A method, apparatus, device and storage medium for low-altitude data quality assessment

Abstract

This invention discloses a method, apparatus, device, and storage medium for low-altitude data quality assessment, relating to the field of data quality assessment technology. The method includes: acquiring first low-altitude data to be assessed; parsing the first low-altitude data to determine a spatiotemporal correlation matrix and data quality indicators corresponding to the first low-altitude data; determining a dynamic weight vector corresponding to the first low-altitude data based on the spatiotemporal correlation matrix and the data quality indicators; and determining a data quality score for the first low-altitude data based on the dynamic weight vector and the data quality indicators. This allows for automatic adjustment of spatiotemporal weights based on performance under different spatiotemporal conditions, improving data fusion quality and thus enhancing the accuracy and adaptability of data quality assessment.

Description

Technical Field

[0001] This invention relates to the field of data quality assessment technology, and in particular to a method, apparatus, device, and storage medium for low-altitude data quality assessment. Background Technology

[0002] As an emerging industry, the low-altitude economy encompasses multiple sectors, including drone logistics, air taxis, aircraft manufacturing, and low-altitude tourism, and is developing rapidly worldwide. With the surge in the number of low-altitude aircraft such as drones and air taxis, the low-altitude economy has generated massive amounts of multi-source heterogeneous data (such as aircraft position signals, meteorological data, and geographic information).

[0003] Low-altitude data is a key element supporting industrial development, but data quality issues have become a critical bottleneck restricting the industry's further development. These massive, multi-source, heterogeneous data differ in their spatiotemporal distribution and acquisition standards, and are affected by factors such as sensor performance degradation and dynamic changes in flight trajectories. This leads to several problems in data quality assessment, such as the inability of existing static weight allocation mechanisms to adapt to the dynamic characteristics of low-altitude data sources changing over time and space, the loss of spatiotemporal correlation information during the quality assessment process, and the lack of dynamic correction capabilities for low-altitude data.

[0004] Traditional static quality assessment methods cannot meet the needs of dynamic monitoring, early warning and assessment of data quality in low-altitude scenarios. As a result, the quality assessment of low-altitude data is affected by the above-mentioned problems, the assessment results are out of touch with the actual situation, resulting in insufficient assessment accuracy and affecting the accuracy of data quality assessment. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for low-altitude data quality assessment, which automatically adjusts spatiotemporal weights based on performance under different spatiotemporal conditions to improve data fusion quality, thereby enhancing the accuracy and adaptability of data quality assessment.

[0006] According to one aspect of the present invention, a method for assessing low-altitude data quality is provided. The method includes:

[0007] Acquire first low-altitude data to be assessed for data quality, wherein the first low-altitude data includes multi-source heterogeneous data within a preset area and a preset time period, and the multi-source heterogeneous data includes at least data content, data type and data structure;

[0008] The first low-altitude data is parsed and processed to determine the spatiotemporal correlation matrix and data quality indicators corresponding to the first low-altitude data. The data quality indicators include at least time precision, spatial precision, data integrity and data consistency.

[0009] Based on the spatiotemporal correlation matrix and the data quality index, determine the dynamic weight vector corresponding to the first low-altitude data;

[0010] The data quality score of the first low-altitude data is determined based on the dynamic weight vector and the data quality index.

[0011] According to another aspect of the present invention, a low-altitude data quality assessment apparatus is provided. The apparatus includes:

[0012] The low-altitude data acquisition module is used to acquire first low-altitude data to be assessed for data quality. The first low-altitude data includes multi-source heterogeneous data within a preset area and a preset time period. The multi-source heterogeneous data includes at least data content, data type, and data structure.

[0013] The low-altitude data parsing module is used to parse and process the first low-altitude data to determine the spatiotemporal correlation matrix and data quality indicators corresponding to the first low-altitude data. The data quality indicators include at least time precision, spatial precision, data integrity and data consistency.

[0014] The dynamic weight determination module is used to determine the dynamic weight vector corresponding to the first low-altitude data based on the spatiotemporal correlation matrix and the data quality index.

[0015] The data quality assessment module is used to determine the data quality score of the first low-altitude data based on the dynamic weight vector and the data quality index.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the low-altitude data quality assessment method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the low-altitude data quality assessment method according to any embodiment of the present invention.

[0021] The technical solution of this invention involves acquiring first low-altitude data to be evaluated for data quality. The first low-altitude data is parsed to determine the corresponding spatiotemporal correlation matrix and data quality index. Based on the spatiotemporal correlation matrix and the data quality index, a dynamic weight vector corresponding to the first low-altitude data is determined. Based on the dynamic weight vector and the data quality index, a data quality score for the first low-altitude data is determined. This solves the problem of spatiotemporal coupling and quality dimension decoupling in multi-source heterogeneous data, and can automatically adjust the spatiotemporal weights according to the data source under different spatiotemporal conditions, improving the accuracy and adaptability of data quality evaluation.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a low-altitude data quality assessment method provided in Embodiment 1 of the present invention;

[0025] Figure 2 This is a flowchart of a low-altitude data quality assessment method provided in Embodiment 2 of the present invention;

[0026] Figure 3 This is a structural diagram of a low-altitude data quality assessment device provided according to Embodiment 3 of the present invention;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the low-altitude data quality assessment method of this invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This is a flowchart of a low-altitude data quality assessment method provided in Embodiment 1 of the present invention. This embodiment is applicable to evaluating the quality of low-altitude multi-source heterogeneous data. The method can be executed by a low-altitude data quality assessment device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0032] S101. Obtain the first low-altitude data to be assessed for data quality.

[0033] The first low-altitude data can refer to the low-altitude data to be assessed for data quality. For example, the first low-altitude data includes heterogeneous data from multiple data sources within a preset area and time period. The preset area and time period can be set according to actual needs, and this invention does not impose specific limitations on them. It should be noted that multi-source heterogeneous data includes at least data content, data type, and data structure. The data structure can include text, tables, and images; the data type can include flight technology data, administrative rule data, and regional economic data, etc.; and the data content can refer to the specific content of the heterogeneous data.

[0034] For example, acquiring the first low-altitude data to be assessed for data quality includes:

[0035] Obtain raw low-altitude data within the target assessment area. Perform data preprocessing on the raw low-altitude data to obtain the first low-altitude data.

[0036] The raw low-altitude data can refer to the raw data collected by sensors or data platforms. The data preprocessing includes at least noise removal, temporal alignment, spatial alignment, and normalization.

[0037] Specifically, the acquired raw low-altitude data is preprocessed step by step to obtain the first low-altitude data. Data preprocessing includes:

[0038] (1) Removal of outliers and noisy data: outlier detection methods based on statistics (such as the 3σ principle) are used to remove data that deviates significantly from the normal range;

[0039] (2) Time alignment: For unevenly distributed data, bilinear interpolation is used for time alignment. The time points that need to be interpolated and their corresponding feature dimensions are identified in the time series. Four nearest neighbor points are found on the time axis and feature dimensions. Based on the relative position of the target point and the neighboring points, the weight coefficients of the time axis and feature dimensions are calculated. The weights and the values ​​of the neighboring points are combined by the bilinear formula to generate the interpolation result of the target point, ensuring the consistency of the data in the time dimension.

[0040] (3) Spatial alignment: Use affine transformation (a coordinate transformation algorithm) to unify the spatial coordinates of different data sources into the same coordinate system and determine the differences in coordinate systems. First, identify the coordinate system parameters of different data sources (such as origin, unit, rotation angle, projection type). If geographic coordinate systems (such as WGS-84, UTM) are involved, ellipsoid parameters and projection transformation need to be considered. Then, select control points and solve the parameters. Select at least 3 pairs of corresponding points (non-collinear) to determine the 6 parameters of the affine transformation (translation, scaling, rotation, shearing). Then solve the linear equation system by least squares method, or use iterative optimization algorithm (such as Levenberg-Marquardt) to improve the accuracy. Calculate the transformation matrix according to the control points, perform batch transformation on all data points, complete the spatial alignment, and unify the spatial coordinates of different data sources into the same coordinate system.

[0041] (4) Standardization: The Z-score standardization method is used to convert the numerical characteristics of different data sources into a distribution with a mean of 0 and a standard deviation of 1.

[0042] S102. The first low-altitude data is parsed and processed to determine the spatiotemporal correlation matrix and data quality indicators corresponding to the first low-altitude data.

[0043] The data quality indicators include at least time accuracy, spatial accuracy, data integrity, and data consistency.

[0044] Specifically, the first low-altitude data is further analyzed to determine the temporal and spatial relationships between various data sources, thereby obtaining the spatiotemporal correlation function, i.e., the spatiotemporal correlation matrix.

[0045] Meanwhile, based on the spatiotemporal dynamic constraints of each data source in the first low-altitude data, the data quality indicators corresponding to the first low-altitude data are calculated, as shown below:

[0046] ;

[0047] in, Indicates data quality indicators, Indicates time precision. Indicates spatial precision. Indicates data integrity. This indicates data consistency.

[0048] It should be noted that there are various methods for calculating and obtaining data quality indicators, and those skilled in the art can choose according to their actual needs. This invention will not describe them one by one.

[0049] S103. Determine the dynamic weight vector corresponding to the first low-altitude data based on the spatiotemporal correlation matrix and the data quality index.

[0050] The dynamic weight vector can refer to the weight proportion of the data source in the data quality credibility assessment, and is used to indicate the weight proportion of the data source under different spatiotemporal conditions.

[0051] Specifically, based on the spatiotemporal correlation matrix and data quality indicators, a dynamic weight allocation algorithm (DWA) is designed to adjust weights according to real-time data characteristics, enabling dynamic adjustment of weights for different data sources under different spatiotemporal conditions, thus obtaining a dynamic weight vector. This invention, through a dynamic weight allocation algorithm under spatiotemporal constraints, can automatically adjust weights based on the performance of data sources under different spatiotemporal conditions, improving the accuracy and adaptability of data quality reliability assessment.

[0052] For example, determining the dynamic weight vector corresponding to the first low-altitude data based on the spatiotemporal correlation matrix and the data quality index includes:

[0053] ));

[0054] in, This refers to the dynamic weight vector. ( ) refers to the dynamic weight allocation function. This refers to the aforementioned data quality indicators. ) refers to the spatiotemporal correlation matrix.

[0055] This invention dynamically adjusts weights based on the real-time performance of the data source and changes in the spatiotemporal environment. For example, in traffic monitoring scenarios, cameras may have a higher weight during the day than at night; in disaster early warning scenarios, sensors closer to disaster areas may have a higher weight.

[0056] S104. Determine the data quality score of the first low-altitude data based on the dynamic weight vector and the data quality index.

[0057] Specifically, the data quality score of the first low-altitude data is calculated based on the dynamic weight vector and data quality indicators.

[0058] For example, determining the data quality score of the first low-altitude data based on the dynamic weight vector and the data quality index includes:

[0059] ;

[0060] in, This refers to the data quality score. Refers to data The dynamic weight vector, Refers to data The data quality metrics mentioned above.

[0061] This invention calculates and determines the data quality score of the first low-altitude data by using dynamic weight vectors and data quality indicators, and outputs and displays the quality score of each data source and the overall quality assessment result of the fused data.

[0062] The technical solution of this invention involves acquiring first low-altitude data to be evaluated for data quality. The first low-altitude data is parsed to determine the corresponding spatiotemporal correlation matrix and data quality index. Based on the spatiotemporal correlation matrix and the data quality index, a dynamic weight vector corresponding to the first low-altitude data is determined. Based on the dynamic weight vector and the data quality index, a data quality score for the first low-altitude data is determined. This solves the problem of spatiotemporal coupling and quality dimension decoupling in multi-source heterogeneous data, and can automatically adjust the spatiotemporal weights according to the data source under different spatiotemporal conditions, improving the accuracy and adaptability of data quality evaluation.

[0063] Example 2

[0064] Figure 2 This is a flowchart of a low-altitude data quality assessment method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment further refines the determination of the spatiotemporal correlation matrix corresponding to the first low-altitude data. For example... Figure 2 As shown, the method includes:

[0065] S201. Obtain the first low-altitude data to be assessed for data quality.

[0066] S202. The first low-altitude data is parsed and processed to determine the data quality index corresponding to the first low-altitude data.

[0067] S203. Perform spatiotemporal dynamic calibration processing on the first low-altitude data to obtain the second low-altitude data.

[0068] Specifically, the first low-altitude data is subjected to spatiotemporal synchronization calibration to obtain the second low-altitude data with spatiotemporal synchronization calibration.

[0069] For example, the first low-altitude data is subjected to spatiotemporal dynamic calibration processing to obtain the second low-altitude data, including:

[0070] Spatiotemporal correlation analysis is performed based on the first low-altitude data and the predetermined theoretical low-altitude data to obtain time offset estimates and spatial rotation parameter estimates.

[0071] Based on the time offset estimation and the spatial rotation parameter estimation, the first low-altitude data is subjected to spatiotemporal calibration to obtain the second low-altitude data.

[0072] Specifically, trace correlation analysis is used to calculate the spatiotemporal correlation between sensors, and to obtain time offset estimates and spatial rotation parameter estimates.

[0073] Time offset estimation: Using high-frequency IMU data as a reference, the time offset of other data sources is calculated through cross-correlation analysis. The specific formula is as follows:

[0074] ;

[0075] in, This is the actual data for the first low-altitude data at time t. This refers to theoretical low-altitude data at time t+td. This is the time offset.

[0076] Spatial rotation parameter estimation: Also based on motion correlation analysis, the spatial rotation parameter estimates between different data sources are calculated. The specific formula is as follows:

[0077] ;

[0078] in, For spatial rotation parameter estimation, This is the spatial data for the first low-altitude data. Spatial data for theoretical low-altitude data.

[0079] Furthermore, time offset estimation and spatial rotation parameter estimation are applied to the data source to achieve spatiotemporal synchronization calibration. A negative time offset indicates that the target data source is delayed compared to the reference data source; a positive value indicates that the target data source is ahead of the reference data source.

[0080] S204. Perform spatiotemporal dimensional fusion processing on the second low-altitude data to obtain the spatiotemporal correlation matrix corresponding to the second low-altitude data.

[0081] Specifically, the spatiotemporal correlation matrix can be obtained by integrating the spatiotemporal correlation information in the second low-altitude data.

[0082] For example, the step of performing spatiotemporal dimensional fusion processing on the second low-altitude data to obtain the spatiotemporal correlation matrix corresponding to the second low-altitude data includes:

[0083] The second low-altitude data is subjected to feature extraction processing to obtain temporal and spatial features;

[0084] Based on the temporal and spatial characteristics, construct the spatial correlation matrix and temporal correlation matrix corresponding to the second low-altitude data;

[0085] Based on the spatial correlation matrix, the temporal correlation matrix, and the predetermined spatiotemporal correlation weights, the spatiotemporal correlation matrix corresponding to the second low-altitude data is determined.

[0086] Specifically, temporal features (such as sampling frequency and temporal resolution) and spatial features (such as spatial resolution and coverage) are extracted from the second low-altitude data. Using methods such as linear regression, spatial correlation matrices S and temporal correlation matrices T are constructed based on the temporal and spatial features, where the matrix elements represent the degree of correlation between the two data sources. A spatiotemporal joint constraint function F(S,T) is designed to fuse the correlation information from the spatial and temporal dimensions.

[0087] Determining the spatiotemporal correlation matrix corresponding to the second low-altitude data includes:

[0088] ;

[0089] in, , , For spatiotemporal correlation weights, It is a spatial incidence matrix. This is a time correlation matrix.

[0090] It should be noted that the present invention can also use the gradient descent algorithm to optimize the fusion weights of the spatiotemporal correlation matrix, so that the model can adapt to the spatiotemporal characteristics of different scenarios.

[0091] S205. Determine the dynamic weight vector corresponding to the first low-altitude data based on the spatiotemporal correlation matrix and the data quality index.

[0092] S206. Determine the data quality score of the first low-altitude data based on the dynamic weight vector and the data quality index.

[0093] This invention utilizes motion correlation analysis and trace correlation algorithms to perform spatiotemporal dynamic calibration on the first low-altitude data to obtain the second low-altitude data. This achieves automated correction of spatiotemporal deviations in multi-source heterogeneous data, reducing the need for manual intervention and improving system efficiency. By employing a spatiotemporal joint constraint function, the second low-altitude data undergoes spatiotemporal dimensional fusion processing to obtain the corresponding spatiotemporal correlation matrix. This considers the correlation between the time and spatial dimensions, avoiding information loss caused by spatiotemporal separation processing, improving data fusion quality, and enhancing the accuracy of data quality assessment. Compared to existing methods, this invention reduces the data processing latency of the UAV management platform, improves airspace resource utilization, significantly enhances the emergency response capability of urban low-altitude traffic, provides quantitative basis for airspace hierarchical management, and provides fundamental support for the value sharing of low-altitude economic data.

[0094] Example 3

[0095] Figure 3 This is a schematic diagram of a low-altitude data quality assessment device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes:

[0096] The low-altitude data acquisition module 301 is used to acquire first low-altitude data to be evaluated for data quality, wherein the first low-altitude data includes multi-source heterogeneous data in a preset area and a preset time period, and the multi-source heterogeneous data includes at least data content, data type and data structure.

[0097] The low-altitude data parsing module 302 is used to parse and process the first low-altitude data to determine the spatiotemporal correlation matrix and data quality indicators corresponding to the first low-altitude data, wherein the data quality indicators include at least time precision, spatial precision, data integrity and data consistency.

[0098] The dynamic weight determination module 303 is used to determine the dynamic weight vector corresponding to the first low-altitude data based on the spatiotemporal correlation matrix and the data quality index.

[0099] The data quality assessment module 304 is used to determine the data quality score of the first low-altitude data based on the dynamic weight vector and the data quality index.

[0100] The technical solution of this invention involves acquiring first low-altitude data to be evaluated for data quality. The first low-altitude data is parsed to determine the corresponding spatiotemporal correlation matrix and data quality index. Based on the spatiotemporal correlation matrix and the data quality index, a dynamic weight vector corresponding to the first low-altitude data is determined. Based on the dynamic weight vector and the data quality index, a data quality score for the first low-altitude data is determined. This solves the problem of spatiotemporal coupling and quality dimension decoupling in multi-source heterogeneous data, and can automatically adjust the spatiotemporal weights according to the data source under different spatiotemporal conditions, improving the accuracy and adaptability of data quality evaluation.

[0101] Optionally, the low-altitude data parsing module 302 includes:

[0102] The second low-altitude data acquisition unit is used to perform spatiotemporal dynamic calibration processing on the first low-altitude data to obtain the second low-altitude data.

[0103] The second correlation matrix determination unit is used to perform spatiotemporal dimension fusion processing on the second low-altitude data to obtain the spatiotemporal correlation matrix corresponding to the second low-altitude data.

[0104] Optionally, the second low-altitude data acquisition unit is specifically used for:

[0105] Spatiotemporal correlation analysis is performed based on the first low-altitude data and the predetermined theoretical low-altitude data to obtain time offset estimates and spatial rotation parameter estimates.

[0106] Based on the time offset estimation and the spatial rotation parameter estimation, the first low-altitude data is subjected to spatiotemporal calibration to obtain the second low-altitude data.

[0107] Optionally, the second correlation matrix determining unit is specifically used for:

[0108] The second low-altitude data is subjected to feature extraction processing to obtain temporal and spatial features;

[0109] Based on the temporal and spatial characteristics, construct the spatial correlation matrix and temporal correlation matrix corresponding to the second low-altitude data;

[0110] Based on the spatial correlation matrix, the temporal correlation matrix, and the predetermined spatiotemporal correlation weights, the spatiotemporal correlation matrix corresponding to the second low-altitude data is determined.

[0111] Optionally, the dynamic weight determination module 303 is specifically used for:

[0112] ));

[0113] in, This refers to the dynamic weight vector. ( ) refers to the dynamic weight allocation function. This refers to the aforementioned data quality indicators. ) refers to the spatiotemporal correlation matrix.

[0114] Optionally, the data quality assessment module 304 is specifically used for:

[0115] ;

[0116] in, This refers to the data quality score. Refers to data The dynamic weight vector, Refers to data The data quality metrics mentioned above.

[0117] Optionally, the low-altitude data acquisition module 301 is specifically used for:

[0118] Acquire raw low-altitude data within the target assessment area;

[0119] The original low-altitude data is preprocessed to obtain the first low-altitude data, wherein the data preprocessing includes at least noise removal, time alignment, spatial alignment and normalization.

[0120] The low-altitude data quality assessment device provided in the embodiments of the present invention can execute the low-altitude data quality assessment method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0121] Example 4

[0122] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0123] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0124] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0125] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as low-altitude data quality assessment methods.

[0126] In some embodiments, the low-altitude data quality assessment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the low-altitude data quality assessment method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the low-altitude data quality assessment method by any other suitable means (e.g., by means of firmware).

[0127] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0131] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0132] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0133] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0134] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for assessing the quality of low-altitude data, characterized in that, include: Acquire first low-altitude data to be assessed for data quality, wherein the first low-altitude data includes multi-source heterogeneous data within a preset area and a preset time period, and the multi-source heterogeneous data includes at least data content, data type and data structure; The first low-altitude data is parsed and processed to determine the spatiotemporal correlation matrix and data quality indicators corresponding to the first low-altitude data. The data quality indicators include at least time precision, spatial precision, data integrity and data consistency. Based on the spatiotemporal correlation matrix and the data quality index, determine the dynamic weight vector corresponding to the first low-altitude data; The data quality score of the first low-altitude data is determined based on the dynamic weight vector and the data quality index. The step of determining the spatiotemporal correlation matrix corresponding to the first low-altitude data based on the first low-altitude data includes: The first low-altitude data is subjected to spatiotemporal dynamic calibration to obtain the second low-altitude data. The second low-altitude data is fused in a spatiotemporal dimension to obtain the spatiotemporal correlation matrix corresponding to the second low-altitude data. The first low-altitude data is subjected to spatiotemporal dynamic calibration processing to obtain the second low-altitude data, including: Spatiotemporal correlation analysis is performed based on the first low-altitude data and the predetermined theoretical low-altitude data to obtain time offset estimates and spatial rotation parameter estimates. Based on the time offset estimation and the spatial rotation parameter estimation, the first low-altitude data is subjected to spatiotemporal calibration to obtain the second low-altitude data. The step of performing spatiotemporal dynamic calibration processing on the first low-altitude data to obtain the second low-altitude data includes: Trajectory correlation analysis is used to calculate the spatiotemporal correlation between sensors, yielding time offset estimates and spatial rotation parameter estimates. The time offset estimate uses high-frequency IMU data as a reference, and cross-correlation analysis is used to calculate the time offset from other data sources. The specific formula is as follows: ； in, This refers to the actual data of the first low-altitude data at time t. The theoretical low-altitude data refers to the theoretical data at time t+td. This is the time offset; The spatial rotation parameter estimation is based on motion correlation analysis, calculating the spatial rotation parameter estimates between different data sources. The specific formula is as follows: ； in, Estimate the spatial rotation parameters. This refers to the spatial data of the first low-altitude data. The spatial data refers to the theoretical low-altitude data. The step of performing spatiotemporal dimensional fusion processing on the second low-altitude data to obtain the spatiotemporal correlation matrix corresponding to the second low-altitude data includes: The second low-altitude data is subjected to feature extraction processing to obtain temporal and spatial features; Based on the temporal and spatial characteristics, construct the spatial correlation matrix and temporal correlation matrix corresponding to the second low-altitude data; Based on the spatial correlation matrix, the temporal correlation matrix, and the predetermined spatiotemporal correlation weights, the spatiotemporal correlation matrix corresponding to the second low-altitude data is determined.

2. The method according to claim 1, characterized in that, The step of determining the dynamic weight vector corresponding to the first low-altitude data based on the spatiotemporal correlation matrix and the data quality index includes: )); in, This refers to the dynamic weight vector. ( ) refers to the dynamic weight allocation function. This refers to the data quality indicators. ) refers to the spatiotemporal correlation matrix, It is a spatial incidence matrix. This is a time correlation matrix.

3. The method according to claim 1, characterized in that, Determining the data quality score of the first low-altitude data based on the dynamic weight vector and the data quality index includes: ; in, This refers to the data quality score. Refers to data The dynamic weight vector, Refers to data The data quality metrics mentioned above.

4. The method according to claim 1, characterized in that, The acquisition of the first low-altitude data to be assessed for data quality includes: Acquire raw low-altitude data within the target assessment area; The original low-altitude data is preprocessed to obtain the first low-altitude data, wherein the data preprocessing includes at least noise removal, time alignment, spatial alignment and normalization.

5. A low-altitude data quality assessment device, characterized in that, include: The low-altitude data acquisition module is used to acquire first low-altitude data to be assessed for data quality. The first low-altitude data includes multi-source heterogeneous data within a preset area and a preset time period. The multi-source heterogeneous data includes at least data content, data type, and data structure. The low-altitude data parsing module is used to parse and process the first low-altitude data to determine the spatiotemporal correlation matrix and data quality indicators corresponding to the first low-altitude data. The data quality indicators include at least time precision, spatial precision, data integrity and data consistency. The dynamic weight determination module is used to determine the dynamic weight vector corresponding to the first low-altitude data based on the spatiotemporal correlation matrix and the data quality index. The data quality assessment module is used to determine the data quality score of the first low-altitude data based on the dynamic weight vector and the data quality index. The low-altitude data parsing module includes: The second low-altitude data acquisition unit is used to perform spatiotemporal dynamic calibration processing on the first low-altitude data to obtain the second low-altitude data. The second correlation matrix determination unit is used to perform spatiotemporal dimension fusion processing on the second low-altitude data to obtain the spatiotemporal correlation matrix corresponding to the second low-altitude data. The second low-altitude data acquisition unit is specifically used for: Trajectory correlation analysis is used to calculate the spatiotemporal correlation between sensors, yielding time offset estimates and spatial rotation parameter estimates. The time offset estimate uses high-frequency IMU data as a reference, and cross-correlation analysis is used to calculate the time offset from other data sources. The specific formula is as follows: ； in, This refers to the actual data of the first low-altitude data at time t. This refers to theoretical low-altitude data at time t+td. This is the time offset; The spatial rotation parameter estimation is based on motion correlation analysis, calculating the spatial rotation parameter estimates between different data sources. The specific formula is as follows: ； in, Estimate the spatial rotation parameters. This refers to the spatial data of the first low-altitude data. The spatial data refers to the theoretical low-altitude data. The second low-altitude data acquisition unit is specifically used for: Spatiotemporal correlation analysis is performed based on the first low-altitude data and the predetermined theoretical low-altitude data to obtain time offset estimates and spatial rotation parameter estimates. Based on the time offset estimation and the spatial rotation parameter estimation, the first low-altitude data is subjected to spatiotemporal calibration to obtain the second low-altitude data. The second correlation matrix determination unit is specifically used for: The second low-altitude data is subjected to feature extraction processing to obtain temporal and spatial features; Based on the temporal and spatial characteristics, construct the spatial correlation matrix and temporal correlation matrix corresponding to the second low-altitude data; Based on the spatial correlation matrix, the temporal correlation matrix, and the predetermined spatiotemporal correlation weights, the spatiotemporal correlation matrix corresponding to the second low-altitude data is determined.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the low-altitude data quality assessment method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the low-altitude data quality assessment method according to any one of claims 1-4.

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