Measurement method and system based on cooperative networking of unmanned aerial vehicle and unmanned surface vehicle

Abstract

The invention relates to a measurement method and system based on cooperative networking of an unmanned aerial vehicle and an unmanned ship, and relates to the technical field of data processing, and the method comprises the steps: carrying out the initialization setting of sensors and communication modules of the unmanned aerial vehicle and the unmanned ship, and determining a signal transmission rate and a link stability factor; establishing a communication link between the unmanned aerial vehicle and the unmanned ship according to the signal transmission rate and the link stability factor, and evaluating the stability of the communication link through the link stability factor; according to the measurement task target, allocating measurement tasks to the unmanned aerial vehicle and the unmanned ship and planning a cooperative path; the unmanned aerial vehicle and the unmanned ship are controlled to collect, transmit and fuse measurement data according to the cooperative path, and a measurement result for the measurement task target is obtained; and evaluating the measurement result, and performing feedback adjustment on the measurement result to generate a target measurement result. According to the invention, the measurement precision and efficiency can be improved.

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] At present, the collaborative measurement method of unmanned aerial vehicles (UAVs) and unmanned submarines (USVs) mainly relies on traditional single-platform operations, where the UAV usually provides aerial perspectives or positioning data, while the USV is responsible for surface or underwater detection and data collection. This operation mode has been widely used in the fields of marine measurement, environmental monitoring, disaster warning, etc.

[0003] However, there is a lack of efficient and real-time data transmission and coordination mechanisms between multiple platforms in collaborative operations, especially in complex environments, where the stability and accuracy of communication links are difficult to guarantee. At the same time, due to the large differences in measurement tasks and real-time data requirements of different platforms, existing methods have certain limitations in data fusion and processing, and cannot achieve efficient integration and analysis of cross-platform data. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a measurement method, system, electronic device 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 cooperative networking of unmanned aerial vehicles and unmanned boats, 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] According to the signal transmission rate and the link stability factor, a communication link is established between the UAV and the unmanned boat to determine the link stability factor, and the stability of the communication link is evaluated by the link stability factor;

[0009] According to the measurement task 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 unmanned boat to collect, transmit and fuse measurement data according to the collaborative path to obtain measurement results for the measurement task target;

[0011] The measurement result is evaluated, and feedback adjustment is performed on the measurement result to generate a target measurement result.

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

[0013] Obtaining a channel bandwidth of a frequency range in which a channel between the UAV and the UAV can transmit;

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

[0015] Obtaining the noise power spectral density of the noise power within the 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 of the channels between the UAV and the UAV, and the number of the channels;

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

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

[0020]

[0021] Among them, R is the signal transmission rate, B is the channel bandwidth, P is the transmission power, and N 0 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 the measurement task objectives includes:

[0026] Determining the measurement requirements of the UAV and the unmanned boat according to 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] In the mission area boundary, determining the path selection probability of the UAV and the path selection probability of the unmanned boat, 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 coordination factor, and the coordinated 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, allowduav 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] Acquire 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] Calculate a first cumulative sum of the products of the first distance and the second distance at each moment;

[0036] Calculate a second cumulative sum value 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 task 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 drone and a second transmission priority of the data collected by the unmanned boat;

[0042] Determine a data transmission order and perform data transmission according to the first transmission priority and the second priority;

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

[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] Based on the time interval, calculating the change amount of the depth data and the change amount of the water quality parameter;

[0047] The second transmission priority is determined based on the change amount of the depth data and its corresponding first weight, and the change amount of 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 credibility of the estimated depth value;

[0051] Acquire the directly measured depth value collected by the unmanned boat, and construct 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 purpose, the present invention also proposes a measurement system based on the collaborative networking of unmanned aerial vehicles and unmanned boats, comprising:

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

[0055] A link establishment module, used 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 through the link stability factor;

[0056] A path planning module, used to assign 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, 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, and perform feedback adjustment on the measurement result to generate a target measurement result.

[0059] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, including: 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 an unmanned aerial vehicle and an unmanned boat as described above.

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

[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 collaborative factors, 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, data fusion methods (such as Bayesian estimation) further improve 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 to ensure that emergency data (such as sudden water quality abnormalities) 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 drone 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, feedback adjustment mechanism, etc. 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 cooperative networking of unmanned aerial vehicles and unmanned boats provided by the present invention;

[0067] Figure 2 A flow chart of a measurement method based on cooperative 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 cooperative 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are 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 as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0073] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field 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 will not be 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 in the present invention.

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

[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 limitations 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 for:

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

[0078] According to the signal transmission rate and the link stability factor, a communication link is established between the UAV and the unmanned boat to determine the link stability factor, and the stability of the communication link is evaluated by the link stability factor;

[0079] According to the measurement task 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 unmanned boat to collect, transmit and fuse measurement data according to the collaborative path to obtain measurement results for the measurement task target;

[0081] The measurement result is evaluated, and feedback adjustment is performed on the measurement result to generate a target measurement result.

[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 in which a channel between the UAV and the UAV can transmit;

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

[0087] Obtaining the noise power spectral density of the noise power within the 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 of the channels between the UAV and the UAV, and the number of the channels;

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

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

[0092]

[0093] Among them, R is the signal transmission rate, B is the channel bandwidth, P is the transmission power, and N 0 is the noise power spectral density.

[0094] In specific implementations, R is usually expressed in bits per second (bps), which indicates the amount of data that can be transmitted per unit time. The R given by this formula is the maximum theoretical transmission rate of the channel, which refers to the maximum data transmission speed that can be achieved through the channel under specific communication link conditions.

[0095] The unit of B is Hertz (Hz). Bandwidth represents the frequency range that 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 watt (W). Transmit power refers to the power used by the transmitter (such as the communication equipment of a drone or unmanned boat) to send a signal. Higher transmit power can increase the strength of the signal, thereby improving the signal propagation distance and signal quality.

[0097] N 0 The unit is watt per hertz (W / Hz). Noise power spectral density represents the noise power per unit bandwidth. In any communication system, noise always exists and it affects the quality of the signal. 0 The larger it is, the stronger the noise in the system will be, thus affecting the effective transmission of the signal.

[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 transmitting more data.

[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 transmitted power of the signal, N 0 is the noise power spectral density, and B is the bandwidth. The larger the ratio, the stronger the signal is relative to the noise, the better the communication quality, and the higher the maximum data transmission rate R of the system.

[0101] Logarithmic function log 2 The figure shows the nonlinear relationship between the signal-to-noise ratio (SNR) and the rate. As the signal-to-noise ratio increases, the increase in transmission rate gradually slows down. This shows that although increasing the signal transmission power or reducing the noise level can increase the rate, the increase in rate is not linear and there is a certain marginal effect.

[0102] Increasing the bandwidth B can increase the signal transmission rate, because the bandwidth determines the frequency range that the signal can occupy, and increasing the bandwidth is equivalent to increasing the amount of information that can be transmitted. Increasing the transmit power P can improve the signal-to-noise ratio (SNR), thereby increasing the data transmission rate. Higher transmit power makes the signal less susceptible to noise interference during propagation. Reducing the noise power spectral density can improve the signal-to-noise ratio, thereby increasing the transmission rate. The smaller the noise, the higher the effectiveness of the signal and the better the communication quality.

[0103] In practical applications, bandwidth and power are usually limited resources, so a trade-off needs to be made between bandwidth and transmission power during design. If the bandwidth is wide but the power is low, or if the bandwidth is narrow but the power is high, the transmission rate of the system will be affected. In complex communication environments, such as densely populated areas of high-rise buildings in cities or on the sea, noise interference may be large. Therefore, the design of the system needs to minimize the impact of noise and use advanced anti-interference technologies (such as channel coding, modulation technology, etc.) to improve communication quality.

[0104] In summary, the present invention provides a mathematical model for the maximum theoretical transmission rate of the system, which helps to understand how to optimize the signal transmission rate by adjusting the bandwidth, transmission power and noise level. Through reasonable 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 unmanned boat according to the signal transmission rate and the link stability factor, determines the link stability factor, and evaluates the stability of the communication link through 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 the signal strength. Gain H iIt is the ratio of the signal strength after the signal is transmitted through the i-th channel to the original transmitted signal strength. The larger the gain, the better the signal propagation effect in the channel and the smaller the signal loss.

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

[0112] Communication link stability threshold S th is a preset value used to determine whether the link is stable. 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 environment, distance, and obstacles. Therefore, by averaging the square value of all channel gains, the overall stability of the system link can be more comprehensively reflected.

[0114] Channel gain H i It is the amplification or attenuation experienced by a signal after passing through a channel. The gain of a channel 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 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 an important parameter for averaging. In multi-band communications, a communication link may consist of multiple sub-channels, and the transmission quality of each channel may vary. By averaging the squared values ​​of all channel gains, the stability of the entire link can be more accurately assessed to prevent fluctuations in a single channel from affecting the overall link stability.

[0117] The link stability factor S is calculated by averaging the square values ​​of all channel gains. A larger S value indicates that the channel gain is generally better, the signal transmission quality is higher, and the communication link is stable. A smaller S value indicates that the signal transmission may be subject to more interference or attenuation, the link stability is poor, and data loss or transmission delay may occur.

[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 the smooth transmission of data. In this case, the communication between the drone and the unmanned boat will be able to maintain a good connection, and the data transmission rate and quality can meet expectations.

[0119] When S≤S th When the signal is low, it indicates that the communication link is unstable. There may be severe signal attenuation, interference or other communication problems, resulting in reduced data transmission quality. In this case, the system may trigger some adjustment mechanisms (such as increasing the transmit power, adjusting the channel selection, switching to other channels, etc.) to restore the stability of the link.

[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 considered 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: According to the measurement task objectives, measurement tasks are assigned to the UAV and the unmanned boat and a collaborative path is planned.

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

[0123] Determining the measurement requirements of the UAV and the unmanned boat according to 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] In the mission area boundary, determining the path selection probability of the UAV and the path selection probability of the unmanned boat, 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 coordination factor, and the coordinated 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, allowduav uav It is the next set of nodes that the drone can choose.

[0130] In the specific implementation, p uav (i,j) is the probability of the drone's path selection, which is used to determine the next move 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 the path (i,j). Pheromones are a factor that simulates the natural ant foraging process and indicates the "attractiveness" of the path. In the ant colony algorithm, the size of the pheromone concentration affects the probability of path selection. The higher the pheromone concentration, the better the path is considered, and the drone is more inclined to choose this path.

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

[0133] α and β are the first and second influencing factors respectively. α controls the influence of pheromone on path selection. A larger α value will make the pheromone concentration have a greater impact on path selection. β is the weight factor of heuristic information, which controls the influence of heuristic information on path selection. A larger β value will make heuristic information play a greater role in path selection.

[0134] alloweduav uav It is the next node set that the drone can choose. During the path selection process, the drone can only choose certain nodes in the allowed node set. This set is dynamically changing and depends on the current position of the drone and the mission goal.

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

[0136] In summary, the present invention is based on the ant colony algorithm and guides the drone to select a path through pheromone concentration and heuristic information. By setting different influencing factors, the role of pheromone and heuristic information in path selection can be adjusted, so that the drone can select a suitable path more intelligently and flexibly to improve the efficiency of task execution. In practical applications, the algorithm can adjust the path selection according to real-time data and gradually optimize the overall path planning.

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

[0138] Acquire 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] Calculate a first cumulative sum of the products of the first distance and the second distance at each moment;

[0140] Calculate a second cumulative sum value 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 the specific implementation, the coordination factor C is used to quantify the degree of cooperation between the UAV and the unmanned boat when performing collaborative tasks. The value of the coordination factor reflects the synergy effect of the two in the task. If C is higher, it means that the path selection and task execution of the two are more coordinated; if C is lower, it means that their coordination effect is poor, and path conflicts or inefficiencies may occur.

[0143] At time t, the distance d between the drone and the target point 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 speed and flight path of the UAV.

[0144] At time t, the distance d from the unmanned boat to the target point 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 means 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 unmanned boat at each moment and sum it up for all time steps.

[0147] d uav (t)×d usv(t) reflects the relative position of the UAV and the unmanned boat at a certain moment. If the distance between the two is small, the product is small, which means that they are closer in path planning and have better coordination. The cumulative summation of the distance product of all time steps can reflect the coordination between the two during the entire mission.

[0148] What is calculated is the square of the distance from the UAV and the UAV to the target point, and the sum is taken. uav (t) 2 and d usv (t) 2 They reflect the relative distances of the two in their respective paths. A larger square value indicates a larger distance difference, indicating that their paths may be uncoordinated and have poor synergy. By accumulating the square of the distance at each time step, the change in the distance between each platform and the target point can be quantified.

[0149] If the C value is high, it means that the two have better synergy. That is to say, during the execution of the mission, the paths of the UAV and the unmanned boat are more coordinated, and they can complete the mission together in the shortest time, avoiding path overlap or conflict, and forming a good collaborative working mode. A high C value means that the two cooperate more closely in path selection, for example: the UAV and the unmanned boat arrive at the target point almost at the same time, or when performing the mission, their paths remain consistent at multiple times, and the mission efficiency is maximized.

[0150] If the C value is low, it means that the synergy between the two is poor. This may be manifested as a conflict between the two in path selection, or due to certain factors (such as unreasonable task allocation, unbalanced path planning, etc.), they are less efficient in performing tasks. A low C value means that the two may go their own way, the distance between them is far, the efficiency of task completion is relatively low, and there may even be a conflict caused by the intersection of the two paths, affecting the smooth execution of the task.

[0151] A longer time step T means that the task takes longer to execute, and the synergy effect may become more obvious as time goes on. If the paths of the UAV and the unmanned boat are planned properly, the synergy over a long period of time will help improve the overall efficiency of the task.

[0152] A shorter time step T means a shorter task time, which may result in an insignificant synergy effect between the two. If the task time 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 the unmanned boat to minimize the distance change between the two, avoid duplicate areas and conflict areas, and improve mission efficiency.

[0155] Task allocation and scheduling optimization: Based on the coordination factor C, task allocation can be adjusted dynamically 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 unmanned boat to collect, transmit and fuse measurement data according to the collaborative path to obtain measurement results for the measurement task 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 drone and a second transmission priority of the data collected by the unmanned boat;

[0163] Determine a data transmission order and perform data transmission according to the first transmission priority and the second priority;

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

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

[0166] Get the preset time interval;

[0167] Based on the time interval, calculating the change amount of the depth data and the change amount of the water quality parameter;

[0168] The second transmission priority is determined based on the change amount of the depth data and its corresponding first weight, and the change amount of 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, ω 1 is the first weight, ω 2 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 is, and it should be transmitted first.

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

[0174] The change in depth data, ΔD, is the depth of the water body measured by sensors such as sonar. ΔD represents the change in depth within a time interval. Depth changes can reflect changes in the water area, such as water level fluctuations.

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

[0176] ω 1 is the first weight, ω 2 is the second weight, which controls the degree of influence of water quality parameter changes and depth data changes on the priority. According to the needs of specific tasks, the weight coefficient can be adjusted to balance the priority of water quality changes 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 task or affect other measurement data, so the amount of change in depth data should also be considered. For example, when conducting seabed exploration, a sharp change in depth may indicate the existence of special conditions on the seabed (such as seabed collapse, sediment accumulation, etc.), and 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 variation in the mission (for example, in water pollution monitoring), you can set ω1 Larger, 2 Smaller, so that changes in water quality data are given higher priority. Conversely, if depth changes are more important to the mission objectives (such as in oceanographic survey missions), ω can be set 1 Smaller, 2 Larger.

[0179] Based on the calculated priority, the system can dynamically decide 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 transmitted to the receiving end in time for processing.

[0180] By giving priority to the transmission of high-priority data, it can be ensured 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, by giving priority to the transmission of the most important data, the system's response speed can be improved and the timely processing of key data can be ensured. This is very important for tasks such as emergency response and environmental monitoring. According to task requirements and real-time data changes, the weight coefficient can be dynamically adjusted to adapt to the priority requirements in different scenarios. For example, the priority of water quality data is increased when the water quality changes suddenly, and the depth data is transmitted first when the water level changes greatly in the measurement area. By giving priority to the transmission of important data, the waste of communication bandwidth can be effectively reduced. Especially in the case of limited bandwidth, giving priority to the transmission of urgent or critical data can avoid excessive transmission of non-critical data, thereby improving the overall performance of the system.

[0182] In summary, the present invention provides a scientific calculation method for data transmission priority, and determines the priority transmission order of data according to the change speed of water quality and depth data and their weights. In practical applications, by adjusting the weight coefficient, the data transmission strategy can be dynamically optimized according to different task requirements to ensure the timely transmission of key data, thereby improving the response efficiency and accuracy of the task.

[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 credibility of the estimated depth value;

[0186] Acquire the directly measured depth value collected by the unmanned boat, and construct 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 fused depth value, 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 direct measurement 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 estimated depth and the unmanned boat's measured depth, 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 it may be affected by noise interference or limited equipment accuracy.

[0194] The probability density function of the estimated depth P(D uav-est ) reflects the reliability or accuracy of the drone's estimated depth. The larger the value, the more reliable the 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 larger weight in the fusion result. In this way, the fused depth value can be dynamically adjusted according to the credibility of different data sources, so that the system can optimally utilize information according to the current situation, thereby obtaining a more accurate depth estimation.

[0197] The present invention fuses data from two different sources, so that the fused depth value is more accurate and can make up for the errors that may exist in a single data source. In complex environments, such as underwater or when the drone cannot directly contact the water surface, combining the estimated depth with the directly measured depth helps to improve the accuracy of the overall depth estimation. Through the probability density function, the system can dynamically adjust the trust in different data sources, thereby enhancing the adaptability of the system. For example, when the drone's estimation result is more reliable, the weight of its depth estimation will be increased, and vice versa.

[0198] The present invention is not limited to data fusion between drones and unmanned boats, but is also applicable to the fusion of other multi-source data. In practical applications, there may be multiple sensors and data sources. The Bayesian method can flexibly weight them to obtain more accurate fusion results. By introducing multiple data sources and fusing them, the system can still rely on data provided by other sensors when a sensor fails or the data is unreliable, thereby enhancing the robustness and reliability of the system.

[0199] In summary, the present invention uses the Bayesian estimation method to perform 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 according to the credibility of each data source, the system can effectively improve the accuracy of depth measurement and enhance the reliability and accuracy of task execution. This method is suitable for data fusion in a variety of environments, and can provide more robust performance, especially in complex or dynamically changing environments.

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

[0201] The measurement results can be evaluated, the corresponding measurement errors can be calculated, and the position adjustment amounts for 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 measurements of the UAV and the unmanned boat after the position adjustments.

[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 a 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 from high-precision measurement equipment or historical data, used as a benchmark for measuring the accuracy of current depth measurements.

[0208] The number of measurement points N represents the number of points at which depth measurement is performed in the entire measurement area. N is usually the number of discrete sampling points in the actual measurement area.

[0209] E th It 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 result is not accurate enough and task adjustment or collaborative planning may be needed.

[0210] (D fusion (i)-D ref (i)) 2 The error of each measurement point is the square of the difference between the fused depth value and the reference depth value. The square operation is to eliminate negative values ​​and highlight larger errors. The larger the square value, the greater the error of the measurement point. The root mean square error E is obtained by summing the squares of the errors of all measurement points, taking the average value and taking the square root. This value can intuitively reflect the average deviation of the depth data during the entire measurement process. The smaller the RMSE value, the higher the measurement accuracy of the system and the closer the measurement result is 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 unmanned boat 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 in the measurement process. Common sources of error include sensor noise, environmental interference, equipment calibration problems, etc. If the calculated E is less than the set error threshold, the measurement result is considered to be 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 operations:

[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 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 noise and errors in the depth data.

[0220] Enhanced collaborative work:

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

[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, the present invention can provide the system with feedback on the accuracy of depth measurement. If the error exceeds the set threshold, the system will provide feedback to adjust the task allocation and collaborative planning to improve the accuracy of the measurement results. This process ensures that the entire measurement task can be completed within a reasonable error range, thereby improving the measurement accuracy of the system and the efficiency of task execution.

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

[0226]

[0227] Among them, Δx is the position adjustment amount, and k is the adjustment coefficient.

[0228] In the specific implementation, the position adjustment amount Δx represents the displacement or position parameter that needs to be adjusted. This amount usually affects the path, flight altitude, navigation speed, etc. of the drone or unmanned boat to improve the accuracy of the mission. When the measurement error is greater than the set threshold, the system will calculate the required adjustment amount according to the formula.

[0229] The adjustment coefficient k is a constant that represents the sensitivity or proportionality factor of the adjustment. The value of this coefficient affects the size of the adjustment. If k is large, the system will make larger adjustments when the error is large; if k is small, the adjustment is small and the system has a higher tolerance for errors.

[0230] E represents the deviation between the actual measured depth value and the reference depth value. This value is calculated by a formula and reflects the accuracy of the current measurement result. The larger the error E, the worse the measurement accuracy and the more adjustments are needed.

[0231] E th is a preset value used to determine whether the accuracy of the measurement result meets the requirements. th , it means that the measurement error exceeds the set tolerance range and needs to be adjusted accordingly.

[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 according to the value to ensure that the task can be executed more accurately.

[0234] When the error exceeds the preset threshold, the system can effectively reduce the measurement error by adjusting the flight path or navigation route to ensure that the depth measurement result is within the tolerance range. By adjusting the 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 regarded as a feedback control mechanism. When the system finds that the measurement result does not meet the requirements, it will feedback and adjust the task execution strategy based on the current error. By adjusting the path, speed, etc., the system can effectively reduce the measurement error and improve the accuracy of task completion.

[0235] In summary, by calculating the required position adjustment amount based on the difference between the measurement error and the set threshold, its role is to automatically adjust the key parameters in the task execution (such as the flight path of the drone or the navigation path of the unmanned boat) to improve the measurement accuracy. The adjustment coefficient controls the sensitivity of the adjustment, allowing the system to flexibly adjust according to different task requirements. Through this adjustment mechanism, the system can respond quickly when the measurement error is too large, optimize the path planning, and ensure the smooth execution of the task and the accuracy of the measurement results.

[0236] See also Figure 3 , 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.

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

[0238] The initial setting module 301 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;

[0239] A link establishing module 302 is used 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 through the link stability factor;

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

[0241] A result determination module 304 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;

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

[0243] See also Figure 4 , Figure 4 Schematic diagram 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] According to the signal transmission rate and the link stability factor, a communication link is established between the UAV and the unmanned boat to determine the link stability factor, and the stability of the communication link is evaluated by the link stability factor;

[0246] According to the measurement task 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 unmanned boat to collect, transmit and fuse measurement data according to the collaborative path to obtain measurement results for the measurement task target;

[0248] The measurement result is evaluated, and feedback adjustment is performed on the measurement result to generate a target measurement result.

[0249] See also Figure 5 , Figure 5 A 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] According to the signal transmission rate and the link stability factor, a communication link is established between the UAV and the unmanned boat to determine the link stability factor, and the stability of the communication link is evaluated by the link stability factor;

[0252] According to the measurement task 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 unmanned boat to collect, transmit and fuse measurement data according to the collaborative path to obtain measurement results for the measurement task target;

[0254] The measurement result is evaluated, and feedback adjustment is performed on the measurement result to generate a target measurement result.

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

[0256] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 generate 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 flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple 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 a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A 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 operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0260] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other 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 can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats, characterized in that: The method comprises: Initialize the sensors and communication modules of the drone and unmanned boat to determine the signal transmission rate and link stability factor; According to the signal transmission rate and the link stability factor, a communication link is established between the UAV and the unmanned boat to determine the link stability factor, and the stability of the communication link is evaluated by the link stability factor; According to the measurement task objectives, the measurement tasks are assigned to the UAV and the unmanned boat and a collaborative path is planned; 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 task target; The measurement result is evaluated, and feedback adjustment is performed on the measurement result to generate a target measurement result.

2. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 1 is characterized in that: The determining of the signal transmission rate and the link stability factor comprises: Obtaining a channel bandwidth of a frequency range in which a channel between the UAV and the UAV can transmit; Obtaining the transmission power used by the UAV and the unmanned boat when sending signals on the channel; Obtaining the noise power spectral density of the noise power within the unit bandwidth of the channel; Determining a signal transmission rate of the channel according to the channel bandwidth, the transmit power and the noise power spectral density; Obtaining the gain of each of the channels between the UAV and the UAV, and the number of the channels; The link stability factor is determined according to the gain of each of the channels and the number of the channels.

3. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 2 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; 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 S th is the communication link stability threshold.

4. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 3 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 the measurement requirements of the UAV and the unmanned boat according to 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; In the mission area boundary, determining the path selection probability of the UAV and the path selection probability of the unmanned boat, 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 coordination factor, and the coordinated path of the UAV and the unmanned boat is determined.

5. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 4 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, allowduav uav It is the next set of nodes that the drone can choose.

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

7. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 6 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 task 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 drone and a second transmission priority of the data collected by the unmanned boat; Determine a data transmission order and perform data transmission according to the first transmission priority and the second priority; The transmitted UAV data and the unmanned boat data are subjected to data fusion processing based on Bayesian estimation to obtain the measurement result.

8. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 7 is 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; Based on the time interval, calculating the change amount of the depth data and the change amount of the water quality parameter; The second transmission priority is determined based on the change amount of the depth data and its corresponding first weight, and the change amount of the water quality parameter and its corresponding second weight.

9. The measurement method based on cooperative networking of unmanned aerial vehicles and unmanned boats according to claim 8 is 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 credibility of the estimated depth value; Acquire the directly measured depth value collected by the unmanned boat, and construct 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.

10. A measurement system based on cooperative networking of unmanned aerial vehicles and unmanned boats, characterized in that: The system comprises: The initial setting module is used to initialize the sensors and communication modules of the drone and unmanned boat, and determine the signal transmission rate and link stability factor; A link establishment module, used 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 through the link stability factor; A path planning module, used to assign measurement tasks to the UAV and the unmanned boat and plan a collaborative path according to the measurement task objectives; A result determination module, 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, and perform feedback adjustment on the measurement result to generate a target measurement result.

Images