A method for constructing a marine mobile distributed detection network

Abstract

The present invention discloses a method for constructing a marine mobile distributed detection network, which relates to the field of marine detection and communication technology. The method is used to solve the problems that existing marine detection networks are difficult to adapt to dynamic changes and are prone to link failure and reconstruction lag. The method obtains communication quality records of detection nodes in the marine environment, screens and marks key nodes, collects their motion trajectories and data traffic, calculates collaboration scores, and obtains the main network collaboration index in combination with real-time communication quality. The calculation accuracy of the collaboration index is improved by splitting and summing, and the detection cost is reduced. Static or dynamic link configuration is selected according to the collaboration index, and link performance is improved using a communication optimization algorithm. Under the dynamic link configuration, signal strength changes and the number of interference signals are detected, the execution time is calculated, and an early warning prompt is issued, thereby improving the communication stability and security of the distributed detection network.

Description

Technical Field

[0001] The present invention relates to the field of ocean detection and communication technology, and more specifically, to a method for constructing an ocean mobile distributed detection network. Background Art

[0002] Marine mobile distributed detection network construction technology involves optimizing node deployment, dynamic networking, and real-time data transmission to ensure efficient and collaborative operation in complex marine environments. This technology, when applied to fields such as marine environmental monitoring, resource exploration, and military reconnaissance, can significantly improve detection efficiency and accuracy. However, existing distributed detection networks have limitations in terms of dynamic adaptability, communication stability, and multi-node collaboration, which hinder their effectiveness in dynamic marine environments.

[0003] The existing technology has the following deficiencies:

[0004] At present, existing ocean exploration networks achieve inter-node communication through fixed communication links. However, this type of link configuration is difficult to adapt to dynamic changes in scenarios with high node mobility and frequent environmental changes. It is prone to problems such as link failure and reconstruction lag, which reduces the allocation and optimization of link resources and increases the risk of communication interruption and the probability of data loss. Therefore, a method for constructing an ocean mobile distributed exploration network is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for constructing a marine mobile distributed detection network, which evaluates the collaborative efficiency of the detection network by analyzing the working status of each detection node in the marine environment, performs a communication quality test after each dynamic networking is completed, selects a communication optimization strategy based on the collaborative efficiency of the comprehensive detection network, calculates the time interval for the next dynamic networking, and issues early warning prompts according to the time interval to adjust the node deployment position to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing a marine mobile distributed detection network, comprising the following steps:

[0008] Step S1: Obtain the location information and communication quality records of each detection node in the marine environment, screen and mark the key nodes of the detection network according to the communication quality records of each node; collect the motion trajectory data of the marked nodes, detect the data transmission volume through the marked nodes during the operation of the detection network, and calculate the data flow;

[0009] Step S2: Calculate the trajectory offset of the marked node based on the movement trajectory data of the marked node, calculate the collaborative score of the corresponding node using the hierarchical analysis method based on the trajectory offset and data flow of each marked node, detect the real-time communication quality of each marked node and calculate the main collaborative index of the entire detection network;

[0010] Step S3: Based on the main cooperation index, a static link configuration or a dynamic link configuration is selected to optimize the detection network using a communication optimization algorithm. After each optimization using the dynamic link configuration, the signal strength change value of the communication link is measured and the signal strength change amount is calculated. The number of interference signals in the communication link is collected to obtain an interference count and recorded.

[0011] Step S4: Calculate the stability coefficient of the current communication link based on the signal strength change of the integrated communication link and the interference count, calculate the execution time of the dynamic link configuration based on the stability coefficient, and issue an early warning prompt.

[0012] In a preferred embodiment, in step S1, the communication quality records of each detection node in the marine environment are called through the historical database, and the communication quality records of each node are the signal strength of each node under the normal operation of the detection network in the historical data. The signal strength of each node under the normal operation of the detection network is used as the regional signal strength of each node, and the regional signal strength of all nodes in the detection network is recorded and the average signal strength is calculated as the signal screening threshold. Nodes below the signal screening threshold are screened out and marked.

[0013] In a preferred embodiment, in step S1, a period of time is selected as analysis time when the detection network is running, and a data flow monitor is used to detect the amount of data transmitted through each marked node when the detection network is running during the analysis time;

[0014] The data flow of each marked node is calculated based on the analysis time and the data transmission volume of each marked node: D divided by t equals F;

[0015] Where D is the amount of data transmitted through the marked node when the detection network is running, t is the analysis time interval, and F is the data flow of the corresponding marked node;

[0016] Two time points are randomly selected during the analysis time to detect the motion trajectory data of each marked node.

[0017] In a preferred embodiment, in step S2, the trajectory data obtained by detecting the next time point is subtracted from the trajectory data obtained by detecting the previous time point during the analysis time to obtain the trajectory offset of the marked node. The collaborative growth coefficient of each marked node is calculated based on the trajectory offset and data flow of the marked node and is used as the collaborative score of each marked node.

[0018] Calculate the collaborative growth rate coefficient of the marked node: the trajectory offset multiplied by the data flow and then squared equals the collaborative growth rate coefficient;

[0019] Among them, the collaborative growth coefficient is the collaborative growth coefficient of the corresponding marked node, and i is the sequence number of each marked node;

[0020] The signal strength sensor is used to detect the real-time communication quality of each marked node, and the collaborative growth rate coefficient of each marked node is used as the collaborative score;

[0021] The collaborative scores are sorted from small to large, and the collaborative weights are set for each marked node after sorting using the hierarchical analysis method.

[0022] In a preferred embodiment, in step S2, the synergy weight is set using the hierarchical analysis method and the main synergy index of the entire detection network is calculated in combination with the real-time communication quality of each marked node. The specific steps are as follows:

[0023] Construct a scoring matrix by comparing the relative importance of each node through the collaborative scoring of each marked node;

[0024] Normalize each column of the scoring matrix so that the sum of each column is equal to 1;

[0025] Calculate the eigenvalue of each factor. The eigenvalue is the weighted average of each column of the scoring matrix. The eigenvalue of each node is used as the quantitative value of the importance of each node. The eigenvalue of each node is used as the collaborative weight of the corresponding marked node. The collaborative weight of each marked node and the real-time communication quality of the corresponding marked node are weighted and summed to obtain the main collaborative index of the entire detection network.

[0026] In a preferred embodiment, in step S3, when the main cooperation index of the detection network is higher than the cooperation index threshold, the detection network is optimized using a static link configuration control communication optimization algorithm;

[0027] When the main cooperation index of the detection network is lower than the cooperation index threshold, the dynamic link configuration control communication optimization algorithm is used to optimize the detection network.

[0028] In a preferred embodiment, in step S3, after each optimization process is completed, the signal strength of the communication link is measured and recorded using a signal strength meter, and the difference between the currently measured signal strength of the communication link and the previously recorded signal strength of the communication link is calculated to obtain a signal strength change of the communication link;

[0029] The communication link is collected according to the set fixed collection period, and interference signals exceeding the preset strength are obtained and their number is counted to obtain the interference count and recorded.

[0030] In a preferred embodiment, in step S4, the stability coefficient of the current communication link is calculated using a polynomial regression algorithm based on the signal strength change of the communication link and the interference count;

[0031] The signal strength change multiplied by the first weight parameter plus the interference count multiplied by the second weight parameter plus the modulation constant equals the stability coefficient;

[0032] Among them, the stability coefficient is the stability coefficient of the current communication link, and the signal strength change is the change in the signal strength of the communication link after the current optimization adjustment;

[0033] The interference count is the interference count after optimization adjustment, the first weight parameter and the second weight parameter are the influence weights of the two parameters respectively, and the modulation constant is a constant value;

[0034] The inverse of the stability coefficient of the current communication link is taken as the adjustment ratio for the next dynamic link configuration;

[0035] The product of the execution time of the current dynamic link configuration and the adjustment ratio is used as the execution time of the next dynamic link configuration.

[0036] In a preferred embodiment, in step S4, the execution time is recorded in real time each time the dynamic link configuration control communication optimization algorithm is used to perform optimization processing;

[0037] When the processing time reaches the execution time of the current round, the static link configuration is switched to optimize the detection network;

[0038] If the execution time of the next dynamic link configuration calculated from the execution time of the current dynamic link configuration is lower than the preset processing time threshold, an early warning is issued and a prompt is given to adjust the node deployment position.

[0039] In a preferred embodiment, in step S4, the early warning prompt is achieved through the linkage of the signal strength meter and the time recording device, ensuring that the communication quality of the detection network is always within a controllable range.

[0040] Technical effects and advantages of the present invention:

[0041] 1. The present invention screens and marks key nodes in the detection network by obtaining the communication quality records of each detection node in the marine environment, collects the motion trajectory data of the marked nodes and detects the data flow passing through the marked nodes during operation, calculates the collaborative score of each marked node based on the trajectory data and data flow of each marked node, calculates the main collaborative index of the detection network based on the collaborative score of each marked node and the real-time communication quality of each marked node, and calculates the main collaborative index of the detection network as a whole by splitting and summing, thereby reducing the detection cost and improving the measurement accuracy of the communication quality. The relative importance of each node is analyzed to facilitate subsequent management calls, and different communication optimization strategies are selected according to the main collaborative index to optimize the detection network using the communication optimization algorithm. When using dynamic link configuration, the changes in the signal strength of the communication link and the number of interference signals are detected, the execution time of the dynamic link configuration is calculated, and early warning prompts are issued, thereby improving the communication security of the marine mobile distributed detection network. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a flow chart of a method for constructing a marine mobile distributed detection network according to the present invention.

[0043] Figure 2 This is a logic block diagram of the collaborative scoring calculation process of marked nodes in a method for constructing a marine mobile distributed detection network according to the present invention.

[0044] Figure 3 This is a calculation logic diagram for adjusting the execution time of dynamic link configuration in a method for constructing a marine mobile distributed detection network according to the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] Example 1

[0047] The present invention provides a method for constructing a marine mobile distributed detection network. Figure 1 , Attachment Figure 2 And attached Figure 3 Provide detailed explanation;

[0048] like Figure 1 As shown in the figure, the overall process includes steps from node screening to communication optimization. Each step is coordinated through data collection, calculation analysis and optimization adjustment to complete the dynamic management of the detection network.

[0049] In step S1, the location information and communication quality records of each detection node in the ocean environment are first obtained. This process relies on the support of the historical database.

[0050] The historical database stores the signal strength data of each node when the detection network is operating normally. These data are extracted as regional signal strength and used to calculate the average signal strength.

[0051] It should be noted that the above-mentioned historical database can be a node communication behavior database based on timestamp records, covering fields such as node ID, geographic location, historical signal strength value (expressed in dBm) and communication success rate, so as to facilitate the time series statistics and horizontal comparison of regional signal quality.

[0052] This average value is set as the signal screening threshold, and nodes below this threshold are considered critical nodes and marked.

[0053] After the marking is completed, the trajectory monitoring device is used to collect the motion trajectory data of the marked node, and the data flow monitor is used to detect the data transmission volume passing through the marked node when the detection network is running.

[0054] The calculation formula for data flow is D divided by t equals F, where D represents the data transmission volume, t is the analysis time interval, and F is the data flow of the corresponding marked node.

[0055] Two time points are randomly selected during the analysis time to detect the motion trajectory data of each marked node to ensure the accuracy of the trajectory offset.

[0056] like Figure 2 As shown in the figure, the trajectory offset of the marked node and the data flow together constitute the basis of the collaborative scoring, and the combination of the two is further used to generate the collaborative growth coefficient through the hierarchical analysis method.

[0057] After entering step S2, the collaborative growth rate coefficient is calculated based on the trajectory offset and data flow of the marked node. This coefficient is used as the collaborative score to participate in the subsequent weight distribution.

[0058] The logic for obtaining the trajectory offset of the marked node is to randomly select two time points within the preset analysis time period, obtain the position information of the marked node at the two time points, and calculate the trajectory offset of the marked node using the Euclidean distance formula;

[0059] It should be noted that the preset analysis time period is obtained by our experimenters based on the average moving speed of the detection node and the average response time of the communication link switching, which will not be elaborated here;

[0060] The calculation formula for the collaborative growth coefficient is the geometric mean method, which is: multiply the trajectory offset by the data flow and then take the square root to obtain the collaborative growth coefficient of the corresponding marked node.

[0061] Subsequently, a signal strength sensor is used to detect the real-time communication quality of each marked node, the collaborative scores are sorted from small to large, and collaborative weights are set for the sorted nodes.

[0062] In this process, a scoring matrix is ​​constructed to compare the relative importance of each node, and each column of the scoring matrix is ​​standardized so that the sum of each column is equal to 1.

[0063] Then the eigenvalue of each factor is calculated, that is, the weighted average of each column of the scoring matrix, and the eigenvalue is used as the quantitative value of the importance of each node.

[0064] Finally, the cooperation weight of each node and the real-time communication quality are weighted and summed to obtain the main cooperation index of the entire detection network.

[0065] The calculation results of the main synergy index directly affect the selection of the next communication optimization strategy.

[0066] In step S3, static link configuration or dynamic link configuration is selected based on the comparison result between the main cooperation index and the cooperation index threshold.

[0067] When the main cooperation index is higher than the cooperation index threshold, the static link configuration control communication optimization algorithm is used to optimize the detection network; otherwise, the dynamic link configuration is used.

[0068] After each optimization process is completed, use a signal strength meter to measure the communication link signal strength and record the current value.

[0069] The signal strength change is obtained by subtracting the signal strength measured at the time from the last recorded value. At the same time, the interference signals in the communication link are counted according to a fixed collection period, and the number of interference signals exceeding the preset strength is obtained and recorded as the interference count.

[0070] The preset intensity was obtained by our experimenters based on the statistical distribution characteristics of the background noise intensity of the communication link in the marine environment and the critical value analysis of the impact of interference on the signal reception quality, which will not be elaborated here.

[0071] like Figure 3 As shown, the signal strength variation and interference count jointly affect the calculation of the stability coefficient, which is completed through the polynomial regression algorithm.

[0072] In step S4, the stability coefficient is calculated as the signal strength change multiplied by the first weight parameter plus the interference count multiplied by the second weight parameter plus the modulation constant, and finally the stability coefficient of the current communication link is obtained.

[0073] Specifically, the calculation formula of the stability coefficient is based on a normalized weighted calculation formula, which is common knowledge among those skilled in the art. For the weighted calculation, the parameters substituted are subjected to de-normalization and quantization processing, i.e., normalization processing;

[0074] It should be noted that the standardization methods include but are not limited to standard linear transformation based on interval scaling, Z-Score standardization method based on statistics, or normalization method based on nonlinear mapping function. The application methods of standardization are not described in detail here.

[0075] The reciprocal of the stability coefficient is used as the adjustment ratio for the next dynamic link configuration. The execution time of the current dynamic link configuration is multiplied by the adjustment ratio to obtain the execution time of the next dynamic link configuration.

[0076] Each time dynamic link configuration is used, the execution time is recorded in real time. When the processing time reaches the execution time, the static link configuration is switched.

[0077] If the next execution time calculated from the current execution time is lower than the preset processing time threshold, the early warning mechanism is triggered and a prompt is given to adjust the node deployment position.

[0078] The design of the early warning mechanism is achieved through the linkage of the signal strength meter and the time recording device to ensure that the communication quality of the detection network is always within the controllable range.

[0079] During the entire implementation process, the data flow and calculation logic between each step are closely connected.

[0080] For example, the marked node screening results in step S1 directly determine the calculation scope of the collaborative score in step S2, and the main collaborative index generated in step S2 provides a basis for the selection of the communication optimization strategy in step S3.

[0081] Similarly, the signal strength change and interference count in step S3 provide input data for the calculation of the stability coefficient in step S4, and the final execution time adjustment plan is fed back to the node deployment adjustment link in step S1.

[0082] This closed-loop design ensures the dynamic optimization capability of the detection network.

[0083] In addition, Figure 1 The overall process from node selection to communication optimization is shown. Figure 2 The calculation process of the collaborative score is described in detail. Figure 3 Explained the adjustment logic of dynamic link configuration execution time.

[0084] The markings in these drawings correspond to the actual implementation process. Figure 2 The trajectory offset and data flow calculation module marked in the figure are directly related to the collaborative speed-up coefficient generation process in step S2, and the attached Figure 3 The signal strength variation and interference count input modules in step S4 are matched with the polynomial regression algorithm calculation process.

[0085] With the aid of the accompanying drawings, those skilled in the art can clearly understand the logical relationship between the steps and the data transmission path, thereby accurately implementing the technical solution of the present invention.

[0086] All calculation formulas and logical judgments involved in this implementation are designed based on actual application scenarios, ensuring the feasibility and practicality of the technical solution. For example, the motion trajectory of detection nodes in marine environments is complex and changeable. By introducing a comprehensive evaluation of trajectory offset and data flow, the actual working status of the node can be more accurately reflected. At the same time, the flexible switching mechanism between dynamic and static link configurations effectively addresses communication interference issues in marine environments and improves the overall stability of the detection network.

[0087] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is supplemented below with reference to a specific application scenario.

[0088] In the marine environment monitoring mission, it is assumed that the detection network consists of multiple buoy nodes, which are deployed in a sea area to monitor data such as water temperature, salinity and ocean current changes.

[0089] Each buoy node transmits the collected data to a central processing platform via wireless communication links. Due to the complex water flow, wind and wave interference, and signal attenuation in the marine environment, the dynamic adaptability and communication stability of the detection network become key challenges.

[0090] The following is combined with Figure 1 , Attachment Figure 2 and attached Figure 3 The specific operation process of the method of the present invention is described in detail.

[0091] First, in step S1, the system obtains the communication quality records of each buoy node through the historical database. These records include signal strength data during normal operation.

[0092] By performing statistical analysis on the signal strength data, the average signal strength of the entire detection network is calculated and set as the signal screening threshold.

[0093] Buoy nodes below the threshold are marked as key nodes. Subsequently, the trajectory monitoring device is used to collect the motion trajectory data of these key nodes, and the data flow monitor is used to detect the data transmission volume within the analysis time interval.

[0094] For example, assuming that a buoy node transmits D = 50MB of data within the analysis time interval t, its data flow F can be calculated using the formula F = D / t.

[0095] At the same time, two time points are randomly selected during the analysis time to detect the motion trajectory of the buoy node to ensure the accuracy of the trajectory offset.

[0096] As attached Figure 2 As shown in the figure, the trajectory offset of the marked node and the data flow together constitute the basis of the collaborative scoring. The two generate a collaborative growth coefficient through the hierarchical analysis method. This process provides a quantitative basis for the subsequent weight distribution.

[0097] After entering step S2, the collaborative speed-up coefficient is calculated based on the trajectory offset and data flow of the marked node.

[0098] For example, if the trajectory offset of a buoy node is 10 meters and the data flow is 5MB / s, its collaborative growth rate coefficient can be calculated using the formula √(10×5).

[0099] Subsequently, a signal strength sensor is used to detect the real-time communication quality of each marked node, the collaborative scores are sorted from small to large, and collaborative weights are set for the sorted nodes.

[0100] During this process, a scoring matrix is ​​constructed to compare the relative importance of each node. Each column of the scoring matrix is ​​normalized so that the sum of each column is equal to 1, and the eigenvalue of each factor is calculated as the quantitative value of the importance of each node.

[0101] Finally, the coordination weight of each node and the real-time communication quality are weighted and summed to obtain the main coordination index of the entire detection network. The level of the main coordination index directly affects the selection of the next communication optimization strategy.

[0102] In step S3, it is determined whether to adopt static link configuration or dynamic link configuration according to the comparison result between the main cooperation index and the cooperation index threshold.

[0103] For example, when the main cooperation index is higher than the cooperation index threshold, the system uses static link configuration to optimize the detection network; otherwise, dynamic link configuration is used.

[0104] After each optimization process is completed, use a signal strength meter to measure the communication link signal strength and record the current value.

[0105] Assume that the signal strength measured this time is -70dBm and the last recorded value is -65dBm, then the signal strength change is -5dBm.

[0106] At the same time, the interference signals in the communication link are counted according to a fixed collection period. Assuming that the number of interference signals exceeding the preset strength is 3, the interference count is 3.

[0107] As attached Figure 3 As shown, the signal strength variation and interference count jointly affect the calculation of the stability coefficient, which is completed through the polynomial regression algorithm.

[0108] In step S4, the stability coefficient is calculated as follows: the signal strength variation multiplied by the first weight parameter plus the interference count multiplied by the second weight parameter plus the modulation constant.

[0109] For example, assuming that the first weight parameter is 0.6, the second weight parameter is 0.4, and the modulation constant is 10, the stability coefficient can be calculated by the formula (-5×0.6)+(3×0.4)+10.

[0110] The reciprocal of the stability coefficient is used as the adjustment ratio for the next dynamic link configuration. The execution time of the current dynamic link configuration is multiplied by the adjustment ratio to obtain the execution time of the next dynamic link configuration.

[0111] Each time a dynamic link configuration is used, the execution time is recorded in real time. When the processing time reaches the execution time, the system switches to the static link configuration.

[0112] If the next execution time calculated from the current execution time is lower than the preset processing time threshold, the early warning mechanism is triggered and a prompt is given to adjust the node deployment position.

[0113] The design of the early warning mechanism is achieved through the linkage of the signal strength meter and the time recording device to ensure that the communication quality of the detection network is always within the controllable range.

[0114] During the entire implementation process, the data flow and calculation logic between each step are closely connected.

[0115] For example, the screening results of the marked nodes in step S1 directly determine the calculation range of the collaborative score in step S2, and the main collaborative index generated in step S2 provides a basis for the selection of the communication optimization strategy in step S3.

[0116] Similarly, the signal strength change and interference count in step S3 provide input data for the calculation of the stability coefficient in step S4, and the final execution time adjustment plan is fed back to the node deployment adjustment link in step S1.

[0117] This closed-loop design ensures the dynamic optimization capability of the detection network.

[0118] In addition, Figure 1 The overall process from node selection to communication optimization is shown. Figure 2 The calculation process of the collaborative score is described in detail. Figure 3 Explained the adjustment logic of dynamic link configuration execution time.

[0119] The notations in these drawings correspond one to one with the actual implementation process.

[0120] For example, Figure 2 The trajectory offset and data flow calculation module marked in the figure are directly related to the collaborative speed-up coefficient generation process in step S2, and the attached Figure 3 The signal strength variation and interference count input modules in step S4 are matched with the polynomial regression algorithm calculation process.

[0121] With the aid of the accompanying drawings, those skilled in the art can clearly understand the logical relationship between the steps and the data transmission path, thereby accurately implementing the technical solution of the present invention.

[0122] In the above application scenarios, by introducing a comprehensive evaluation of trajectory offset and data flow, the actual working status of the buoy node can be more accurately reflected.

[0123] At the same time, the flexible switching mechanism between dynamic link configuration and static link configuration effectively addresses the communication interference problem in the marine environment and improves the overall stability of the detection network.

[0124] For example, in an actual monitoring mission, when a buoy node has a large position offset and a significant drop in data traffic due to water flow impact, the system calculates that its collaborative growth coefficient is low, and thus marks it as a key node and adjusts the communication optimization strategy.

[0125] This process not only improves the collaborative efficiency of the detection network, but also ensures the reliability of data transmission, providing strong support for marine environment monitoring tasks.

[0126] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0127] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0128] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0129] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0130] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0131] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0133] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0134] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0135] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for constructing a marine mobile distributed detection network, characterized by: The following steps are involved: Step S1: Obtain the location information and communication quality records of each detection node in the marine environment, screen and mark the key nodes of the detection network according to the communication quality records of each node; collect the motion trajectory data of the marked nodes, detect the data transmission volume through the marked nodes during the operation of the detection network, and calculate the data flow; Step S2: Calculate the trajectory offset of the marked node based on the movement trajectory data of the marked node, calculate the collaborative score of the corresponding node using the hierarchical analysis method based on the trajectory offset and data flow of each marked node, detect the real-time communication quality of each marked node and calculate the main collaborative index of the entire detection network; Calculate the collaborative growth rate coefficient of the marked node: the trajectory offset multiplied by the data flow and then squared equals the collaborative growth rate coefficient; Among them, the collaborative growth coefficient is the collaborative growth coefficient of the corresponding marked node, and i is the sequence number of each marked node; The signal strength sensor is used to detect the real-time communication quality of each marked node, and the collaborative growth rate coefficient of each marked node is used as the collaborative score; Construct a scoring matrix by comparing the relative importance of each node through the collaborative scoring of each marked node; Normalize each column of the scoring matrix so that the sum of each column is equal to 1; Calculate the eigenvalue of each factor. The eigenvalue is the weighted average of each column of the scoring matrix. The eigenvalue of each node is used as the quantitative value of the importance of each node. The eigenvalue of each node is used as the collaborative weight of the corresponding marked node. The collaborative weight of each marked node and the real-time communication quality of the corresponding marked node are weighted and summed to obtain the main collaborative index of the entire detection network. Step S3: Based on the main cooperation index, a static link configuration or a dynamic link configuration is selected to optimize the detection network using a communication optimization algorithm. After each optimization using the dynamic link configuration, the signal strength change value of the communication link is measured and the signal strength change amount is calculated. The number of interference signals in the communication link is collected to obtain an interference count and recorded. Step S4: Calculate the stability coefficient of the current communication link based on the signal strength change of the communication link and the interference count, calculate the execution time of the dynamic link configuration based on the stability coefficient, and issue an early warning prompt; In step S4, the stability coefficient of the current communication link is calculated using a polynomial regression algorithm based on the signal strength change of the communication link and the interference count; The signal strength change multiplied by the first weight parameter plus the interference count multiplied by the second weight parameter plus the modulation constant equals the stability coefficient; Among them, the stability coefficient is the stability coefficient of the current communication link, and the signal strength change is the change in the signal strength of the communication link after the current optimization adjustment; The interference count is the interference count after optimization adjustment, the first weight parameter and the second weight parameter are the influence weights of the two parameters respectively, and the modulation constant is a constant value; The inverse of the stability coefficient of the current communication link is taken as the adjustment ratio for the next dynamic link configuration; The product of the execution time of the current dynamic link configuration and the adjustment ratio is used as the execution time of the next dynamic link configuration.

2. The method for constructing a marine mobile distributed detection network according to claim 1, wherein: In step S1, the communication quality records of each detection node in the marine environment are called through the historical database. The communication quality record of each node is the signal strength of each node under the normal operation of the detection network in the historical data. The signal strength of each node under the normal operation of the detection network is used as the regional signal strength of each node. The regional signal strength of all nodes in the detection network is recorded and the average signal strength is calculated as the signal screening threshold. Nodes below the signal screening threshold are screened out and marked.

3. The method for constructing a marine mobile distributed detection network according to claim 2, wherein: In step S1, a period of time is selected as analysis time when the detection network is running, and a data flow monitor is used to detect the amount of data transmitted through each marked node when the detection network is running during the analysis time; The data flow of each marked node is calculated based on the analysis time and the data transmission volume of each marked node: D divided by t equals F; Where D is the amount of data transmitted through the marked node when the detection network is running, t is the analysis time interval, and F is the data flow of the corresponding marked node; Two time points are randomly selected during the analysis time to detect the motion trajectory data of each marked node.

4. The method for constructing a marine mobile distributed detection network according to claim 3, wherein: In step S2, the motion trajectory data detected at the next time point is subtracted from the trajectory data detected at the previous time point within the analysis time to obtain the trajectory offset of the marked node; The collaborative scores are sorted from small to large, and the collaborative weights are set for each marked node after sorting using the hierarchical analysis method.

5. The method for constructing a marine mobile distributed detection network according to claim 1, wherein: In step S3, when the main cooperation index of the detection network is higher than the cooperation index threshold, the static link configuration control communication optimization algorithm is used to optimize the detection network; When the main cooperation index of the detection network is lower than the cooperation index threshold, the dynamic link configuration control communication optimization algorithm is used to optimize the detection network.

6. The method for constructing a marine mobile distributed detection network according to claim 5, characterized in that: In step S3, after each optimization process is completed, the signal strength of the communication link is measured and recorded using a signal strength meter, and the difference between the current measured signal strength of the communication link and the last recorded signal strength of the communication link is calculated to obtain a signal strength change of the communication link; The communication link is collected according to the set fixed collection period, and interference signals exceeding the preset strength are obtained and their number is counted to obtain the interference count and recorded.

7. The method for constructing a marine mobile distributed detection network according to claim 1, characterized in that: In step S4, the execution time is recorded in real time each time the dynamic link configuration control communication optimization algorithm is used to perform optimization processing; When the processing time reaches the execution time of the current round, the static link configuration is switched to optimize the detection network; If the execution time of the next dynamic link configuration calculated from the execution time of the current dynamic link configuration is lower than the preset processing time threshold, an early warning is issued and a prompt is given to adjust the node deployment position.

8. The method for constructing a marine mobile distributed detection network according to claim 1, wherein: In step S4, the early warning prompt is realized by the linkage of the signal strength meter and the time recording device, ensuring that the communication quality of the detection network is always within the controllable range.

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