A measurement method and system based on collaborative networking of unmanned aerial vehicles and unmanned boats

Abstract

The present invention relates to a measurement method and system based on the collaborative networking of an unmanned aerial vehicle (UAV) and an unmanned boat, and relates to the field of data processing technology. The method includes: initializing the sensors and communication modules of the UAV and the unmanned boat, determining the signal transmission rate and the link stability factor; establishing a communication link between the UAV and the unmanned boat based on the signal transmission rate and the link stability factor, and evaluating the stability of the communication link through the link stability factor; assigning measurement tasks to the UAV and the unmanned boat and planning a collaborative path based on the measurement mission objectives; controlling the UAV and the unmanned boat to collect, transmit, and fuse measurement data based on the collaborative path to obtain measurement results for the measurement mission objectives; evaluating the measurement results, and performing feedback adjustment on the measurement results to generate target measurement results. The present invention can improve measurement accuracy and efficiency.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a measurement method, system, electronic equipment and non-transient computer-readable storage medium based on collaborative networking of unmanned aerial vehicles and unmanned boats. Background Art

[0002] Currently, collaborative surveying methods using unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) primarily rely on traditional single-platform operations, with the UAV typically providing aerial perspectives or positioning data, while the USV handles surface or underwater detection and data collection. This operational model has been widely used in fields such as oceanographic surveying, environmental monitoring, and disaster warning.

[0003] However, collaborative operations lack efficient, real-time data transmission and coordination mechanisms across multiple platforms. This makes it difficult to ensure the stability and accuracy of communication links, especially in complex environments. Furthermore, due to the significant differences in measurement tasks and real-time data requirements across different platforms, existing methods have limitations in data fusion and processing, making it impossible to achieve efficient cross-platform data integration and analysis. Summary of the Invention

[0004] In response to the technical problems existing in the prior art, the present invention provides a measurement method, system, electronic equipment and non-transitory computer-readable storage medium based on the collaborative networking of unmanned aerial vehicles and unmanned boats, which can improve measurement accuracy and efficiency.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] The present invention provides a measurement method based on the collaborative networking of an unmanned aerial vehicle (UAV) and an unmanned boat, the method comprising:

[0007] Initialize the sensors and communication modules of the drone and unmanned boat to determine the signal transmission rate and link stability factor;

[0008] Establishing a communication link between the UAV and the unmanned boat based on the signal transmission rate and the link stability factor, determining the link stability factor, and evaluating the stability of the communication link using the link stability factor;

[0009] According to the measurement mission objectives, the measurement tasks are assigned to the UAV and the unmanned boat and a collaborative path is planned;

[0010] Controlling the UAV and the UAV to collect, transmit, and fuse measurement data according to the collaborative path to obtain measurement results for the measurement mission target;

[0011] The measurement results are evaluated and feedback-adjusted to generate target measurement results.

[0012] Optionally, determining the signal transmission rate and the link stability factor includes:

[0013] Obtaining a channel bandwidth of a frequency range transmittable through a channel between the UAV and the UAV;

[0014] Obtaining the transmission power used by the UAV and the UAV when sending signals on the channel;

[0015] Obtaining a noise power spectral density of noise power within a unit bandwidth of the channel;

[0016] determining a signal transmission rate of the channel according to the channel bandwidth, the transmit power, and the noise power spectral density;

[0017] Obtaining the gain of each channel between the UAV and the UAV, and the number of channels;

[0018] The link stability factor is determined according to the gain of each channel and the number of channels.

[0019] Optionally, the signal transmission rate is expressed as:

[0020]

[0021] Where R is the signal transmission rate, B is the channel bandwidth, P is the transmit power, and N0 is the noise power spectral density;

[0022] The link stability factor is expressed as:

[0023]

[0024] Where S is the link stability factor, H i is the gain of the ith channel, n is the number of channels, when S>S th When S th is the communication link stability threshold.

[0025] Optionally, allocating measurement tasks to the UAV and the unmanned boat and planning collaborative paths according to measurement task objectives includes:

[0026] Determining measurement requirements for the UAV and the unmanned boat based on the measurement mission objectives;

[0027] Determining the mission area boundary of the measurement mission target according to the measurement requirements of the UAV and the unmanned boat;

[0028] Determining the path selection probability of the UAV and the path selection probability of the unmanned boat within the mission area boundary, and planning the initial path of the UAV and the initial path of the unmanned boat;

[0029] The initial path of the UAV and the initial path of the unmanned boat are optimized according to the cooperation factor, and the cooperation path of the UAV and the unmanned boat is determined.

[0030] Optionally, the drone path selection probability is expressed as:

[0031]

[0032] Among them, p uav (i, j) is the probability of drone path selection, τ(i, j) is the pheromone concentration on path (i, j), η(i, j) is the heuristic information on path (i, j), α and β are the first and second influencing factors respectively, and alloweduav uav It is the next set of nodes that the drone can choose.

[0033] Optionally, the synergistic factor is determined by the following steps:

[0034] Obtaining a first distance between the UAV and a target point, and a second distance between the UAV and the target point at different times;

[0035] Calculating a first cumulative sum of the product of the first distance and the second distance at each moment;

[0036] Calculating a second cumulative sum of the sum of the square of the first distance and the square of the second distance at each moment;

[0037] The cooperation factor is determined according to the first cumulative sum value and the second cumulative sum value.

[0038] Optionally, controlling the UAV and the unmanned boat to collect, transmit, and fuse measurement data according to the collaborative path to obtain a measurement result for the measurement mission target includes:

[0039] Acquiring aerial image data and terrain height data collected by the drone;

[0040] Acquiring underwater depth data and water quality parameter data collected by the unmanned boat;

[0041] Determining a first transmission priority of the data collected by the UAV and a second transmission priority of the data collected by the unmanned boat;

[0042] determining a data transmission order according to the first transmission priority and the second priority and performing data transmission;

[0043] The transmitted UAV data and the unmanned boat data are subjected to data fusion processing based on Bayesian estimation to obtain the measurement results.

[0044] Optionally, the second transmission priority of the data collected by the unmanned boat is determined by the following steps:

[0045] Get the preset time interval;

[0046] Calculating the change in the depth data and the change in the water quality parameter based on the time interval;

[0047] The second transmission priority is determined based on the change in the depth data and its corresponding first weight, and the change in the water quality parameter and its corresponding second weight.

[0048] Optionally, the measurement result is determined by the following steps:

[0049] Processing the terrain height data collected by the drone according to Bayesian estimation to convert it into an estimated depth value;

[0050] Constructing an estimated depth probability density function representing the reliability of the estimated depth value;

[0051] Obtaining a directly measured depth value collected by the unmanned boat, and constructing a directly measured depth probability density function representing the credibility of the directly measured depth value;

[0052] The fused measurement result is determined according to the estimated depth value, the directly measured depth value, the estimated depth probability density function, and the directly measured depth probability density function.

[0053] In addition, to achieve the above-mentioned purpose, the present invention also proposes a measurement system based on the collaborative networking of UAVs and unmanned boats, comprising:

[0054] The initial setting module is used to initialize the sensors and communication modules of the UAV and the unmanned boat, and determine the signal transmission rate and link stability factor;

[0055] a link establishing module, configured to establish a communication link between the UAV and the unmanned boat according to the signal transmission rate and the link stability factor, determine the link stability factor, and evaluate the stability of the communication link using the link stability factor;

[0056] A path planning module, configured to allocate measurement tasks to the UAV and the unmanned boat and plan a collaborative path according to the measurement task objectives;

[0057] A result determination module is used to control the UAV and the unmanned boat to collect, transmit and fuse measurement data according to the collaborative path to obtain a measurement result for the measurement task target;

[0058] The result evaluation module is used to evaluate the measurement result, perform feedback adjustment on the measurement result, and generate a target measurement result.

[0059] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing a measurement method based on the collaborative networking of drones and unmanned boats as described above.

[0060] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, which stores a computer software program. When the computer software program is executed by the processor, it implements a measurement method based on the collaborative networking of drones and unmanned boats as described above.

[0061] The beneficial effects of the present invention are:

[0062] (1) The present invention ensures that the UAV and the unmanned boat maintain an efficient collaborative relationship during the execution of the task by introducing a collaborative path planning and task allocation mechanism. The improved ant colony algorithm is used to plan the movement paths of the two, and the path selection is optimized through the collaborative factor, so that the two can reasonably divide the work within the measurement area and avoid path overlap, thereby improving the coverage efficiency of the measurement area and reducing the time cost of repeated measurements. In addition, the data fusion method (such as Bayesian estimation) further improves the data accuracy. By fusing the measurement data of the UAV and the unmanned boat, the error of the single platform measurement can be effectively compensated, and the credibility and accuracy of the measurement results are enhanced.

[0063] (2) The present invention uses a communication link based on multi-band communication technology and OFDM protocol to ensure the stability and efficiency of data transmission between the UAV and the unmanned boat. A link stability factor is introduced to monitor the link quality to ensure the stability of the communication link. This makes data transmission less susceptible to interference in complex environments, greatly improving the reliability of task execution. A data priority mechanism is introduced to dynamically adjust the priority of data transmission by calculating the change in water quality and depth data, ensuring that emergency data (such as sudden water quality anomalies) can be transmitted first, thereby improving the system's response capability to emergencies and ensuring the timeliness and accuracy of real-time data processing.

[0064] (3) The present invention introduces multiple intelligent decision-making mechanisms such as task requirement analysis, path planning, and data transmission priority, so that the system can be flexibly adjusted according to real-time conditions. The UAV and the unmanned boat automatically adjust the working mode according to the environment and task requirements, and can adjust the task execution strategy according to real-time error feedback, such as adjusting the measurement path, flight altitude or navigation speed through feedback, thereby continuously optimizing the system's measurement accuracy and task completion during the execution process. For example, in the measurement result evaluation stage, by calculating the error, if the measurement error exceeds the set threshold, the system can automatically adjust the path planning and task allocation to reduce the error and improve the measurement accuracy. This mechanism enables the system to adapt to environmental changes and task requirements, increasing the system's adaptability in complex environments.

[0065] In summary, the present invention not only improves the measurement accuracy and efficiency, but also enhances the stability, adaptability and anti-interference ability of the system through a number of innovative means such as efficient collaborative path planning, data priority transmission, Bayesian data fusion, and feedback adjustment mechanism. It has broad application prospects and can provide reliable technical support for multi-platform collaborative measurement in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A scene diagram of a measurement method based on collaborative networking of drones and unmanned boats provided by the present invention;

[0067] Figure 2 A flow chart of a measurement method based on collaborative networking of unmanned aerial vehicles and unmanned boats provided by the present invention;

[0068] Figure 3 A schematic diagram of the structure of a measurement system based on collaborative networking of unmanned aerial vehicles and unmanned boats provided by the present invention;

[0069] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0070] Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0072] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0073] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0074] See also Figure 1 , Figure 1 This is a scene diagram of a measurement method based on the collaborative networking of drones and unmanned boats provided by the present invention. Figure 1 As shown, the terminal and server are connected via a network, such as a wired or wireless network. Terminals include, but are not limited to, portable devices such as mobile phones and tablets installed with various network platform applications, as well as fixed devices such as computers, kiosks, and advertising machines. The server provides various business services to users, including service push servers and user recommendation servers.

[0075] It should be noted that Figure 1 The scenario diagram of a measurement method based on collaborative networking of drones and unmanned boats is only an example. The terminal, server, and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate any limitation on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0076] Among them, the terminal can be used to:

[0077] Initialize the sensors and communication modules of the drone and unmanned boat to determine the signal transmission rate and link stability factor;

[0078] Establishing a communication link between the UAV and the unmanned boat based on the signal transmission rate and the link stability factor, determining the link stability factor, and evaluating the stability of the communication link using the link stability factor;

[0079] According to the measurement mission objectives, the measurement tasks are assigned to the UAV and the unmanned boat and a collaborative path is planned;

[0080] Controlling the UAV and the UAV to collect, transmit, and fuse measurement data according to the collaborative path to obtain measurement results for the measurement mission target;

[0081] The measurement results are evaluated and feedback-adjusted to generate target measurement results.

[0082] See also Figure 2 , provides a flow chart of a measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats of the present invention, comprising the following steps:

[0083] Step 201: Initialize the sensors and communication modules of the UAV and the UAV to determine the signal transmission rate and link stability factor.

[0084] In some embodiments, step 201 may include:

[0085] Obtaining a channel bandwidth of a frequency range transmittable through a channel between the UAV and the UAV;

[0086] Obtaining the transmission power used by the UAV and the UAV when sending signals on the channel;

[0087] Obtaining a noise power spectral density of noise power within a unit bandwidth of the channel;

[0088] determining a signal transmission rate of the channel according to the channel bandwidth, the transmit power, and the noise power spectral density;

[0089] Obtaining the gain of each channel between the UAV and the UAV, and the number of channels;

[0090] The link stability factor is determined according to the gain of each channel and the number of channels.

[0091] In some embodiments, the signal transmission rate is expressed as:

[0092]

[0093] Where R is the signal transmission rate, B is the channel bandwidth, P is the transmit power, and N0 is the noise power spectral density.

[0094] In practice, R is typically expressed in bits per second (bps), representing the amount of data that can be transmitted per unit time. The formula for R is the maximum theoretical transmission rate of the channel, which refers to the maximum data transmission speed achievable through the channel under specific communication link conditions.

[0095] The unit of B is Hertz (Hz). Bandwidth represents the frequency range a channel can transmit. Generally, the larger the channel bandwidth, the higher the transmission rate. In wireless communication systems, bandwidth is one of the key factors that determine the maximum data transmission rate.

[0096] The unit of "P" is usually watts (W). Transmit power refers to the power used by the transmitter (such as the communication equipment of a drone or unmanned watercraft) to send a signal. Higher transmit power increases signal strength, thereby improving signal propagation distance and signal quality.

[0097] N0 is measured in watts per hertz (W / Hz). Noise power spectral density (PSD) represents the noise power per unit bandwidth. Noise is always present in any communication system and can affect signal quality. The larger the N0, the greater the system noise, which can affect effective signal transmission.

[0098] B represents the bandwidth of the channel, which directly affects the transmission rate of the communication system. The larger the bandwidth, the higher the signal transmission rate R will be, because the bandwidth increases the frequency range that the signal can carry, thus allowing more data to be transmitted.

[0099] Signal-to-Noise Ratio (SNR) is a key factor affecting communication quality. In the formula, It is the ratio of signal to noise, which represents the signal quality in the channel.

[0100] Where P is the signal's transmitted power, N0 is the noise power spectral density, and B is the bandwidth. A larger ratio indicates a stronger signal relative to the noise, better communication quality, and a higher maximum data rate, R, for the system.

[0101] The logarithmic function log2 demonstrates the nonlinear relationship between the signal-to-noise ratio (SNR) and the transmission rate. As the SNR increases, the increase in transmission rate slows down. This indicates that while increasing the signal transmission power or reducing the noise level can increase the transmission rate, the increase in rate is not linear and exhibits a certain marginal effect.

[0102] Increasing bandwidth B can increase signal transmission rates. Bandwidth determines the frequency range a signal can occupy, so increasing bandwidth increases the amount of information that can be transmitted. Increasing transmit power P improves the signal-to-noise ratio (SNR), thereby increasing data transmission rates. Higher transmit power reduces the likelihood of signal interference during transmission. Reducing noise power spectral density improves the signal-to-noise ratio, thereby increasing transmission rates. Lower noise levels increase signal effectiveness and communication quality.

[0103] In practical applications, bandwidth and power are often limited resources, so a trade-off must be struck during design. A wide bandwidth with low power, or a narrow bandwidth with high power, will both affect the system's transmission rate. In complex communication environments, such as densely populated urban areas or on the sea, noise interference can be significant. Therefore, system design must minimize the impact of noise and utilize advanced anti-interference technologies (such as channel coding and modulation techniques) to improve communication quality.

[0104] In summary, this invention provides a mathematical model for the system's maximum theoretical transmission rate, helping to understand how to optimize signal transmission rates by adjusting bandwidth, transmit power, and noise levels. Through rational system design and optimization, efficient and stable communication between drones and unmanned boats can be ensured, thereby improving the efficiency and accuracy of multi-platform collaborative operations.

[0105] Step 202 establishes a communication link between the UAV and the UAV based on the signal transmission rate and the link stability factor, determines the link stability factor, and evaluates the stability of the communication link using the link stability factor.

[0106] In some embodiments, the link stability factor is expressed as:

[0107]

[0108] Where S is the link stability factor, H i is the gain of the ith channel, n is the number of channels, when S>S th When S th is the communication link stability threshold.

[0109] In the specific implementation, the link stability factor S is used to reflect the stability of the entire communication link. Specifically, it is the average of the square values ​​of all channel gains. If S reaches a certain threshold S th , it means that the communication link is stable and can ensure smooth data transmission.

[0110] The gain H of the i-th channel i Reflects the quality of the channel or signal strength. Gain H iThe gain is the ratio of the signal strength after the signal is transmitted through the i-th channel to the original transmitted signal strength. The greater the gain, the better the signal propagation in the channel and the less signal loss.

[0111] The number of channels, n, refers to the total number of channels used for data transmission in a communication system. Different channels may have different gains, and the system averages the gains of all channels to determine the overall stability of the link.

[0112] Communication link stability threshold S th It is a preset value used to determine whether the link is stable. If the calculated S is greater than S th , the link is considered stable; if S is less than or equal to S th , it means that the link is unstable, there may be signal interference or transmission problems, and the system needs to be adjusted.

[0113] Specifically, the channel gain H i Represents the transmission capacity of each channel, which is closely related to factors such as transmission quality, signal loss, and interference. In wireless communications, the transmission quality of each channel may vary due to factors such as the environment, distance, and obstacles. Therefore, averaging the squared gain of all channels provides a more comprehensive reflection of the overall stability of the system link.

[0114] Channel gain H i The gain of a signal is the amount of amplification or attenuation it experiences after passing through a channel. Channel gain depends not only on the physical characteristics of the channel itself but also on environmental factors such as distance, frequency, weather, obstacles, and noise.

[0115] |H i | 2 The purpose is to obtain the degree of influence of gain on the signal, and on this basis comprehensively consider the signal strength of each channel.

[0116] The number of channels, n, is a crucial parameter for averaging calculations. In multi-band communications, a communication link may consist of multiple sub-channels, each with varying transmission quality. Averaging the squared gains of all channels allows for a more accurate assessment of overall link stability, preventing fluctuations in a single channel from impacting overall link stability.

[0117] The link stability factor (S) is calculated by averaging the squared gains of all channels. A larger S value indicates generally good channel gain, high signal transmission quality, and a stable communication link. A smaller S value indicates that signal transmission may be subject to significant interference or attenuation, resulting in poor link stability and potentially leading to data loss or transmission delays.

[0118] When S>S th When , the communication link is considered stable, Sth is the communication link stability threshold, and the signal transmission quality is high enough to ensure smooth data transmission. In this case, the communication between the UAV and the unmanned boat will be able to maintain a good connection, and the data transmission rate and quality can meet the expectations.

[0119] When S≤S th If the signal is not stable, the communication link is unstable. This indicates that there may be severe signal attenuation, interference, or other communication issues that have degraded data transmission quality. In this case, the system may trigger some adjustment mechanisms (such as increasing transmit power, adjusting channel selection, switching to another channel, etc.) to restore link stability.

[0120] The present invention calculates the link stability factor S by calculating the square average of all channel gains, which can fully reflect the overall communication performance of the system. th When the link is stable, the system can continue to transmit data. Otherwise, adjustment measures need to be taken to ensure the stability of the communication link.

[0121] Step 203: Allocate measurement tasks to the UAV and the unmanned boat according to the measurement task objectives and plan a collaborative path.

[0122] In some embodiments, step 203 may include:

[0123] Determining measurement requirements for the UAV and the unmanned boat based on the measurement mission objectives;

[0124] Determining the mission area boundary of the measurement mission target according to the measurement requirements of the UAV and the unmanned boat;

[0125] Determining the path selection probability of the UAV and the path selection probability of the unmanned boat within the mission area boundary, and planning the initial path of the UAV and the initial path of the unmanned boat;

[0126] The initial path of the UAV and the initial path of the unmanned boat are optimized according to the cooperation factor, and the cooperation path of the UAV and the unmanned boat is determined.

[0127] In some embodiments, the drone path selection probability is expressed as:

[0128]

[0129] Among them, p uav(i, j) is the probability of drone path selection, τ(i, j) is the pheromone concentration on path (i, j), η(i, j) is the heuristic information on path (i, j), α and β are the first and second influencing factors respectively, and alloweduav uav It is the next set of nodes that the drone can choose.

[0130] In the specific implementation, p uav (i, j) is the drone's path selection probability, which is used to determine the next movement in the drone's path planning. The larger the path selection probability, the more likely the drone is to choose this path.

[0131] τ(i,j) is the pheromone concentration on path (i,j). Pheromones are a factor that simulates the natural foraging process of ants and indicate the "attractiveness" of a path. In the ant colony algorithm, the pheromone concentration affects the probability of path selection. A higher pheromone concentration indicates that the path is considered better, and the drone is more likely to choose this path.

[0132] η(i, j) is the heuristic information on path (i, j). Heuristic information is typically defined based on specific environmental information or the requirements of the target mission. For example, it can be factors such as distance, time, and resource consumption. Heuristic information η(i, j) is used to guide the drone to choose a more appropriate path. In this solution, the heuristic information may be related to the actual efficiency of the path (such as distance and speed).

[0133] α and β are the primary and secondary influencing factors, respectively. α controls the degree of influence of pheromones on path selection. A larger α value results in a greater impact of pheromone concentration on path selection. β is the weighting factor for heuristic information, controlling the degree of influence of heuristic information on path selection. A larger β value results in a greater role for heuristic information in path selection.

[0134] alloweduav uav The set of nodes that the drone can choose as the next node. During the path selection process, the drone can only choose certain nodes in the allowed set of nodes. This set is dynamic and depends on the drone's current position and mission objectives.

[0135] is a normalization factor that ensures that the sum of the path selection probabilities is 1. By [τ(i,k)] α ×[η(i,k)] β The sum is calculated so that the probability ratio of each path is reasonable.

[0136] In summary, this invention, based on the ant colony algorithm, uses pheromone concentration and heuristic information to guide drone path selection. By setting different influencing factors, the proportion of pheromone and heuristic information in path selection can be adjusted, enabling drones to more intelligently and flexibly select appropriate paths, thereby improving mission execution efficiency. In practical applications, this algorithm can adjust path selection based on real-time data and gradually optimize overall path planning.

[0137] In some embodiments, the synergistic factor can be determined by the following steps:

[0138] Obtaining a first distance between the UAV and a target point, and a second distance between the UAV and the target point at different times;

[0139] Calculating a first cumulative sum of the product of the first distance and the second distance at each moment;

[0140] Calculating a second cumulative sum of the sum of the square of the first distance and the square of the second distance at each moment;

[0141] The cooperation factor is determined according to the first cumulative sum value and the second cumulative sum value.

[0142] In practice, the synergy factor (C) quantifies the degree of coordination between the UAV and the unmanned boat during a collaborative mission. The value of the synergy factor reflects the synergy between the two during the mission. A higher C indicates more coordinated path selection and mission execution; a lower C indicates poorer coordination, potentially leading to path conflicts or inefficiencies.

[0143] The distance d from the drone to the target point at time t uav (t) represents the straight-line distance from the current position of the UAV to the mission target point, which usually changes over time. This distance is related to factors such as the UAV's speed and flight path.

[0144] The distance d from the unmanned boat to the target point at time t usv (t) represents the straight-line distance from the current position of the unmanned boat to the mission target point, which will also change with time and be affected by factors such as navigation speed and path selection.

[0145] The total time step T refers to the total time length of the task, which indicates the duration of the task, or the number of time steps sampled during the execution of the task.

[0146] It is to calculate the product of the distance between the UAV and the UAV at each moment and sum it over all time steps.

[0147] d uav (t)×d usv(t) reflects the relative positions of the UAV and the UAV at a given moment. A smaller distance between the two results in a smaller product, indicating closer proximity during path planning and better coordination. The cumulative summation of the distance products over all time steps reflects the coordination between the two throughout the mission.

[0148] The calculation is the square of the distance from the UAV and the UAV to the target point, and the sum is calculated. uav (t) 2 and d usv (t) 2 The squared distances reflect the relative distances between the two platforms in their respective paths. Larger squared values ​​indicate a larger distance difference, suggesting that their paths may be uncoordinated and their synergy may be poor. By summing the squared distances at each time step, we can quantify the change in distance between each platform and the target point.

[0149] A high C value indicates good synergy between the two. This means that during mission execution, the paths of the drone and the unmanned boat are well coordinated, allowing them to complete the mission together in the shortest possible time, avoiding path overlap or conflict, and forming a well-coordinated working model. A high C value indicates close coordination between the two in path selection. For example, the drone and unmanned boat approach the target point almost simultaneously, or their paths remain consistent at multiple moments during the mission, maximizing mission efficiency.

[0150] A low C value indicates poor synergy between the two. This may manifest as conflict in path selection, or due to factors such as irrational task allocation or imbalanced path planning, resulting in low efficiency in task execution. A low C value means that the two systems may be pursuing separate paths, separated by a large distance, resulting in relatively low task completion efficiency. There is even the possibility that their paths may intersect, leading to conflict and hindering smooth task execution.

[0151] A longer time step T means a longer mission execution time, and the collaborative effect may become more obvious over time. If the paths of the UAV and the unmanned boat are planned properly, long-term collaboration will help improve the overall efficiency of the mission.

[0152] A shorter time step T indicates a shorter task duration, which may result in a less significant synergy effect between the two. If the task duration is short and the path selection of the two is not well coordinated, it may result in a lower synergy factor value.

[0153] In practical applications, by optimizing the synergy factor C, the following goals can be achieved:

[0154] Path planning optimization: Adjust the paths of the drone and unmanned boat to minimize the distance change between the two, avoid overlapping and conflicting areas, and improve mission efficiency.

[0155] Task allocation and scheduling optimization: Based on the coordination factor C, task allocation can be dynamically adjusted to ensure that drones and unmanned boats can better collaborate throughout the mission and avoid waste of resources.

[0156] Real-time adjustment: As the task progresses, the system can adjust the task execution strategy based on the real-time value of the collaboration factor C to ensure that the two maintain efficient collaboration throughout the entire task.

[0157] In summary, the present invention can optimize the task execution effect, avoid path conflicts and improve the overall task efficiency by calculating the distances from both to the target point at each time step and considering the coordination of path selection.

[0158] Step 204: Control the UAV and the UAV according to the collaborative path to collect, transmit, and fuse measurement data to obtain a measurement result for the measurement mission target.

[0159] In some embodiments, step 204 may include:

[0160] Acquiring aerial image data and terrain height data collected by the drone;

[0161] Acquiring underwater depth data and water quality parameter data collected by the unmanned boat;

[0162] Determining a first transmission priority of the data collected by the UAV and a second transmission priority of the data collected by the unmanned boat;

[0163] determining a data transmission order according to the first transmission priority and the second priority and performing data transmission;

[0164] The transmitted UAV data and the unmanned boat data are subjected to data fusion processing based on Bayesian estimation to obtain the measurement results.

[0165] In some embodiments, the second transmission priority of the data collected by the unmanned vehicle is determined by the following steps:

[0166] Get the preset time interval;

[0167] Calculating the change in the depth data and the change in the water quality parameter based on the time interval;

[0168] The second transmission priority is determined based on the change in the depth data and its corresponding first weight, and the change in the water quality parameter and its corresponding second weight.

[0169] In some embodiments, the second transmission priority may be expressed as:

[0170]

[0171] Among them, P priority is the data transmission priority, ΔQ is the change in water quality parameters, ΔD is the change in depth data, Δt is the time interval, ω1 is the first weight, and ω2 is the second weight.

[0172] In the specific implementation, the data transmission priority P priority It is used to determine which data should be transmitted first based on the change in water quality parameters and depth data. The larger the priority value, the more important the data is, the higher the priority, and it should be transmitted first.

[0173] The change in water quality parameters, ΔQ, refers to water quality data (such as water temperature, pH, dissolved oxygen, turbidity, etc.) measured by sensors. ΔQ is the change in a water quality parameter over a specific time period, indicating fluctuations or sudden changes in water quality.

[0174] Depth data delta D (ΔD) is the depth of a body of water measured by sensors like sonar. ΔD represents the change in depth over a time interval. Depth changes can reflect changes in the water body, such as water level fluctuations.

[0175] The time interval Δt is the period of time used to calculate the change in water quality parameters and depth data, usually in seconds or minutes. It is the basis for the change calculation and represents the rate of change in water quality or depth during this period.

[0176] ω1 is the first weight, and ω2 is the second weight, controlling the degree to which changes in water quality parameters and depth data affect the priority, respectively. Depending on the needs of the specific task, the weight coefficients can be adjusted to balance the priority of water quality and depth changes in data transmission.

[0177] Indicates the rate of change in depth. If the depth of the water area changes significantly (such as a sudden rise or fall in the water level), it may affect the execution of the mission or affect other measurement data, so the change in depth data should also be considered. For example, when conducting seabed exploration, a sudden change in depth may indicate the presence of special seabed conditions (such as seabed collapse, sediment accumulation, etc.). This information needs to be transmitted first to ensure the integrity and real-time nature of the data.

[0178] If water quality monitoring is more important than depth changes in the mission (for example, in water pollution monitoring), ω1 can be set larger and ω2 smaller, so that changes in water quality data will be given higher priority. Conversely, if depth changes are more important to the mission objectives (for example, in ocean measurement missions), ω1 can be set smaller and ω2 larger.

[0179] Based on the calculated priorities, the system can dynamically determine which data should be transmitted first. For example, if water quality parameters change dramatically at a certain moment (such as water pollution), water quality data will be transmitted first to ensure that relevant data is delivered to the receiving end for processing in a timely manner.

[0180] By prioritizing the transmission of high-priority data, we can ensure that the most critical data can be processed in a timely manner under limited communication resources, thereby improving the effectiveness of task execution.

[0181] In scenarios where multiple sensors collect data and need to transmit it in real time, prioritizing the most important data can improve system response speed and ensure timely processing of critical data. This is crucial for tasks such as emergency response and environmental monitoring. Based on task requirements and real-time data changes, weighting coefficients can be dynamically adjusted to meet the priority requirements in different scenarios. For example, water quality data can be given higher priority when water quality changes suddenly, while depth data can be prioritized when the water level in the measurement area fluctuates significantly. Prioritizing the transmission of important data effectively reduces wasted communication bandwidth. Especially when bandwidth is limited, prioritizing the transmission of urgent or critical data can avoid excessive transmission of non-critical data, thereby improving overall system performance.

[0182] In summary, this invention provides a scientific calculation method for data transmission priority, determining the priority order of data transmission based on the changing speed of water quality and depth data and their weighting. In practical applications, by adjusting the weighting coefficients, data transmission strategies can be dynamically optimized according to different task requirements, ensuring the timely transmission of critical data, thereby improving task response efficiency and accuracy.

[0183] In some embodiments, the measurement results may be determined by:

[0184] Processing the terrain height data collected by the drone according to Bayesian estimation to convert it into an estimated depth value;

[0185] Constructing an estimated depth probability density function representing the reliability of the estimated depth value;

[0186] Obtaining a directly measured depth value collected by the unmanned boat, and constructing a directly measured depth probability density function representing the credibility of the directly measured depth value;

[0187] The fused measurement result is determined according to the estimated depth value, the directly measured depth value, the estimated depth probability density function, and the directly measured depth probability density function.

[0188] In some embodiments, the measurement results may be expressed as:

[0189]

[0190] Among them, D fusion is the depth value after fusion, D uav-est is the estimated depth value of the drone, D usv is the depth value directly measured by the unmanned boat, P(D uav-est ) is the probability density function of the estimated depth, P(D usv ) is the probability density function of the directly measured depth.

[0191] In the specific implementation, the fused depth value D fusion The final depth value is obtained by fusing depth data from two different sources: estimated depth and directly measured depth. The fused depth value combines the advantages of the drone's depth estimation and the unmanned vehicle's depth measurement, providing more accurate depth information.

[0192] D uav-est It is the depth estimation performed by the UAV using indirect methods such as terrain height data, which may have certain errors due to environmental factors (such as sensor error, flight path, etc.).

[0193] D usv It is the underwater depth directly measured by the unmanned boat through sonar or other underwater sensors. Usually, this value is relatively accurate, but may be affected by noise interference or equipment accuracy limitations.

[0194] The probability density function of the estimated depth P(D uav-est ) reflects the reliability or accuracy of the UAV's estimated depth. A larger value indicates a more reliable estimated depth, and vice versa.

[0195] The probability density function of directly measuring depth P(D usv ) indicates the reliability of the depth value directly measured by the unmanned boat. The larger the value, the more reliable the measurement result.

[0196] Specifically, when the credibility of a data source is high, for example, P(D uav-est ) or P(D usv ) is larger, the corresponding depth value will have a greater weight in the fusion result. In this way, the fused depth value can be dynamically adjusted according to the credibility of different data sources, allowing the system to optimally utilize information based on the current situation, thereby obtaining a more accurate depth estimate.

[0197] By fusing data from two different sources, the present invention creates a more accurate depth estimate, compensating for potential errors from a single data source. In complex environments, such as underwater or when the drone cannot directly access the water surface, combining estimated depth with directly measured depth helps improve the accuracy of the overall depth estimate. Using a probability density function, the system can dynamically adjust its confidence in different data sources, enhancing the system's adaptability. For example, when a drone's estimate is more reliable, its depth estimate is given a higher weight, and vice versa.

[0198] This invention is not limited to data fusion between drones and unmanned watercraft; it is also applicable to the fusion of other multi-source data. In practical applications, there may be multiple sensors and data sources. Bayesian methods can flexibly weight these data sources to obtain more accurate fusion results. By integrating and fusing multiple data sources, the system can still rely on data provided by other sensors even if one sensor fails or the data is unreliable, thereby enhancing the robustness and reliability of the system.

[0199] In summary, this invention uses a Bayesian estimation method to perform a weighted fusion of depth data from two different sources (estimated depth and directly measured depth) to obtain a more accurate depth estimate. By dynamically adjusting the weights based on the credibility of each data source, the system can effectively improve the accuracy of depth measurement and enhance the reliability and precision of task execution. This method is suitable for data fusion in a variety of environments, and can provide more robust performance in complex or dynamically changing environments.

[0200] Step 205: Evaluate the measurement result, perform feedback adjustment on the measurement result, and generate a target measurement result.

[0201] The measurement results can be evaluated, the corresponding measurement errors can be calculated, and the position adjustment amount of the UAV and the unmanned boat can be determined based on the measurement errors. The target measurement results can then be generated based on the measurement of the UAV and the unmanned boat after the position adjustment.

[0202] In some embodiments, the measurement error can be expressed as:

[0203]

[0204] Where E is the measurement error, D ref (i) is the reference depth value, N is the number of measurement points, if E>E th ,Feedback adjustment task allocation and collaborative planning,E th is the error threshold.

[0205] In the specific implementation, the measurement error E represents the average deviation between the fused depth value and the reference depth value. The smaller the error, the higher the measurement accuracy of the system, and vice versa.

[0206] Reference depth value D ref (i) is the depth D estimated by the drone uav-est and the unmanned boat to measure the depth D usv The fused depth value combines the advantages of different data sources and can provide more accurate depth estimation.

[0207] Reference depth value D ref (i) It is a known or pre-determined true depth value, usually obtained from high-precision measurement equipment or historical data, and is used as a benchmark for measuring the accuracy of the current depth measurement.

[0208] The number of measurement points N represents the number of points in the entire measurement area where depth measurements are taken. N is usually the number of discrete sampling points in the actual measurement area.

[0209] E th Is a preset value used to determine whether the measurement result meets the accuracy requirements. If the calculated measurement error E is greater than the threshold E th , it means that the measurement results are not accurate enough and task adjustment or collaborative planning may be needed.

[0210] (D fusion (i)-D ref (i)) 2 The error at each measurement point is the square of the difference between the fused depth value and the reference depth value. This squaring operation eliminates negative values ​​and emphasizes larger errors. A larger square indicates a greater error at that measurement point. The root mean square error (RMSE) is calculated by summing the squared errors of all measurement points, taking the average, and taking the square root. This value intuitively reflects the average deviation of the depth data throughout the measurement process. A smaller RMSE value indicates higher measurement accuracy and closer the measurement results are to the true value.

[0211] The error value E is mainly affected by the following factors:

[0212] Accuracy of depth measurement: The estimation by the UAV and the direct measurement by the UAV will affect the accuracy of the fused depth value.

[0213] Quality of reference data: The reference depth must be accurate and reliable. If the reference value itself is biased, the error value will also be affected.

[0214] Number of measurement points: The number of measurement points N reflects the coverage of the measurement area and the density of data sampling. The more sampling points there are, the more the average value of the error can reflect the global accuracy.

[0215] The calculated E represents the error of the entire measurement process. The smaller the error value, the more accurate the data during the measurement process. Common sources of error include sensor noise, environmental interference, equipment calibration issues, etc. If the calculated E is less than the set error threshold, the measurement result is considered accurate enough and the task can continue. If the calculated E is greater than the set error threshold, it means that the error of the measurement result is too large and measures need to be taken to adjust it. At this time, the system can trigger the following actions:

[0216] Adjust the flight path of the drone:

[0217] If the drone's estimated depth error is large, you can improve its measurement accuracy by adjusting its flight altitude, flight speed, or sensor settings. The drone can also perform more detailed flight path planning as needed to reduce the estimation error.

[0218] Adjust the navigation path of the unmanned boat:

[0219] If there are deviations in the direct measurements of the unmanned boat, it may be necessary to replan the navigation route of the unmanned boat to ensure that the sensor collects data at the optimal position and reduce the noise and error of the depth data.

[0220] Enhanced collaborative work:

[0221] By analyzing the calculation results of E, the coordinated planning between the UAV and the unmanned boat can be adjusted to optimize their path selection and task allocation. For example, if the measurement accuracy of one platform is poor, the measurement task load for that platform can be increased while the task load for another platform can be reduced to optimize the measurement results of the overall mission.

[0222] Increase the density of measurement points:

[0223] If the measurement coverage is insufficient or the error is large, the density of measurement points can be increased and the sampling frequency of measurement data can be increased to improve the accuracy of the data.

[0224] In summary, this invention provides the system with feedback on depth measurement accuracy. If the error exceeds a set threshold, the system uses this feedback to adjust task allocation and collaborative planning to improve measurement accuracy. This process ensures that the entire measurement task can be completed within a reasonable error range, thereby improving the system's measurement accuracy and task execution efficiency.

[0225] In some embodiments, the position adjustment amount can be expressed as:

[0226]

[0227] Where Δx is the position adjustment amount and k is the adjustment coefficient.

[0228] In practice, the position adjustment Δx represents the displacement or position parameter that needs to be adjusted. This amount typically affects the path, altitude, and speed of the drone or unmanned watercraft to improve mission accuracy. When the measurement error exceeds a set threshold, the system calculates the required adjustment based on this formula.

[0229] The adjustment coefficient k is a constant that represents the sensitivity or proportionality factor of the adjustment. Its value influences the magnitude of the adjustment. A larger k value results in a larger adjustment when the error is large; a smaller k value results in a smaller adjustment and a higher tolerance for error.

[0230] E represents the deviation between the actual measured depth and the reference depth. This value is calculated using a formula and reflects the accuracy of the current measurement. A larger error, E, indicates a less accurate measurement and requires more adjustments.

[0231] E th Is a preset value used to determine whether the accuracy of the measurement results meets the requirements. th , it means that the measurement error exceeds the set tolerance range and corresponding adjustments are required.

[0232] When E<E th hour, A negative value or zero will be obtained, indicating that no position adjustment is required. The system considers the measurement to be accurate enough and the task can continue.

[0233] When E>E th hour, A positive value will be obtained, indicating that the measurement result of the system is inaccurate and the position needs to be adjusted based on the value to ensure that the task can be executed more accurately.

[0234] When the error exceeds a preset threshold, the system adjusts the flight path or navigation route to effectively reduce measurement errors and ensure depth measurements are within tolerance. By adjusting displacement or position, the system can quickly adapt to environmental changes and improve measurement accuracy. The calculation and adjustment process of this formula can be considered a feedback control mechanism. If the system detects that the measurement results do not meet requirements, it uses the current error as feedback to adjust the task execution strategy. By adjusting path, speed, and other factors, the system can effectively reduce measurement errors and improve task completion accuracy.

[0235] In summary, by calculating the required position adjustment based on the difference between the measurement error and a set threshold, the system automatically adjusts key parameters during mission execution (such as the flight path of a drone or the navigation path of an unmanned vehicle) to improve measurement accuracy. The adjustment coefficient controls the sensitivity of this adjustment, allowing the system to flexibly adapt to different mission requirements. Through this adjustment mechanism, the system can quickly respond to excessive measurement errors, optimize path planning, and ensure smooth mission execution and accurate measurement results.

[0236] See also Figure 3 , Figure 3 This is a structural schematic diagram of a measurement system based on collaborative networking of unmanned aerial vehicles and unmanned boats provided by the present invention.

[0237] like Figure 3 As shown, a measurement system based on a collaborative network of unmanned aerial vehicles and unmanned boats proposed in an embodiment of the present invention includes:

[0238] Initial setting module 301 is used to initialize the sensor and communication modules of the UAV and the UAV, and determine the signal transmission rate and link stability factor;

[0239] A link establishing module 302 is configured to establish a communication link between the UAV and the UAV based on the signal transmission rate and the link stability factor, determine the link stability factor, and evaluate the stability of the communication link using the link stability factor;

[0240] A path planning module 303 is used to assign measurement tasks to the UAV and the UAV and plan a collaborative path according to the measurement task objectives;

[0241] A result determination module 304 is configured to control the UAV and the UAV to collect, transmit, and fuse measurement data according to the collaborative path to obtain a measurement result for the measurement mission target;

[0242] The result evaluation module 305 is used to evaluate the measurement result, perform feedback adjustment on the measurement result, and generate a target measurement result.

[0243] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0244] Initialize the sensors and communication modules of the drone and unmanned boat to determine the signal transmission rate and link stability factor;

[0245] Establishing a communication link between the UAV and the unmanned boat based on the signal transmission rate and the link stability factor, determining the link stability factor, and evaluating the stability of the communication link using the link stability factor;

[0246] According to the measurement mission objectives, the measurement tasks are assigned to the UAV and the unmanned boat and a collaborative path is planned;

[0247] Controlling the UAV and the UAV to collect, transmit, and fuse measurement data according to the collaborative path to obtain measurement results for the measurement mission target;

[0248] The measurement results are evaluated and feedback-adjusted to generate target measurement results.

[0249] See also Figure 5 , Figure 5 Schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0250] Initialize the sensors and communication modules of the drone and unmanned boat to determine the signal transmission rate and link stability factor;

[0251] Establishing a communication link between the UAV and the unmanned boat based on the signal transmission rate and the link stability factor, determining the link stability factor, and evaluating the stability of the communication link using the link stability factor;

[0252] According to the measurement mission objectives, the measurement tasks are assigned to the UAV and the unmanned boat and a collaborative path is planned;

[0253] Controlling the UAV and the UAV to collect, transmit, and fuse measurement data according to the collaborative path to obtain measurement results for the measurement mission target;

[0254] The measurement results are evaluated and feedback-adjusted to generate target measurement results.

[0255] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0256] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0257] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0258] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0259] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0260] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0261] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A measurement method based on collaborative networking of unmanned aerial vehicles and unmanned boats, characterized in that: The method comprises: Initializing and setting the sensors and communication modules of the UAV and the UAV to determine the signal transmission rate and link stability factor includes: obtaining the channel bandwidth of the frequency range in which the channel between the UAV and the UAV can be transmitted; obtaining the transmit power used by the UAV and the UAV when sending signals on the channel; obtaining the noise power spectral density of the noise power within the unit bandwidth of the channel; determining the signal transmission rate of the channel based on the channel bandwidth, the transmit power, and the noise power spectral density; obtaining the gain of each channel between the UAV and the UAV, and the number of channels; and determining the link stability factor based on the gain of each channel and the number of channels. The link stability factor is expressed as: Where S is the link stability factor, H i is the gain of the ith channel, n is the number of channels, when S>S th When , it means the communication link is stable, S th is the communication link stability threshold; establishing a communication link between the UAV and the unmanned boat according to the signal transmission rate and the link stability factor, and evaluating the stability of the communication link using the link stability factor; According to the measurement mission objectives, the measurement tasks are assigned to the UAV and the unmanned boat and a collaborative path is planned; Controlling the UAV and the UAV to collect, transmit, and fuse measurement data according to the collaborative path to obtain measurement results for the measurement mission target; The measurement results are evaluated and feedback-adjusted to generate target measurement results.

2. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 1 is characterized in that: The signal transmission rate is expressed as: Where R is the signal transmission rate, B is the channel bandwidth, P is the transmit power, and N0 is the noise power spectral density.

3. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 2 is characterized in that: The allocating measurement tasks to the UAV and the unmanned boat and planning collaborative paths according to the measurement task objectives includes: Determining measurement requirements for the UAV and the unmanned boat based on the measurement mission objectives; Determining the mission area boundary of the measurement mission target according to the measurement requirements of the UAV and the unmanned boat; Determining the path selection probability of the UAV and the path selection probability of the unmanned boat within the mission area boundary, and planning the initial path of the UAV and the initial path of the unmanned boat; The initial path of the UAV and the initial path of the unmanned boat are optimized according to the collaborative factor, and the collaborative path of the UAV and the unmanned boat is determined.

4. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 3 is characterized in that: The UAV path selection probability is expressed as: Among them, p uav (i, j) is the probability of drone path selection, τ(i, j) is the pheromone concentration on path (i, j), η(i, j) is the heuristic information on path (i, j), α and β are the first and second influencing factors respectively, and alloweduav uav It is the next set of nodes that the drone can choose.

5. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 4 is characterized in that: The synergistic factor is determined by the following steps: Obtaining a first distance between the UAV and a target point, and a second distance between the UAV and the target point at different times; Calculating a first cumulative sum of the product of the first distance and the second distance at each moment; Calculating a second cumulative sum of the sum of the square of the first distance and the square of the second distance at each moment; The cooperation factor is determined according to the first cumulative sum value and the second cumulative sum value.

6. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 5 is characterized in that: The controlling the UAV and the unmanned boat to collect, transmit, and fuse measurement data according to the collaborative path to obtain measurement results for the measurement mission target includes: Acquiring aerial image data and terrain height data collected by the drone; Acquiring underwater depth data and water quality parameter data collected by the unmanned boat; Determining a first transmission priority of the data collected by the UAV and a second transmission priority of the data collected by the unmanned boat; determining a data transmission order according to the first transmission priority and the second priority and performing data transmission; The transmitted UAV data and the unmanned boat data are subjected to data fusion processing based on Bayesian estimation to obtain the measurement results.

7. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 6, characterized in that: The second transmission priority of the data collected by the unmanned boat is determined by the following steps: Get the preset time interval; Calculating the change in the depth data and the change in the water quality parameter based on the time interval; The second transmission priority is determined based on the change in the depth data and its corresponding first weight, and the change in the water quality parameter and its corresponding second weight.

8. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 7, characterized in that: The measurement results are determined by the following steps: Processing the terrain height data collected by the drone according to Bayesian estimation to convert it into an estimated depth value; Constructing an estimated depth probability density function representing the reliability of the estimated depth value; Obtaining a directly measured depth value collected by the unmanned boat, and constructing a directly measured depth probability density function representing the credibility of the directly measured depth value; The fused measurement result is determined according to the estimated depth value, the directly measured depth value, the estimated depth probability density function, and the directly measured depth probability density function.

9. A measurement system based on collaborative networking of drones and unmanned boats, characterized in that: The system comprises: The initial setting module is used to initialize the sensors and communication modules of the UAV and the UAV, determine the signal transmission rate and link stability factor, and obtain the channel bandwidth of the frequency range in which the channel between the UAV and the UAV can be transmitted; obtain the transmission power used by the UAV and the UAV when sending signals on the channel; obtain the noise power spectral density of the noise power within the unit bandwidth of the channel; determine the signal transmission rate of the channel based on the channel bandwidth, the transmission power and the noise power spectral density; obtain the gain of each channel between the UAV and the UAV, and the number of channels; determine the link stability factor based on the gain of each channel and the number of channels; the link stability factor is expressed as: Where S is the link stability factor, H i is the gain of the ith channel, n is the number of channels, when S>S th When , it means the communication link is stable, S th is the communication link stability threshold; a link establishing module, configured to establish a communication link between the UAV and the unmanned boat according to the signal transmission rate and the link stability factor, determine the link stability factor, and evaluate the stability of the communication link using the link stability factor; A path planning module, configured to allocate measurement tasks to the UAV and the unmanned boat and plan a collaborative path according to the measurement task objectives; A result determination module is used to control the UAV and the unmanned boat to collect, transmit and fuse measurement data according to the collaborative path to obtain a measurement result for the measurement task target; The result evaluation module is used to evaluate the measurement result, perform feedback adjustment on the measurement result, and generate a target measurement result.

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