for modern urban management, which can help improve the level of urban management and services. However, the existing technology lacks an automatic planning method for drone inspection routes 11, and relies on manual pilots to fly according to needs and adjust the flight trajectory as the inspection route 11. The inspection route 11 obtained in this way lacks pertinence, so it is necessary to study the control system of drones 10 and the generation method of inspection routes 11. Please refer to the attached Figure 1 , which is the inspection route 11 generated by the intelligent drone inspection system provided in this specification according to the historical inspection records, and the distribution of different event probabilities 12, and different colors represent different events. An airport is set on or near the inspection route 11. The drone 10 flies along the inspection route 11 at a basically fixed speed and altitude, and shoots the image below. Exemplarily, the drone 10 can shoot a range of 3 km on both sides of the inspection route 11.

[0031] The system provided by this application has the following features: Figure 2 The system architecture shown, Figure 2 is a schematic diagram of the system architecture in the embodiment of the present application, such as Figure 2 As shown, the system architecture includes a server 20 and a client 30, and the client 30 is deployed with an interactive interface, see the attached Figure 3 The interactive interface is used to upload the target area range, historical inspection records, view real-time inspection images, control the drone 10 to take off or return, view the drone status, etc., wherein the interactive interface can be run on the terminal device in the form of a browser, or in the form of an independent application (APP), etc. The specific display form of the client 30 is not limited here. The server 20 involved in this application can be an independent physical server 20, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms. The terminal device can be a smart phone, tablet computer, laptop computer, PDA, personal computer, smart speaker, smart TV, smart watch, vehicle-mounted device, wearable device, etc., but is not limited thereto. The terminal device and the server 20 can be directly or indirectly connected by wired or wireless communication, which is not limited in this application. The number of servers 20 and terminal devices is also not limited.

[0032] An intelligent drone inspection system, please see the attached Figure 4 , perform the following steps: Step S101) Read the target area to be inspected and the historical inspection records.

[0033] Step S102) Generate an event heat map of the target area based on historical inspection records, and the event heat map records the probability distribution of events occurring at each location within the target area. Figure 5 The events include accumulated construction waste, exposed garbage, illegal parking, illegal buildings, traffic jams and other urban management related events.

[0034] Please see attached Figure 6 The method for the system to generate an event heat map of the target area based on historical inspection records includes: Step S201) The target area is divided into grids of a preset size, and the number of events occurring in each grid is marked according to the historical inspection records.

[0035] Step S202) traverses each square marked with the number of events in turn, reads the geographic description information and building information of the square, obtains surrounding squares with similar geographic description information and building information to the square, and records them as a similar square set.

[0036] Step S203) Calculate the maximum and average values ​​of the number of events marked in each square in the set of similar squares, obtain an equivalent value based on the maximum and average values, and set the number of events marked in each square in the set of similar squares as the equivalent value.

[0037] Step S204) After the equivalent values ​​of all the squares are obtained, the probability of an event occurring in each square is calculated according to the equivalent values ​​of all the squares to obtain an event heat map.

[0038] The number of events that occur in each grid can be directly obtained from the historical inspection records. However, the occurrence of events recorded in the historical inspection records has a certain degree of contingency. In this embodiment, in order to make each grid more accurately reflect the probability of the occurrence of an event, a set of similar grids is obtained based on geographic description information and building information. The maximum and average values ​​of the number of events in all similar grid sets are calculated. The equivalent value is obtained according to the maximum and average values. Exemplarily, the weighted mean of the maximum and average values ​​is used as the equivalent value. For example, the equivalent value = 0.3 × maximum value + 0.2 × average value. Furthermore, different weights are used according to the numerical range of the maximum value and average value. For example, when the maximum value and average value are greater than 30, the weight used is 0.45, when the maximum value and average value are between 20-30, the weight used is 0.35, when the maximum value and average value are between 10-20, the weight used is 0.2, and when the maximum value and average value are between 0-10, the weight used is 0.15.

[0039] Please see the attached Figure 7 The method for the system to obtain surrounding squares with similar geographic description information and building information to the square includes: Step S301) calculating the similarity of the geographical description information within the preset surrounding range of the grid, which is recorded as geographical similarity; Step S302) identifying an image of a square within the preset surrounding range to obtain the number of building elements in the image; Step S303) calculating the correlation between the building elements in pairs, and obtaining an average correlation according to the correlation between the building elements in pairs; Step S304) Calculate the weighted mean of the geographic similarity and the average correlation, and classify the squares whose difference with the weighted mean of the square is greater than a preset lower limit as the approximate surrounding squares of the square.

[0040] Exemplarily, the geographic description information includes topography, climate conditions, vegetation coverage, human geography characteristics, and address description. The method for calculating the similarity of the geographic description information is to calculate the text similarity of the fields of topography, climate conditions, vegetation coverage, human geography characteristics, and address description respectively, and the average value of the similarity of these texts is used as the average value of the geographic description information. The probability of the same type of event occurring in blocks with similar geographic description information is relatively close. Exemplarily, a block is a dry, flat, hard, bare idle ground, and the adjacent block or another block with a close distance is also a dry, flat, hard, bare idle ground. The probability of the two blocks having accumulated construction waste is similar. Assume that the geographic similarity of the two is 0.9. Since it is an idle ground, the number of building elements is 0, so the correlation is 0, and the average correlation is also 0. If the first block A has 4 accumulated construction wastes in the historical inspection records, and the other block B has never had accumulated construction wastes. However, in this embodiment, by calculating the weighted mean of the geographic similarity and average correlation between square A and square B, assuming that the calculation result of the weighted mean is 0.72 and the lower limit is 0.7, square B will be classified as an approximate surrounding square of square A. After calculating the equivalent value through square A, square B and other squares, the number of events in square B is set to the equivalent value. At this time, even if the number of events of accumulated construction waste in square B is 0, after calculation in this embodiment, the number of events of accumulated construction waste set for square B will not be 0, but the equivalent value obtained by calculation.

[0041] The method for the system to calculate the correlation between the building elements comprises: Reading a sample building image, identifying a building element in the sample building image, wherein the building element belongs to a preconfigured building element set; Combine the architectural elements in pairs and obtain the probability that each pair of architectural elements appears in the same sample architectural image; The correlation between each pair of building elements is obtained according to the probability.

[0042] Architectural elements include buildings, walls, columns, balconies, courtyards, landscapes, ground, drainage equipment, etc. The probability of a wall and a courtyard appearing in the same sample building image is relatively high, exemplarily 0.85, so the correlation between the wall and the courtyard can be set to 0.85.

[0043] Step S103) According to the event heat map, the target area is divided into several sub-areas, wherein the difference in the probability of the event occurring is less than a preset threshold. Exemplarily, considering only the event of accumulated construction waste, the probability of the event occurring in most areas is 0, so most areas are divided into a sub-area, and the probability of the event occurring is 0 or close to 0. In the remaining areas, several sub-areas are divided according to the rule that the probability of the event occurring is similar. The sub-areas are not necessarily continuous areas. To avoid forming too many sub-areas, the threshold for the difference in the probability of the event occurring should be set to a larger value. Exemplarily, for a single type of event, forming 2-4 sub-areas is a more recommended implementation method. On the other hand, generating multiple sub-areas for each type of event will result in too many sub-areas. Therefore, this embodiment adopts the average value of the probabilities of all types of events as the basis for dividing sub-areas. That is, after the event heat maps of all types of events are superimposed into one event heat map, it is used for the division of sub-areas. It means that when the total probability of multiple types of events occurring in an area is high, it will be divided into a sub-area.

[0044] Step S104 ) generates a set of inspection routes 11 and inspection frequencies for each sub-area, wherein the set of inspection routes 11 includes a plurality of inspection routes 11 .

[0045] Please see attached Figure 8 The method of generating a set of inspection routes and inspection frequencies for each sub-area by the system includes: Step S401) Calculate the average probability of the event occurring in all the squares of the sub-region, and arrange all the sub-regions in descending order according to the average probability.

[0046] Step S402) Setting the inspection frequency of each sub-area according to the average probability.

[0047] Step S403) Read the sub-regions in sequence, generate a curve of a preset length, and place the curve in the sub-region.

[0048] Step S404) Calculate the average probability of the blocks that the curve passes through, and the set of blocks in the sub-area that are extended along the normal direction of the curve and cover a preset length, and calculate the weighted sum of the average probability and the number of blocks in the set of blocks.

[0049] Step S405) The curve is adjusted until the weighted sum corresponding to the curve is maximum.

[0050] Step S406) Determine whether the preset length of the normal extension of the curve covers the sub-region.

[0051] Step S407) If the sub-region is not covered, a new curve is generated and placed in the portion of the sub-region that is not covered by the preset length of the normal extension of the curve.

[0052] Step S408) Calculate the average probability of the blocks that the new curve passes through, and the set of blocks in the sub-area that are extended along the normal direction of the new curve and cover a preset length, and calculate the weighted sum of the average probability and the number of blocks in the set of blocks.

[0053] Step S409) The new curve is adjusted until the weighted sum corresponding to the new curve is the largest.

[0054] Step S410) New curves are continuously generated according to the above steps until all the curves are normally extended to a preset length to cover the sub-region.

[0055] Arrange all the sub-areas in descending order according to the average probability, and prioritize the sub-areas with higher average probability. The average probability sets the inspection frequency of each sub-area. For example, when the average probability is greater than 0.5, the daily inspection frequency is 3 times; when the average probability is greater than 0.2 and less than 0.5, the daily inspection frequency is 2 times; when the average probability is less than 0.2, the daily inspection frequency is 1 time.

[0056] On the other hand, in another embodiment, the method of generating a set of inspection routes 11 and inspection frequencies for each sub-area by the system further includes: Whenever a new curve is generated, all the generated curves are adjusted to maximize the weighted sum.

[0057] When a curve of a preset length is generated for a sub-region, one is selected from a plurality of preset lengths according to the size of the sub-region. The maximum length of the curve is the range of the drone 10. Different types of drones 10 correspond to different maximum lengths of the curve. The maximum length of the generated curve is the range of the drone 10. The length of the generated curve can be selected from 1 / 8, 1 / 6, 1 / 4, 1 / 2, 2 / 3, 3 / 4 and 1 maximum length. The specific selection method can be performed using the method disclosed in the art, and the process does not require creative labor.

[0058] Step S105 ) Generate an airport location according to the set of inspection routes 11 , and control the drone 10 to cyclically execute the inspection routes 11 in the set of inspection routes 11 in accordance with the inspection frequency.

[0059] In this embodiment, the inspection route 11 includes several inspection routes 11, and one of the inspection routes 11 is selected in turn for each inspection. When the sub-area is large, it is difficult for a single inspection route 11 to cover the sub-area, so multiple inspection routes 11 are used to cover it. The inspection frequency can be appropriately increased.

[0060] The method of generating a set of inspection routes and inspection frequencies for each sub-area by the system further includes: Whenever a new curve is generated, all the generated curves are adjusted to maximize the weighted sum. When a new curve is added, all the curves are adjusted to optimize the combination of multiple curves.

[0061] On the other hand, please see the attached Fig. 9 In this embodiment, an intelligent drone inspection system includes: Reading module 100, reads the target area to be inspected and historical inspection records; A heat map module 200 generates an event heat map of the target area based on historical inspection records, wherein the event heat map records the probability distribution of events occurring at each location within the target area; A division module 300 is configured to divide the target area into a plurality of sub-areas according to the event heat map, wherein the sub-areas are areas where the difference in probability of an event occurring is less than a preset threshold; A generation module 400 generates a set of inspection routes 11 and inspection frequencies for each sub-area, wherein the set of inspection routes 11 includes a plurality of inspection routes 11; The execution module 500 generates the airport location according to the set of inspection routes 11, and controls the drone to cyclically execute the inspection routes 11 in the set of inspection routes 11 in accordance with the inspection frequency.

[0062] Please see attached Fig.10 This is a real-time image of an exemplary airport. The airport has charging equipment for charging drones.

[0063] See also Fig.11 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification is shown.

[0064] like Fig.11As shown, the electronic device 1100 may include: at least one processor 1101, at least one network interface 1104, a user interface 1103, a memory 1105 and at least one communication bus 1102. Among them, the communication bus 1102 can be used to realize the connection and communication of the above-mentioned components. Among them, the user interface 1103 may include a button, and the optional user interface may also include a standard wired interface and a wireless interface. Among them, the network interface 1104 may include but is not limited to a Bluetooth module, an NFC module, a Wi-Fi module, etc. Among them, the processor 1101 may include one or more processing cores. The processor 1101 uses various interfaces and lines to connect various parts in the entire electronic device 1100, and executes various functions and processes data of the routing device 1100 by running or executing instructions, programs, code sets or instruction sets stored in the memory 1105, and calling data stored in the memory 1105. Optionally, the processor 1101 can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor 1101 may integrate one or a combination of CPU, GPU and modem, etc. Among them, the CPU mainly processes the operating system, user interface and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communication.

[0065] It is understandable that the above-mentioned modem may not be integrated into the processor 1101, but may be implemented by a separate chip.

[0066] Among them, the memory 1105 may include RAM or ROM. Optionally, the memory 1105 includes a non-transitory computer-readable medium. The memory 1105 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1105 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1105 may also be at least one storage device located away from the aforementioned processor 1101. The memory 1105 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application. The processor 1101 may be used to call the application stored in the memory 1105 and execute the methods in the above-mentioned multiple embodiments.

[0067] The embodiments of this specification also provide a computer-readable storage medium, which stores instructions, and when the instructions are executed on a computer or a processor, the computer or the processor executes the multiple steps in the above embodiments. If the components of the above electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0068] The embodiments of this specification also provide a computer program product, including a computer program, which implements multiple steps in the above embodiments when executed by a processor.

[0069] In the absence of conflict, the technical features in this embodiment and implementation scheme can be combined arbitrarily.

[0070] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes a plurality of computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of this specification is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes multiple available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

[0071] When implemented by hardware or firmware, the aforementioned method flow is programmed into the hardware circuit to obtain the corresponding hardware circuit structure and realize the corresponding function. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit, and its logic function is determined by the user programming the device. The designer programs by himself to "integrate" a digital system on a PLD, without asking a chip manufacturer to design and make a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when writing program development, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL), and HDL is not just one, but many. Those skilled in the art should also be aware that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit to easily obtain the hardware circuit that implements the logic method flow.

[0072] The embodiments described above are merely preferred embodiments of this specification and are not intended to limit the scope of this specification. Without departing from the design spirit of this specification, various modifications and improvements made to the technical solutions of this specification by ordinary technicians in this field should fall within the scope of protection determined by the claims of this specification.

Claims

1. An intelligent drone inspection system, characterized in that: The system performs the following steps: Read the target area to be inspected and historical inspection records; Generate an event heat map of the target area based on historical inspection records, where the event heat map records the probability distribution of events occurring at each location within the target area; Dividing the target area into a plurality of sub-areas according to the event heat map, wherein the sub-areas are areas where the difference in probability of an event occurring is less than a preset threshold; Generate an inspection route set and an inspection frequency for each sub-area, wherein the inspection route set includes a plurality of inspection routes; The airport location is generated according to the inspection route set, and the UAV is controlled to cyclically execute the inspection routes in the inspection route set in sequence according to the inspection frequency.

2. The intelligent unmanned aerial vehicle inspection system according to claim 1, characterized in that: The method for the system to generate an event heat map of the target area according to historical inspection records includes: Divide the target area into grids of preset size, and mark the number of events occurring in each grid according to the historical inspection records; Traverse each square marked with the number of events in turn, read the geographic description information and building information of the square, obtain surrounding squares with similar geographic description information and building information to the square, and record them as a set of similar squares; Calculating the maximum value and the average value of the number of events marked in each square in the set of similar squares, obtaining an equivalent value according to the maximum value and the average value, and setting the number of events marked in each square in the set of similar squares as the equivalent value; After obtaining the equivalent values ​​of all the squares, the probability of an event occurring in each square is calculated according to the equivalent values ​​of all the squares to obtain an event heat map.

3. The intelligent unmanned aerial vehicle inspection system according to claim 2, characterized in that: The method for the system to obtain surrounding grids with geographic description information and building information similar to the grid includes: Calculate the similarity of the geographic description information within the preset surrounding range of the grid, which is recorded as geographic similarity; Recognize the image of the squares within the preset surrounding range to obtain the number of building elements in the image; Calculating the correlation between the building elements in pairs, and obtaining an average correlation according to the correlation between the building elements in pairs; The weighted mean of the geographic similarity and the average correlation is calculated, and the squares whose difference with the weighted mean of the square is greater than a preset lower limit are classified as approximate surrounding squares of the square.

4. The intelligent unmanned aerial vehicle inspection system according to claim 3, characterized in that: The method for the system to calculate the correlation between the building elements comprises: Reading a sample building image, identifying a building element in the sample building image, wherein the building element belongs to a preconfigured building element set; Combine the architectural elements in pairs and obtain the probability that each pair of architectural elements appears in the same sample architectural image; The correlation between each pair of building elements is obtained according to the probability.

5. An intelligent drone inspection system according to any one of claims 1 to 4, characterized in that: The method for the system to generate an inspection route set and inspection frequency for each sub-area includes: Calculate the average probability of the event occurring in all the squares of the sub-region, and arrange all the sub-regions in descending order according to the average probability; Setting the inspection frequency of each of the sub-areas according to the average probability; Reading the sub-regions in sequence, generating a curve of a preset length, and placing the curve in the sub-region; Calculate the average probability of the blocks that the curve passes through, and the set of blocks in the sub-area that are extended along the normal direction of the curve and covered by a preset length, and calculate the weighted sum of the average probability and the number of blocks in the set of blocks; Adjusting the curve until the weighted sum corresponding to the curve is maximum; Determine whether the preset length of the normal extension of the curve covers the sub-area; If the sub-region is not covered, a new curve is generated, and the new curve is placed in the portion of the sub-region that is not covered by the preset length of the normal extension of the curve; Calculate the average probability of the blocks that the new curve passes through, and the set of blocks in the sub-area that are extended along the normal direction of the new curve and covered by a preset length, and calculate the weighted sum of the average probability and the number of blocks in the set of blocks; Adjusting the new curve until the weighted sum corresponding to the new curve is the largest; New curves are continuously generated according to the above steps until all the curves are normally extended to a preset length to cover the sub-region.

6. The intelligent unmanned aerial vehicle inspection system according to claim 5, characterized in that: The method for the system to generate an inspection route set and an inspection frequency for each sub-area also includes: Whenever a new curve is generated, all the generated curves are adjusted to maximize the weighted sum.

7. An intelligent drone inspection system, characterized in that: include: Reading module, reads the target area to be inspected and historical inspection records; A heat map module generates an event heat map of the target area based on historical inspection records, wherein the event heat map records the probability distribution of events occurring at each location within the target area; A division module, which divides the target area into a plurality of sub-areas according to the event heat map, wherein the sub-areas are areas where the difference in the probability of an event occurring is less than a preset threshold; A generation module generates an inspection route set and an inspection frequency for each sub-area, wherein the inspection route set includes a plurality of inspection routes; The execution module generates the airport location according to the inspection route set, and controls the UAV to cyclically execute the inspection routes in the inspection route set in sequence according to the inspection frequency.

8. An electronic device, characterized in that including a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method executed by the system as claimed in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method executed by the system according to any one of claims 1 to 6 is implemented.

10. Computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method executed by the system as claimed in any one of claims 1 to 6 is implemented.

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