Intelligent sensor-based light intensity detection device and unmanned aerial vehicle

Abstract

The application relates to the technical field of wireless measurement, and particularly discloses a light intensity detection device based on an intelligent sensor and a unmanned aerial vehicle, which comprises a cruise and sensing control module that obtains light intensity detection data and space surveying and mapping data of a current position based on an intelligent sensor carried by the unmanned aerial vehicle; a cruise calibration control module that calibrates a current cruise route in real time based on all the obtained space surveying and mapping data, and controls the unmanned aerial vehicle to continue cruising based on the calibrated cruise route; and a light intensity thermal analysis module that generates a three-dimensional space surveying and mapping model of a target space range in real time based on all the obtained space surveying and mapping data, and performs light intensity thermal analysis on all the obtained light intensity detection data based on the three-dimensional space surveying and mapping model until complete light intensity detection results of the target space range are obtained; efficient and accurate acquisition and analysis of target space light intensity and space information are realized, work efficiency is improved, and the difficulty and risk of manual operation are reduced.

Description

Technical Field

[0001] This invention relates to the field of wireless measurement technology, and in particular to a light intensity detection device and a drone based on a smart sensor. Background Technology

[0002] Currently, light intensity detection devices are equipment that utilize advanced sensing technology and precise measuring elements to accurately measure and analyze light intensity in different environments. Typical light intensity detection devices are usually equipped with highly sensitive photoelectric sensors that convert light signals into electrical signals, which are then processed and calculated through internal circuits and algorithms to obtain accurate light intensity values. Existing light intensity data detection technologies for large-area spaces utilize advanced optical sensing equipment and data acquisition systems to comprehensively and continuously monitor light intensity over a wide area. This technology typically deploys multiple distributed detection nodes, transmitting the collected data to a central processing system via wireless or wired networks to construct a light intensity distribution map over a large area.

[0003] However, in practical applications, due to the vastness of the testing area and the diversity of the environment, there are many technical shortcomings. First, the cost of testing equipment is high, and large-scale deployment will bring huge economic pressure. Second, data synchronization and calibration between different testing nodes are difficult, which can easily lead to data deviation and inconsistency. Third, complex environmental conditions may cause damage and failure of testing equipment, increasing the difficulty and cost of maintenance.

[0004] Therefore, this invention proposes a light intensity detection device and a drone based on a smart sensor. Summary of the Invention

[0005] This invention provides a light intensity detection device and a drone based on intelligent sensors. The navigation and sensor control module controls the drone to navigate along an initial route and acquire light intensity detection data and spatial mapping data, enabling preliminary detection of the target space and providing foundational data for subsequent analysis and calibration. The navigation calibration control module performs real-time calibration of the navigation route based on the acquired spatial mapping data, ensuring the drone can more accurately cover the target space and improving the completeness and accuracy of data acquisition. This real-time calibration function helps adapt to the complex environment and obstacles that may exist within the target space, reducing data loss or errors caused by inaccurate initial routes. The light intensity thermal analysis module generates a three-dimensional spatial mapping model based on the spatial mapping data and analyzes the light intensity detection data, providing a comprehensive and intuitive presentation of the light intensity distribution in the target space. Complete light intensity detection results help to gain a deeper understanding of the illumination characteristics of the target space, providing valuable reference for applications such as lighting design and energy management. The entire solution, through drone-based patrol, calibration, and data analysis, avoids the economic burden of distributed deployment in existing technologies. It achieves efficient and accurate acquisition and analysis of target space light intensity and spatial information, improving work efficiency and reducing the difficulty and risk of manual operation. It also reduces maintenance costs. Furthermore, it provides scientific data support for decision-making and optimization in related fields, contributing to more rational resource allocation and environmental improvement.

[0006] This invention provides a light intensity detection device based on a smart sensor, comprising:

[0007] The cruise and sensor control module is used to control the UAV to cruise within the target space based on the initial cruise route. At the same time, it uses the intelligent sensors on the UAV to acquire light intensity detection data and spatial mapping data of the current location.

[0008] The cruise calibration control module is used to calibrate the current cruise route in real time based on all acquired spatial mapping data, obtain a calibrated cruise route, and control the UAV to continue cruise based on the calibrated cruise route.

[0009] The light intensity thermal analysis module is used to generate a three-dimensional spatial mapping model of the target spatial range in real time based on all acquired spatial mapping data, and to perform light intensity thermal analysis on all acquired light intensity detection data based on the three-dimensional spatial mapping model until the complete light intensity detection results of the target spatial range are obtained.

[0010] Preferably, the cruise and sensor control module includes:

[0011] The first cruise control submodule is used to control the UAV to cruise within the target space based on the initial cruise route.

[0012] The sensing and control submodule is used to acquire light intensity detection data of the drone's current location and spatial mapping data within the mappable range corresponding to the drone's current location based on the intelligent sensors on board the drone.

[0013] Preferably, the cruise calibration control module includes:

[0014] The 3D spatial mapping submodule is used to perform continuous 3D spatial fitting based on all acquired spatial mapping data to obtain a partial 3D spatial mapping model.

[0015] The cruise route calibration submodule is used to calibrate the current cruise route in real time based on a partial 3D spatial mapping model to obtain a calibrated cruise route.

[0016] The second cruise control submodule is used to control the UAV to continue cruising based on the calibrated cruise route.

[0017] Preferably, the light intensity thermal analysis module includes:

[0018] The light intensity data verification submodule is used to verify all the acquired light intensity detection data based on the latest partially obtained 3D spatial mapping model, and obtain the current light intensity detection data verification result;

[0019] The cruise route recalibration submodule is used to verify the results based on the 3D spatial mapping model and the current light intensity detection data, and determine whether a re-detection is needed. If so, the current cruise route is recalibrated to obtain the latest calibrated cruise route; otherwise, the judgment result is retained.

[0020] The light intensity thermal analysis submodule is used to control the UAV to cruise within the target space range based on the latest calibration cruise route. When the latest calibration cruise route has been traversed and the verification results based on the three-dimensional spatial mapping model, all acquired light intensity detection data, and the current light intensity detection data determine that no re-detection is required, light intensity thermal analysis is performed on all acquired light intensity detection data based on the three-dimensional spatial mapping model to obtain the complete light intensity detection results for the target space range.

[0021] Preferably, the cruise route recalibration submodule includes:

[0022] The detection location filtering unit is used to filter out the detection location points corresponding to all light intensity detection data that have failed verification from all the acquired light intensity detection data based on the verification results of the current light intensity detection data.

[0023] The error distribution evaluation unit is used to evaluate the relative value of the spatial distribution of errors of all acquired light intensity detection data based on the distribution of all detection location points in the three-dimensional spatial mapping model.

[0024] The evaluation value comparison and judgment unit is used to determine whether the relative value of the error spatial distribution of all acquired light intensity detection data is not less than a preset threshold. If so, it is determined that the detection needs to be repeated and the current cruise route needs to be recalibrated to obtain the latest calibrated cruise route. Otherwise, it is determined that the detection does not need to be repeated and the judgment result is retained.

[0025] Preferably, the error distribution evaluation unit includes:

[0026] The neighborhood space partitioning sub-unit is used to partition the first-order neighborhood reference space region of each detection location point in the three-dimensional spatial mapping model based on the distribution of all detection location points in the three-dimensional spatial mapping model and the first-order neighborhood size.

[0027] The first error distribution evaluation subunit is used to calculate the relative value of the error spatial distribution of all acquired light intensity detection data based on all groups of overlapping first-order neighborhood reference spatial regions when there are overlapping first-order neighborhood reference spatial regions in the first-order neighborhood reference spatial regions of all detection location points.

[0028] The neighbor spacing determination subunit is used to determine the spacing between each detection location point and each adjacent detection location point as the neighbor spacing of each detection location point when there are no overlapping first-order neighbor reference space regions in the first-order neighbor reference space regions of all detection location points.

[0029] The second error distribution evaluation subunit is used to calculate the relative value of the spatial distribution of errors of all acquired light intensity detection data based on all neighboring distances of all detection location points.

[0030] Preferably, the first error distribution evaluation subunit includes:

[0031] The second-order neighborhood size determination end is used to determine the second-order neighborhood size based on all groups of overlapping first-order neighborhood reference space regions when there are overlapping first-order neighborhood reference space regions in the first-order neighborhood reference space regions of all detection location points.

[0032] The second-order neighborhood space division segment is used to divide the second-order neighborhood reference space region of each detection location point in the three-dimensional spatial mapping model based on the second-order neighborhood size.

[0033] The first error distribution evaluation end is used to determine the third-order neighborhood size based on the second-order neighborhood reference space regions that overlap with all groups, until the size of each dimension in the latest determined neighborhood size is greater than the size of the corresponding dimension of the three-dimensional spatial mapping model. Then, based on all-order neighborhood reference space regions of all currently determined detection location points, the relative value of the error spatial distribution of all acquired light intensity detection data is calculated.

[0034] The neighborhood size includes the dimensions used to measure the three-dimensional spatial mapping model.

[0035] Preferably, the first error distribution evaluation end calculates the relative value of the spatial distribution of errors of all acquired light intensity detection data based on all order neighborhood reference space regions of all currently determined detection location points, including:

[0036] ;

[0037] In the formula, Let m represent the relative values ​​of the spatial distribution of errors for all acquired light intensity detection data, m be the maximum order of all order neighborhood reference space regions for all currently determined detection locations, and N be the total number of detection locations. This represents the total number of all detection locations whose corresponding j-th order neighborhood reference space region overlaps with the j-th order neighborhood reference space region of the remaining detection locations. Let X be the dimension along the x-axis in the preset coordinate system contained in the j-th order neighborhood size, and let X be the dimension along the x-axis in the preset coordinate system contained in the most recently determined neighborhood size. Let Y be the dimension along the y-axis in the preset coordinate system contained in the j-th order neighborhood size, and let Y be the dimension along the y-axis in the preset coordinate system contained in the most recently determined neighborhood size. Z represents the dimensions in the k-th order neighborhood size that lie along the vertical axis in the preset coordinate system, and Z represents the dimensions in the newly determined neighborhood size that lie along the vertical axis in the preset coordinate system.

[0038] Preferably, the second error distribution evaluation subunit includes:

[0039] The neighbor spacing filtering end is used to determine the maximum neighbor spacing, minimum neighbor spacing, and average neighbor spacing for each detection location point;

[0040] The second error distribution evaluation end is used to calculate the relative values ​​of the spatial distribution of errors for all acquired light intensity detection data based on the maximum and minimum neighbor distances and the average neighbor distance of all detection location points, including:

[0041] ;

[0042] In the formula, Let N be the relative value of the spatial distribution of errors for all acquired light intensity detection data, and N be the total number of detection locations. This refers to the dimension of the 3D spatial mapping model along the horizontal axis in the preset coordinate system. This refers to the dimension of the 3D spatial mapping model along the vertical axis in the preset coordinate system. This refers to the dimension of the 3D spatial mapping model along the vertical coordinate axis in the preset coordinate system. The maximum neighbor spacing of the i-th detection location point. Let be the minimum neighbor spacing of the i-th detection location. Let be the average neighbor spacing of the i-th detection location.

[0043] This invention provides a drone, which is applied to any of the above-mentioned light intensity detection devices based on intelligent sensors, and is equipped with:

[0044] Intelligent light intensity sensor is used to acquire light intensity detection data of the drone's current location in real time;

[0045] LiDAR sensors are used to acquire spatial mapping data within the mappable range corresponding to the current location of the UAV in real time.

[0046] The beneficial effects of this invention compared to existing technologies are as follows: The cruise and sensing control module can control the UAV to cruise along an initial cruise route and acquire light intensity detection data and spatial mapping data, achieving preliminary detection of the target space and providing basic data for subsequent analysis and calibration. The cruise calibration control module performs real-time calibration of the cruise route based on the acquired spatial mapping data, ensuring that the UAV can more accurately cover the target space and improving the completeness and accuracy of data acquisition. This real-time calibration function helps to adapt to the complex environment and obstacles that may exist in the target space, reducing data loss or errors caused by inaccurate initial routes. The light intensity thermal analysis module generates a three-dimensional spatial mapping model based on the spatial mapping data and analyzes the light intensity detection data, which can comprehensively and intuitively present the light intensity distribution of the target space. Complete light intensity detection results help to understand the illumination characteristics of the target space, providing valuable reference for applications such as lighting design and energy management. The entire solution, through UAV cruise, calibration, and data analysis, avoids the economic pressure caused by distributed deployment in existing technologies, achieves efficient and accurate acquisition and analysis of light intensity and spatial information of the target space, improves work efficiency, and reduces the difficulty and risk of manual operation. It also reduces maintenance costs. It provides scientific data support for decision-making and optimization in related fields, contributing to more rational resource allocation and environmental improvement.

[0047] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0048] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0049] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0050] Figure 1 This is a schematic diagram of a light intensity detection device based on a smart sensor in an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of a drone in an embodiment of the present invention. Detailed Implementation

[0052] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0053] Example 1: This invention provides a light intensity detection device based on a smart sensor, referencing... Figure 1 ,include:

[0054] The cruise and sensor control module is used to control the UAV to cruise within the target space based on the initial cruise route. At the same time, it uses the intelligent sensors on the UAV to acquire light intensity detection data and spatial mapping data of the current location.

[0055] The cruise calibration control module is used to calibrate the current cruise route in real time based on all acquired spatial mapping data, obtain a calibrated cruise route, and control the UAV to continue cruise based on the calibrated cruise route.

[0056] The light intensity thermal analysis module is used to generate a three-dimensional spatial mapping model of the target spatial range in real time based on all acquired spatial mapping data, and to perform light intensity thermal analysis on all acquired light intensity detection data based on the three-dimensional spatial mapping model until the complete light intensity detection results of the target spatial range are obtained.

[0057] In this embodiment, the initial cruise route controls the drone to cruise within the target space. Simultaneously, the drone is equipped with intelligent sensors: this means the pre-set initial flight path directs the drone to fly within a specific target space, and the intelligent sensors on the drone function during flight. For example, the initial cruise route might cause the drone to fly from the upper left corner of the target space to the lower right corner, while the intelligent sensors begin collecting data.

[0058] In this embodiment, the target spatial range refers to the specific area where light intensity detection and spatial mapping are required. For example, the interior of a large warehouse or an outdoor park.

[0059] In this embodiment, the light intensity detection data is specific numerical information about the light intensity at a certain location, obtained by a smart sensor. For example, the light intensity detected at a certain point is 1000 lux.

[0060] In this embodiment, the spatial mapping data includes relevant data on spatial features such as the shape, size, and obstacle positions of the target space. For example, the length, width, and height of the target space, or the coordinates of a certain object.

[0061] In this embodiment, the calibration cruise route is a new flight route obtained by correcting and adjusting the initial cruise route based on the acquired spatial mapping data. For example, if an obstacle is detected ahead, the route is adjusted to avoid it.

[0062] In this embodiment, the complete light intensity detection results for the target space are the final comprehensive information about the light intensity distribution obtained after a comprehensive detection of the entire target space. This includes the light intensity values ​​at each location, the trend of light intensity changes, and the comparison of light intensity in different regions.

[0063] The beneficial effects of the above technologies are as follows: The cruise and sensor control module enables the UAV to cruise along an initial route and acquire light intensity detection data and spatial mapping data, achieving preliminary detection of the target space and providing basic data for subsequent analysis and calibration. The cruise calibration control module calibrates the cruise route in real time based on the acquired spatial mapping data, ensuring that the UAV can more accurately cover the target space and improving the completeness and accuracy of data acquisition. This real-time calibration function helps adapt to the complex environment and obstacles that may exist in the target space, reducing data loss or errors caused by inaccurate initial routes. The light intensity thermal analysis module generates a three-dimensional spatial mapping model based on the spatial mapping data and analyzes the light intensity detection data, providing a comprehensive and intuitive presentation of the light intensity distribution in the target space. Complete light intensity detection results help to gain a deeper understanding of the illumination characteristics of the target space, providing valuable reference for applications such as lighting design and energy management. The entire solution, through UAV cruise, calibration, and data analysis, avoids the economic pressure caused by distributed deployment in existing technologies, achieving efficient and accurate acquisition and analysis of light intensity and spatial information in the target space, improving work efficiency, and reducing the difficulty and risk of manual operation. It also reduces maintenance costs. It provides scientific data support for decision-making and optimization in related fields, contributing to more rational resource allocation and environmental improvement.

[0064] Example 2: Based on Example 1, the cruise and sensor control module includes:

[0065] The first cruise control submodule is used to control the UAV to cruise within the target space based on the initial cruise route.

[0066] The sensing and control submodule is used to acquire light intensity detection data of the drone's current location and spatial mapping data within the mappable range corresponding to the drone's current location based on the intelligent sensors on board the drone.

[0067] The beneficial effects of the above technologies are as follows: The first cruise control submodule controls the UAV to cruise according to the initial cruise route, ensuring that the UAV can begin to explore the target space in a planned manner, laying the foundation for subsequent data acquisition. The sensing control submodule uses the intelligent sensors on the UAV to acquire light intensity detection data and spatial mapping data, realizing the collection of key information about the target space. This clearly defined design allows the cruise and sensing functions to work independently yet collaboratively, improving the stability and reliability of the system. It enables more accurate acquisition of specific data about the UAV's current position, avoiding data collection chaos and omissions. It provides a clear and accurate data source for subsequent data analysis and processing, helping to improve the efficiency and accuracy of the entire system. It helps reduce the complexity of the system, facilitating individual optimization and maintenance of each submodule. It can better adapt to different target spaces and mission requirements, achieving more efficient detection and data acquisition by flexibly adjusting the parameters and working modes of the submodules.

[0068] Example 3: Based on Example 1, the cruise calibration control module includes:

[0069] The 3D spatial mapping submodule is used to perform continuous 3D spatial fitting based on all acquired spatial mapping data to obtain a partial 3D spatial mapping model.

[0070] The cruise route calibration submodule is used to calibrate the current cruise route in real time based on a partial 3D spatial mapping model to obtain a calibrated cruise route.

[0071] The second cruise control submodule is used to control the UAV to continue cruising based on the calibrated cruise route.

[0072] In this embodiment, a partial 3D spatial mapping model is obtained by continuously fitting all acquired spatial mapping data. This means integrating and simulating various collected spatial measurement data using mathematical methods to construct a partial 3D model of the target space. For example, the coordinates and distance information of some points are first acquired, and then a 3D shape model of a local area is generated through a fitting algorithm.

[0073] In this embodiment, some 3D spatial mapping models are 3D models that only cover a part of the target space, obtained through the above fitting process. For example, a 3D representation of only a corner or part of a floor of the target warehouse.

[0074] In this embodiment, the current cruise route is calibrated in real time based on a partial 3D spatial mapping model to obtain a calibrated cruise route. Based on the spatial conditions reflected in this constructed 3D model, the ongoing cruise route is adjusted and corrected to obtain a new flight route more suitable for the current space environment. For example, if a prominent obstacle is detected in the partial model, the route is adjusted to avoid it, and the resulting new route is the calibrated cruise route.

[0075] The beneficial effects of the above technologies are as follows: The 3D spatial mapping submodule, by continuously fitting the acquired spatial mapping data in 3D space, can construct a partial 3D spatial mapping model, providing a basic spatial information reference for the calibration of the cruise route. The cruise route calibration submodule performs real-time calibration of the current cruise route based on the partial 3D spatial mapping model, enabling the UAV's flight path to be dynamically adjusted according to the actual spatial conditions, improving the comprehensiveness and accuracy of the detection. The second cruise control submodule controls the UAV to continue cruise based on the calibrated cruise route, ensuring that the UAV can fly along the optimized path, effectively avoiding repeated detection and missed areas, and improving work efficiency. This modular design makes the function of each submodule more focused and clear, facilitating targeted optimization and troubleshooting. It can better cope with the complex and ever-changing environment and unknown situations in the target space, adjust the UAV's flight strategy in a timely manner, and ensure the integrity and reliability of data acquisition. It helps to improve the adaptability and flexibility of the entire system, enabling it to operate efficiently and stably under different target spaces and mission requirements. It provides strong technical support for achieving accurate detection and data acquisition of the target space, and provides high-quality data assurance for subsequent analysis and application.

[0076] Example 4: Based on Example 1, the light intensity thermal analysis module includes:

[0077] The light intensity data verification submodule is used to verify all the acquired light intensity detection data based on the latest partially obtained 3D spatial mapping model, and obtain the current light intensity detection data verification result;

[0078] The cruise route recalibration submodule is used to verify the results based on the 3D spatial mapping model and the current light intensity detection data, and determine whether a re-detection is needed. If so, the current cruise route is recalibrated to obtain the latest calibrated cruise route; otherwise, the judgment result is retained.

[0079] The light intensity thermal analysis submodule is used to control the UAV to cruise within the target space range based on the latest calibration cruise route. When the latest calibration cruise route has been traversed and the verification results based on the three-dimensional spatial mapping model, all acquired light intensity detection data, and the current light intensity detection data determine that no re-detection is required, light intensity thermal analysis is performed on all acquired light intensity detection data based on the three-dimensional spatial mapping model to obtain the complete light intensity detection results for the target space range.

[0080] In this embodiment, based on the newly obtained partial 3D spatial mapping model, all acquired light intensity detection data are verified to obtain the current light intensity detection data verification result: the spatial information provided by the newly constructed partial 3D spatial mapping model is used to check whether all acquired light intensity detection data is accurate and reasonable. For example, the position of the detection point in the model is compared with the expected illumination to determine whether the light intensity data meets expectations, thereby obtaining the verification result.

[0081] In this embodiment, the current cruise route is recalibrated to obtain a latest calibrated cruise route. Based on the new situation and judgment results, the currently used cruise route is further adjusted and optimized to obtain an updated and more accurate flight route. For example, if omissions or inaccuracies are found in previous detections, the route is replanned to supplement the detections.

[0082] In this embodiment, a light intensity thermal analysis is performed on all acquired light intensity detection data based on a three-dimensional spatial mapping model to obtain complete light intensity detection results for the target space: by integrating the entire three-dimensional spatial mapping model and all collected light intensity detection data, a comprehensive understanding of the light intensity distribution within the target space is obtained through specific analysis methods (such as thermal map analysis), including high and low light intensity regions and trends, ultimately resulting in complete and accurate light intensity detection results.

[0083] The beneficial effects of the above technologies are as follows: The light intensity data verification submodule verifies the light intensity detection data using the latest partial 3D spatial mapping model, ensuring the accuracy and reliability of the light intensity data and providing a high-quality data foundation for subsequent analysis. The cruise route recalibration submodule can determine whether re-detection and corresponding cruise route calibration are needed based on the 3D spatial mapping model and the light intensity detection data verification results, improving the targeting and efficiency of detection. This dynamic calibration mechanism can adapt to complex target space environments and light intensity distributions, ensuring that the UAV can collect light intensity data more comprehensively and accurately. The light intensity thermal analysis submodule performs light intensity thermal analysis under specific conditions, obtaining more complete and accurate light intensity detection results, providing strong support for a deeper understanding of the illumination characteristics of the target space. The design of the entire module realizes fine-grained control over light intensity detection and UAV cruise, improving the system's flexibility and adaptability. It helps reduce unnecessary flights and data collection, lowering system energy consumption and operating costs. It can provide more valuable light intensity distribution information for applications in related fields, such as lighting planning and energy management, promoting the development and application of related technologies.

[0084] Example 5: Based on Example 4, the cruise route recalibration submodule includes:

[0085] The detection location filtering unit is used to filter out the detection location points corresponding to all light intensity detection data that have failed verification from all the acquired light intensity detection data based on the verification results of the current light intensity detection data.

[0086] The error distribution evaluation unit is used to evaluate the relative value of the spatial distribution of errors of all acquired light intensity detection data based on the distribution of all detection location points in the three-dimensional spatial mapping model.

[0087] The evaluation value comparison and judgment unit is used to determine whether the relative value of the error spatial distribution of all acquired light intensity detection data is not less than a preset threshold. If so, it is determined that the detection needs to be repeated and the current cruise route needs to be recalibrated to obtain the latest calibrated cruise route. Otherwise, it is determined that the detection does not need to be repeated and the judgment result is retained.

[0088] In this embodiment, based on the current light intensity detection data verification results, the detection location points corresponding to all light intensity detection data that failed verification are screened from all acquired light intensity detection data: according to the current verification status of the light intensity detection data, the specific detection locations corresponding to those data judged as unqualified or inaccurate are identified from all previously acquired light intensity detection data. For example, if the verification results show abnormal light intensity data at certain locations, these locations are identified.

[0089] In this embodiment, the specific location where the light intensity is detected is defined by its spatial coordinates. For example, in a room, the coordinates of a corner would be a detection location.

[0090] In this embodiment, the relative value of the spatial distribution of errors of all acquired light intensity detection data takes into account the distribution of errors of all detected light intensity data in the entire three-dimensional space, and is a relative value obtained by a certain calculation method to measure the relative uniformity of error distribution.

[0091] In this embodiment, the preset threshold is a pre-set standard value used to compare with the relative value of the error spatial distribution to determine whether the route needs to be re-detected and calibrated.

[0092] In this embodiment, the current cruise route is recalibrated to obtain a new calibrated cruise route. Based on the new judgment and analysis results, the existing cruise route is readjusted and optimized to obtain a more accurate and suitable new route. For example, if it is found that the previous route missed some areas with large errors, the route is replanned to supplement the detection of these areas.

[0093] The beneficial effects of the above technologies are as follows: The detection location screening unit can accurately identify the detection location points corresponding to the light intensity detection data that failed verification, providing a clear object for subsequent error distribution evaluation and helping to improve the targeting of calibration. The error distribution evaluation unit evaluates the relative value of the spatial distribution of errors based on the distribution position of the detection location points in the three-dimensional spatial mapping model, realizing the quantification and spatial analysis of light intensity detection data errors. The evaluation value comparison and judgment unit scientifically determines whether the cruise route needs to be re-detected and calibrated by comparing with a preset threshold, avoiding unnecessary repetitive operations while ensuring the accuracy and reliability of the data. This refined evaluation and judgment mechanism can effectively improve the accuracy and completeness of light intensity detection, reducing erroneous analysis and decisions caused by errors. It helps to optimize the UAV's cruise path, enabling it to collect accurate light intensity data more efficiently, improving the overall system efficiency and performance. It can flexibly adjust the detection strategy according to the actual situation, adapting to target spaces and light intensity distributions of different complexities, enhancing the system's adaptability and versatility. It provides a strong guarantee for obtaining more comprehensive and accurate target space light intensity detection results, providing more valuable data support for applications in related fields.

[0094] Example 6: Based on Example 5, the error distribution evaluation unit includes:

[0095] The neighborhood space partitioning sub-unit is used to partition the first-order neighborhood reference space region of each detection location point in the three-dimensional spatial mapping model based on the distribution of all detection location points in the three-dimensional spatial mapping model and the first-order neighborhood size.

[0096] The first error distribution evaluation subunit is used to calculate the relative value of the error spatial distribution of all acquired light intensity detection data based on all groups of overlapping first-order neighborhood reference spatial regions when there are overlapping first-order neighborhood reference spatial regions in the first-order neighborhood reference spatial regions of all detection location points.

[0097] The neighbor spacing determination subunit is used to determine the spacing between each detection location point and each adjacent detection location point as the neighbor spacing of each detection location point when there are no overlapping first-order neighbor reference space regions in the first-order neighbor reference space regions of all detection location points.

[0098] The second error distribution evaluation subunit is used to calculate the relative value of the spatial distribution of errors of all acquired light intensity detection data based on all neighboring distances of all detection location points.

[0099] In this embodiment, the first-order neighborhood size is a metric used to define the size of the neighborhood range around the detection location point. For example, it is set to 1 meter, which means a range with a radius of 1 meter centered on the detection location point.

[0100] In this embodiment, the first-order neighborhood reference space region of each detection location point refers to a specific spatial range determined around each detection location point according to the set first-order neighborhood size. For example, if the detection location point is a point in a room, a spherical or cubic spatial region is determined with this point as the center and a first-order neighborhood size of 1 meter.

[0101] The beneficial effects of the above technologies are as follows: The neighborhood space partitioning subunit provides a clear spatial range for evaluating error distribution by dividing the first-order neighborhood reference space region of the detection location points, which helps to analyze local error situations more accurately. The first error distribution evaluation subunit calculates the relative value of error spatial distribution when there are overlapping first-order neighborhood reference space regions, which can effectively handle the error evaluation of dense detection location points and improve the accuracy and reliability of the evaluation. The neighbor spacing determination subunit determines the neighbor spacing of detection location points when there are no overlapping regions, providing the necessary data preparation for another evaluation method and increasing the flexibility and comprehensiveness of the evaluation. The second error distribution evaluation subunit calculates the relative value of error spatial distribution based on the neighbor spacing, which can give reasonable error evaluation results under different spatial distribution conditions. The design of the entire error distribution evaluation unit can adapt to various complex detection location point distributions and comprehensively and accurately evaluate the error spatial distribution of light intensity detection data. It helps to more scientifically determine whether re-detection and recalibration of the cruise route are needed, improving the decision-making accuracy and efficiency of the system. It provides strong support for optimizing light intensity detection and UAV cruise strategies, thereby obtaining higher quality light intensity detection results and a more efficient detection process.

[0102] Example 7: Based on Example 6, the first error distribution evaluation subunit includes:

[0103] The second-order neighborhood size determination end is used to determine the second-order neighborhood size based on all groups of overlapping first-order neighborhood reference space regions when there are overlapping first-order neighborhood reference space regions in the first-order neighborhood reference space regions of all detection location points.

[0104] The second-order neighborhood space division segment is used to divide the second-order neighborhood reference space region of each detection location point in the three-dimensional spatial mapping model based on the second-order neighborhood size.

[0105] The first error distribution evaluation end is used to determine the third-order neighborhood size based on the second-order neighborhood reference space regions that overlap with all groups, until the size of each dimension in the latest determined neighborhood size is greater than the size of the corresponding dimension of the three-dimensional spatial mapping model. Then, based on all-order neighborhood reference space regions of all currently determined detection location points, the relative value of the error spatial distribution of all acquired light intensity detection data is calculated.

[0106] The neighborhood size includes the dimensions used to measure the three-dimensional spatial mapping model.

[0107] In this embodiment, determining the size of the second-order neighborhood based on the overlapping first-order neighborhood reference space regions of all groups means that the size of the second-order neighborhood is calculated and determined using a specific algorithm or rule based on the overlap of the first-order neighborhood reference space regions. For example, if multiple first-order neighborhoods overlap significantly, the size of the second-order neighborhood may be appropriately increased.

[0108] In this embodiment, the second-order neighborhood size is a metric size that further expands the neighborhood range based on the first-order neighborhood. For example, if the first-order neighborhood size is 1 meter, the second-order neighborhood size might be 2 meters.

[0109] In this embodiment, the second-order neighborhood reference space region of each detection location point is the spatial range defined by the detection location point as the center and according to the second-order neighborhood size.

[0110] In this embodiment, the determination of the third-order neighborhood size is similar to the determination of the second-order neighborhood size, based on the overlapping second-order neighborhood reference space regions of all groups. The size of the third-order neighborhood is calculated and determined according to the overlap of the second-order neighborhood reference space regions.

[0111] In this embodiment, the three dimensions used to measure the three-dimensional spatial mapping model typically refer to the length, width, and height, which are used to comprehensively describe the size and shape of the three-dimensional spatial mapping model.

[0112] The beneficial effects of the above technologies are as follows: The second-order neighborhood size determination end can determine a suitable second-order neighborhood size based on the overlapping first-order neighborhood reference space region, providing a more refined spatial division basis for further error assessment. The second-order neighborhood space division segment divides the second-order neighborhood reference space region based on the determined size, which helps to more comprehensively consider the relationship between adjacent detection location points and improve the accuracy of error assessment. The first error distribution assessment end, by continuously determining higher-order neighborhood sizes and reference space regions until specific conditions are met, can fully consider a wider range of spatial correlations, thereby more accurately calculating the relative value of the spatial distribution of light intensity detection data errors. This progressively expanding and refined assessment method can adapt to the distribution of detection location points with different densities and complexities, ensuring reliable error assessment results under various conditions. It helps to more accurately judge the error distribution of light intensity detection data, providing a more scientific and convincing basis for whether re-detection and recalibration of the cruise route are needed. It can improve the overall system's quality control level of light intensity detection data, optimize the UAV's cruise path and detection strategy, and improve the system's working efficiency and performance. This provides strong technical support for obtaining more comprehensive and accurate target spatial light intensity information, which helps related application fields make more rational decisions and plans.

[0113] Example 8: Based on Example 7, the first error distribution evaluation end calculates the relative value of the error spatial distribution of all acquired light intensity detection data based on the all-order neighborhood reference space regions of all currently determined detection location points, including:

[0114] ;

[0115] In the formula, Let m represent the relative values ​​of the spatial distribution of errors for all acquired light intensity detection data, m be the maximum order of all order neighborhood reference space regions for all currently determined detection locations, and N be the total number of detection locations. This represents the total number of all detection locations whose corresponding j-th order neighborhood reference space region overlaps with the j-th order neighborhood reference space region of the remaining detection locations. Let X be the dimension along the x-axis in the preset coordinate system contained in the j-th order neighborhood size, and let X be the dimension along the x-axis in the preset coordinate system contained in the most recently determined neighborhood size. Let Y be the dimension along the y-axis in the preset coordinate system contained in the j-th order neighborhood size, and let Y be the dimension along the y-axis in the preset coordinate system contained in the most recently determined neighborhood size. Z represents the dimensions in the k-th order neighborhood size that lie along the vertical axis in the preset coordinate system, and Z represents the dimensions in the newly determined neighborhood size that lie along the vertical axis in the preset coordinate system.

[0116] In this embodiment, the newly determined neighborhood size is the maximum order neighborhood size that determines the current maximum order neighborhood reference space region.

[0117] The beneficial effects of the above technologies are as follows: they can accurately calculate the relative value of the spatial distribution of errors in light intensity detection data, which helps to better understand the error situation of light intensity detection data. They consider factors such as the size of all order neighborhood reference regions of the detection location point and multiple coordinate axis directions, making error assessment more comprehensive and accurate. They provide important reference for the analysis and processing of light intensity detection data, helping to improve the accuracy and reliability of light intensity detection.

[0118] Example 9: Based on Example 6, the second error distribution evaluation subunit includes:

[0119] The neighbor spacing filtering end is used to determine the maximum neighbor spacing, minimum neighbor spacing, and average neighbor spacing for each detection location point;

[0120] The second error distribution evaluation end is used to calculate the relative values ​​of the spatial distribution of errors for all acquired light intensity detection data based on the maximum and minimum neighbor distances and the average neighbor distance of all detection location points, including:

[0121] ;

[0122] In the formula, Let N be the relative value of the spatial distribution of errors for all acquired light intensity detection data, and N be the total number of detection locations. This refers to the dimension of the 3D spatial mapping model along the horizontal axis in the preset coordinate system. This refers to the dimension of the 3D spatial mapping model along the vertical axis in the preset coordinate system. This refers to the dimension of the 3D spatial mapping model along the vertical coordinate axis in the preset coordinate system. The maximum neighbor spacing of the i-th detection location point. Let be the minimum neighbor spacing of the i-th detection location. Let be the average neighbor spacing of the i-th detection location.

[0123] In this embodiment, the preset coordinate system is a reference coordinate system pre-defined in a specific technical application scenario to describe information such as the position and orientation of an object. For example, in a three-dimensional light intensity detection scenario, a point in space can be used as the origin, and three mutually perpendicular directions can be used as the x-axis, y-axis, and z-axis to establish the preset coordinate system. For instance, a fixed point on the ground where the drone takes off can be used as the origin, with the horizontal forward direction as the positive x-axis, the horizontal rightward direction as the positive y-axis, and the vertical upward direction as the positive z-axis. In this way, under this preset coordinate system, the position of the drone and each detection point can be represented by a set of coordinate values ​​(x, y, z), which facilitates various calculations and analyses, such as calculating the relative value of the spatial distribution error of the light intensity detection data.

[0124] In this embodiment, the dimension of the three-dimensional spatial mapping model in the horizontal (or vertical, or longitudinal) coordinate axis direction in the preset coordinate system refers to the length or range of the model in the horizontal (or vertical, or longitudinal) coordinate axis direction.

[0125] In this embodiment, the maximum neighbor distance, minimum neighbor distance, and average neighbor distance of each detection location point refer to the maximum, minimum, and average distances between each detection location point and its surrounding adjacent detection location points, respectively.

[0126] The beneficial effects of the above techniques are as follows: By determining the maximum, minimum, and average neighbor spacing of each detection location, a more comprehensive understanding of the distribution of detection locations can be obtained, providing multi-dimensional data support for error assessment. Calculating the relative value of the spatial distribution error of light intensity detection data using a specific formula considers the neighbor spacing of all detection locations and the dimensional information of the three-dimensional spatial mapping model, making error assessment more accurate and reliable. This facilitates a better analysis of the error characteristics of light intensity detection data, providing an effective method and means to improve the accuracy and quality of light intensity detection.

[0127] Example 10: This invention provides a drone, applied to any of the smart sensor-based light intensity detection devices in Examples 1 to 9, equipped with:

[0128] Intelligent light intensity sensor is used to acquire light intensity detection data of the drone's current location in real time;

[0129] LiDAR sensors are used to acquire spatial mapping data within the mappable range corresponding to the current location of the UAV in real time.

[0130] In this embodiment, the measurable range corresponding to the current location refers to the area within which the lidar sensor can effectively measure and acquire spatial information at the specific location of the drone. For example, a spherical area with a radius of 50 meters centered on the drone is the measurable range corresponding to the current location.

[0131] In this embodiment, a lidar sensor is a device that measures distance and acquires information about the surrounding environment by emitting a laser beam and receiving the reflected laser light. It can quickly and accurately measure spatial information such as the position, shape, and distance of objects, providing data support for spatial mapping of unmanned aerial vehicles (UAVs). For example, common vehicle-mounted lidar can help vehicles identify surrounding obstacles and road conditions.

[0132] The beneficial effects of the above technologies are as follows: Equipped with an intelligent light intensity sensor, the drone can acquire accurate light intensity detection data in real time, providing a direct and crucial data source for light intensity analysis and related applications. The lidar sensor enables real-time acquisition of spatial mapping data within the measurable range, facilitating the construction of accurate 3D spatial models and providing a spatial foundation for the calibration and analysis of light intensity detection. The combined use of these two sensors allows the drone to simultaneously collect light intensity and spatial information, achieving multi-dimensional data acquisition and improving the comprehensiveness and integration of the data. When applied to the aforementioned light intensity detection device, it can fully utilize the functions of each module, improving the overall system's operating efficiency and detection accuracy. It provides strong technical support for drone light intensity detection tasks in complex environments, enabling it to adapt to different scenarios and needs. It helps improve the drone's autonomous detection capabilities and intelligence level, reducing reliance on human intervention. It provides richer and more accurate data for research and applications in related fields, such as urban planning and energy management, promoting the development and innovation of related technologies.

[0133] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A light intensity detection device based on a smart sensor, characterized in that, include: The cruise and sensor control module is used to control the UAV to cruise within the target space based on the initial cruise route. At the same time, it uses the intelligent sensors on the UAV to acquire light intensity detection data and spatial mapping data of the current location. The cruise calibration control module is used to calibrate the current cruise route in real time based on all acquired spatial mapping data, obtain a calibrated cruise route, and control the UAV to continue cruise based on the calibrated cruise route. The light intensity thermal analysis module is used to generate a three-dimensional spatial mapping model of the target spatial range in real time based on all acquired spatial mapping data, and to perform thermal map analysis on all acquired light intensity detection data based on the three-dimensional spatial mapping model until the complete light intensity detection results of the target spatial range are obtained. The light intensity and thermal analysis module includes: The light intensity data verification submodule is used to verify all the acquired light intensity detection data based on the latest partially obtained 3D spatial mapping model, and obtain the current light intensity detection data verification result; The cruise route recalibration submodule is used to verify the results based on the 3D spatial mapping model and the current light intensity detection data, and determine whether a re-detection is needed. If so, the current cruise route is recalibrated to obtain the latest calibrated cruise route; otherwise, the judgment result is retained. The light intensity thermal analysis submodule is used to control the UAV to cruise within the target space range based on the latest calibration cruise route. When the latest calibration cruise route has been traversed and the verification results based on the three-dimensional spatial mapping model, all acquired light intensity detection data, and the current light intensity detection data determine that no re-detection is required, a thermal map analysis is performed on all acquired light intensity detection data based on the three-dimensional spatial mapping model to obtain the complete light intensity detection results for the target space range. The cruise route recalibration submodule includes: The detection location filtering unit is used to filter out the detection location points corresponding to all light intensity detection data that have failed verification from all the acquired light intensity detection data based on the verification results of the current light intensity detection data. The error distribution evaluation unit is used to evaluate the relative value of the spatial distribution of errors of all acquired light intensity detection data based on the distribution of all detection location points in the three-dimensional spatial mapping model. The evaluation value comparison and judgment unit is used to determine whether the relative value of the error spatial distribution of all acquired light intensity detection data is not less than a preset threshold. If so, it is determined that the detection needs to be repeated and the current cruise route needs to be recalibrated to obtain the latest calibrated cruise route. Otherwise, it is determined that the detection does not need to be repeated and the judgment result is retained.

2. The light intensity detection device based on a smart sensor according to claim 1, characterized in that, Cruise and sensor control module, including: The first cruise control submodule is used to control the UAV to cruise within the target space based on the initial cruise route. The sensing and control submodule is used to acquire light intensity detection data of the drone's current location and spatial mapping data within the mappable range corresponding to the drone's current location based on the intelligent sensors on board the drone.

3. The light intensity detection device based on a smart sensor according to claim 1, characterized in that, Cruise calibration control module, including: The 3D spatial mapping submodule is used to perform continuous 3D spatial fitting based on all acquired spatial mapping data to obtain a partial 3D spatial mapping model. The cruise route calibration submodule is used to calibrate the current cruise route in real time based on a partial 3D spatial mapping model to obtain a calibrated cruise route. The second cruise control submodule is used to control the UAV to continue cruising based on the calibrated cruise route.

4. The light intensity detection device based on a smart sensor according to claim 1, characterized in that, Error distribution evaluation unit, including: The neighborhood space partitioning sub-unit is used to partition the first-order neighborhood reference space region of each detection location point in the three-dimensional spatial mapping model based on the distribution of all detection location points in the three-dimensional spatial mapping model and the first-order neighborhood size. The first error distribution evaluation subunit is used to calculate the relative value of the error spatial distribution of all acquired light intensity detection data based on the first-order neighboring reference spatial regions of all detection location points when there are overlapping first-order neighboring reference spatial regions. The neighbor spacing determination subunit is used to determine the spacing between each detection location point and each adjacent detection location point as the neighbor spacing of each detection location point when there are no overlapping first-order neighbor reference space regions in the first-order neighbor reference space regions of all detection location points. The second error distribution evaluation subunit is used to calculate the relative value of the spatial distribution of errors of all acquired light intensity detection data based on all neighboring distances of all detection location points.

5. The light intensity detection device based on a smart sensor according to claim 4, characterized in that, The first error distribution evaluation subunit includes: The second-order neighborhood size determination end is used to determine the second-order neighborhood size based on all groups of overlapping first-order neighborhood reference space regions when there are overlapping first-order neighborhood reference space regions in the first-order neighborhood reference space regions of all detection location points. The second-order neighborhood space division segment is used to divide the second-order neighborhood reference space region of each detection location point in the three-dimensional spatial mapping model based on the second-order neighborhood size. The first error distribution evaluation end is used to determine the third-order neighborhood size based on the second-order neighborhood reference space regions that overlap with all groups, until the size of each dimension in the latest determined neighborhood size is greater than the size of the corresponding dimension of the three-dimensional spatial mapping model. Then, based on all-order neighborhood reference space regions of all currently determined detection location points, the relative value of the error spatial distribution of all acquired light intensity detection data is calculated. The neighborhood size includes the dimensions used to measure the three-dimensional spatial mapping model.

6. The light intensity detection device based on a smart sensor according to claim 5, characterized in that, The first error distribution evaluation end calculates the relative values ​​of the spatial distribution of errors for all acquired light intensity detection data based on all order neighborhood reference space regions of all currently determined detection location points, including: ; In the formula, Let m represent the relative values ​​of the spatial distribution of errors for all acquired light intensity detection data, m be the maximum order of all order neighborhood reference space regions for all currently determined detection locations, and N be the total number of detection locations. This represents the total number of all detection locations whose corresponding j-th order neighborhood reference space region overlaps with the j-th order neighborhood reference space region of the remaining detection locations. Let X be the dimension along the x-axis in the preset coordinate system contained in the j-th order neighborhood size, and let X be the dimension along the x-axis in the preset coordinate system contained in the most recently determined neighborhood size. Let Y be the dimension along the y-axis in the preset coordinate system contained in the j-th order neighborhood size, and let Y be the dimension along the y-axis in the preset coordinate system contained in the most recently determined neighborhood size. Z represents the dimensions in the preset coordinate system contained in the j-th order neighborhood size along the vertical axis, and Z represents the dimensions in the preset coordinate system contained in the latest determined neighborhood size along the vertical axis.

7. The light intensity detection device based on a smart sensor according to claim 4, characterized in that, The second error distribution evaluation subunit includes: The neighbor spacing filtering end is used to determine the maximum neighbor spacing, minimum neighbor spacing, and average neighbor spacing for each detection location point; The second error distribution evaluation end is used to calculate the relative values ​​of the spatial distribution of errors for all acquired light intensity detection data based on the maximum and minimum neighbor distances and the average neighbor distance of all detection location points, including: ; In the formula, Let N be the relative value of the spatial distribution of errors for all acquired light intensity detection data, and N be the total number of detection locations. This refers to the dimension of the 3D spatial mapping model along the horizontal axis in the preset coordinate system. This refers to the dimension of the 3D spatial mapping model along the vertical axis in the preset coordinate system. This refers to the dimension of the 3D spatial mapping model along the vertical coordinate axis in the preset coordinate system. The maximum neighbor spacing of the i-th detection location point. Let be the minimum neighbor spacing of the i-th detection location. Let be the average neighbor spacing of the i-th detection location.

8. A drone, characterized in that, The light intensity detection device based on a smart sensor, applicable to any one of claims 1 to 7, is equipped with: Intelligent light intensity sensor is used to acquire light intensity detection data of the drone's current location in real time; LiDAR sensors are used to acquire spatial mapping data within the mappable range corresponding to the current location of the UAV in real time.

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