Sea-land integrated measurement system and method based on multi-sensor fusion

Abstract

The invention provides a sea-land integrated measurement system and method based on multi-sensor fusion, and relates to the technical field of data processing. The system comprises a data acquisition module used for acquiring measurement data of a target area through various types of sensors and generating original data including spatial position information and measurement attribute information; the space-time synchronization module is used for adjusting the data generated by each sensor to a unified time reference and space reference coordinate system; the data fusion module is used for generating three-dimensional topographic data of the target area; the task distribution module is used for dividing the target area into a plurality of task areas according to the distribution characteristics of the three-dimensional topographic data and generating corresponding operation instructions; and the collaborative operation module is used for coordinating measurement behaviors of the operation platform in different task areas. According to the technical scheme, the precision and efficiency of the sea-land integrated measurement system in the aspects of multi-sensor data fusion and multi-platform collaborative operation can be improved.

Description

Background Art

[0002] With the continuous improvement of modern measurement needs, the application of integrated sea and land measurement systems in environmental monitoring, topographic mapping and resource exploration has become more and more extensive. However, existing technologies have exposed many limitations when dealing with complex measurement scenarios, especially in terms of multi-sensor data fusion accuracy and multi-platform collaborative operation efficiency. Due to the diversity of measurement environments, data acquisition from different sensors is affected by noise interference, physical limitations and environmental changes, making it difficult to effectively unify data in time and space. Especially in the sea-land boundary area, underwater sonar equipment is susceptible to echo interference, resulting in discontinuous measurement results; the onshore laser radar has increased sampling data noise due to strong water surface reflection, making it difficult to form an organic connection with underwater data. In addition, existing systems usually rely on a single platform operation, with a limited operating range, and lack of efficient coordination mechanisms between multiple platforms, which is prone to unreasonable task allocation, rigid path planning and difficult data integration. These problems directly affect the accuracy and completeness of the measurement results, and it is difficult to meet the needs of high-precision measurement in multiple scenarios.

[0003] Therefore, in view of the shortcomings of existing technologies in multi-sensor data fusion and multi-platform collaborative operations, it is urgent to propose a new sea-land integrated measurement system that integrates multiple sensors, supports multi-platform collaborative operations and realizes efficient data fusion to meet the needs of complex measurement scenarios. Summary of the invention

[0004] The purpose of the embodiments of the present disclosure is to provide a land-sea integrated measurement system and method based on multi-sensor fusion, thereby at least to a certain extent improving the accuracy and efficiency of the land-sea integrated measurement system in terms of multi-sensor data fusion and multi-platform collaborative operation.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0006] According to a first aspect of an embodiment of the present disclosure, a land-sea integrated measurement system based on multi-sensor fusion is provided, the system comprising: a data acquisition module, for acquiring measurement data of a target area through various types of sensors, and generating raw data including spatial position information and measurement attribute information; a time-space synchronization module, for performing time alignment processing and spatial coordinate conversion processing on the raw data, and adjusting the data generated by each sensor to a unified time reference and spatial reference coordinate system; a data fusion module, for performing fusion processing on multi-source data according to a weighted algorithm based on the data adjusted by the time-space synchronization module, and generating three-dimensional terrain data of the target area; a task allocation module, for dividing the target area into multiple task areas and generating corresponding operation instructions according to the distribution characteristics of the three-dimensional terrain data and the operation status information of multiple operation platforms; a collaborative operation module, for executing the operation instructions, coordinating the measurement behaviors of the operation platforms in different task areas, and collecting and updating the operation status information of each operation platform in real time.

[0007] According to a second aspect of an embodiment of the present disclosure, a land-sea integrated measurement method based on multi-sensor fusion is provided, the method comprising: acquiring measurement data of a target area through multiple types of sensors, and generating raw data including spatial position information and measurement attribute information; performing time alignment processing and spatial coordinate conversion processing on the raw data, and adjusting the data generated by each sensor to a unified time reference and spatial reference coordinate system; based on the data adjusted by the time-space synchronization module, performing fusion processing on the multi-source data according to a weighted algorithm, and generating three-dimensional terrain data of the target area; according to the distribution characteristics of the three-dimensional terrain data, combined with the operating status information of multiple operating platforms, dividing the target area into multiple task areas and generating corresponding operating instructions; according to the operating instructions, coordinating the measurement behaviors of the operating platforms in different task areas, and collecting and updating the operating status information of each operating platform in real time.

[0008] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the above-mentioned integrated land and sea measurement method based on multi-sensor fusion is implemented.

[0009] According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the land-sea integrated measurement method based on multi-sensor fusion is implemented.

[0010] The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects: The land-sea integrated measurement system based on multi-sensor fusion in the exemplary embodiment of the present disclosure includes a data acquisition module, a time-space synchronization module, a data analysis module, a data fusion module, a task allocation module and a collaborative operation module.

[0011] Among them, the data acquisition module can realize the coordinated work of various types of sensors in the target area, solve the problems of limited measurement range and discontinuous data of a single sensor in complex scenes, and provide multi-source, high-coverage measurement information for subsequent data processing; through the time alignment processing and spatial coordinate conversion processing of the time-space synchronization module, the data of different sensors are unified to the same time base and spatial reference coordinate system, overcoming the limitations of timestamp errors and spatial position offsets among multiple sensors, laying the foundation for the effective fusion of multi-source data; through the data fusion module, multi-source data are fused based on a weighted algorithm, which can integrate the measurement results of each sensor, improve the overall consistency of the data, and solve the problem of difficulty in fusion between heterogeneous data. The problem of insufficient measurement accuracy caused by connection is solved, while ensuring the integrity and accuracy of three-dimensional terrain data in complex terrain; through the task allocation module, combined with the distribution characteristics of three-dimensional terrain data and the operating status information of multiple work platforms, it can dynamically divide the task area and generate work instructions, solving the problems of insufficient coverage and low resource utilization efficiency of a single platform operation mode in complex scenes, and realizing the high efficiency of multi-platform collaborative work; through the collaborative work module, the measurement behaviors of multiple work platforms are coordinated in real time and the operating status is collected, which can dynamically adapt to changes in the measurement site, solving the defects of rigid task allocation and low efficiency of multi-platform collaboration in the existing technology, and further ensuring the continuity and stability of measurement tasks in complex environments.

[0012] Based on the mutual cooperation among the above modules, the present invention significantly improves the measurement accuracy, operation efficiency and environmental adaptability of the integrated land and sea measurement system in multiple scenarios through full process optimization from data acquisition, synchronous calibration, data fusion to task allocation and collaborative control, based on multi-sensor data fusion.

[0013] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0015] Figure 1The present invention schematically shows a composition diagram of a land-sea integrated measurement system based on multi-sensor fusion according to some embodiments of the present disclosure.

[0016] Figure 2 The schematic diagram schematically shows the composition of another land-sea integrated measurement system based on multi-sensor fusion according to some embodiments of the present disclosure.

[0017] Figure 3 A flow chart of a land-sea integrated measurement method based on multi-sensor fusion according to some embodiments of the present disclosure is schematically shown.

[0018] Figure 4 A schematic diagram of the structure of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0019] Figure 5 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0020] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts. DETAILED DESCRIPTION

[0021] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Instead, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0022] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the" and "the" used in this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0023] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0024] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete and will fully convey the concept of the example embodiments to those skilled in the art.

[0025] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0026] Furthermore, the drawings are only schematic illustrations and are not necessarily drawn to scale. The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0027] With the continuous improvement of modern measurement needs, the integrated land and sea measurement system is increasingly used in environmental monitoring, topographic mapping, resource exploration and other fields. However, due to the complexity of the measurement environment and the limitations of technical implementation, related technologies are difficult to meet the needs of high-precision measurement in multiple scenarios.

[0028] Related measurement systems usually rely on a single sensor or a single platform, which greatly limits the scope and accuracy of data acquisition. For example, single-beam and multi-beam sensors have certain advantages in underwater topographic measurement, but in the sea-land boundary area, due to the drastic change in water depth and significant interference of echo signals, the measurement results are prone to discontinuity or reduced accuracy. Although equipment such as lidar performs well in the field of onshore surveying and mapping, its sampled data often has significant noise in areas with strong surface reflection, making it difficult to effectively connect with underwater data. The resulting fusion barriers between heterogeneous data directly affect the integrity and reliability of topographic data. In addition, most existing measurement systems are based on single-platform operations, which makes it difficult to effectively cover complex areas. For example, although shipborne equipment is suitable for large-scale underwater measurements, it is limited in operation in shallow waters or nearshore environments; although the UAV platform has high flexibility, its endurance and load limitations determine that the operation time and range are short; land mobile equipment is difficult to adapt to the needs of non-land areas. This single-platform operation mode cannot give full play to the strengths of various platforms, often resulting in extended measurement time, inefficient resource utilization, and even measurement blind spots in complex terrain.

[0029] In order to solve some or all of the above technical problems, the present disclosure provides a land-sea integrated measurement system based on multi-sensor fusion. Figure 1 The schematic diagram of the composition of the land-sea integrated measurement system based on multi-sensor fusion according to some embodiments of the present disclosure is schematically shown. Figure 1 As shown, the land-sea integrated measurement system based on multi-sensor fusion can include the following modules: The data acquisition module 1 is used to obtain the measurement data of the target area through various types of sensors and generate raw data including spatial position information and measurement attribute information; The time-space synchronization module 2 is used to perform time alignment processing and space coordinate conversion processing on the raw data, and adjust the data generated by each sensor to a unified time reference and space reference coordinate system; A data fusion module 3 is used to fuse the multi-source data according to a weighted algorithm based on the data adjusted by the spatiotemporal synchronization module, and generate three-dimensional terrain data of the target area; The task allocation module 4 is used to divide the target area into multiple task areas and generate corresponding operation instructions according to the distribution characteristics of the three-dimensional terrain data and the operation status information of multiple operation platforms; The collaborative operation module 5 is used to execute the operation instructions, coordinate the measurement behaviors of the operation platforms in different task areas, and collect and update the operation status information of each operation platform in real time.

[0030] Specifically, the above-mentioned integrated land and sea measurement system based on multi-sensor fusion includes multiple modules, and each module cooperates with each other to achieve full process optimization from data collection to collaborative operation.

[0031] The data acquisition module 1 measures the target area through various types of sensors, including single-beam, multi-beam and laser radar, etc. Each sensor obtains different types of spatial position information and measurement attribute information according to its specific working mode. The data acquisition module can dynamically adjust the sampling parameters of the sensor according to the measurement scene to ensure that high-precision raw data is collected in a complex environment. These raw data provide a unified data basis for the processing of subsequent modules by adding timestamps and sensor identifiers.

[0032] The spatiotemporal synchronization module 2 performs time alignment and spatial coordinate conversion processing on the raw data generated by the data acquisition module 1. Through time alignment processing, the spatiotemporal synchronization module adjusts the data of various types of sensors to a unified time reference to eliminate data offset caused by sensor timestamp errors. The spatial coordinate conversion processing realizes the conversion between various sensor measurement coordinate systems and the global reference coordinate system through the rotation matrix and translation vector calibrated by external parameters, so that all data are consistent under a unified spatial reference frame.

[0033] The data fusion module 3 fuses the multi-source data adjusted by the spatiotemporal synchronization module 2 according to the weighted algorithm. During the fusion process, the data fusion module calculates the weights of the signal-to-noise ratio, sampling accuracy and other parameters of each sensor data, and generates three-dimensional terrain data by weighted average. In order to further improve the accuracy of the fusion results, the data fusion module also uses the error optimization method to correct the fused data, eliminate the spatial deviation caused by the measurement error, and finally generate a high-precision three-dimensional terrain model. These terrain data provide accurate spatial distribution information for task allocation.

[0034] The task allocation module 4 dynamically divides the measurement area and generates work instructions based on the three-dimensional terrain data generated by the data fusion module 3 and the operating status information of multiple operating platforms. The task allocation module first evaluates the workload of the measurement area based on the complexity and distribution characteristics of the terrain data, and generates the optimal task allocation plan based on the real-time operating status of each platform, including the speed, endurance and operating coverage of the platform. During the allocation process, the task allocation module uses an optimization algorithm to maximize the balance of workload between platforms, ensuring the rationality of resource utilization and the efficiency of task execution.

[0035] The collaborative operation module 5 is responsible for executing the operation instructions generated by the task allocation module 4 and coordinating the operation behavior of the operation platform in real time. The collaborative operation module dynamically adjusts the operation path and execution strategy of the platform by monitoring the operation status information of the operation platform, including location, operation progress and resource usage. At the same time, the collaborative operation module 5 realizes data sharing and status synchronization through real-time communication between multiple platforms to ensure the continuity of collaborative operations of various platforms during the measurement process. In addition, when the operation platform needs to adjust the scope of operation due to environmental changes or emergencies, the module can respond quickly and reallocate tasks to maintain the continuity of the operation and the integrity of the measurement results.

[0036] Through the division of labor and cooperation of the above modules, the system realizes the organic connection of the functional chain from data collection to task execution, and can provide efficient, accurate and stable integrated sea and land measurement capabilities in complex measurement scenarios.

[0037] In addition, reference Figure 2 As shown, in other embodiments of the present disclosure, the integrated land and sea measurement system based on multi-sensor fusion also includes a heterogeneous data processing module 6 and a communication module 7, wherein the heterogeneous data processing module 6 can be used to receive the measurement data acquired by the collaborative operation module, and perform hierarchical storage, rapid retrieval and dynamic compression processing on different types of measurement data; the communication module 7 can be used to establish a data interaction channel between multiple modules, and share the measurement data processed by the heterogeneous data processing module in real time among multiple operation platforms, and synchronously transmit the status information of the operation platforms.

[0038] Specifically, the heterogeneous data processing module 6 receives the measurement data obtained by the collaborative operation module, and performs hierarchical storage, rapid retrieval and dynamic compression processing on different types of data. The heterogeneous data processing module classifies and stores real-time data, high-frequency data and historical data according to the importance of the data, where real-time data is stored in high-performance memory, high-frequency data is stored in high-speed storage devices, and historical data is stored through compression algorithms to save resources. At the same time, the heterogeneous data processing module realizes rapid positioning and access of data through a three-dimensional spatial index mechanism, and uses a dynamic compression algorithm to reduce the overhead of data transmission and storage, providing data support for the efficient operation of the system.

[0039] The communication module 7 establishes a data interaction channel between the modules of the system, and supports real-time data sharing and status information synchronization between multiple operating platforms. The communication module adopts a multi-mode communication architecture, including 5G communication, ad hoc network and satellite communication, and can dynamically switch the communication mode according to network conditions and operating requirements. Through the intelligent routing mechanism, the module prioritizes high-bandwidth channels for real-time data, and non-emergency data is transmitted through paths with lower loads. In addition, the communication module has a communication redundancy function, which automatically switches to the backup channel when the communication mode fails to ensure the continuity and stability of data transmission. The communication module is also responsible for sharing the data processed by the heterogeneous data processing module between operating platforms, and synchronizing the status information of the operating platform to support multi-platform collaborative operations and dynamic task adjustments.

[0040] Next, the land-sea integrated measurement system based on multi-sensor fusion in the above exemplary embodiment will be further described.

[0041] refer to Figure 2 As shown, the data acquisition module 1 may include a beam depth measurement unit 11, a signal strength processing unit 12, a laser distance measurement unit 13, a beam direction control unit 14 and a boundary judgment unit 15. Among them: The beam depth measurement unit 11 can be used to control the multi-beam sonar to transmit sound waves and receive echoes to obtain water depth data. The calculation of the depth data is based on the propagation speed of the sound waves and the echo time, using the formula: in, Indicates the depth value, The speed at which sound waves propagate in a medium. Indicates the time interval from the emission to the reception of sound waves. The speed of sound wave propagation is corrected according to environmental parameters (such as water temperature, salinity and pressure). The correction formula is: in, represents the base propagation speed, Indicates water temperature, Indicates salinity, Indicates pressure, Represents the correction factor. Sound waves are transmitted and received through a multi-beam array, and the emission angle and coverage area of ​​each beam are determined by the array design. Array calibration ensures that the beam coverage area accurately corresponds to the target area.

[0042] The signal strength processing unit 12 may be configured to calculate the signal strength according to the following formula: The intensity distribution of the echo signal of the multi-beam sonar is calculated, where, Indicates time The signal strength, Indicates time The signal power, represents the attenuation coefficient, which is related to the propagation path and environmental noise. represents the starting time of intensity distribution calculation, Represents the base of the natural logarithm. Signal strength processing filters out background noise and enhances the signal-to-noise ratio of valid signals by performing frequency and time domain analysis on the received signal. The dynamic threshold algorithm is used to distinguish target signals from background noise to ensure the accuracy of valid signal extraction.

[0043] The laser ranging unit 13 can be used to obtain the distance value of the target area through the pulse flight time of the laser radar. The distance value can be calculated by the following formula: in, Indicates the measured distance, represents the speed of light, Indicates the time interval between pulse transmission and reception, Represents the refractive index of the medium. The laser pulse width and frequency are controlled by modulating the source to balance the ranging accuracy and data sampling efficiency.

[0044] The beam direction control unit 14 can be used to adjust the beam direction of the multi-beam sonar in real time according to the geometric characteristics of the target area. The beam direction is based on the following formula: Angle adjustment is performed, wherein represents the angle between the beam and the target direction, represents the beam direction vector, is the target position vector. The beam direction control unit achieves full coverage of the target area by adjusting the beam direction. The real-time adjustment of the beam pointing is combined with electronic control and array feedback optimization, and the optimal beam coverage path is determined through an iterative algorithm to maximize the signal coverage range.

[0045] In addition, in order to ensure the temporal and spatial consistency of data collected by different sensors, this module uniformly calibrates the timestamps of all sensors. The timestamp calibration process is based on the linear time interpolation formula: in, Indicates the timestamp after calibration, Represents a reference timestamp, Indicates the current timestamp, represents the original time interval, Represents the target time interval. The calibrated timestamp data is further used to construct the spatiotemporal distribution matrix : in, represents the spatial coordinates of the sampling points, Indicates the corresponding timestamp, Represents the total number of sampling points. Spatiotemporal distribution matrix It provides accurate spatiotemporal alignment input for subsequent data fusion.

[0046] The boundary judgment unit 15 can be used to judge the boundary of the measurement area in the target terrain environment, and the boundary is obtained by fitting the plane equation: To determine, , , represents the components of the plane normal vector, represents the plane intercept, Represents the spatial coordinates of the sampling point, and the target terrain environment represents a complex terrain environment.

[0047] Next reference Figure 2 The spatiotemporal synchronization module 2 may include a time calibration unit 21, a space calibration unit 22, and a joint optimization unit 23. Among them: The time calibration unit 21 may be used according to: Determine the time offset, where represents the sampling frequency of the inertial measurement unit, Indicates the global navigation satellite system signal delay time, The time offset is represented, and the calibration time is calculated according to the time offset. The time calibration unit calibrates the timestamp of the sensor by calculating the time offset, thereby achieving time alignment of different sensor data.

[0048] The spatial calibration unit 22 can be used to transform the spatial coordinates of the sensor through the rotation matrix and translation vector calibrated by external parameters. The transformation process of the spatial coordinates is expressed as: in, represents the global coordinates, represents the local coordinates of the sensor, represents the translation vector, represents a rotation matrix, which satisfies the orthogonality condition: ,in, Represents the unit matrix. Through the combination of rotation and translation, the spatial calibration unit realizes the mapping of multi-sensor data in a unified coordinate system. The rotation matrix and translation vector can be obtained through calibration experiments. The calibration process combines the global coordinates of the known reference point and the local measurement coordinates of the sensor, and uses the least squares method for optimization.

[0049] The joint optimization unit 23 may be used to jointly optimize the time calibration and the space calibration by constructing a comprehensive error objective function, wherein the error objective function is: in, represents the total error, represents the number of sampling points, Indicates The global coordinates of the points, Indicates The reference coordinates corresponding to the points are and Represent the calibrated timestamp and reference timestamp respectively, Represents the weight factor between time error and space error. Through the optimization algorithm, the objective function E Minimize, thereby improving the accuracy of both temporal and spatial calibration. The optimization process combines the gradient descent method and constraints to ensure the stability and reliability of the calibration results.

[0050] Next reference Figure 2 The data fusion module 3 may include a weighted calculation unit 31, a center point calculation unit 32, an error optimization unit 33 and a data representation unit 34. Among them: The weighted calculation unit 31 can be used to perform weighted fusion on the data according to the preset weights based on the measurement results of different sensors to fuse the depth values. according to: To determine, , , are the depth values ​​collected by different sensors. , , represents the weighting coefficient, satisfying The weighted calculation unit performs weighted data fusion based on the measurement results of different sensors to ensure the comprehensive accuracy of multi-source data. The weighting coefficient is dynamically adjusted according to the signal strength and signal-to-noise ratio of each sensor to ensure that high-quality data has a greater weight in the fusion process. The calculation formula can be expressed as: in, Indicates The weighting coefficients of the sensors are Indicates The signal-to-noise ratio of the sensor, Indicates the index of the sensor, and its value range is 1 to 3. Through the above dynamic adjustment mechanism, low-quality data can be suppressed and high-quality data can be enhanced, thereby improving the stability and reliability of the deep fusion results.

[0051] The center point calculation unit 32 can be used to integrate the sampling points of the multi-source measurement data into a unified structure according to the three-dimensional space grid. The coordinates of the center point of the grid are according to: To determine, Represents the coordinates of the center point after fusion, represents the coordinates of each sampling point, Represents the total number of sampling points. The center point calculation unit integrates the sampling points of multi-source measurement data into a three-dimensional grid to build a unified spatial data structure. This calculation method is based on the geometric distribution of all sampling points to ensure the consistency and uniformity of the fused data in space. In order to further improve the calculation accuracy, outliers can also be removed. The judgment basis of outliers is: in, Represents the distance threshold. By removing abnormal points that exceed the threshold, the module effectively reduces the interference of noise data on the fusion results.

[0052] The error optimization unit 33 can be used to construct an objective function according to the spatial deviation of the sensor data and perform error optimization on the fused data, wherein the optimized objective function for: in, Indicates The global coordinates of the points, Indicates The optimization process minimizes the objective function To reduce the spatial deviation of multi-source data, the optimization algorithm uses the gradient descent method, and the iterative formula can be expressed as: in, Indicates The coordinate value after iterations, represents the learning rate, Represents the gradient of the objective function. Through optimization iterations, the 3D terrain model data with minimized error is finally output, and the optimized data is stored in a gridded form to provide accurate spatial reference for subsequent task allocation modules and collaborative operation modules.

[0053] The data representation unit 34 can be used to store the error-optimized fusion data in a gridded form, and output unified three-dimensional terrain model data to the task allocation module and the collaborative operation module.

[0054] Specifically, the data representation unit is used to uniformly grid the fused 3D terrain data and generate a compressed data structure suitable for storage and transmission. The core of this unit is to achieve efficient organization and storage of terrain data through space segmentation, point cloud encoding and data compression.

[0055] First, the data representation unit divides the fused 3D terrain data into grids according to spatial regions. The volume of the grid unit can be defined as: in, Indicates The volume of a grid cell, Respectively represent the grid cells in The length in three directions. The grid division adopts an adaptive resolution strategy, dynamically adjusting the size of the grid unit according to the complexity of the terrain data. High-resolution grids are used in complex areas, and low-resolution grids are used in flat areas. The complexity is determined based on the gradient change, and the gradient formula can be expressed as: in, Represents the gradient value of the three-dimensional terrain function, express about The partial derivative of the direction, express about The partial derivative of the direction, express about The partial derivatives in the direction of the gradient are divided into smaller grid cells to capture more details.

[0056] Secondly, the data representation unit encodes the point cloud data in each grid unit. The encoding format can be expressed as: in, represents the coordinates of the point cloud, They represent the three-dimensional spatial position of the points respectively. To reduce storage redundancy, the point cloud data in the grid can be deduplicated and streamlined.

[0057] Finally, the data representation unit uses a dynamic compression algorithm to compress the encoded point cloud data. The compressed data structure It is expressed as: in, Represents the relative position change between adjacent points. By encoding the discretized coordinate differences, the module can significantly reduce the amount of data while retaining the main features of the terrain data. By dynamically adjusting the compression parameters, the module can achieve the best balance between data accuracy and storage efficiency.

[0058] Finally, the compressed data output by the data representation unit is used for rapid access and transmission by subsequent modules, while providing an efficient data representation method for topographic measurement tasks in complex scenarios.

[0059] Next reference Figure 2 The task allocation module 4 may include a regional complexity evaluation unit 41, a task optimization unit 42 and a path generation unit 43. Among them: The regional complexity evaluation unit 41 may be used to calculate the complexity of the grid cells in the measurement area based on the three-dimensional terrain data generated by the data fusion module, wherein the complexity is based on: To determine, Indicates The terrain complexity of each grid cell, represents the volume of the grid cell, and Respectively represent the terrain height and The rate of change of direction. During the calculation process, the side length of the grid unit Dynamically adjust according to the gradient value to capture the detail changes in different areas. Complexity assessment results are used to identify high-complexity areas so as to prioritize the allocation of measurement resources and tasks.

[0060] The task optimization unit 42 can be used to combine the calculation results of the regional complexity evaluation unit and generate a task allocation plan that satisfies the minimum operation cost according to the speed, coverage and operation cost of the operation platform, wherein the optimization objective function is: in, represents the optimization goal, Indicates the total number of operating platforms, Indicates The unit cost of a work platform, Indicates the working area of ​​the working platform. Indicates the speed of the working platform, Represents the remaining operating time of the operating platform. The optimization process minimizes the total cost of the task while ensuring the operating efficiency by adjusting the allocation ratio of the operating platform. In the specific implementation, the gradient descent method can be used to iteratively solve the optimization objective function. The update formula for each iteration can be expressed as: in, represents the learning rate, represents the partial derivative of the objective function with respect to the cost parameter. This method ensures the convergence and robustness of the task allocation scheme in dynamic adjustment.

[0061] The path generation unit 43 can be used to generate an optimal path for each work platform based on terrain gradient calculation in combination with the task allocation plan generated by the task optimization unit, wherein the path planning avoids high slope areas and minimizes the total length of the work path.

[0062] Specifically, the optimization goal of path planning is to minimize the total path length. The calculation formula of the path length can be expressed as: in, represents the path length, Respectively indicate the paths in The velocity component in the direction, Indicates the total time of path planning. The path is generated by the dynamic planning algorithm, and the area with a smaller gradient is selected as the next target point at each step, thereby avoiding the operation risk caused by the high slope area. The selection of path points is based on the following conditions: in, represents the gradient of the path point, is the preset gradient threshold. Path points that meet the conditions are selected first to ensure the feasibility and safety of the path.

[0063] The path data finally output by the path generation unit is represented in serialized form and transmitted to the operation platform in real time to guide its operation behavior and dynamically adjust the measurement strategy. Through the collaborative work of the three units of regional complexity assessment, task optimization and path generation, the task allocation module can efficiently allocate measurement tasks and achieve optimal configuration and utilization of resources in complex terrain.

[0064] Next reference Figure 2 The collaborative operation module 5 may include a task adjustment unit 51, a data interaction unit 52, a path planning unit 53 and a status monitoring unit 54. Among them: The task adjustment unit 51 can be used to dynamically adjust the task allocation according to the real-time status and external environmental conditions of multiple operating platforms. Specifically, the task adjustment unit dynamically adjusts the task allocation by collecting the operating status and external environmental conditions of multiple operating platforms in real time, combining the capabilities of the platforms and the priority of the tasks. The unit generates an optimal allocation plan based on current conditions by monitoring the remaining flight time, current workload, geographic location, and task progress of the platform. During operation, if the status of a platform changes, for example, the flight time of a drone is close to its limit, the task adjustment unit can immediately reallocate its unfinished tasks to other suitable operating platforms, such as ground equipment or ship platforms. During the adjustment process, the module will comprehensively consider the time cost of platform switching and the efficiency of task completion, and give priority to the completion of high-priority tasks.

[0065] The data interaction unit 52 can be used to realize real-time data sharing between multiple operating platforms through communication links, and coordinate different platforms to supplement measurement data as needed. Specifically, the data interaction unit realizes data sharing and task coordination between multiple operating platforms through communication links. The module establishes a dynamic data interaction mechanism between platforms by transmitting measurement results, task status and path planning information in real time. When a platform encounters signal interference or data loss during measurement, the data interaction unit can coordinate other platforms to supplement the measurement. For example, when a ship-borne platform encounters measurement difficulties in shallow water areas, it can instruct the drone to use laser radar to supplement data collection in the corresponding area, thereby ensuring the integrity and consistency of the data. The communication method of the data interaction unit supports multi-mode switching, such as using a high-speed and low-latency communication link for close-range operations, and maintaining a stable connection between platforms through ad hoc networks or satellite communications for long-range operations.

[0066] The path planning unit 53 can be used to dynamically plan the working path according to the characteristics of the working platform, give priority to the flying platform to perform macro measurement, and instruct the ground equipment to complete local supplementary measurement. Specifically, the path planning unit dynamically plans the optimal working path for each platform according to the characteristics of the working platform. In path planning, the flying platform can be given priority to complete large-scale macro measurement tasks, such as covering a large area of ​​flat area; for areas with complex terrain or high resolution requirements, ground equipment or ship-borne platforms are assigned to perform local detailed supplementary measurement. The path planning unit can adjust the priority of the path according to the complexity of the terrain, and select a path with a lower slope and avoiding obstacles to ensure the safe operation of the platform. The path generation process is updated in real time. When the platform needs to readjust the working range due to environmental changes, the path planning unit will dynamically modify the original path planning to minimize the operation time and path duplication.

[0067] The status monitoring unit 54 can be used to collect the operating status information of multiple operating platforms in real time, and pass the operating status information to other modules to optimize task execution. Specifically, the status monitoring unit collects and manages the operating status information of multiple operating platforms in real time. The status monitoring unit monitors the power, position, speed, task completion progress and sensor operating status of the platform. The collected status information generates a real-time status report through the processing mechanism within the module, and passes the report to other modules to assist in optimizing task execution. For example, when the power of the ground equipment drops to a safe threshold, the status monitoring unit passes this information to the task adjustment unit to reallocate the unfinished task area of ​​the equipment. In addition, the status monitoring unit can also trigger an alarm and coordinate other modules for emergency processing when detecting an abnormal state of the platform (such as sensor failure or path deviation) to ensure the smooth progress of the entire measurement task.

[0068] Next reference Figure 2 , the heterogeneous data processing module 6 may include a data stratification unit 61, a spatial index unit 62 and a dynamic compression unit 63. Among them: The data stratification unit 61 can be used to divide the measurement data obtained by the collaborative operation module into real-time access data, high-frequency access data and historical data according to the type and importance of the data. Specifically, the data stratification unit can classify and filter the measurement data into real-time data and historical data according to real-time access requirements and storage requirements. Real-time data includes high-frequency measurement results and platform status information, which are used to support dynamic task adjustment and real-time decision-making; historical data includes long-term archived measurement data and analysis results, which are stored in the low-frequency access layer to reduce the occupancy of storage resources. The classification is based on the timestamp and importance of the data, which can be expressed as follows through the priority sorting function: in, Indicates data priority, Indicates the data update frequency, Indicates the maximum allowed delay time. Real-time data is preferentially stored in high-frequency access areas, while historical data is archived in low-frequency access areas to achieve efficient management of data resources.

[0069] The spatial index unit 62 can be used to quickly retrieve the measurement data based on a three-dimensional spatial index algorithm. The three-dimensional spatial index algorithm is constructed using a three-dimensional tree structure. The boundary of each node is determined by the minimum coordinate and the maximum coordinate. The distribution density of the points in the node is as follows: To determine, represents the distribution density, represents the node volume, represents the distribution function of the points within the node, , and Respectively represent the nodes in x axis, y Axis and z The minimum value in the axis direction, , and Respectively represent the nodes in x axis, y Axis and z The maximum value along the axis direction. High-density areas are split into smaller nodes to support fine-grained queries; low-density areas are merged to reduce the depth and complexity of the index tree.

[0070] The node generation process adopts a recursive partitioning strategy to determine the boundary coordinates of the child nodes based on the distribution characteristics of the points in the region. When the distribution density within the node is lower than the set threshold, the partitioning stops and the current node is marked as a leaf node. The positioning process is achieved by matching whether it meets the following conditions: Nodes that meet the conditions are retrieved as target nodes, and the measurement data they contain are returned.

[0071] The dynamic compression unit 63 can be used to compress and store the historical data in a decentralized and encoded manner, wherein the compressed representation of the data points is in the form of a three-dimensional vector. ,in Represents the discretization resolution. Data compression is achieved by quantizing the original coordinates. The compression ratio can be defined as: in, represents the compression ratio, Indicates the total size of the original data. Indicates the total size of the compressed data. To further improve storage efficiency, the unit aggregates sparse point cloud data and merges data points of low-density nodes by increasing the resolution.

[0072] The compressed data is stored in a hierarchical structure, with each layer containing the spatial range of the node, the discretization resolution, and the encoded point cloud data. When decompression is required, the dynamic compression unit reconstructs the original data format through inverse quantization and index tables. The reconstruction process relies on the original reference points and encoded data in the compressed storage to ensure that the decompressed data can meet the measurement accuracy requirements.

[0073] The dynamic compression unit also supports real-time decompression access, which directly applies compressed data to query and analysis tasks through a fast decoding algorithm, avoiding the time overhead caused by full decompression. This segmented and differential storage mechanism significantly improves data storage efficiency and access performance, and provides a reliable technical foundation for data management in multiple scenarios.

[0074] Next reference Figure 2 , the communication module 7 may include a multi-mode communication unit 71 and a route optimization unit 72. Wherein: The multi-mode communication unit 71 can be used to establish a data transmission link between a ship platform, a drone platform and land equipment through 5G communication, ad hoc network and satellite communication. Specifically, the multi-mode communication unit establishes a data transmission link between the operating platforms through different communication methods, including three modes: 5G communication, ad hoc network and satellite communication. 5G communication is suitable for short-distance data transmission scenarios with high bandwidth and low latency, and is used to realize real-time data exchange between ships, drones and land equipment, such as the transmission of measurement results, path planning information and platform status data. The ad hoc network mode provides flexible network connection under conditions of weak signal coverage or complex terrain, and constructs a mesh structure through dynamic node selection, so that the operating platform can exchange data with surrounding nodes to ensure the continuity and stability of communication. Satellite communication is used for operations over long distances or deep in the ocean, and provides wide-area coverage through satellites to achieve long-distance transmission of data and reliable transmission of instructions. According to the operating environment and real-time communication requirements, the system will dynamically switch the communication mode to ensure that the data transmission link is always efficient and stable.

[0075] The switching method of communication mode can be based on the dynamic evaluation of multiple parameters. Specifically, the communication unit monitors indicators such as signal strength, bandwidth utilization, and transmission delay in real time, and selects the communication method that best suits the current scenario through priority rules. When the signal strength of 5G communication is lower than the preset threshold, it will switch to ad hoc network communication and use distributed nodes to maintain data exchange. For long-distance operation scenarios, when ground communication is not feasible, the communication unit maintains long-distance data transmission by switching to satellite mode. In order to improve the anti-interference capability of the link, the communication frequency band and signal power will be dynamically adjusted according to the environmental interference conditions to ensure the stability of data transmission.

[0076] The route optimization unit 72 can be used to dynamically select a transmission path based on network bandwidth, signal strength and data priority. Specifically, the route optimization unit dynamically selects the optimal transmission path through a comprehensive analysis of network bandwidth, signal strength and data priority. The path selection is based on a multi-dimensional network performance evaluation, including delay, packet loss rate and bandwidth occupancy, and the paths are prioritized through a weighted scoring mechanism. The scoring formula is: in, represents the score of the path, Indicates the available bandwidth, Indicates the transmission delay, Indicates the packet loss rate, They represent the scoring weight coefficients respectively. The path with the highest score is selected as the main transmission path.

[0077] The data priority dynamic sorting mechanism ensures that key data is transmitted first. For example, status monitoring information and task adjustment instructions will be assigned to the path with the highest score, while non-real-time data such as measurement results will be transmitted through the suboptimal path, thus avoiding delays in key data and network congestion. The path selection process will also be dynamically updated. When the performance of the current path decreases, it will automatically switch to the backup path to ensure the continuity and efficiency of data transmission.

[0078] In the land-sea integrated measurement system based on multi-sensor fusion in the above embodiment, the data acquisition module combines multiple types of sensors to collect multi-dimensional information of the target area and generates raw data containing spatial position information and measurement attribute information. This design effectively makes up for the shortcomings of the acquisition range and accuracy of a single sensor, can adapt to a variety of complex measurement scenarios, and enhances the integrity and diversity of the measurement data.

[0079] The spatiotemporal synchronization module adjusts the data collected by multiple sensors to a unified time base and spatial reference coordinate system through time alignment processing and spatial coordinate conversion. The time calibration unit reduces the timestamp error through a high-precision calibration algorithm, while the spatial calibration unit converts the local coordinate system of the sensor based on the external parameter calibration method to achieve high-precision spatiotemporal consistency of data from different sources. The joint optimization unit minimizes the time and space calibration errors by constructing an error objective function, significantly improving the fusion quality of multi-source data and providing an accurate and reliable foundation for subsequent processing.

[0080] The data fusion module fuses multi-source data through a weighted algorithm, integrating the measurement results of different sensors into unified three-dimensional terrain data. The weighted calculation unit assigns weights based on the characteristics of the sensor, solving the problem of inconsistent accuracy and credibility among multi-source data; the center point calculation unit integrates the sampling points of multi-source data in a unified manner, improving the standardization and consistency of spatial data representation. The error optimization unit reduces local deviations in the data fusion process through objective function optimization, ensuring the overall accuracy of the fused data. At the same time, the data representation unit outputs a unified three-dimensional terrain model, laying a data foundation for subsequent task allocation and collaborative work.

[0081] The task allocation module divides the target area into multiple task areas and generates corresponding work instructions based on the distribution characteristics of three-dimensional terrain data and the operating status information of multiple work platforms. The regional complexity assessment unit realizes the precise deployment of measurement resources by performing complexity analysis on terrain data; the task optimization unit generates the optimal allocation plan based on the platform characteristics, reducing the operation cost; the path generation unit effectively reduces the task execution time while ensuring the safety of the path, and improves the overall operation efficiency and flexibility of the system.

[0082] The collaborative operation module coordinates the measurement behaviors of multiple operation platforms in real time during the task execution process, and dynamically monitors their operating status. The task adjustment unit dynamically adjusts the task allocation through the analysis of real-time status and external environmental conditions, improving the efficiency of collaboration between platforms; the data interaction unit uses efficient communication links to achieve real-time data sharing, enabling different platforms to complement each other in measurement, ensuring the integrity and continuity of measurement data; the path planning unit dynamically adjusts the operation path according to the characteristics of the operation platform, giving full play to the advantages of different platforms in specific scenarios; the status monitoring unit collects platform status information in real time and transmits it to other modules to support the optimization decision of task execution, further enhancing the system's adaptability in a dynamic environment.

[0083] The heterogeneous data processing module significantly improves data management efficiency by classifying, retrieving and compressing measurement data. The data stratification unit classifies measurement data by type and importance, achieving rapid response to high-frequency data and efficient storage of historical data; the spatial index unit uses a three-dimensional tree index structure to quickly retrieve measurement data, shortening the response time of data access; the dynamic compression unit compresses and stores historical data through differentiation and encoding, reducing storage space occupancy and optimizing long-term data management performance.

[0084] The communication module establishes data transmission links between different operating platforms through the multi-mode communication unit, and dynamically selects the optimal transmission path through the routing optimization unit. The multi-mode communication unit can dynamically switch the communication mode according to the operating environment and the distance between platforms, and maintain efficient data transmission in different scenarios; the routing optimization unit dynamically adjusts the transmission path by real-time monitoring of network bandwidth, signal strength and data priority, avoiding network congestion and data delay, and ensuring the communication stability and data interaction efficiency of the system in complex environments.

[0085] Through the collaborative work of various modules, the above technical solutions have demonstrated significant advantages in multi-sensor data fusion, efficient collaboration of operating platforms, and high-precision measurement in complex environments, providing comprehensive and reliable technical support for multi-scenario applications.

[0086] It should be noted that although several modules or units of the land-sea integrated measurement system based on multi-sensor fusion are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0087] Secondly, in an exemplary embodiment of the present disclosure, a land-sea integrated measurement method based on multi-sensor fusion is also provided. Figure 3 As shown, the land-sea integrated measurement method based on multi-sensor fusion may include the following steps: Step S310, obtaining measurement data of the target area through various types of sensors, and generating raw data including spatial position information and measurement attribute information; Step S320, performing time alignment processing and space coordinate conversion processing on the raw data, and adjusting the data generated by each sensor to a unified time reference and space reference coordinate system; Step S330, based on the data adjusted by the spatiotemporal synchronization module, multi-source data are fused according to a weighted algorithm to generate three-dimensional terrain data of the target area; Step S340, dividing the target area into multiple task areas and generating corresponding operation instructions according to the distribution characteristics of the three-dimensional terrain data and the operation status information of multiple operation platforms; Step S350: Coordinate the measurement behaviors of the operation platforms in different task areas according to the operation instructions, and collect and update the operation status information of each operation platform in real time.

[0088] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0089] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned land-sea integrated measurement method based on multi-sensor fusion is also provided.

[0090] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware embodiments, complete software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0091] Refer to the following Figure 4 hereinafter describes an electronic device 400 according to such an embodiment of the present disclosure. Figure 4 The electronic device 400 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0092] like Figure 4 As shown, the electronic device 400 is in the form of a general computing device. The components of the electronic device 400 may include but are not limited to: the at least one processing unit 410, the at least one storage unit 420, a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410), and a display unit 440.

[0093] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 executes the steps according to various exemplary embodiments of the present disclosure described in the above “Exemplary Method” section of this specification. The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 421 and / or a cache storage unit 422, and may further include a read-only storage unit (ROM) 423.

[0094] The storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0095] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0096] The electronic device 400 may also communicate with one or more external devices 470 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication may be performed through an input / output (I / O) interface 450. In addition, the electronic device 400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 through a bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0097] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0098] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.

[0099] refer to Figure 5 As shown, a program product 500 for implementing the above-mentioned land-sea integrated measurement method based on multi-sensor fusion according to an embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0100] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0101] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0102] The program code contained on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, electromagnetic waves, etc., or any suitable combination of the foregoing.

[0103] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0104] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present disclosure, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0105] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the embodiment of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiment of the present disclosure.

[0106] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and examples are to be considered as exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0107] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A land-sea integrated measurement system based on multi-sensor fusion, characterized in that: include: A data acquisition module is used to obtain measurement data of the target area through various types of sensors and generate raw data including spatial position information and measurement attribute information; A time-space synchronization module is used to perform time alignment processing and space coordinate conversion processing on the raw data, and adjust the data generated by each sensor to a unified time reference and space reference coordinate system; A data fusion module, used to fuse the multi-source data according to a weighted algorithm based on the data adjusted by the spatiotemporal synchronization module, and generate three-dimensional terrain data of the target area; A task allocation module, for dividing the target area into multiple task areas and generating corresponding work instructions according to the distribution characteristics of the three-dimensional terrain data and the operation status information of multiple work platforms; The collaborative operation module is used to execute the operation instructions, coordinate the measurement behaviors of the operation platforms in different task areas, and collect and update the operation status information of each operation platform in real time.

2. The integrated land and sea measurement system based on multi-sensor fusion according to claim 1 is characterized in that: Also includes: A heterogeneous data processing module is used to receive the measurement data acquired by the collaborative operation module, and perform hierarchical storage, rapid retrieval and dynamic compression processing on different types of measurement data; The communication module is used to establish a data interaction channel between multiple modules, and to share the measurement data processed by the heterogeneous data processing module in real time among multiple operating platforms, while synchronously transmitting the status information of the operating platforms.

3. The integrated land and sea measurement system based on multi-sensor fusion according to claim 1 is characterized in that: The data acquisition module comprises: Beam depth measurement unit, used to control the multi-beam sonar to transmit sound waves and receive echoes to obtain water depth data; The signal strength processing unit is used to: The intensity distribution of the echo signal of the multi-beam sonar is calculated, where: Indicates time The signal strength, Indicates time The signal power, represents the attenuation coefficient, represents the starting time of intensity distribution calculation, represents the base of natural logarithms; A laser ranging unit is used to obtain the distance value of the target area through the pulse flight time of the laser radar; A beam direction control unit is used to adjust the beam direction of the multi-beam sonar in real time according to the geometric characteristics of the target area, wherein the beam direction is based on: Angle adjustment is performed, wherein represents the angle between the beam and the target direction, represents the beam direction vector, is the target position vector; The boundary judgment unit is used to judge the boundary of the measurement area in the target terrain environment, and the boundary is determined by fitting the plane equation: To determine, , , represents the components of the plane normal vector, represents the plane intercept, Represents the spatial coordinates of the sampling points.

4. The integrated land and sea measurement system based on multi-sensor fusion according to claim 1 is characterized in that: The space-time synchronization module comprises: Time calibration unit for: Determine the time offset, where represents the sampling frequency of the inertial measurement unit, Indicates the global navigation satellite system signal delay time, indicating a time offset, and calculating a calibration time according to the time offset; The spatial calibration unit is used to transform the spatial coordinates of the sensor through the rotation matrix and translation vector calibrated by external parameters. The transformation process of the spatial coordinates is expressed as: in, represents the global coordinates, represents the local coordinates of the sensor, represents the translation vector, represents a rotation matrix, which satisfies the orthogonality condition: ,in, represents the identity matrix; The joint optimization unit is used to jointly optimize the time calibration and the space calibration by constructing a comprehensive error objective function, wherein the error objective function is: in, represents the total error, represents the number of sampling points, Indicates The global coordinates of the points, Indicates The reference coordinates corresponding to the points are and Represent the calibrated timestamp and reference timestamp respectively, Represents the weight factor between temporal error and spatial error.

5. The integrated land and sea measurement system based on multi-sensor fusion according to claim 1 is characterized in that: The data fusion module comprises: The weighted calculation unit is used to perform weighted fusion of data based on the measurement results of different sensors according to preset weights to fuse the depth value according to: To determine, , , are the depth values ​​collected by different sensors. , , represents the weighting coefficient, satisfying ; The center point calculation unit is used to integrate the sampling points of multi-source measurement data into a unified structure according to the three-dimensional space grid. The coordinates of the center point of the grid according to: To determine, represents the coordinates of each sampling point, Indicates the total number of sampling points; The error optimization unit is used to construct an objective function based on the spatial deviation of the sensor data and optimize the error of the fused data, where the optimization objective function for: in, Indicates The global coordinates of the points, Indicates The fusion coordinates of the points; The data representation unit is used to store the error-optimized fusion data in a gridded form and output unified three-dimensional terrain model data to the task allocation module and the collaborative operation module.

6. The integrated land and sea measurement system based on multi-sensor fusion according to claim 1 is characterized in that: The task allocation module includes: A regional complexity evaluation unit is used to calculate the complexity of grid cells in the measurement area based on the three-dimensional terrain data generated by the data fusion module, wherein the complexity is based on: To determine, Indicates The terrain complexity of each grid cell, represents the volume of the grid cell, and Respectively represent the terrain height and rate of change of direction; The task optimization unit is used to combine the calculation results of the regional complexity evaluation unit and generate a task allocation plan that satisfies the minimum operation cost according to the speed, coverage and operation cost of the operation platform, wherein the optimization objective function is: in, represents the optimization goal, Indicates the total number of operating platforms, Indicates The unit cost of a work platform, Indicates the working area of ​​the working platform. Indicates the speed of the working platform, Indicates the remaining operating time of the operating platform; The path generation unit is used to generate the optimal path for each work platform based on terrain gradient calculation in combination with the task allocation plan generated by the task optimization unit.

7. The integrated land and sea measurement system based on multi-sensor fusion according to claim 1 is characterized in that: The collaborative operation module includes: A task adjustment unit, used to dynamically adjust task allocation according to the real-time status of multiple operating platforms and external environmental conditions; Data exchange unit, used to achieve real-time data sharing among multiple working platforms through communication links, and coordinate different platforms to supplement measurement data as needed; The path planning unit is used to dynamically plan the operation path according to the characteristics of the operation platform, give priority to the flight platform to perform macro measurement, and instruct the ground equipment to complete local supplementary measurement; The status monitoring unit is used to collect the operating status information of multiple working platforms in real time and transmit the operating status information to other modules to optimize task execution.

8. The integrated land and sea measurement system based on multi-sensor fusion according to claim 2 is characterized in that: The heterogeneous data processing module includes: A data stratification unit, for dividing the measurement data acquired by the collaborative operation module into real-time access data, high-frequency access data and historical data according to the type and importance of the data; The spatial index unit is used to quickly retrieve the measurement data based on a three-dimensional spatial index algorithm. The three-dimensional spatial index algorithm is constructed using a three-dimensional tree structure. The boundary of each node is determined by the minimum coordinate and the maximum coordinate. The distribution density of the points in the node is as follows: To determine, represents the distribution density, represents the node volume, represents the distribution function of the points within the node, , and Respectively represent the nodes in x axis, y Axis and z The minimum value in the axis direction, , and Respectively represent the nodes in x axis, y Axis and z Maximum value in the axis direction; The dynamic compression unit is used to compress and store the historical data in a decentralized and encoded manner.

9. The integrated land and sea measurement system based on multi-sensor fusion according to claim 2 is characterized in that: The communication module comprises: A multi-mode communication unit for establishing data transmission links between ship platforms, UAV platforms and land equipment through 5G communication, ad hoc networks and satellite communications; Route optimization unit, used to dynamically select transmission paths based on network bandwidth, signal strength and data priority.

10. A land-sea integrated measurement method based on multi-sensor fusion, applied to the land-sea integrated measurement system based on multi-sensor fusion as described in any one of claims 1 to 9, characterized in that: The method comprises: Acquire measurement data of the target area through various types of sensors and generate raw data including spatial location information and measurement attribute information; Performing time alignment processing and space coordinate conversion processing on the raw data, adjusting the data generated by each sensor to a unified time reference and space reference coordinate system; Based on the data adjusted by the spatiotemporal synchronization module, the multi-source data are fused according to a weighted algorithm to generate three-dimensional terrain data of the target area; According to the distribution characteristics of the three-dimensional terrain data and in combination with the operation status information of multiple operation platforms, the target area is divided into multiple task areas and corresponding operation instructions are generated; According to the operation instructions, the measurement behaviors of the operation platforms in different task areas are coordinated, and the operation status information of each operation platform is collected and updated in real time.

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