Cooperative operation method and system of multi-type surveying equipment

Abstract

The application discloses a multi-type scanning equipment cooperative operation method and system, and relates to the technical field of intelligent control, which comprises the following steps: reading a target detection task and a target equipment set; based on the target detection task, evaluating the influence degree of the target equipment set to determine an equipment influence coefficient set; monitoring and collecting a to-be-detected area to obtain detection water area influence data; based on the detection task and the detection water area influence data, matching the working frequency of the target equipment set to generate an adaptive frequency threshold set; taking the adaptive frequency threshold set as a solution set space, taking a predetermined frequency interference threshold as a constraint, and based on a detection quality evaluation function, optimizing the working frequency of the target equipment set; obtaining an optimized frequency set based on the optimization result, and performing task detection based on the optimized frequency set and the target equipment set. The application solves the technical problem that multiple devices exist mutual interference due to frequency problems in the process of simultaneous operation in the prior art, and achieves the technical effect of improving detection efficiency and accuracy.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to a collaborative operation method and system for multiple types of scanning and measuring equipment. Background Technology

[0002] With the continuous development of unmanned technology, unmanned surface vessels (USVs) are being used more and more widely in fields such as marine exploration, environmental monitoring, and resource surveys. The various types of surveying equipment carried by USVs can perform a variety of tasks, including water quality testing, seabed topography mapping, and biological resource surveys, greatly facilitating scientific research and engineering practice. However, in practical applications, the coordinated operation of multiple surveying devices on USVs still faces many challenges.

[0003] Unmanned surface vessels (USVs) are equipped with a variety of devices, including but not limited to sonar, water quality analyzers, and optical cameras, each with its own specific working principles and performance characteristics. Ensuring that these devices work collaboratively and avoid mutual interference during USV navigation is a crucial issue. Summary of the Invention

[0004] This application provides a method and system for the collaborative operation of multiple types of scanning and testing equipment, which is used to address the technical problem of mutual interference caused by frequency issues when multiple devices are operating simultaneously in the prior art.

[0005] In view of the above problems, this application provides a collaborative operation method and system for multiple types of scanning and measuring equipment.

[0006] The first aspect of this application provides a method for collaborative operation of multiple types of scanning and measuring equipment, the method comprising:

[0007] The process involves: reading the target detection task and the target equipment set, where the detection task includes the detection task type, measurement accuracy requirements, and measurement resolution requirements; evaluating the impact of each target equipment set based on the target detection task to determine the equipment impact coefficient set; monitoring and collecting data on the area to be detected according to preset water area impact factors to obtain water area impact data; matching the operating frequencies of the target equipment set with the detection task and the water area impact data to generate an adaptive frequency threshold set, where each target equipment corresponds one-to-one with an adaptive frequency threshold; using the adaptive frequency threshold set as the solution space and a predetermined frequency interference threshold as a constraint, optimizing the equipment operating frequencies of the target equipment set based on a detection quality evaluation function constructed from the equipment impact coefficient set; obtaining an optimized frequency set based on the optimization results, and executing the detection task based on the optimized frequency set and the target equipment set.

[0008] A second aspect of this application provides a collaborative operation system for multiple types of scanning and measuring equipment, the system comprising:

[0009] The system includes: a target information reading module, which reads the target detection task and the target equipment set, wherein the detection task includes the detection task type, measurement accuracy requirements, and measurement resolution requirements; an impact assessment module, which performs impact assessments on the target equipment set based on the target detection task to determine the equipment impact coefficient set; a detection water area impact data acquisition module, which monitors and collects data on the area to be detected according to preset water area impact factors to obtain detection water area impact data; and an adaptation frequency threshold set generation module, which... Based on the detection task and the impact data of the detected water area, the operating frequencies of the target equipment set are matched to generate an adaptive frequency threshold set, wherein there is a one-to-one correspondence between the target equipment and the adaptive frequency threshold; an optimization module, using the adaptive frequency threshold set as the solution space and a predetermined frequency interference threshold as a constraint, optimizes the operating frequencies of the equipment in the target equipment set based on a detection quality evaluation function, wherein the detection quality evaluation function is constructed based on the equipment impact coefficient set; a task detection module, obtaining an optimized frequency set based on the optimization results, and performing task detection based on the optimized frequency set and the target equipment set.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] This application reads a target detection task and a set of target equipment. The detection task includes the detection task type, measurement accuracy requirements, and measurement resolution requirements. Based on the target detection task, the impact of each set of target equipment is assessed to determine the set of equipment impact coefficients. Monitoring and data collection are performed on the area to be detected according to preset water area impact factors to obtain water area impact data. Based on the detection task and the water area impact data, the operating frequencies of the target equipment set are matched to generate a set of suitable frequency thresholds, where there is a one-to-one correspondence between target equipment and suitable frequency thresholds. Using the suitable frequency threshold set as the solution space and a predetermined frequency interference threshold as a constraint, the operating frequencies of the target equipment set are optimized based on a detection quality evaluation function constructed from the set of equipment impact coefficients. An optimized frequency set is obtained based on the optimization results, and the detection task is executed based on the optimized frequency set and the target equipment set. This invention solves the technical problem of mutual interference due to frequency issues when multiple devices operate simultaneously in the prior art. Through impact assessment, water area impact factor monitoring, and frequency matching and optimization techniques, it achieves optimized collaborative operation of multiple devices, thereby improving detection efficiency and accuracy. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the collaborative operation method for multiple types of scanning and measuring equipment provided in the embodiments of this application;

[0014] Figure 2 This is a schematic diagram of the collaborative operation system structure of multiple types of scanning and measuring equipment provided in the embodiments of this application.

[0015] Figure labeling: Target information reading module 11, Impact assessment module 12, Impact data acquisition module for the probed water area 13, Adaptation frequency threshold set generation module 14, Optimization module 15, Task detection module 16. Detailed Implementation

[0016] This application provides a collaborative operation method and system for multiple types of scanning equipment. It addresses the technical problem of mutual interference caused by frequency issues when multiple devices operate simultaneously in the prior art. Through impact assessment, water area impact factor monitoring, and frequency matching and optimization techniques, it optimizes the collaborative operation of multiple devices, thereby improving detection efficiency and accuracy.

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0019] Example 1

[0020] like Figure 1 As shown, this application provides a collaborative operation method for multiple types of scanning and measuring equipment, the method comprising:

[0021] Step S100: Read the target detection task and target device set, wherein the detection task includes the detection task type, measurement accuracy requirements, and measurement resolution requirements;

[0022] In this embodiment, reading the target detection task is the first step in the collaborative operation, involving parsing and extracting the specific content and requirements of the detection task. The target detection task includes the detection task type, measurement accuracy requirements, and measurement resolution requirements.

[0023] The type of exploration mission refers to the main purpose and nature of the mission. For example, it could be a depth measurement mission, which aims to obtain depth data of a specific body of water; it could also be a topographic scanning mission, which is used to depict underwater topography; or it could be a current velocity measurement mission, which is used to monitor the velocity and direction of water flow, etc.

[0024] Measurement accuracy requirements refer to the requirements for the accuracy of the detection data, expressed in specific numerical values ​​or error ranges. For example, the error in water depth measurement is required to be within ±0.1 meters.

[0025] Measurement resolution requirements refer to the spatial resolution requirements for the probed data. Higher resolution means more detailed probed data, reflecting more nuanced information. For example, in terrain scanning missions, high-resolution data is needed to accurately depict subtle changes in underwater topography.

[0026] The target equipment set refers to the collection of various scanning and measuring devices that can be used for detection missions. These devices have different operating principles, performance parameters, and applicable ranges. The target equipment set includes various types such as multibeam sonar, single-beam sonar, side-scan sonar, shallow profilers, and acoustic Doppler current profilers. Each device has its unique operating principle and performance characteristics, such as operating frequency, beam angle, and measurement range. In actual operation, the determination of the target equipment set often needs to be flexibly adjusted according to the actual situation. For example, the type, quantity, or configuration of equipment may be adjusted based on the specific requirements of the detection mission, the characteristics of the aquatic environment, and the actual availability of equipment.

[0027] Step S200: Based on the target detection mission, conduct an impact assessment on the target equipment set to determine the equipment impact coefficient set;

[0028] In this application embodiment, the impact assessment should follow certain principles, including the mission adaptability principle, the performance priority principle, and the comprehensive benefit principle. The mission adaptability principle assesses whether the equipment is suitable for the needs of the detection mission, such as whether the equipment's working range and accuracy meet the mission requirements. The performance priority principle prioritizes equipment with superior performance and good stability, while meeting mission requirements. The comprehensive benefit principle comprehensively considers factors such as equipment cost, maintenance difficulty, and service life, selecting the equipment with the highest overall benefit.

[0029] Impact assessment employs various methods, such as expert scoring, analytic hierarchy process (AHP), and fuzzy comprehensive evaluation. The specific method chosen depends on the actual situation. Here, we will use expert scoring as an example. First, based on the requirements of the detection mission and the characteristics of the equipment, specific indicators for impact assessment are determined, such as measurement accuracy, resolution, stability, and adaptability. Then, an assessment team composed of experienced and knowledgeable experts evaluates the target equipment set. Experts score each piece of equipment according to the evaluation indicators, forming preliminary assessment results. Finally, the expert scoring results are processed and analyzed, such as calculating the average score and standard deviation, to obtain a comprehensive score for each piece of equipment.

[0030] Based on the above evaluation results, the influence coefficient of each device in a specific detection mission is determined, and a set of device influence coefficients is obtained.

[0031] Step S300: Monitor and collect data on the area to be detected according to the preset water area impact factors to obtain the water area impact data;

[0032] In this embodiment, preset water area influencing factors are first defined. These factors include information such as water flow velocity, water temperature, water depth, water turbidity, biological density, and underwater topography, which affect the detection capability, signal transmission, and measurement accuracy of the scanning equipment. Appropriate influencing factors are selected for monitoring based on the specific requirements of the detection mission and the characteristics of the target equipment set.

[0033] After identifying the influencing factors, representative monitoring points are selected based on factors such as the topography, landforms, and water flow characteristics of the area to be monitored.

[0034] Based on the preset water area impact factors, appropriate monitoring instruments are selected and configured. These instruments possess high precision, high stability, and good adaptability, ensuring accurate measurement and recording of various data. Monitoring parameters, including sampling frequency and data acquisition time, are set according to the requirements of the detection mission. Following the set monitoring parameters and acquisition plan, the monitoring instruments are activated to acquire data on the impact of the detected water area.

[0035] Step S400: Based on the detection task and the impact data of the detected water area, match the operating frequency of the target equipment set to generate an adaptive frequency threshold set, wherein the target equipment and the adaptive frequency threshold correspond one-to-one;

[0036] In this embodiment, the collected data on the impact of the monitored water area are analyzed in depth. This data includes information on multiple aspects such as water temperature, salinity, flow velocity, and water level, which affect the performance of the target equipment in the aquatic environment. By analyzing this data, the characteristics of the aquatic environment and its impact on equipment performance are determined.

[0037] A comprehensive evaluation is conducted on the target device set in terms of its operating frequency range, stability, and anti-interference capabilities.

[0038] Based on the requirements of the detection mission and the impact data of the detected water area, the operating frequencies of the target equipment are matched. In this process, operating frequencies that meet both the requirements of the detection mission and are adaptable to the water environment are selected, taking into account the performance characteristics of the equipment and the influence of the water environment. Frequency matching is performed individually for each target device to ensure optimal performance.

[0039] After matching the operating frequencies of all target devices, these frequencies are organized into a set of adaptive frequency thresholds. Each target device corresponds to an adaptive frequency threshold.

[0040] Step S500: Using the adapted frequency threshold set as the solution space and the predetermined frequency interference threshold as a constraint, optimize the operating frequency of the target device set based on the detection quality evaluation function. The detection quality evaluation function is constructed based on the device influence coefficient set.

[0041] In this embodiment of the application, the solution space is the set of adaptive frequency thresholds, which includes the frequency range in which each target device can work stably in different aquatic environments.

[0042] A predetermined frequency interference threshold serves as a constraint, limiting the selection of the device's operating frequency. In practical applications, the device's operating frequency may be affected by interference from other devices or the external environment. Therefore, setting an interference threshold ensures that the selected operating frequency is not subject to excessive interference, thereby guaranteeing the stability and accuracy of the detection.

[0043] The detection quality evaluation function is a standard used to measure the quality of equipment operating at different frequencies. This function is constructed based on a set of equipment influence coefficients, which includes the degree to which each device affects detection quality at different frequencies. By comprehensively considering these influence coefficients, an evaluation function reflecting detection quality is built. A higher output value of this function indicates a better operating frequency for the corresponding equipment.

[0044] During the optimization process, several parameters are set, such as the choice of optimization algorithm, the number of iterations, and the search step size. The setting of these parameters directly affects the efficiency and results of the optimization. Therefore, these parameters should be initialized appropriately based on the actual situation before starting the optimization process.

[0045] Based on the solution space, constraints, and probe quality evaluation function, an optimization algorithm is executed. In each iteration, the algorithm adjusts the search direction and step size of the operating frequency according to the current operating frequency and the output value of the probe quality evaluation function, in order to find the frequency point that achieves the optimal probe quality.

[0046] When the optimization algorithm converges or reaches the preset number of iterations, it outputs the optimal solution, which corresponds to the operating frequency of the device with the highest detection quality.

[0047] Through the above process, the optimized frequency set of the target device set is obtained.

[0048] Step S600: Obtain an optimized frequency set based on the optimization results, and perform task detection based on the optimized frequency set and the target device set.

[0049] In this embodiment of the application, the optimized frequency set includes the optimal operating frequency for each target device under a given aquatic environment and detection mission requirements.

[0050] The optimized frequency set is matched with the target device set, and the corresponding operating frequency is configured for each target device.

[0051] After configuring the target device set, preparations are made before executing the mission. These include checking the connectivity and communication of the devices, confirming the specific requirements and objectives of the probe mission, and developing a detailed mission execution plan.

[0052] According to the mission execution plan, the target equipment set will be activated to conduct the detection mission. During the detection process, the equipment will collect and process data at an optimized operating frequency.

[0053] Furthermore, step S200 in the method provided in the application embodiment further includes:

[0054] Obtain the set of device performance parameters for the target device set;

[0055] Based on a predetermined expert evaluation group, the impact of the target detection mission and the equipment performance parameter set are evaluated respectively, and a set of equipment impact coefficients is generated.

[0056] The method for constructing the pre-selected evaluation expert group is as follows:

[0057] The predetermined evaluation expert group includes M evaluation experts, and each evaluation expert has a professional characteristic identifier, wherein the professional characteristics include at least years of practice, historical evaluation accuracy rate and industry achievements, and M is an integer greater than 20;

[0058] Based on the analytic hierarchy process, a professional competence assessment is conducted according to the aforementioned years of experience, historical assessment accuracy, and industry achievements, generating a professionalism coefficient.

[0059] The evaluation experts are labeled according to the professionalism coefficient to obtain a predetermined evaluation expert group, wherein the output of the predetermined evaluation expert group is the comprehensive result of the outputs of M evaluation experts.

[0060] In this embodiment, the target device set is first analyzed, and the main performance parameters of each device are listed. These parameters include the device's accuracy, stability, response time, operating range, power consumption, etc. Next, performance parameter data for each device is collected by consulting the device's technical documentation. The collected performance parameter data is then organized to construct a device performance parameter set for the target device set.

[0061] The pre-selected evaluation expert group consists of M evaluation experts, with a minimum of 20 experts. These experts possess relevant professional backgrounds and extensive experience, enabling them to accurately evaluate the target detection mission and the equipment performance parameter set. Based on their respective professional knowledge and experience, the evaluation experts will conduct impact assessments of the target detection mission and the equipment performance parameter set. Based on each expert's evaluation results, a weighted calculation using a professional expertise coefficient will be performed to generate a set of equipment impact coefficients. This set of equipment impact coefficients contains the impact coefficient of each piece of equipment on the detection mission.

[0062] When assembling the evaluation expert group, experts with relevant professional backgrounds and extensive experience are selected, ensuring they possess advantages in areas such as years of experience, historical evaluation accuracy, and industry achievements. Based on the analytic hierarchy process (AHP), the professional competence of the evaluation experts is assessed based on their years of experience, historical evaluation accuracy, and industry achievements. According to the AHP evaluation results, a professionalism coefficient is generated for each evaluation expert. The professionalism coefficient reflects the expert's professional competence and evaluation ability in the relevant field. The evaluation experts are then labeled according to their professionalism coefficients, resulting in the predetermined evaluation expert group.

[0063] Furthermore, before monitoring and collecting data on the area to be detected based on preset water area impact factors, the method further includes:

[0064] A first target device is selected from the target device set. Using the first target device as a constraint, a first detection record dataset is obtained by network retrieval. The first detection record data includes information on the detected water area and detection result error.

[0065] The first water area impact index is selected from the preset water area impact indexes. The preset water area impact indexes include at least information such as water flow velocity, water temperature, water depth, water turbidity, biological density, and underwater topography.

[0066] Based on a single analysis strategy, the first detection record dataset is extracted according to the first water area impact index, and the error impact scalar analysis is performed based on the extraction results to obtain the first error impact scalar.

[0067] Based on the first error impact scalar, a first error impact scalar sequence is generated, and multiple error impact scalar sequences are obtained by sequential analysis, wherein each error impact scalar sequence corresponds one-to-one with the target device;

[0068] The information of the multiple error impact scalar sequences is integrated to generate an overall error impact scalar sequence, and the water area impact index of the first N overall error impact scalars in the overall error impact scalar sequence is extracted and set as the preset water area impact factor, where N is an integer greater than 1.

[0069] In this embodiment, one device is randomly selected from the set of target devices as the first target device. Using the first target device as a constraint, a dataset of detection records related to that device is obtained through database queries or other methods. These records include detailed information about the detected water area, such as its geographical location, water type, and error data of the detection results.

[0070] From the preset water area impact indicators, the most relevant or most important indicator is selected as the first water area impact indicator based on the specific needs of the detection mission and the characteristics of the first target equipment.

[0071] Based on a single analysis strategy, such as thresholding or statistical analysis, the first detection record dataset is extracted according to the first water area impact index. Detection records related to the first water area impact index are selected, and error data from these records is extracted. The extracted error data is analyzed to calculate the error impact scalar, quantifying the degree of influence of the first water area impact index on the detection result error.

[0072] Arrange the error impact scalars of the first target device in chronological or other logical order to generate the first error impact scalar sequence. Repeat the above process to perform error impact scalar analysis on other devices in the target device set and generate corresponding error impact scalar sequences. Ensure that each target device has a corresponding error impact scalar sequence.

[0073] Multiple error impact scalar sequences are integrated to generate a single overall error impact scalar sequence. This sequence synthesizes the error impact of all target devices under different aquatic environments. The top N aquatic impact indices corresponding to the overall error impact scalar sequences are extracted from this sequence. These indices have the most significant impact on the detection result error and are therefore set as preset aquatic impact factors.

[0074] Furthermore, based on the detection mission and the impact data of the detected water area, the method further includes matching the operating frequency of the target equipment set.

[0075] Select the first equipment performance parameter of the first target device from the set of equipment performance parameters;

[0076] Using the performance parameters of the first device as constraints, a first sample dataset is retrieved, wherein the first sample data includes sample detection tasks, sample water area impact data, and sample working frequency thresholds;

[0077] Using the first sample dataset as training data, supervised learning is performed on the frequency matching channel built based on the feedforward neural network to obtain the first convergent frequency matching sub-channel.

[0078] A convergence frequency matching channel is generated based on the first convergence frequency matching sub-channel, and the operating frequency matching of the target device set is performed through the convergence frequency matching channel.

[0079] In this embodiment, firstly, a comprehensive analysis of the device performance parameter set is performed to understand the meaning of each parameter and its impact on device performance. This includes, but is not limited to, parameters such as device processing speed, power consumption, and stability. Then, any device is selected from the target device set as the first target device. Based on the characteristics and requirements of the first target device, relevant parameters are selected from the device performance parameter set as the first device performance parameters.

[0080] Based on the selected first equipment performance parameters, corresponding constraints are set. These constraints are used to retrieve sample data that meets the conditions from the database. According to the set constraints, sample data is retrieved from relevant resources. The retrieved sample data includes information such as sample detection tasks, sample water area impact data, and sample operating frequency thresholds. The retrieved sample data is then processed and cleaned to ensure its accuracy and completeness. Finally, a first sample dataset containing the required information is constructed.

[0081] A frequency-matching channel is constructed using a feedforward neural network. The first sample dataset is used as training data, and the supervised learning objective and loss function are set. By adjusting the weights and biases of the neural network, the network output is matched to the working frequency threshold in the sample data. Through multiple iterations of training, the neural network gradually approaches the optimal solution. In each iteration, the error between the network output and the sample data is calculated, and the network weights and biases are updated using the backpropagation algorithm. When the neural network reaches a certain convergence accuracy on the training data, training is stopped and the current network state is saved. This converged network state is the first converged frequency-matching sub-channel.

[0082] The first convergent frequency matching sub-channel is integrated as a core component into the overall frequency matching channel. This sub-channel outputs an appropriate operating frequency threshold based on equipment performance parameters and environmental factors. A complete frequency matching channel architecture is designed, receiving target equipment performance parameters, detection mission requirements, and real-time water impact data as input. Other necessary algorithms and strategies, such as optimization algorithms, decision trees, or rule sets, are incorporated into the channel architecture to enhance the accuracy and robustness of frequency matching. These algorithms and strategies can work collaboratively with the first convergent frequency matching sub-channel to determine the optimal operating frequency. The generated convergent frequency matching channel is then parameter-tuned and optimized to ensure it accurately responds to the frequency matching requirements of different equipment and environments.

[0083] For each device in the target device set, its relevant performance parameters, such as processing speed, storage capacity, and power consumption, are collected. These parameters are then input into the convergence frequency matching channel. Data on the target device's current operating environment, including factors such as water area impact, is obtained. The device performance parameters and operating environment data are input into the convergence frequency matching channel, where calculations and analyses are performed using internal algorithms and strategies. Based on the input data and the calculation results from the internal algorithms, the convergence frequency matching channel outputs one or more operating frequencies suitable for the current device and environment.

[0084] Finally, the operating frequency of the convergence frequency matching channel output is configured to the corresponding target device.

[0085] Furthermore, step S500 in the method provided in the application embodiment further includes:

[0086] Using the set of adaptive frequency thresholds as a space, multiple device frequency combinations are randomly generated, and these multiple device frequency combinations are set as multiple initial solutions.

[0087] Based on the predetermined frequency interference threshold, the multiple initial solutions are filtered to obtain a standard initial solution set;

[0088] A detection quality evaluation function is constructed based on the set of equipment influence coefficients, and the standard initial solution set is used as the optimization space. Based on the detection quality evaluation function, the operating frequency of the target equipment set is optimized.

[0089] In this embodiment, multiple device frequency combinations are randomly generated within the space defined by the adaptive frequency threshold set. Each combination represents a possible device operating frequency configuration.

[0090] Frequency interference thresholds are used to measure the degree of mutual interference between the operating frequencies of devices. When the operating frequencies of devices are too close, mutual interference will occur, affecting the detection quality. Therefore, a predetermined frequency interference threshold is set to ensure that the operating frequencies of devices do not interfere with each other.

[0091] For each randomly generated initial solution, i.e., a combination of device frequencies, the interference level between the operating frequencies of each device within that combination is calculated. If the interference between the operating frequencies of devices in a combination exceeds a predetermined threshold, that combination is discarded. The solution set obtained after this filtering process is the standard initial solution set.

[0092] When calculating the interference level between the operating frequencies of the internal devices, a suitable interference measurement method should first be defined. Commonly used measurement methods include the absolute value of the frequency difference and the frequency ratio.

[0093] For a randomly generated combination of device frequencies, calculate the interference value between any two device operating frequencies. First, select any two devices from the combination to form a pair. Calculate the difference between the operating frequencies of the two devices in the pair. This difference can be an absolute frequency difference or a normalized frequency difference, depending on the chosen interference measurement method. Substitute the calculated frequency difference into the interference measurement method to obtain the interference value between the two devices. Repeat the above steps until the interference values ​​between all device pairs in the combination have been calculated. For each combination of device frequencies, summarize the interference values ​​between all device pairs.

[0094] The aggregated interference values ​​are compared with a predetermined frequency interference threshold. If the interference value exceeds the threshold, it indicates that there is significant mutual interference in the operating frequency configuration of the devices in the initial solution, which will affect the detection quality. Therefore, this initial solution is discarded and not considered in the subsequent optimization process. After the above screening process, the remaining initial solution set is the standard initial solution set.

[0095] When constructing a detection quality evaluation function, the evaluation indicators for detection quality must first be clearly defined. These indicators include detection accuracy, stability, and response speed. The detection quality evaluation function is then constructed by combining the set of equipment influence coefficients with the evaluation indicators. The specific form of the evaluation function can be a weighted sum, where the influence coefficient of each device serves as a weight, multiplied by its corresponding performance indicator and then summed.

[0096] A solution is selected from the standard initial solution set as the current optimal solution, and the corresponding probe quality evaluation function value is set to the current optimal value. An optimization algorithm, such as a genetic algorithm or particle swarm optimization algorithm, is used to iteratively search within the standard initial solution set. In each iteration, new solutions are generated according to the strategy of the optimization algorithm, and the probe quality evaluation function values ​​of these solutions are calculated. The evaluation function values ​​of the newly generated solutions are compared with the current optimal value. If the evaluation function value of the new solution is better, the current optimal solution and the optimal value are updated. Appropriate termination conditions are set, such as reaching the maximum number of iterations or the optimal solution not showing significant improvement in multiple consecutive iterations. When the termination conditions are met, the iteration stops, and the current optimal solution is output as the optimization result for the device operating frequency.

[0097] Furthermore, based on the set of equipment influence coefficients, a detection quality evaluation function is constructed, and the method further includes:

[0098] Based on the equipment influence coefficient, the target equipment in the target equipment set is weighted according to the influence weight, resulting in multiple equipment weights;

[0099] The detection quality evaluation function is constructed by weighting the device frequencies based on the multiple device weights.

[0100] The expression for the detection quality evaluation function is:

[0101]

[0102] Where P is the detection quality evaluation coefficient, N is the number of devices in the target device set, and v i M represents the device frequency weight for the i-th device. i K represents the detection accuracy of the i-th device. i W represents the detection resolution of the i-th device. i T characterizes the stability of the detection results of the i-th device. i Characterizes the detection duration of the i-th device.

[0103] In this embodiment, an influence weight is assigned to each device in the target device set based on the device influence coefficient. The influence weight represents the relative importance of each device in the overall detection mission. The weight setting is linear, meaning the influence coefficient is used directly as the weight.

[0104] Device frequency weighting is the process of associating the influence weight of devices with specific device frequencies. When performing device frequency weighting, the available frequency range for each device is determined. These ranges are constrained by the device's physical limitations or mission requirements. After determining the device's frequency range, these frequency ranges are weighted by assigning different weight values ​​to them based on the device's influence weight. This allows devices with higher weights to have a larger search space or higher priority during the frequency optimization process.

[0105] After mapping is completed, a detection quality evaluation function is constructed. This function is a mathematical model that quantitatively evaluates the detection quality under different combinations of device frequencies. It comprehensively considers the performance indicators and weights of the devices to guide the frequency optimization process. This function is used to assign the performance indicator M of each device... i K i W i T i Its frequency weight v i Multiply the results and sum them to obtain a comprehensive detection quality evaluation coefficient P. The higher the detection quality evaluation coefficient, the better the detection quality under a given combination of equipment frequencies.

[0106] Furthermore, using the standard initial solution set as the optimization space, and based on the detection quality evaluation function, the operating frequency of the target device set is optimized. The method further includes:

[0107] The standard initial solution set is evaluated based on the detection quality evaluation function to generate several quality evaluation coefficients;

[0108] The standard initial solutions in the standard initial solution set are arranged in descending order of quality evaluation coefficients to generate a standard initial solution sequence;

[0109] The first Q solutions in the standard initial solution sequence are designated as the head solution, and the last Z solutions are designated as the tail solution, where the sum of Q and Z is the number of standard initial solutions, and Z is much larger than Q.

[0110] Using the Q head solutions as leaders, cluster the Z tail solutions to generate Q neighborhoods;

[0111] Within the Q neighborhoods, the tail solutions within the neighborhoods are adjusted based on a preset update step size, with the head solution as the selected direction, to obtain Q updated neighborhoods. If the updated tail solution within an updated neighborhood does not meet the predetermined frequency interference threshold, then the tail solution is not updated.

[0112] Within the Q update domains, if there exists a tail solution whose quality evaluation coefficient is greater than that of the head solution, then the tail solution replaces and updates the head solution.

[0113] Continuously iterate and optimize until the preset number of times threshold is met, and output the current Q update domains;

[0114] The optimal update domain is determined based on the Q update domains, and the head solution of the optimal update domain is selected as the optimization result.

[0115] In this embodiment, for each solution in the standard initial solution set, the constructed probe quality evaluation function is used to calculate the corresponding quality evaluation coefficient. Based on the calculated quality evaluation coefficients, the solutions in the standard initial solution set are arranged in descending order of coefficients to generate a standard initial solution sequence.

[0116] In the standard initial solution sequence, the first Q solutions are selected as the head solutions, which have high quality evaluation coefficients and are considered relatively optimal. Meanwhile, the last Z solutions are selected as the tail solutions, which have relatively low quality evaluation coefficients. The selection of Q and Z is determined based on the problem size and requirements, with Z being significantly larger than Q.

[0117] Using Q head solutions as leaders, cluster the Z tail solutions. Clustering assigns the tail solutions to the neighborhoods represented by the head solutions based on some similarity or distance metric. This forms Q neighborhoods, each consisting of one head solution and a set of tail solutions.

[0118] In each domain, the frequency combination represented by the head solution performs well in terms of detection quality. Using the head solution as the selected direction, the tail solutions are adjusted to move closer to the frequency combination represented by the head solution, aiming to improve the detection quality of the tail solutions. Next, the tail solutions within the domain are adjusted based on a preset update step size. The update step size determines the magnitude of the frequency change during each tail solution adjustment. The choice of step size needs to be determined according to the specific problem. During the adjustment process, the updated frequency combination of each tail solution is calculated, and it is evaluated whether it meets the preset frequency interference threshold. If the updated tail solution does not meet this threshold, i.e., its frequency combination causes significant interference, then this tail solution will not be updated, and its original frequency combination will be maintained. Through this adjustment process, the tail solutions within each domain are fine-tuned according to the direction of the head solution and the preset update step size to improve the detection quality as much as possible while remaining within an acceptable frequency interference range. Finally, each domain will obtain an updated set of tail solutions, which, together with the original head solutions, constitute Q updated domains.

[0119] After adjusting the tail solutions within the domain, the quality evaluation coefficients of the tail solutions and the head solutions in each domain are compared. If a tail solution has a higher quality evaluation coefficient than the head solution, the head and tail solutions are replaced and updated to further improve the quality of solutions within the domain.

[0120] The above steps are iteratively repeated until a preset threshold number of iterations is reached. In each iteration, the solution is adjusted and updated based on the current solution set and the probe quality evaluation function to gradually approach the optimal solution.

[0121] After reaching the preset number of iterations, the current Q update neighborhoods are output. Then, based on certain selection criteria, such as the average quality evaluation coefficient, the optimal update neighborhood is determined from the Q update neighborhoods. Finally, the head solution of the optimal update neighborhood is selected as the final optimization result, i.e., the optimal combination of device frequencies.

[0122] In summary, the embodiments of this application have at least the following technical effects:

[0123] This application reads a target detection task and a set of target equipment. The detection task includes the detection task type, measurement accuracy requirements, and measurement resolution requirements. Based on the target detection task, the impact of each set of target equipment is assessed to determine the set of equipment impact coefficients. Monitoring and data collection are performed on the area to be detected according to preset water area impact factors to obtain water area impact data. Based on the detection task and the water area impact data, the operating frequencies of the target equipment set are matched to generate a set of suitable frequency thresholds, where there is a one-to-one correspondence between target equipment and suitable frequency thresholds. Using the suitable frequency threshold set as the solution space and a predetermined frequency interference threshold as a constraint, the operating frequencies of the target equipment set are optimized based on a detection quality evaluation function constructed from the set of equipment impact coefficients. An optimized frequency set is obtained based on the optimization results, and the detection task is executed based on the optimized frequency set and the target equipment set. This invention solves the technical problem of mutual interference due to frequency issues when multiple devices operate simultaneously in the prior art. Through impact assessment, water area impact factor monitoring, and frequency matching and optimization techniques, it achieves optimized collaborative operation of multiple devices, thereby improving detection efficiency and accuracy.

[0124] Example 2

[0125] Based on the same inventive concept as the collaborative operation method of multiple types of scanning and measuring equipment in the foregoing embodiments, such as Figure 2 As shown, this application provides a collaborative operation system for multiple types of scanning and measuring equipment. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0126] The target information reading module 11 reads the target detection task and the target equipment set, wherein the detection task includes the detection task type, measurement accuracy requirements, and measurement resolution requirements;

[0127] Impact assessment module 12, based on the target detection task, performs impact assessment on the target equipment set respectively, and determines the equipment impact coefficient set;

[0128] The water area impact data acquisition module 13 monitors and collects data on the area to be detected according to preset water area impact factors to obtain water area impact data.

[0129] The frequency threshold set generation module 14 generates a frequency threshold set by matching the working frequency of the target equipment set with the detection task and the impact data of the detected water area, wherein the target equipment and the frequency threshold are in one-to-one correspondence.

[0130] The optimization module 15 uses the adapted frequency threshold set as the solution space and a predetermined frequency interference threshold as a constraint to optimize the operating frequency of the target device set based on the detection quality evaluation function. The detection quality evaluation function is constructed based on the device influence coefficient set.

[0131] The task detection module 16 obtains an optimized frequency set based on the optimization results, and performs task detection based on the optimized frequency set and the target device set.

[0132] Furthermore, the influence assessment module 12 is also used to implement the following functions:

[0133] Obtain the set of device performance parameters for the target device set;

[0134] Based on a predetermined expert evaluation group, the impact of the target detection mission and the equipment performance parameter set are evaluated respectively, and a set of equipment impact coefficients is generated.

[0135] The method for constructing the pre-selected evaluation expert group is as follows:

[0136] The predetermined evaluation expert group includes M evaluation experts, and each evaluation expert has a professional characteristic identifier, wherein the professional characteristics include at least years of practice, historical evaluation accuracy rate and industry achievements, and M is an integer greater than 20;

[0137] Based on the analytic hierarchy process, a professional competence assessment is conducted according to the aforementioned years of experience, historical assessment accuracy, and industry achievements, generating a professionalism coefficient.

[0138] The evaluation experts are labeled according to the professionalism coefficient to obtain a predetermined evaluation expert group, wherein the output of the predetermined evaluation expert group is the comprehensive result of the outputs of M evaluation experts.

[0139] Furthermore, the influence assessment module 12 is also used to implement the following functions:

[0140] A first target device is selected from the target device set. Using the first target device as a constraint, a first detection record dataset is obtained by network retrieval. The first detection record data includes information on the detected water area and detection result error.

[0141] The first water area impact index is selected from the preset water area impact indexes. The preset water area impact indexes include at least information such as water flow velocity, water temperature, water depth, water turbidity, biological density, and underwater topography.

[0142] Based on a single analysis strategy, the first detection record dataset is extracted according to the first water area impact index, and the error impact scalar analysis is performed based on the extraction results to obtain the first error impact scalar.

[0143] Based on the first error impact scalar, a first error impact scalar sequence is generated, and multiple error impact scalar sequences are obtained by sequential analysis, wherein each error impact scalar sequence corresponds one-to-one with the target device;

[0144] The information of the multiple error impact scalar sequences is integrated to generate an overall error impact scalar sequence, and the water area impact index of the first N overall error impact scalars in the overall error impact scalar sequence is extracted and set as the preset water area impact factor, where N is an integer greater than 1.

[0145] Furthermore, the influence assessment module 12 is also used to implement the following functions:

[0146] Select the first equipment performance parameter of the first target device from the set of equipment performance parameters;

[0147] Using the performance parameters of the first device as constraints, a first sample dataset is retrieved, wherein the first sample data includes sample detection tasks, sample water area impact data, and sample working frequency thresholds;

[0148] Using the first sample dataset as training data, supervised learning is performed on the frequency matching channel built based on the feedforward neural network to obtain the first convergent frequency matching sub-channel.

[0149] A convergence frequency matching channel is generated based on the first convergence frequency matching sub-channel, and the operating frequency matching of the target device set is performed through the convergence frequency matching channel.

[0150] Furthermore, the optimization module 15 is also used to implement the following functions:

[0151] Using the set of adaptive frequency thresholds as a space, multiple device frequency combinations are randomly generated, and these multiple device frequency combinations are set as multiple initial solutions.

[0152] Based on the predetermined frequency interference threshold, the multiple initial solutions are filtered to obtain a standard initial solution set;

[0153] A detection quality evaluation function is constructed based on the set of equipment influence coefficients, and the standard initial solution set is used as the optimization space. Based on the detection quality evaluation function, the operating frequency of the target equipment set is optimized.

[0154] Furthermore, the optimization module 15 is also used to implement the following functions:

[0155] Based on the equipment influence coefficient, the target equipment in the target equipment set is weighted according to the influence weight, resulting in multiple equipment weights;

[0156] The detection quality evaluation function is constructed by weighting the device frequencies based on the multiple device weights.

[0157] The expression for the detection quality evaluation function is:

[0158]

[0159] Where P is the detection quality evaluation coefficient, N is the number of devices in the target device set, and v i M represents the device frequency weight for the i-th device. i K represents the detection accuracy of the i-th device. i W represents the detection resolution of the i-th device. i T characterizes the stability of the detection results of the i-th device. i Characterizes the detection duration of the i-th device.

[0160] Furthermore, the optimization module 15 is also used to implement the following functions:

[0161] The standard initial solution set is evaluated based on the detection quality evaluation function to generate several quality evaluation coefficients;

[0162] The standard initial solutions in the standard initial solution set are arranged in descending order of quality evaluation coefficients to generate a standard initial solution sequence;

[0163] The first Q solutions in the standard initial solution sequence are designated as the head solution, and the last Z solutions are designated as the tail solution, where the sum of Q and Z is the number of standard initial solutions, and Z is much larger than Q.

[0164] Using the Q head solutions as leaders, cluster the Z tail solutions to generate Q neighborhoods;

[0165] Within the Q neighborhoods, the tail solutions within the neighborhoods are adjusted based on a preset update step size, with the head solution as the selected direction, to obtain Q updated neighborhoods. If the updated tail solution within an updated neighborhood does not meet the predetermined frequency interference threshold, then the tail solution is not updated.

[0166] Within the Q update domains, if there exists a tail solution whose quality evaluation coefficient is greater than that of the head solution, then the tail solution replaces and updates the head solution.

[0167] Continuously iterate and optimize until the preset number of times threshold is met, and output the current Q update domains;

[0168] The optimal update domain is determined based on the Q update domains, and the head solution of the optimal update domain is selected as the optimization result.

[0169] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0170] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0171] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for collaborative operation of multiple types of scanning and measuring equipment, characterized in that, The method includes: Read the target detection task and target device set, wherein the detection task includes the detection task type, measurement accuracy requirements, and measurement resolution requirements; Based on the target detection mission, the impact of each set of target devices is evaluated to determine the set of device impact coefficients; Based on the preset water area impact factors, the area to be detected is monitored and data is collected to obtain the impact data of the detected water area. Based on the detection mission and the impact data of the detected water area, the operating frequency of the target equipment set is matched to generate an adaptive frequency threshold set, wherein the target equipment and the adaptive frequency threshold are in one-to-one correspondence. Using the set of adaptive frequency thresholds as the solution space and a predetermined frequency interference threshold as a constraint, the operating frequency of the target device set is optimized based on the detection quality evaluation function, which is constructed based on the set of device influence coefficients. An optimized frequency set is obtained based on the optimization results, and a task detection is performed based on the optimized frequency set and the target device set; Using the adapted frequency threshold set as the solution space and a predetermined frequency interference threshold as a constraint, the operating frequencies of the target device set are optimized based on a detection quality evaluation function. This detection quality evaluation function is constructed based on the device influence coefficient set and includes: Using the set of adaptive frequency thresholds as a space, multiple device frequency combinations are randomly generated, and these multiple device frequency combinations are set as multiple initial solutions. Based on the predetermined frequency interference threshold, the multiple initial solutions are filtered to obtain a standard initial solution set; A detection quality evaluation function is constructed based on the set of equipment influence coefficients, and the standard initial solution set is used as the optimization space. Based on the detection quality evaluation function, the operating frequency of the target equipment set is optimized. Using the standard initial solution set as the optimization space, and based on the detection quality evaluation function, the operating frequency of the target device set is optimized, including: The standard initial solution set is evaluated based on the detection quality evaluation function to generate several quality evaluation coefficients; The standard initial solutions in the standard initial solution set are arranged in descending order of quality evaluation coefficients to generate a standard initial solution sequence; The first Q solutions in the standard initial solution sequence are designated as the head solution, and the last Z solutions are designated as the tail solution, where the sum of Q and Z is the number of standard initial solutions, and Z is much larger than Q. Using the Q head solutions as leaders, cluster the Z tail solutions to generate Q neighborhoods; Within the Q neighborhoods, the tail solutions within the neighborhoods are adjusted based on a preset update step size, with the head solution as the selected direction, to obtain Q updated neighborhoods. If the updated tail solution within an updated neighborhood does not meet the predetermined frequency interference threshold, then the tail solution is not updated. Within the Q update domains, if there exists a tail solution whose quality evaluation coefficient is greater than that of the head solution, then the tail solution replaces and updates the head solution. Continuously iterate and optimize until the preset number of times threshold is met, and output the current Q update domains; The optimal update domain is determined based on the Q update domains, and the head solution of the optimal update domain is selected as the optimization result.

2. The method according to claim 1, characterized in that, Based on the target detection mission, the impact assessment of the target device set is performed, including: Obtain the set of device performance parameters for the target device set; Based on a predetermined expert evaluation group, the impact of the target detection mission and the equipment performance parameter set are evaluated respectively, and a set of equipment impact coefficients is generated. The method for constructing the pre-selected evaluation expert group is as follows: The predetermined evaluation expert group includes M evaluation experts, and each evaluation expert has a professional characteristic identifier, wherein the professional characteristics include years of experience, historical evaluation accuracy rate and industry achievements, and M is an integer greater than 20; Based on the analytic hierarchy process, a professional competence assessment is conducted according to the aforementioned years of experience, historical assessment accuracy, and industry achievements, generating a professionalism coefficient. The evaluation experts are labeled according to the professionalism coefficient to obtain a predetermined evaluation expert group, wherein the output of the predetermined evaluation expert group is the comprehensive result of the outputs of M evaluation experts.

3. The method according to claim 2, characterized in that, Based on preset water area impact factors, monitoring and data collection are conducted on the area to be detected, which previously included: A first target device is selected from the target device set. Using the first target device as a constraint, a first detection record dataset is obtained by network retrieval. The first detection record data includes information on the detected water area and detection result error. The first water area impact index is selected from the preset water area impact indexes. The preset water area impact indexes include water flow velocity, water temperature, water depth, water turbidity, biological density, and underwater topography information. Based on a single analysis strategy, the first detection record dataset is extracted according to the first water area impact index, and the error impact scalar analysis is performed based on the extraction results to obtain the first error impact scalar. Based on the first error impact scalar, a first error impact scalar sequence is generated, and multiple error impact scalar sequences are obtained by sequential analysis, wherein each error impact scalar sequence corresponds one-to-one with the target device; The information of the multiple error impact scalar sequences is integrated to generate an overall error impact scalar sequence, and the water area impact index of the first N overall error impact scalars in the overall error impact scalar sequence is extracted and set as the preset water area impact factor, where N is an integer greater than 1.

4. The method according to claim 2, characterized in that, Based on the detection mission and the impact data of the detected water area, the operating frequency of the target equipment set is matched, including: Select the first equipment performance parameter of the first target device from the set of equipment performance parameters; Using the performance parameters of the first device as constraints, a first sample dataset is retrieved, wherein the first sample data includes sample detection tasks, sample water area impact data, and sample working frequency thresholds; Using the first sample dataset as training data, supervised learning is performed on the frequency matching channel built based on the feedforward neural network to obtain the first convergent frequency matching sub-channel. A convergence frequency matching channel is generated based on the first convergence frequency matching sub-channel, and the operating frequency matching of the target device set is performed through the convergence frequency matching channel.

5. The method according to claim 1, characterized in that, Based on the set of equipment influence coefficients, a detection quality evaluation function is constructed, including: Based on the equipment influence coefficient, the target equipment in the target equipment set is weighted according to the influence weight, resulting in multiple equipment weights; The detection quality evaluation function is constructed by weighting the device frequencies based on the multiple device weights. The expression for the detection quality evaluation function is: ； Where P is the detection quality evaluation coefficient, and N is the number of devices in the target device set. Let i be the device frequency weight of the i-th device. Characterizes the detection accuracy of the i-th device. Characterizes the detection resolution of the i-th device. Characterizes the stability of the detection results of the i-th device. Characterizes the detection duration of the i-th device.

6. A collaborative operation system for multiple types of scanning and measuring equipment, characterized in that, The system is used to execute the collaborative operation method of multiple types of scanning and measuring equipment as described in any one of claims 1-5, including: The target information reading module reads the target detection task and the target equipment set, wherein the detection task includes the detection task type, measurement accuracy requirements, and measurement resolution requirements; An impact assessment module, which assesses the impact of the target equipment set based on the target detection task, and determines the equipment impact coefficient set; The water area impact data acquisition module monitors and collects data on the area to be detected based on preset water area impact factors to obtain water area impact data. An adaptive frequency threshold set generation module, which generates an adaptive frequency threshold set by matching the operating frequency of the target equipment set with the detection task and the impact data of the detected water area, wherein the target equipment and the adaptive frequency threshold are in one-to-one correspondence. The optimization module uses the adapted frequency threshold set as the solution space and a predetermined frequency interference threshold as a constraint to optimize the operating frequency of the target device set based on the detection quality evaluation function. The detection quality evaluation function is constructed based on the device influence coefficient set. The task detection module obtains an optimized frequency set based on the optimization results, and performs task detection based on the optimized frequency set and the target device set.

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