Broadband ad hoc network signal intelligent enhancement system and method, computer equipment and medium

Abstract

The invention relates to a broadband ad hoc network signal intelligent enhancement system and method, computer equipment and a medium. The system comprises a signal simulation prediction module, a signal monitoring and early warning module, an intelligent signal enhancement module and an order task module. The signal simulation prediction module is used for calculating the signal coverage range and the signal coverage intensity of the broadband ad hoc network equipment; the signal monitoring and early warning module is used for monitoring the signal state of the broadband ad hoc network equipment and giving an alarm; the intelligent signal enhancement module is used for intelligently providing broadband ad hoc network equipment signal relay strategies and position suggestions; and the order task module is used for commanding the control node to execute tasks related to signal enhancement. By adopting the system, the accuracy, high efficiency and stability of broadband ad hoc network signal management can be improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a broadband ad hoc network signal intelligent enhancement system, method, computer equipment and medium. Background Art

[0002] Wireless channel simulation uses mathematical models and simulation tools to simulate the signal transmission process in a wireless communication environment. Through simulation, the wireless channel characteristics in different scenarios can be simulated, such as multipath effect, Doppler frequency shift, dynamic delay, etc., so as to evaluate the performance of the communication system. Coverage refers to the transmission distance and coverage area of ​​wireless signals in a specific geographical area. This range is usually determined by the signal transmission strength and propagation characteristics of the wireless access point, and is affected by many factors.

[0003] In the field of wireless channel simulation and coverage prediction, there are wireless channel simulation software such as Matlab, RadioMobile, and RFCloud. The above software can predict the median transmission fading of wireless signals in free space caused by the irregularity of the terrain, thereby helping users understand the propagation of radio waves in specific environments. In the early stages of wireless communication system design, the above software simulation can predict and evaluate the performance of the wireless communication system, thereby guiding the selection of parameters for the preliminary design, such as the location of the site, the layout of the antenna, the setting of the transmission power, and the sensitivity requirements of the receiver, thereby optimizing the layout of the wireless communication system. Using the above software, before actual deployment, by comparing and selecting different design schemes, the layout and parameter settings of the wireless communication system can be optimized, the communication quality and efficiency can be improved, and a lot of cost and time can be saved.

[0004] The above simulation planning software is suitable for network static calculation analysis and planning evaluation. However, broadband self-organizing networks have the characteristics of real-time dynamic changes. The above simulation planning software does not access the actual broadband self-organizing network radio station and cannot perform real-time signal monitoring and signal enhancement. It is not suitable for broadband self-organizing networks with dynamic changes. Secondly, the typical process of broadband self-organizing network wireless signal management includes planning stage, deployment stage, adjustment stage and evaluation stage. The above simulation planning software mainly works in the planning stage and no longer works after the actual deployment stage. It cannot serve the entire process of wireless signal management well, and it cannot unify the command and control and work coordination of wireless signal management behaviors. Finally, the above simulation planning software can only change the prediction results by manually modifying the model parameters, and cannot automatically learn and train according to the field test results to optimize the accuracy of the simulation prediction results. Summary of the invention

[0005] Based on this, it is necessary to provide a broadband ad hoc network signal intelligent enhancement system, method, computer equipment and medium to address the above technical problems.

[0006] A method for intelligently enhancing signals in a broadband ad hoc network, the system comprising:

[0007] A signal simulation prediction module, a signal monitoring and early warning module, an intelligent signal enhancement module and an order task module, wherein the intelligent signal enhancement module is connected to the signal simulation prediction module, the signal monitoring and early warning module and the order task module respectively;

[0008] The signal monitoring and early warning module is connected to the signal simulation prediction module and the broadband ad hoc network respectively, and is used to perform signal monitoring on nodes in the broadband ad hoc network, obtain monitoring results, and perform signal early warning according to the monitoring results to obtain early warning information;

[0009] The signal simulation prediction module includes a signal coverage model and a pre-trained model adjustment network. The model adjustment network obtains the monitoring results output by the signal monitoring and early warning module in real time, processes the monitoring results to obtain statistical factors, and the signal coverage model predicts the signal coverage range and signal coverage strength of each node in the broadband ad hoc network according to the preset deterministic factors and the statistical factors, and transmits them to the intelligent signal enhancement module;

[0010] The intelligent signal enhancement module is used to obtain the warning information transmitted in real time by the signal monitoring and warning module, obtain the node to be enhanced that triggers the warning according to the warning information, generate a signal enhancement plan according to the signal coverage range and signal coverage strength of the node to be enhanced, generate a signal enhancement task according to the signal enhancement plan and transmit it to the order task module;

[0011] The order task module is connected to the broadband self-organizing network and is used to coordinate the relevant nodes in the broadband self-organizing network to perform the signal enhancement task to achieve signal enhancement of the node to be enhanced.

[0012] A method for intelligently enhancing a broadband ad hoc network signal, the method comprising:

[0013] Perform signal monitoring on nodes in the broadband ad hoc network to obtain monitoring results, and perform signal warnings based on the monitoring results to obtain warning information;

[0014] The monitoring results are obtained by adjusting the network through a pre-trained model, the monitoring results are processed to obtain statistical factors, and the signal coverage range and signal coverage strength of each node in the broadband ad hoc network are predicted according to the preset deterministic factors and the statistical factors through a signal coverage model;

[0015] Acquire the warning information, obtain the node to be enhanced that triggers the warning according to the warning information, generate a signal enhancement plan according to the signal coverage range and signal coverage strength of the node to be enhanced, and generate a signal enhancement task according to the signal enhancement plan;

[0016] Coordinate relevant nodes in the broadband ad hoc network to perform the signal enhancement task to achieve signal enhancement of the node to be enhanced.

[0017] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0018] Perform signal monitoring on nodes in the broadband ad hoc network to obtain monitoring results, and perform signal warnings based on the monitoring results to obtain warning information;

[0019] The monitoring results are obtained by adjusting the network through a pre-trained model, the monitoring results are processed to obtain statistical factors, and the signal coverage range and signal coverage strength of each node in the broadband ad hoc network are predicted according to the preset deterministic factors and the statistical factors through a signal coverage model;

[0020] Acquire the warning information, obtain the node to be enhanced that triggers the warning according to the warning information, generate a signal enhancement plan according to the signal coverage range and signal coverage strength of the node to be enhanced, and generate a signal enhancement task according to the signal enhancement plan;

[0021] Coordinate relevant nodes in the broadband ad hoc network to perform the signal enhancement task to achieve signal enhancement of the node to be enhanced.

[0022] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0023] Perform signal monitoring on nodes in the broadband ad hoc network to obtain monitoring results, and perform signal warnings based on the monitoring results to obtain warning information;

[0024] The monitoring results are obtained by adjusting the network through a pre-trained model, the monitoring results are processed to obtain statistical factors, and the signal coverage range and signal coverage strength of each node in the broadband ad hoc network are predicted according to the preset deterministic factors and the statistical factors through a signal coverage model;

[0025] Acquire the warning information, obtain the node to be enhanced that triggers the warning according to the warning information, generate a signal enhancement plan according to the signal coverage range and signal coverage strength of the node to be enhanced, and generate a signal enhancement task according to the signal enhancement plan;

[0026] Coordinate relevant nodes in the broadband ad hoc network to perform the signal enhancement task to achieve signal enhancement of the node to be enhanced.

[0027] The above-mentioned broadband self-organizing network signal intelligent enhancement system, method, computer equipment and medium monitor node signals in real time through the signal monitoring and early warning module, and can issue an early warning in time once the signal is abnormal, ensuring that the problem is discovered in time. The signal simulation prediction module adjusts the network with the help of the signal coverage model and the pre-training model, and accurately predicts the signal strength by integrating deterministic and statistical factors, providing a basis for subsequent adjustments. The intelligent signal enhancement module generates plans and tasks based on the early warning information, and responds to signal problems efficiently. The order task module coordinates the nodes to execute tasks and ensures the implementation of the enhancement plan. When the signal quality decreases, the previous calculation results are used for adjustment, and the enhancement plan is intelligently generated to adapt to dynamic changes in the network. The embodiments of the present invention can realize the intelligent enhancement of broadband self-organizing network signals and improve the accuracy, efficiency and stability of broadband self-organizing network signal management. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A structural block diagram of a broadband ad hoc network signal intelligent enhancement system in one embodiment;

[0029] Figure 2 This is a schematic diagram of the structure of a signal monitoring and early warning module in one embodiment;

[0030] Figure 3 A schematic diagram of the structure of a simulation prediction module in one embodiment;

[0031] Figure 4 A schematic diagram of a signal coverage model optimization process in one embodiment;

[0032] Figure 5 A schematic diagram of the structure of a signal enhancement module in an embodiment;

[0033] Figure 6 A schematic diagram of the structure of an order task module in one embodiment;

[0034] Figure 7 A schematic diagram of a process of a method for intelligently enhancing a broadband ad hoc network signal in one embodiment;

[0035] Figure 8 A schematic diagram of a process of adaptive network management of a broadband ad hoc network in one embodiment;

[0036] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] In one embodiment, Figure 1 As shown, a broadband ad hoc network signal intelligent enhancement system is provided, comprising:

[0039] A signal simulation prediction module, a signal monitoring and early warning module, an intelligent signal enhancement module and an order task module, wherein the intelligent signal enhancement module is connected to the signal simulation prediction module, the signal monitoring and early warning module and the order task module respectively;

[0040] The signal monitoring and early warning module is connected to the signal simulation prediction module and the broadband ad hoc network respectively, and is used to monitor the signals of the nodes in the broadband ad hoc network, obtain the monitoring results, and perform signal early warning according to the monitoring results to obtain early warning information;

[0041] The signal simulation prediction module includes a signal coverage model and a pre-trained model adjustment network. The model adjustment network obtains the monitoring results output by the signal monitoring and early warning module in real time, processes the monitoring results to obtain statistical factors, and the signal coverage model predicts the signal coverage range and signal coverage strength of each node in the broadband ad hoc network according to the pre-set deterministic factors and statistical factors and transmits them to the intelligent signal enhancement module;

[0042] The intelligent signal enhancement module is used to obtain the warning information transmitted in real time by the signal monitoring and warning module, obtain the node to be enhanced that triggers the warning according to the warning information, generate a signal enhancement plan according to the signal coverage range and signal coverage strength of the node to be enhanced, generate a signal enhancement task according to the signal enhancement plan and transmit it to the order task module;

[0043] The order task module is connected to the broadband self-organizing network and is used to coordinate the relevant nodes in the broadband self-organizing network to perform signal enhancement tasks and realize signal enhancement of the nodes to be enhanced.

[0044] In the above-mentioned broadband self-organizing network signal intelligent enhancement system, the node signal is monitored in real time through the signal monitoring and early warning module. Once the signal is abnormal, an early warning can be issued in time to ensure that the problem is discovered in time. The signal simulation prediction module adjusts the network with the help of the signal coverage model and the pre-training model, and accurately predicts the signal strength by integrating deterministic and statistical factors to provide a basis for subsequent adjustments. The intelligent signal enhancement module generates plans and tasks based on the early warning information to deal with signal problems efficiently. The order task module coordinates the nodes to execute tasks and ensures the implementation of the enhancement plan. When the signal quality decreases, the previous calculation results are used for adjustment, and the enhancement plan is intelligently generated to adapt to dynamic changes in the network. The embodiments of the present invention can realize the intelligent enhancement of broadband self-organizing network signals and improve the accuracy, efficiency and stability of broadband self-organizing network signal management.

[0045] exist Figure 1In the system, the signal simulation prediction module is used to calculate the signal coverage range and signal coverage strength of broadband self-organizing network equipment; the signal monitoring and early warning module is used to monitor the signal status of broadband self-organizing network equipment and issue an alarm; the intelligent signal enhancement module is used to intelligently provide broadband self-organizing network equipment signal relay strategy and location suggestions; the order task module is used to command and control nodes to perform tasks related to signal enhancement. The full process management of broadband self-organizing network wireless signals is realized.

[0046] In one embodiment, Figure 2 As shown, a structural schematic diagram of a signal monitoring and early warning module is provided, and the signal monitoring and early warning module includes a signal monitoring submodule and a signal early warning submodule; the signal monitoring submodule is used to read the whole network node information of the broadband self-organizing network in real time to obtain the monitoring result; the signal early warning submodule is used to obtain the signal quality of each node according to the monitoring result, and perform abnormal judgment according to the size relationship between the signal quality of the node and the preset early warning threshold. When it is judged to be abnormal, the signal early warning is triggered to obtain the early warning information of the node.

[0047] In this embodiment, the signal monitoring and early warning module of the present application provides signal monitoring and early warning functions. The signal monitoring and early warning module monitors the signal status by reading the broadband ad hoc network full network device information in real time, and sets the early warning threshold as the signal early warning trigger condition. The signal monitoring and early warning module presets three levels of signal early warning thresholds: excellent, good and general, and the signal quality includes excellent, good, general and lost connection. The signal quality is excellent, the signal quality is good, the signal quality is general to the node loss of connection, indicating that the signal gradient decreases. When the monitored signal has a quality degradation behavior, the signal quality degradation early warning is triggered. In particular, when the node is lost, the node loss of connection early warning is also triggered. The early warning can be actively ignored for a period of time. During this period, the node whose early warning is ignored will no longer generate a signal early warning; the early warning can also be actively eliminated through signal enhancement, and the node signal reaches or exceeds the set early warning threshold by moving the node or adding a relay. The signal enhancement scheme is provided by the signal enhancement module. The signal monitoring and early warning module feeds back the monitored node signal to the artificial neural network of the signal simulation prediction module as training data. The signal monitoring and early warning module transmits the current node position, node signal, early warning threshold and early warning information to the intelligent signal enhancement module, thereby calculating the signal enhancement scheme.

[0048] In one embodiment, an abnormality is determined based on the relationship between the signal quality of the node and a preset warning threshold. When it is determined to be abnormal, a signal warning is triggered, and the warning information of the node is obtained, including: setting a warning threshold of a corresponding level according to the current communication environment; obtaining the signal quality of the current node, and if the signal quality level is less than the warning threshold, it is determined to be abnormal, triggering a signal warning, and obtaining the warning information of the node; the warning information includes a quality degradation warning and a node loss warning.

[0049] Specifically, the situations in which signal warnings are triggered include: when the threshold is excellent: nodes with good, general and lost signal quality trigger signal warnings; when the threshold is good: nodes with general and lost signal quality trigger signal warnings; when the threshold is general: nodes with lost signal quality trigger signal warnings.

[0050] In one embodiment, the step of training a model adjustment network includes: obtaining the signal coverage range and signal coverage strength of each node of a broadband self-organizing network in different communication environments to obtain a measured data set, using a signal coverage model to predict the signal coverage range and signal coverage strength of each node of a broadband self-organizing network in different communication environments to obtain a calculated data set; calculating the signal strength error of nodes with the same position and environmental factors according to the measured data set and the calculated data set to obtain a signal strength error data set; constructing a model adjustment network, the model adjustment network includes an input layer, a hidden layer and an output layer, receiving signal strength error data in the signal strength error data set through the input layer, performing nonlinear mapping on the signal strength error data through the hidden layer, performing linear combination on the output of the hidden layer through the output layer, and outputting a statistical factor; training the model adjustment network according to the signal strength error data set to obtain a trained model adjustment network.

[0051] like Figure 3 The structural diagram of the simulation prediction module is shown in FIG. Figure 3 The irregular terrain model in the model regulation network is Figure 3The artificial neural network in the signal simulation prediction module analyzes, calculates and predicts the communication coverage through the irregular terrain model (ITM model). This model predicts the propagation loss of radio waves by considering multiple influencing factors, and uses a series of mathematical formulas to calculate the propagation loss. These factors include terrain height, landform type, height and power of receiving and transmitting equipment, frequency, and weather conditions. Based on the irregular terrain model, the signal simulation prediction module can calculate the signal coverage and signal coverage strength of broadband ad hoc network devices. The calculation results are numerical results such as receiving power and field strength with geographic location information, and the regional coverage map is presented on the map software. At the same time, the signal coverage data is persistently stored and provided to the intelligent signal enhancement module. In order to improve the prediction accuracy of the signal simulation prediction module, the signal simulation prediction module divides the factors affecting radio wave propagation into deterministic factors and statistical factors. Terrain height, height and power of receiving and transmitting equipment are classified as deterministic factors, and landform type, weather conditions, time-varying factors, spatial variation factors and scene variation factors are classified as statistical factors. The signal simulation prediction module uses artificial neural networks to model statistical factors and trains them through measured data to improve the prediction accuracy of irregular terrain models. The measured data is obtained by organizing personnel and broadband ad hoc network equipment to conduct field tests. During the test, the signal monitoring and early warning module continuously records time, weather, location, signal strength, signal-to-noise ratio and other data as training data to train the artificial neural network model to adjust statistical factors. A large number of training data samples support the statistical factor model based on artificial neural networks to approach reality.

[0052] Specifically, the model formula for defining node signal coverage is as follows:

[0053] LR=(Tx(lat,lon,height,freq,power),Rx(lat,lon,height),DEM)

[0054]

[0055] Among them, LR is the Longley-Rice irregular terrain model, Tx (lat, lon, height, freq, power) is the longitude, latitude, height, signal frequency and transmission power of the transmitting site, Rx (lat, lon, height) is the longitude, latitude and height of the receiving site, DEM is the terrain map. P is the signal strength of the receiving site, C k , C t and C d It is a statistical factor that relates the model to environmental factors.

[0056] Taking into account the complexity of the model and the accuracy of the prediction, the following objective function is set:

[0057]

[0058] ΔP=P view -P calc

[0059] Among them, P view is the actual measured signal strength, P calc is the signal strength predicted by the model, and ΔP is the signal strength deviation. 1 ,ω 2 ,ω 3 is the weight, u 2 (t) is the control value. The goal of the learning algorithm is to find a set of C k , C t and C d value to minimize F.

[0060] The measured data set is obtained through field tests, and the calculated data set is obtained through signal coverage model prediction. The measured data set and the calculated data set are cleaned, transformed and processed to obtain a series of node signal strength error data sets {ΔP} with the same location and environmental factors:

[0061] {ΔP}={ΔP 1 ,ΔP 2 ,ΔP 3 ...ΔP n}

[0062] In this embodiment, the model adjustment network adopts RBF neural network, which consists of input layer, hidden layer and output layer. The input layer is responsible for receiving external input data, the hidden layer performs nonlinear mapping on the input data through radial basis function, and the output layer performs linear combination on the results of the hidden layer to obtain the final output. Through training, the RBF neural network can learn the dynamic characteristics of the node signal coverage model and provide the optimal C k , C t and C d Model parameter parameters, such as Figure 4 The signal coverage model optimization process diagram shown in FIG. 1 is a flow chart of the signal coverage model optimization process. The input of the system is the ΔP node signal strength error, Error(ΔP) is the system control error, {xn}, {wn} and {kn} are the input signal, weight coefficient and output of the RBF neural network respectively, and Yout is the actual output of the system.

[0063] The process of RBF neural network optimization signal coverage model is as follows:

[0064] 1) Initialize the neural unit weight coefficient {wn}, neuron ratio coefficient and learning rate of the RBF neural network;

[0065] 2) Continuously sample the input and output signals of the system to obtain the input and output of the system, and calculate the system control error Error (ΔP);

[0066] 3) Using supervised learning rules, update and adjust the weight coefficient {wn};

[0067] 4) After updating the weight coefficient, recalculate the RBF neural network output {kn};

[0068] 5) Return to step 2 and start the next sampling.

[0069] In this embodiment, the system of the present invention realizes the "more accurate with use" of broadband ad hoc wireless channel simulation and coverage prediction. A neural network model between signal strength and statistical factors affecting signal propagation coverage is constructed through an artificial neural network, and the neural network model is trained using field test data, and the statistical factor parameters output by the neural network model are continuously improved to form a feedback system for adjusting the prediction model with measured data, thereby achieving the effect of "more accurate with use" of the prediction model.

[0070] In one embodiment, the signal enhancement scheme includes a node movement strategy, a node movement strategy and a hybrid enhancement strategy; the signal enhancement task includes a node movement task and a request relay task; generating a signal enhancement task according to the signal enhancement scheme includes: if the adopted signal enhancement scheme is a node movement strategy, then generating a node movement task according to the mobile node and the target area; if the adopted signal enhancement scheme is a node relay strategy, then generating a request relay task according to the relay node; if the adopted signal enhancement scheme is a hybrid enhancement strategy, then generating a node movement task according to the mobile node and the mobile position, and generating a request relay task according to the relay node.

[0071] like Figure 5The structure diagram of the signal enhancement module shown in the figure, the intelligent signal enhancement module provides the signal enhancement solution function. The intelligent signal enhancement module supports two strategies: node movement and adding relays. For all nodes with current signal quality degradation and loss of connection, the signal simulation prediction module's coverage range and coverage strength analysis results are called. In an ad hoc network, the timing of strategy selection of the intelligent signal enhancement module depends on many factors. When the nodes are sparsely distributed, the cost of movement is low, and the signal can be improved by short-distance movement, or when local interference is severe and can be avoided by movement, such as outdoor temporary activity venues or scenes affected by specific interference sources, a mobile strategy is adopted. If the nodes are relatively fixed and limited in movement, such as sensor networks in office buildings, or when connectivity needs to be quickly restored in an emergency, such as emergency rescue scenarios, a relay strategy is selected. In a complex network environment, there are both movable nodes and fixed nodes and a single strategy cannot solve the problem, such as an urban block ad hoc network, or when the network performance needs to be gradually optimized, such as a long-term ad hoc network, a hybrid strategy of movement and relay is adopted to more effectively improve the signal quality and ensure stable network operation. If the node movement strategy is adopted, a movement task is initiated to the node that needs to be moved; if the node relay strategy is adopted, a task requesting signal relay is initiated; if the mixed movement and relay strategy is adopted, a node movement task and a task requesting signal relay are initiated at the same time. The intelligent signal enhancement module is connected to the order task module. When the signal enhancement scheme is calculated, a node movement or request relay task can be generated. The task information includes the mobile node, relay node, task target area, etc. The task is automatically issued or reported after it is generated.

[0072] In this embodiment, both the signal movement task and the signal relay task can be achieved through the existing technology, among which the signal relay task can be achieved through the Chinese patent "A method, device, equipment and medium for automatic relay planning in a self-organizing network" previously applied by the applicant. According to the automatic relay planning algorithm, the position information of all nodes to be connected in the self-organizing network (including nodes to be enhanced and potential relay nodes), the signal target threshold of the nodes to be enhanced and the parameters related to the signal coverage range of the enhanced (or relay) nodes are input and processed, and the precise position and connection relationship of the determined relay nodes and other information are output. This information will be used to guide the actual deployment of relay nodes in the self-organizing network to achieve signal relay tasks, enhance network signal quality, and ensure stable communication between nodes. It will not be repeated here.

[0073] In one embodiment, coordinating relevant nodes in a broadband self-organizing network to perform a signal enhancement task to achieve signal enhancement of the node to be enhanced includes: if the signal enhancement task is a node movement task, the management node sends the node movement task to the member node, the member node parses the node movement task, and if the current member node is a mobile node, it moves to the target area and reports the task completion to the management node; if the signal enhancement task is a request relay task, the current member node reports the request relay task to the management node, and after the management node confirms the task, it sends a relay node to the target area and reports the task completion.

[0074] like Figure 6 The structural diagram of the order task module shown in the figure provides node command and control and work coordination capabilities. The order task module includes task processes such as task issuance, reporting, order acceptance, execution, completion and evaluation. The order task includes the issuance of node movement tasks and the reporting of request relay tasks. For node movement tasks, the management node module issues movement tasks to member nodes. Member nodes receive tasks, confirm tasks, move to the target area of ​​tasks, and report task completion. The system confirms and evaluates the completion of tasks. For request relay tasks, member nodes report tasks to the management node. After the upper node confirms the task, it dispatches relay nodes to the target area to confirm the completion of tasks. The order task module confirms and evaluates the completion of tasks.

[0075] In this embodiment, the system of the present invention realizes the command control and collaborative work of the wireless signal management behavior of the broadband self-organizing network. Through the order task, the manager can issue the node movement task to the node member, the node member confirms the task information, executes the node movement and reports the task completion, and the system automatically evaluates the task completion; the node member can apply for the signal relay task from the manager, the manager confirms the task information, dispatches the relay node and confirms the task completion, and the system automatically evaluates the task completion. Through the above tasks, the command control and collaborative work of the wireless signal management of the broadband self-organizing network are realized.

[0076] In one embodiment, Figure 7 As shown, a method for intelligently enhancing a broadband ad hoc network signal is provided, comprising the following steps:

[0077] Step 702, monitoring the signals of the nodes in the broadband ad hoc network to obtain monitoring results, and performing signal warning according to the monitoring results to obtain warning information;

[0078] Step 704, obtain monitoring results by adjusting the network through the pre-trained model, process the monitoring results to obtain statistical factors, and predict the signal coverage range and signal coverage strength of each node in the broadband ad hoc network according to the preset deterministic factors and statistical factors through the signal coverage model;

[0079] Step 706, obtaining warning information, obtaining the node to be enhanced that triggers the warning according to the warning information, generating a signal enhancement plan according to the signal coverage range and signal coverage strength of the node to be enhanced, and generating a signal enhancement task according to the signal enhancement plan;

[0080] Step 708: coordinate the related nodes in the broadband ad hoc network to perform the signal enhancement task to achieve signal enhancement of the node to be enhanced.

[0081] In this embodiment, the method of the present invention realizes the full process management of broadband ad hoc wireless signals. Figure 8 The flowchart of the adaptive network management of broadband self-organizing networks shown in the figure first connects to the broadband self-organizing network equipment for operation. During the network deployment phase, the system can monitor the signal strength in real time and issue warnings. When the signal quality degrades and a warning is generated, the system can use the propagation coverage calculated in the planning phase to adjust the network signal and intelligently generate a signal enhancement plan. After the enhancement plan is executed, the signal enhancement effect can be evaluated. This is suitable for the broadband self-organizing network field with real-time dynamic changes, and realizes the closed loop of the entire process of broadband self-organizing network wireless signal management planning, deployment, adjustment, and evaluation.

[0082] It should be understood that although Figure 7 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 7 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0083] For the specific limitations of the broadband ad hoc network signal intelligent enhancement method, please refer to the above limitations of the broadband ad hoc network signal intelligent enhancement system, which will not be repeated here.

[0084] Each module in the above-mentioned broadband ad hoc network signal intelligent enhancement system can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0085] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig. 9As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for intelligently enhancing broadband self-organizing network signals is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0086] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0087] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method in the above embodiment when executing the computer program.

[0088] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0089] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0090] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A broadband ad hoc network signal intelligent enhancement system, characterized in that: The system includes a signal simulation prediction module, a signal monitoring and early warning module, an intelligent signal enhancement module and an order task module, wherein the intelligent signal enhancement module is connected to the signal simulation prediction module, the signal monitoring and early warning module and the order task module respectively; The signal monitoring and early warning module is connected to the signal simulation prediction module and the broadband ad hoc network respectively, and is used to perform signal monitoring on nodes in the broadband ad hoc network, obtain monitoring results, and perform signal early warning according to the monitoring results to obtain early warning information; The signal simulation prediction module includes a signal coverage model and a pre-trained model adjustment network. The model adjustment network obtains the monitoring results output by the signal monitoring and early warning module in real time, processes the monitoring results to obtain statistical factors, and the signal coverage model predicts the signal coverage range and signal coverage strength of each node in the broadband ad hoc network according to the preset deterministic factors and the statistical factors, and transmits them to the intelligent signal enhancement module; The intelligent signal enhancement module is used to obtain the warning information transmitted in real time by the signal monitoring and warning module, obtain the node to be enhanced that triggers the warning according to the warning information, generate a signal enhancement plan according to the signal coverage range and signal coverage strength of the node to be enhanced, generate a signal enhancement task according to the signal enhancement plan and transmit it to the order task module; The order task module is connected to the broadband self-organizing network and is used to coordinate the relevant nodes in the broadband self-organizing network to perform the signal enhancement task to achieve signal enhancement of the node to be enhanced.

2. The system according to claim 1, characterized in that The signal monitoring and early warning module includes a signal monitoring submodule and a signal early warning submodule; The signal monitoring submodule is used to read the full network node information of the broadband ad hoc network in real time to obtain the monitoring results; The signal warning submodule is used to obtain the signal quality of each node according to the monitoring results, and to make an abnormality judgment according to the size relationship between the signal quality of the node and the preset warning threshold. When it is judged to be abnormal, a signal warning is triggered to obtain the warning information of the node.

3. The system according to claim 2, characterized in that The abnormality is judged based on the relationship between the signal quality of the node and the pre-set warning threshold. When it is judged to be abnormal, the signal warning is triggered, and the warning information of the node includes: Set the warning threshold of the corresponding level according to the current communication environment; The signal quality of the current node is obtained. If the signal quality level is less than the warning threshold, it is determined to be abnormal, triggering a signal warning, and obtaining the warning information of the node; the warning information includes a quality degradation warning and a node loss warning.

4. The system according to claim 1, characterized in that The step of training the model conditioning network comprises: Acquire the signal coverage range and signal coverage strength of each node of the broadband ad hoc network in different communication environments to obtain a measured data set, and use the signal coverage model to predict the signal coverage range and signal coverage strength of each node of the broadband ad hoc network in different communication environments to obtain a calculated data set; Calculate the signal strength errors of nodes having the same position and environmental factors according to the measured data set and the calculated data set to obtain a signal strength error data set; Constructing a model adjustment network, the model adjustment network comprising an input layer, a hidden layer and an output layer, receiving signal strength error data in a signal strength error data set through the input layer, performing nonlinear mapping on the signal strength error data through the hidden layer, and performing linear combination on the output of the hidden layer through the output layer to output a statistical factor; The model adjustment network is trained according to the signal strength error data set to obtain a trained model adjustment network.

5. The system according to claim 1, characterized in that The signal enhancement scheme includes a node movement strategy, a node movement strategy and a hybrid enhancement strategy; the signal enhancement task includes a node movement task and a request relay task; Generating signal enhancement tasks according to the signal enhancement scheme includes: If the adopted signal enhancement scheme is a node movement strategy, a node movement task is generated according to the mobile node and the target area; If the adopted signal enhancement scheme is the node relay strategy, a request relay task is generated according to the relay node; If the adopted signal enhancement scheme is a hybrid enhancement strategy, a node moving task is generated according to the mobile node and the mobile position, and a request relay task is generated according to the relay node.

6. The system according to claim 1, characterized in that Coordinating relevant nodes in the broadband ad hoc network to perform the signal enhancement task to achieve signal enhancement of the node to be enhanced includes: If the signal enhancement task is a node movement task, the management node sends the node movement task to the member node, and the member node parses the node movement task. If the current member node is a mobile node, it moves to the target area and reports the task completion to the management node; If the signal enhancement task is a request relay task, the current member node reports the request relay task to the management node. After the management node confirms the task, it sends a relay node to the target area and reports the task completion.

7. A method for intelligently enhancing signals in a broadband ad hoc network, characterized in that: The method comprises: Perform signal monitoring on nodes in the broadband ad hoc network to obtain monitoring results, and perform signal warnings based on the monitoring results to obtain warning information; The monitoring results are obtained by adjusting the network through a pre-trained model, the monitoring results are processed to obtain statistical factors, and the signal coverage range and signal coverage strength of each node in the broadband ad hoc network are predicted according to the preset deterministic factors and the statistical factors through a signal coverage model; Acquire the warning information, obtain the node to be enhanced that triggers the warning according to the warning information, generate a signal enhancement plan according to the signal coverage range and signal coverage strength of the node to be enhanced, and generate a signal enhancement task according to the signal enhancement plan; Coordinate relevant nodes in the broadband ad hoc network to perform the signal enhancement task to achieve signal enhancement of the node to be enhanced.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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