Monitoring network organization method, device, equipment, program product and storage medium

Abstract

The application relates to the technical field of network organization, and provides a network organization monitoring method, device, equipment, program product and storage medium. The method comprises the following steps: obtaining optimal positions and optimal connection paths of a plurality of target monitoring nodes in a monitoring network based on a target path search algorithm; and adjusting the current positions and current connection paths of the plurality of target monitoring nodes based on the optimal positions and the optimal connection paths. The target path search algorithm is determined based on a target pheromone updating algorithm and a target path safety updating algorithm. The target pheromone updating algorithm updates pheromone concentration based on real-time characteristic data of a monitoring target and a monitoring environment, and the target path safety updating algorithm updates a safety function value of a path based on real-time characteristic data of a path obstacle. The application can quickly adjust the positions of the monitoring nodes and the connection paths when the monitoring target, the monitoring environment and the path obstacle change in real time, thereby improving the monitoring effect.

Description

Technical Field

[0001] This application relates to the field of network organization technology, specifically to a method, apparatus, device, program product, and storage medium for monitoring network organization. Background Technology

[0002] In traditional surveillance network systems, monitoring nodes such as cameras and sensors are often statically configured, meaning their locations and connection paths with other monitoring nodes are fixed once deployed. This method can be effective when the monitored targets and environment are relatively stable, but with the acceleration of urbanization and the diversification of monitoring needs, traditional static surveillance networks face increasing challenges.

[0003] Changes in monitoring targets, such as changes in the flow of people within the monitoring area, changes in the monitoring environment, such as changes in temperature and humidity within the monitoring area, or changes in obstacles, such as the construction of new buildings or the destruction or relocation of old buildings, can all lead to the original layout of monitoring nodes becoming unreasonable. Traditional static organization methods of monitoring networks cannot adapt to changes in monitoring targets, monitoring environments, and obstacles. Therefore, they cannot quickly adjust the position and connection path of monitoring nodes in the face of these changes, thus affecting the monitoring effect. Summary of the Invention

[0004] This application provides a monitoring network organization method, apparatus, device, program product, and storage medium to solve the technical problem that traditional static organization methods of monitoring networks cannot adapt to changes in monitoring targets, monitoring environments, and obstacles, and therefore cannot quickly adjust the position and connection path of monitoring nodes in the face of these changes, thereby affecting the monitoring effect.

[0005] In a first aspect, embodiments of this application provide a method for monitoring network organization, including:

[0006] Based on the target path search algorithm, the optimal location and optimal connection path of multiple target monitoring nodes in the monitoring network are obtained;

[0007] Based on the optimal location and the optimal connection path, adjust the current location and current connection path of the multiple target monitoring nodes;

[0008] The target path search algorithm is determined based on the target pheromone update algorithm and the target path safety update algorithm. The target pheromone update algorithm is an algorithm that updates the pheromone concentration in real time based on the real-time feature data of the monitored target in the monitoring network and the real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm that updates the path safety function value in real time based on the real-time feature data of the path obstacles in the monitoring network.

[0009] In one embodiment, the optimal location of any target monitoring node is determined based on the following method:

[0010] Based on the target data between the target monitoring node and multiple other monitoring nodes, multiple current selection probabilities are obtained for multiple connection paths between the target monitoring node and the multiple other monitoring nodes.

[0011] The maximum current selection probability is determined from the plurality of current selection probabilities, and the positions of other monitoring nodes on the connection path corresponding to the maximum current selection probability are determined as the optimal position of the target monitoring node;

[0012] The target data includes the current pheromone concentration of the connection path, the current inverse distance between the target monitoring node and the other monitoring nodes, and the current security function value of the connection path;

[0013] The current pheromone concentration is obtained by updating the historical pheromone concentration of the connection path based on the historical motion data of the monitored target and the historical sensitivity of the monitored environment.

[0014] The current security function value is obtained based on the current position of the path obstacle on the connection path and the current sensitivity of the monitoring environment.

[0015] In one embodiment, the target data may also include the current energy consumption and task priority of the monitoring node.

[0016] In one embodiment, adjusting the current location and current connection path of the plurality of target monitoring nodes based on the optimal location and the optimal connection path includes:

[0017] Adjust the current positions of the multiple target monitoring nodes to their corresponding optimal positions;

[0018] Activate the optimal connection path among the current connection paths of the multiple target monitoring nodes, and close the non-optimal connection paths among the current connection paths of the multiple target monitoring nodes;

[0019] The optimal connection path is the connection path whose historical pheromone concentration is greater than the concentration threshold.

[0020] In one embodiment, adjusting the current location and current connection path of the plurality of target monitoring nodes includes:

[0021] Real-time monitoring of the monitoring network's performance data, and performance evaluation and optimization of the monitoring network based on the performance data;

[0022] The performance data includes the coverage area, data transmission latency, and energy consumption of the multiple target monitoring nodes.

[0023] In one embodiment, the coverage area of ​​any target monitoring node is determined based on the sensing range of the target monitoring node for the monitored target;

[0024] The data transmission delay of any target monitoring node is determined based on the difference between the actual speed and the expected speed of the monitored target collected by the target monitoring node.

[0025] Secondly, embodiments of this application provide a network organization monitoring device, comprising:

[0026] The path search module is used to: obtain the optimal location and optimal connection path of multiple target monitoring nodes in the monitoring network based on the target path search algorithm;

[0027] The network organization module is used to: adjust the current position and current connection path of the plurality of target monitoring nodes based on the optimal position and the optimal connection path;

[0028] The target path search algorithm is determined based on the target pheromone update algorithm and the target path safety update algorithm. The target pheromone update algorithm is an algorithm that updates the pheromone concentration in real time based on the real-time feature data of the monitored target in the monitoring network and the real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm that updates the path safety function value in real time based on the real-time feature data of the path obstacles in the monitoring network.

[0029] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the monitoring network organization method described in the first aspect.

[0030] Fourthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the monitoring network organization method described in the first aspect.

[0031] Fifthly, embodiments of this application provide a non-transitory computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the monitoring network organization method described in the first aspect.

[0032] The monitoring network organization method, apparatus, equipment, program product, and storage medium provided in this application, based on a target path search algorithm, obtain the optimal positions and optimal connection paths of multiple target monitoring nodes in the monitoring network, and adjust the current positions and current connection paths of the multiple target monitoring nodes based on the optimal positions and optimal connection paths. Since the target path search algorithm is determined based on a target pheromone update algorithm and a target path security update algorithm—the target pheromone update algorithm is an algorithm that updates pheromone concentration in real time based on real-time feature data of the monitored targets and the real-time feature data of the monitoring environment in which the monitoring network is located, and the target path security update algorithm is an algorithm that updates the path security function value in real time based on real-time feature data of path obstacles in the monitoring network—the acquisition of the optimal positions and optimal connection paths of the target monitoring nodes fully considers the real-time changes of the monitored targets, the monitoring environment, and path obstacles. This ensures that the adjustment of the current positions and current connection paths of the monitoring nodes is performed in response to changes in the monitored targets, the monitoring environment, and path obstacles, thereby achieving rapid adjustment of the monitoring node positions and connection paths in the face of real-time changes in the monitored targets, the monitoring environment, and path obstacles, and improving the monitoring effect. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is one of the flowcharts illustrating the monitoring network organization method provided in the embodiments of this application;

[0035] Figure 2 This is a second flowchart illustrating the monitoring network organization method provided in the embodiments of this application;

[0036] Figure 3 This is a schematic diagram of the structure of the monitoring network organization device provided in the embodiments of this application;

[0037] Figure 4 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] Figure 1 This is one of the flowcharts illustrating the monitoring network organization method provided in this application embodiment. (Refer to...) Figure 1 This application provides a method for monitoring network organization, which may include:

[0040] 101. Based on the target path search algorithm, obtain the optimal location and optimal connection path of multiple target monitoring nodes in the monitoring network;

[0041] 102. Based on the optimal location and optimal connection path, adjust the current location and current connection path of multiple target monitoring nodes;

[0042] The target path search algorithm is determined based on the target pheromone update algorithm and the target path safety update algorithm. The target pheromone update algorithm is an algorithm that updates the pheromone concentration in real time based on the real-time feature data of the monitored target in the monitoring network and the real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm that updates the path safety function value in real time based on the real-time feature data of the path obstacles in the monitoring network.

[0043] In step 101, the target path search algorithm can be an improved ant colony algorithm based on the target pheromone update algorithm and the target path security update algorithm, which is formed on the basis of the traditional ant colony algorithm. The improved ant colony algorithm can be used to determine the optimal location and optimal connection path of the target monitoring node.

[0044] The monitoring network organization method provided in this embodiment, based on a target path search algorithm, obtains the optimal positions and optimal connection paths of multiple target monitoring nodes in the monitoring network. Based on these optimal positions and connection paths, the current positions and connection paths of the multiple target monitoring nodes are adjusted. Since the target path search algorithm is determined based on a target pheromone update algorithm and a target path safety update algorithm—the target pheromone update algorithm updates the pheromone concentration in real time based on real-time feature data of the monitored targets and the monitoring environment in which the monitoring network is located, and the target path safety update algorithm updates the path safety function value in real time based on real-time feature data of path obstacles in the monitoring network—the acquisition of the optimal positions and optimal connection paths of the target monitoring nodes fully considers the real-time changes of the monitored targets, the monitoring environment, and path obstacles. This ensures that the adjustment of the current positions and connection paths of the monitoring nodes is performed in response to changes in the monitored targets, the monitoring environment, and path obstacles, thereby achieving rapid adjustment of the monitoring node positions and connection paths in the face of real-time changes in the monitored targets, the monitoring environment, and path obstacles, and improving the monitoring effect.

[0045] Furthermore, traditional static organization methods for monitoring networks, due to the relatively fixed locations and connection paths of monitoring nodes, often fail to adjust monitoring resources in a timely manner to focus on the incident area in the event of sudden public events such as traffic accidents or natural disasters. This reduces the accuracy of the monitored data, directly impacting the efficiency of event handling and the practical application value of the monitoring network. The method in this embodiment, however, can adjust the location and connection paths of monitoring nodes based on real-time changes in the monitored target. Therefore, it ensures that the monitoring network can quickly respond and adjust monitoring resources to focus on the incident area during emergencies, improving the accuracy of the monitored data. This, in turn, enhances event handling efficiency and the practical application value of the monitoring network, effectively supporting safety management and emergency response.

[0046] Figure 2 This is the second flowchart illustrating the monitoring network organization method provided in this application embodiment. (Refer to...) Figure 2 In one embodiment, the optimal location of any target monitoring node can be determined based on the following method:

[0047] 201. Based on the target data between the target monitoring node and multiple other monitoring nodes, obtain multiple current selection probabilities corresponding to multiple connection paths between the target monitoring node and multiple other monitoring nodes;

[0048] 202. Determine the maximum current selection probability from multiple current selection probabilities, and determine the positions of other monitoring nodes on the connection path corresponding to the maximum current selection probability as the optimal position of the target monitoring node.

[0049] The target data includes the current pheromone concentration of the connection path, the current inverse distance between the target monitoring node and other monitoring nodes, and the current security function value of the connection path;

[0050] The current pheromone concentration is obtained by updating the historical pheromone concentration of the connection path based on the historical motion data of the monitored target and the historical sensitivity of the monitoring environment; the current security function value is obtained based on the current position of the obstacles on the connection path and the current sensitivity of the monitoring environment.

[0051] In step 201, the current time Target monitoring node Other monitoring nodes The current selection probability corresponding to the connection path between them It can be represented as follows:

[0052] (2-1)

[0053] in, It is the current moment. Target monitoring node Other monitoring nodes The current pheromone concentration of the connecting paths, It is the current moment. Target monitoring node Other monitoring nodes The current inverse distance between them It is the current moment. Target monitoring node position vector Other monitoring nodes position vector The current security function value of the connecting paths; It is the target monitoring node The set of all other monitoring nodes within the communication range. It is the current moment. Target monitoring node and The Middle The current pheromone concentration of the connection path between other monitoring nodes. It is the current moment. Target monitoring node and The Middle The current inverse distance between the other monitoring nodes It is the current moment. Target monitoring node position vector and The Middle Location vectors of other monitoring nodes The current security function value of the connecting paths; , , It is the adjustment coefficient.

[0054] Furthermore, It can be obtained based on the following formula:

[0055] (2-2)

[0056] in, It was the previous moment Target monitoring node Other monitoring nodes Historical pheromone concentration of the connecting paths between them It was the previous moment No. A monitoring target at the target monitoring node Other monitoring nodes The amount of pheromone left behind when moving along the connection path between them. It was the previous moment No. A monitoring target at the target monitoring node Other monitoring nodes The velocity vector moving along the connecting path between them It is the first A monitoring target at the target monitoring node Other monitoring nodes The maximum speed of movement on the connecting path between them. It was the previous moment Historical sensitivity of the monitoring environment It was the previous moment Target monitoring node The position vector, It was the previous moment No. The location vector of each monitored target It refers to the pheromone decay rate, which controls the rate at which pheromones decay over time, ensuring that during path selection, older pheromones gradually lose their effectiveness, while newer pheromones receive greater weight. , , These are adjustment coefficients, which respectively control the influence of the speed at which the monitored target moves on the amount of pheromone left by the monitored target, the sensitivity of the monitoring environment on the amount of pheromone left by the monitored target, and the distance between the monitoring node and the monitored target on the amount of pheromone left by the monitored target.

[0057] As can be seen from the above, the pheromone concentration update mechanism has been given special attention and improvement in this embodiment. Considering the real-time movement of the monitored target and the dynamic changes of the monitoring environment, the update of the pheromone concentration not only reflects the path selection results of the monitored target group, but also incorporates the real-time changes of the monitored target and the monitoring environment, such as the speed and position changes of the monitored target and the sensitivity changes of the monitoring environment. Through this dynamic adjustment, the distribution of pheromones can more realistically reflect the status of the monitoring network, thereby improving the accuracy and efficiency of path selection.

[0058] Additionally, at the initial moment Target monitoring node Other monitoring nodes Initial pheromone concentration of the connecting paths It can be obtained based on the following formula:

[0059] ;

[0060] in, It is the initial moment Target monitoring node The position vector, It is the initial moment Other monitoring nodes The position vector, It is a spatial distance scaling factor. It is the initial moment No. The location vector of each monitored target It is the adjustment coefficient.

[0061] in, It can be obtained based on the following formula:

[0062] ;

[0063] in, yes The three-dimensional coordinates This refers to the initial deployment density of the monitoring nodes. It is the target monitoring node At the initial azimuth angle in the horizontal direction, It is the target monitoring node At the initial azimuth angle in the vertical direction, It is the initial height adjustment function value based on terrain data. Similarly, this will not be elaborated upon here.

[0064] In the initialization phase of the improved ant colony algorithm, unlike the uniform initial pheromone distribution typically used in traditional ant colony algorithms, the initial pheromone layout in this embodiment reflects the movement trend of the monitored target and the influence of environmental factors such as terrain. This allows the improved ant colony algorithm to effectively perform path search and optimization for specific monitoring tasks in the initial stage. For each monitoring task, the initialization of pheromones not only relies on the traditional random distribution but also on the dynamic characteristics of the monitored target and the initial data of the monitoring nodes to ensure that the initial pheromones reflect the actual monitoring needs.

[0065] Simultaneously, during the initialization phase, communication and sensing ranges can be configured for each monitoring node, and these ranges can be dynamically adjusted using an adaptive adjustment algorithm based on the physical location of the monitoring node and the expected monitoring area. To optimize network monitoring efficiency and coverage, the target monitoring node... The communication range and sensing range can be dynamically adjusted based on environmental adaptability and monitoring requirements, according to the following formula:

[0066] ;

[0067] ;

[0068] in, It is the current moment. Target monitoring node Communication range, The target monitoring node at the initial moment Communication range, It is the current moment. Environmental factors The measured values ​​reflect the combined effects of environmentally sensitive factors such as temperature, humidity, and light intensity in the monitored environment. It is the current moment. Target monitoring node The sensing range, The target monitoring node at the initial moment The sensing range, It is the adjustment coefficient. It is the current moment. No. The location vector of each monitored target.

[0069] in, The update can be based on the following formula:

[0070] ;

[0071] in, It was the previous moment Environmental factors The measured value, yes and Between environmental factors The measurement difference Environmental factors The rate of change is used to better reflect the dynamic changes in the monitored environment. The larger the value, the greater the fluctuation of the environmental factors.

[0072] It's important to note that the communication range refers to the maximum distance each monitoring node can transmit and receive signals over, essentially the maximum range at which monitoring nodes can exchange data. Within this range, monitoring nodes can exchange information, forming the communication topology of the entire monitoring network. If the distance between monitoring nodes exceeds the communication range, they cannot communicate directly and may need to use intermediate nodes for data transmission. The sensing range refers to the maximum distance each monitoring node can detect the monitored target. Within this range, monitoring nodes can sense and detect the presence and dynamic information (such as location and speed) of the monitored target and transmit this information to the monitoring network.

[0073] Furthermore, combining with formula (2-2), at the current time... Current sensitivity of the monitoring environment It can be represented as follows:

[0074] ;

[0075] in, It is basic environmental sensitivity. Environmental factors The weighting of environmental factors, through a monitoring environment sensitivity formula that integrates environmental factor weights, can improve the overall adaptability and responsiveness of the monitoring network under changes in the monitoring environment.

[0076] Furthermore, combining formula (2-1). Used to evaluate the corresponding connection path in The security of the moment is considered, taking into account the current position of obstacles on the corresponding connection path and the current sensitivity of the monitoring environment. Specifically, the current moment... Obstacles on any connecting path Current position vector It can be represented as follows:

[0077] ;

[0078] in, It was the previous moment Obstacles on the same path of the connection Historical position vector, It is the current moment. Obstacles along the path The velocity vector of movement, It was the previous moment With the current moment The time difference.

[0079] In this embodiment, when selecting the optimal location, in addition to relying on the pheromone concentration of the connection path and the inverse distance between monitoring nodes, the safety of the connection path is also assessed based on the location of obstacles along the path and the sensitivity of the monitoring environment. During the dynamic changes of the monitored target and the monitoring environment, this effectively avoids potential risks in connection path selection, enhances the reliability of connection path selection, and thus obtains the accurate current selection probability of each connection path, thereby acquiring the optimal location. In this process, this embodiment optimizes the collection, processing, and utilization of various real-time data. By integrating the real-time location and speed of the monitored target, the positional changes of obstacles along the path, and other environmentally sensitive data, the accuracy and response speed of data processing are improved.

[0080] In one embodiment, the target data also includes the current energy consumption and task priority of the monitoring node.

[0081] Among them, any monitoring node at the current time Operation status Current energy consumption It can be represented as follows:

[0082] ;

[0083] Among them, the operation status Including standby, activity, and sleep modes. It is the monitoring node at the initial moment energy consumption , and It is the adjustment coefficient.

[0084] By incorporating the current energy consumption of monitoring nodes into the selection of connection paths, the current energy consumption of monitoring nodes can be used as a constraint or optimization objective to obtain the current selection probability of the connection path, thereby avoiding the selection of connection paths with excessive energy consumption and improving the overall energy efficiency of the monitoring network.

[0085] Among them, the task of any monitoring node Task priority It can be represented as follows:

[0086] ;

[0087] in, It is a task The inherent priority coefficient, It is the monitoring node For the task Service capability contribution It represents the total number of monitoring nodes in the monitoring network.

[0088] By incorporating the task priority of monitoring nodes into the consideration of connection path selection, the selection weight of connection paths can be adjusted according to the task priority of different tasks, thereby obtaining the current selection probability of the connection path. This allows the connection path of monitoring nodes with high task priority to obtain a greater current selection probability and be selected first.

[0089] On the one hand, changes in monitoring tasks, such as shifting from routine monitoring to security for large-scale events, often involve changes in task priorities, requiring the monitoring network to be quickly reconfigured to address new security threats and monitoring priorities. On the other hand, without changing the network configuration, the monitoring system often needs to operate continuously around the clock, resulting in a large amount of unnecessary computation and data transmission. This not only increases the load on the monitoring network but may also lead to premature aging of monitoring equipment.

[0090] This embodiment incorporates the current energy consumption and task priority of monitoring nodes into the connection path selection. On the one hand, it can obtain the optimal location based on task priority and adjust the monitoring nodes accordingly, thereby enabling rapid response to new security threats and monitoring priorities. It has high flexibility and scalability, and can adjust monitoring strategies and network layouts according to different application scenarios, adapting to monitoring needs in various complex scenarios such as urban security monitoring, environmental monitoring, and traffic management. On the other hand, it can obtain the optimal location based on current energy consumption and adjust the monitoring nodes accordingly, thereby avoiding a large amount of unnecessary computational data transmission, reducing the load on the monitoring network, extending the service life of monitoring equipment, and thus extending the overall operating time of the monitoring network and reducing maintenance costs. In other words, this embodiment can maximize resource utilization efficiency and the execution quality of monitoring tasks while ensuring the monitoring efficiency of the monitoring network.

[0091] In one embodiment, adjusting the current location and current connection path of multiple target monitoring nodes based on the optimal location and optimal connection path may include:

[0092] Adjust the current position of multiple target monitoring nodes to the corresponding optimal position, activate the optimal connection path in the current connection path of multiple target monitoring nodes, and close the non-optimal connection path in the current connection path of multiple target monitoring nodes.

[0093] The optimal connection path is the one where the historical pheromone concentration is greater than the concentration threshold.

[0094] At the target monitoring node The position adjustment amount during the process of adjusting from the current position to its optimal position It can be obtained from the following formula:

[0095] ;

[0096] in, It is the target monitoring node The optimal position vector, It is the target monitoring node The current position vector, It adjusts the step size coefficient to control the target monitoring node. The position adjustment speed and amplitude.

[0097] In addition, the connection path whose pheromone concentration was greater than the concentration threshold at the previous moment can be identified as the optimal connection path, and the optimal connection path can be activated at the current moment while other connection paths are closed.

[0098] It should be noted that adjusting the location and connection path of the target monitoring node includes not only moving the physical location of the target monitoring node, but also adjusting the routing table and connection policies in the monitoring network.

[0099] This embodiment ensures that the target monitoring node is smoothly adjusted to the optimal position by setting the position adjustment amount of the target monitoring node; by determining the optimal connection path, activating the optimal connection path, and closing the non-optimal connection path, the communication efficiency and data transmission cost of the entire monitoring network can be optimized.

[0100] In one embodiment, after adjusting the current location and current connection path of multiple target monitoring nodes, the following can be included:

[0101] Real-time monitoring of network performance data; performance evaluation and optimization of the network based on the data.

[0102] Performance data includes the coverage of multiple target monitoring nodes, data transmission latency, and energy consumption.

[0103] The coverage area of ​​any target monitoring node is determined based on the sensing range of the target monitored by that target monitoring node, and the data transmission delay of any target monitoring node is determined based on the difference between the actual speed and the expected speed of the target monitored by that target monitoring node.

[0104] Specifically, the current moment Target monitoring node Coverage It can be obtained from the following formula:

[0105] ;

[0106] in, It is the adjustment coefficient.

[0107] In the calculation formula, The exponent is -1, indicating that the coverage range changes as the inverse of the distance between the monitored target and the target monitoring node. This means that the closer the monitored target is to the target monitoring node, the more significant the adjustment of the coverage range. This is a linear adjustment relationship used for precise dynamic adjustment of the coverage range.

[0108] In the calculation formula, The index is -2, which means that changes in the distance between the monitored target and the monitored node have a greater impact on the sensing range of the monitored node, and are mainly used for optimizing and adjusting the sensing range.

[0109] The above coverage calculation formula introduces a dynamic adjustment mechanism based on the sensing capability of the target monitoring node and the changing characteristics of the monitoring target. This enables the calculation of coverage to effectively cope with rapidly changing monitoring needs and improve the spatial coverage capability and response sensitivity of the monitoring network.

[0110] Specifically, the current moment Target monitoring node The first collection The actual speed of each monitored target It can be represented as follows:

[0111] ;

[0112] Current moment Target monitoring node The first collection The expected speed of each monitored target It can be represented as follows:

[0113] ;

[0114] in, It is the first The speed baseline of each monitored target It is a natural constant. Is with the first The attenuation coefficient associated with each monitored target It is the current moment. No. Each monitoring target and its monitoring node The distance between them It is the current moment. No. The rate of change of direction of each monitored target.

[0115] In the formula for expected speed, ;

[0116] in, It is the first The changing angular velocity of each monitored target It is the first The initial phase of each monitored target.

[0117] and The deviation could be due to data transmission delays at the target monitoring node, or it could be due to the target not moving as expected. and The difference can measure the data transmission delay of the target monitoring node, thereby reflecting the timeliness and response speed of the overall information transmission of the monitoring network through the data transmission delay of each target monitoring node, and can be used to evaluate the effectiveness and timeliness of data transmission. It can also be used to determine whether the monitored target conforms to the expected movement trajectory, so as to adjust the tracking strategy for the monitored target in a timely manner and improve the tracking accuracy.

[0118] Similarly, by monitoring the actual energy consumption of each target monitoring node in the network, the energy consumption performance of the adjusted monitoring network under different operating states can be evaluated, thereby optimizing the overall energy efficiency.

[0119] When the real-time characteristic data of the monitoring network changes for various reasons, the relevant steps of this application are repeated to continuously optimize the monitoring network.

[0120] Traditional methods often lack systematic evaluation and optimization mechanisms in the process of achieving self-organization of monitoring networks, making it difficult to guarantee the performance of the network after adjustment, and lacking the necessary flexibility and scalability to cope with possible more complex environmental and demand changes in the future.

[0121] This embodiment continuously monitors and evaluates network performance, and makes adjustments and optimizations based on the actual monitoring results. This ensures that the entire monitoring network continues to operate in the optimal state, effectively improving the overall robustness of the monitoring network and enabling it to withstand the impact of environmental interference and equipment failure.

[0122] The monitoring network organization device provided in the embodiments of this application is described below. The monitoring network organization device described below can be referred to in correspondence with the monitoring network organization method described above.

[0123] Figure 3 This is a schematic diagram of the structure of the monitoring network organization device provided in an embodiment of this application. (Refer to...) Figure 3 This application provides a monitoring network organization device, which may include:

[0124] The path search module 301 is used to: obtain the optimal location and optimal connection path of multiple target monitoring nodes in the monitoring network based on the target path search algorithm;

[0125] The network organization module 302 is used to: adjust the current position and current connection path of the plurality of target monitoring nodes based on the optimal position and the optimal connection path;

[0126] The target path search algorithm is determined based on the target pheromone update algorithm and the target path safety update algorithm. The target pheromone update algorithm is an algorithm that updates the pheromone concentration in real time based on the real-time feature data of the monitored target in the monitoring network and the real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm that updates the path safety function value in real time based on the real-time feature data of the path obstacles in the monitoring network.

[0127] The monitoring network organization device provided in this embodiment obtains the optimal positions and optimal connection paths of multiple target monitoring nodes in the monitoring network based on a target path search algorithm. Based on these optimal positions and connection paths, it adjusts the current positions and current connection paths of the multiple target monitoring nodes. Since the target path search algorithm is determined based on a target pheromone update algorithm and a target path safety update algorithm—the latter updating pheromone concentration in real-time based on real-time feature data of the monitored targets and the monitoring environment, and the former updating the path safety function value in real-time based on real-time feature data of path obstacles—the acquisition of the optimal positions and optimal connection paths of the target monitoring nodes fully considers the real-time changes of the monitored targets, the monitoring environment, and path obstacles. This ensures that the adjustment of the current positions and current connection paths of the monitoring nodes is performed in response to changes in these factors, thereby achieving rapid adjustment of the monitoring node positions and connection paths in the face of real-time changes in the monitored targets, the monitoring environment, and path obstacles, thus improving monitoring effectiveness.

[0128] In one embodiment, the path search module 301 is specifically used for:

[0129] Based on the target data between the target monitoring node and multiple other monitoring nodes, multiple current selection probabilities are obtained for multiple connection paths between the target monitoring node and the multiple other monitoring nodes.

[0130] The maximum current selection probability is determined from the plurality of current selection probabilities, and the positions of other monitoring nodes on the connection path corresponding to the maximum current selection probability are determined as the optimal position of the target monitoring node;

[0131] The target data includes the current pheromone concentration of the connection path, the current inverse distance between the target monitoring node and the other monitoring nodes, and the current security function value of the connection path;

[0132] The current pheromone concentration is obtained by updating the historical pheromone concentration of the connection path based on the historical motion data of the monitored target and the historical sensitivity of the monitored environment.

[0133] The current security function value is obtained based on the current position of the path obstacle on the connection path and the current sensitivity of the monitoring environment.

[0134] In one embodiment, the target data may also include the current energy consumption and task priority of the monitoring node.

[0135] In one embodiment, the network organization module 302 is specifically used for:

[0136] Adjust the current positions of the multiple target monitoring nodes to their corresponding optimal positions;

[0137] Activate the optimal connection path among the current connection paths of the multiple target monitoring nodes, and close the non-optimal connection paths among the current connection paths of the multiple target monitoring nodes;

[0138] The optimal connection path is the connection path whose historical pheromone concentration is greater than the concentration threshold.

[0139] In one embodiment, an adjusted evaluation module (not shown in the figure) is further included for:

[0140] Real-time monitoring of the monitoring network's performance data, and performance evaluation and optimization of the monitoring network based on the performance data;

[0141] The performance data includes the coverage area, data transmission latency, and energy consumption of the multiple target monitoring nodes.

[0142] In one embodiment, the coverage area of ​​any target monitoring node is determined based on the sensing range of the target monitoring node for the monitored target;

[0143] The data transmission delay of any target monitoring node is determined based on the difference between the actual speed and the expected speed of the monitored target collected by the target monitoring node.

[0144] Figure 4 is a schematic diagram of the structure of the electronic device provided in an embodiment of this application, as shown below. Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call a computer program in the memory 430 to execute steps of a monitoring network organization method, such as:

[0145] Based on the target path search algorithm, the optimal location and optimal connection path of multiple target monitoring nodes in the monitoring network are obtained;

[0146] Based on the optimal location and the optimal connection path, adjust the current location and current connection path of the multiple target monitoring nodes;

[0147] The target path search algorithm is determined based on the target pheromone update algorithm and the target path safety update algorithm. The target pheromone update algorithm is an algorithm that updates the pheromone concentration in real time based on the real-time feature data of the monitored target in the monitoring network and the real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm that updates the path safety function value in real time based on the real-time feature data of the path obstacles in the monitoring network.

[0148] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0149] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the monitoring network organization method provided in the above embodiments, such as including:

[0150] Based on the target path search algorithm, the optimal location and optimal connection path of multiple target monitoring nodes in the monitoring network are obtained;

[0151] Based on the optimal location and the optimal connection path, adjust the current location and current connection path of the multiple target monitoring nodes;

[0152] The target path search algorithm is determined based on the target pheromone update algorithm and the target path safety update algorithm. The target pheromone update algorithm is an algorithm that updates the pheromone concentration in real time based on the real-time feature data of the monitored target in the monitoring network and the real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm that updates the path safety function value in real time based on the real-time feature data of the path obstacles in the monitoring network.

[0153] On the other hand, embodiments of this application also provide a non-transitory computer-readable storage medium storing a computer program thereon, the computer program being used to cause a processor to execute the steps of the monitoring network organization method provided in the above embodiments, for example including:

[0154] Based on the target path search algorithm, the optimal location and optimal connection path of multiple target monitoring nodes in the monitoring network are obtained;

[0155] Based on the optimal location and the optimal connection path, adjust the current location and current connection path of the multiple target monitoring nodes;

[0156] The target path search algorithm is determined based on the target pheromone update algorithm and the target path safety update algorithm. The target pheromone update algorithm is an algorithm that updates the pheromone concentration in real time based on the real-time feature data of the monitored target in the monitoring network and the real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm that updates the path safety function value in real time based on the real-time feature data of the path obstacles in the monitoring network.

[0157] The non-transitory computer-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0158] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. 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 the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0159] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for organizing a monitoring network, characterized in that, include: Based on the target path search algorithm, the optimal locations and optimal connection paths of multiple target monitoring nodes in the monitoring network are obtained, including: The current time is obtained based on the following formula. Target monitoring node Other monitoring nodes The current selection probability corresponding to the connection path between them : ； in, It is the current moment. Target monitoring node Other monitoring nodes The current pheromone concentration of the connecting paths, It is the current moment. Target monitoring node Other monitoring nodes The current inverse distance between them It is the current moment. Target monitoring node position vector Other monitoring nodes position vector The current security function value of the connecting paths; It is the target monitoring node The set of all other monitoring nodes within the communication range. It is the current moment. Target monitoring node and The Middle The current pheromone concentration of the connection path between other monitoring nodes. It is the current moment. Target monitoring node and The Middle The current inverse distance between the other monitoring nodes It is the current moment. Target monitoring node position vector and The Middle Location vectors of other monitoring nodes The current security function value of the connecting paths; , , It is the adjustment coefficient; in: ； in, It was the previous moment Target monitoring node Other monitoring nodes Historical pheromone concentration of the connecting paths between them It was the previous moment No. A monitoring target at the target monitoring node Other monitoring nodes The amount of pheromone left behind when moving along the connection path between them. It was the previous moment No. A monitoring target at the target monitoring node Other monitoring nodes The velocity vector moving along the connecting path between them It is the first A monitoring target at the target monitoring node Other monitoring nodes The maximum speed of movement on the connecting path between them. It was the previous moment Historical sensitivity of the monitoring environment It was the previous moment Target monitoring node The position vector, It was the previous moment No. The location vector of each monitored target It is the pheromone decay rate. , , It is the adjustment coefficient; Among them, the current moment Current sensitivity of the monitoring environment It can be represented as follows: ； in, It is basic environmental sensitivity. Environmental factors The weight, It is the current moment. Environmental factors The measured value; The maximum current selection probability is determined from multiple current selection probabilities, and the positions of other monitoring nodes on the connection path corresponding to the maximum current selection probability are determined as the optimal position of the target monitoring node. The current security function value is obtained based on the current position of the obstacles on the connection path and the current sensitivity of the monitoring environment; The optimal connection path is the one where the historical pheromone concentration is greater than the concentration threshold. Based on the optimal location and the optimal connection path, adjust the current location and current connection path of the multiple target monitoring nodes.

2. The monitoring network organization method according to claim 1, characterized in that, The factors influencing the current selection probability also include the current energy consumption and task priority of the monitoring node.

3. The monitoring network organization method according to claim 1, characterized in that, The step of adjusting the current position and current connection path of the multiple target monitoring nodes based on the optimal position and the optimal connection path includes: Adjust the current positions of the multiple target monitoring nodes to their corresponding optimal positions; The optimal connection path among the current connection paths of the multiple target monitoring nodes is activated, and the non-optimal connection paths among the current connection paths of the multiple target monitoring nodes are closed.

4. The monitoring network organization method according to claim 1, characterized in that, After adjusting the current location and current connection path of the multiple target monitoring nodes, the following steps are included: Real-time monitoring of the monitoring network's performance data, and performance evaluation and optimization of the monitoring network based on the performance data; The performance data includes the coverage area, data transmission latency, and energy consumption of the multiple target monitoring nodes.

5. The monitoring network organization method according to claim 4, characterized in that, The coverage area of ​​any target monitoring node is determined based on the sensing range of the target monitoring node for the monitored target; The data transmission delay of any target monitoring node is determined based on the difference between the actual speed and the expected speed of the monitored target collected by the target monitoring node.

6. A monitoring network organization device, characterized in that, For performing the monitoring network organization method of claim 1, comprising: The path search module is used to: obtain the optimal location and optimal connection path of multiple target monitoring nodes in the monitoring network based on the target path search algorithm; The network organization module is used to: adjust the current position and current connection path of the multiple target monitoring nodes based on the optimal position and the optimal connection path.

7. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the monitoring network organization method according to any one of claims 1 to 5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the monitoring network organization method according to any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the monitoring network organization method according to any one of claims 1 to 5.

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