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

Abstract

The invention relates to the technical field of network organization, and provides a monitoring network organization method, device and equipment, a program product and a 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; adjusting the current positions and the 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 security updating algorithm, and the target pheromone updating algorithm updates the pheromone concentration based on real-time feature data of a monitoring target and a monitoring environment; the target path security update algorithm updates the security function value of the path based on the real-time feature data of the path obstacle. According to the invention, the position of the monitoring node and the connection path can be rapidly adjusted when facing real-time changes of the monitoring target, the monitoring environment and the path obstacle, and the monitoring effect is improved.

Description

Technical Field

[0001] The present application relates to the technical field of network organization, and in particular to a method, apparatus, device, program product and storage medium for monitoring network organization. Background Art

[0002] In traditional monitoring network systems, monitoring nodes such as cameras and sensors are often configured statically. Once deployed, their locations and connection paths with other monitoring nodes are fixed. This method can work to a certain extent when the monitoring target and monitoring environment are relatively stable. However, with the acceleration of urbanization and the diversification of monitoring needs, traditional static monitoring networks are facing more and more challenges.

[0003] Changes in monitoring targets, such as changes in the flow of people in the monitoring area, changes in the monitoring environment, such as changes in temperature and humidity in the monitoring area, or changes in obstacles, such as the construction of new buildings or the destruction and relocation of old buildings, will cause the original monitoring node layout to be unreasonable. The static organization method of the traditional monitoring network cannot adapt to the changes in monitoring targets, monitoring environment and obstacles. Therefore, it is impossible to quickly adjust the location and connection path of the monitoring nodes in the face of these changes, thus affecting the monitoring effect. Summary of the invention

[0004] The embodiments of the present application provide a monitoring network organization method, device, equipment, program product and storage medium to solve the technical problem that the traditional static organization method of the monitoring network cannot adapt to the changes in the monitoring target, monitoring environment and obstacles, and therefore cannot quickly adjust the position and connection path of the monitoring node in the face of these changes, thereby affecting the monitoring effect.

[0005] In a first aspect, an embodiment of the present application provides a monitoring network organization method, including: Based on the target path search algorithm, the optimal position and optimal connection path of multiple target monitoring nodes in the monitoring network are obtained; Based on the optimal position and the optimal connection path, adjusting the current positions and current connection paths of the multiple target monitoring nodes; 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 is an algorithm for real-time updating of pheromone concentration based on real-time feature data of monitoring targets in the monitoring network and real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on real-time feature data of path obstacles in the monitoring network.

[0006] In one embodiment, the optimal position of any target monitoring node is determined based on the following method: Based on the target data between the target monitoring node and the plurality of other monitoring nodes, obtaining a plurality of current selection probabilities corresponding to a plurality of connection paths between the target monitoring node and the plurality of other monitoring nodes; Determine a maximum current selection probability from the 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; 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 safety function value of the connection path; The current pheromone concentration is obtained by updating the historical pheromone concentration of the connection path based on the historical motion data of the monitoring target and the historical sensitivity of the monitoring environment; The current safety 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.

[0007] In one embodiment, the target data also includes the current energy consumption and task priority of the monitoring node.

[0008] In one embodiment, 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 includes: Adjusting the current positions of the multiple target monitoring nodes to corresponding optimal positions; activating an optimal connection path among the current connection paths of the multiple target monitoring nodes, and closing a non-optimal connection path among the current connection paths of the multiple target monitoring nodes; The optimal connection path is a connection path whose historical pheromone concentration is greater than a concentration threshold.

[0009] In one embodiment, after adjusting the current positions and current connection paths of the multiple target monitoring nodes, the method further comprises: Monitoring the performance data of the monitoring network in real time, and performing performance evaluation and optimization on the monitoring network based on the performance data; The performance data includes coverage, data transmission delay and energy consumption of the multiple target monitoring nodes.

[0010] In one embodiment, the coverage range of any target monitoring node is determined based on the sensing range of the target monitoring node to the monitoring target; The data transmission delay of any target monitoring node is determined based on the difference between the actual speed of the monitoring target collected by the target monitoring node and the expected speed.

[0011] In a second aspect, an embodiment of the present application provides a monitoring network organization device, including: The path search module is used to obtain the optimal position and optimal connection path of multiple target monitoring nodes in the monitoring network based on the target path search algorithm; A network organization module, used to: adjust 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 update algorithm and a target path safety update algorithm. The target pheromone update algorithm is an algorithm for real-time updating of pheromone concentration based on real-time feature data of monitoring targets in the monitoring network and real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on real-time feature data of path obstacles in the monitoring network.

[0012] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory storing a computer program, wherein when the processor executes the program, the steps of the monitoring network organization method described in the first aspect are implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the monitoring network organization method described in the first aspect.

[0014] In a fifth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium, comprising a computer program, which, when executed by a processor, implements the steps of the monitoring network organization method described in the first aspect.

[0015] The monitoring network organization method, device, equipment, program product and storage medium provided by the present application obtain the optimal position and optimal connection path of multiple target monitoring nodes in the monitoring network based on the target path search algorithm, and adjust the current position and current connection path of multiple target monitoring nodes based on the optimal position and optimal connection path. Since 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 for real-time updating of pheromone concentration based on the real-time feature data of the monitoring target in the monitoring network and the real-time feature data of the monitoring environment in which the monitoring network is located, and the target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on the real-time feature data of the path obstacles in the monitoring network, therefore, the acquisition of the optimal position and optimal connection path of the target monitoring node fully considers the real-time changes of the monitoring target, the monitoring environment and the path obstacles, so that the current position adjustment and the current connection path adjustment of the monitoring node are carried out in accordance with the changes of the monitoring target, the monitoring environment and the path obstacles, thereby realizing the rapid adjustment of the monitoring node position and the connection path in the face of the real-time changes of the monitoring target, the monitoring environment and the path obstacles, and improving the monitoring effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is one of the flow charts of the monitoring network organization method provided in the embodiment of the present application; Figure 2 This is the second flow chart of the monitoring network organization method provided in the embodiment of the present application; Figure 3 It is a structural diagram of a monitoring network organization device provided in an embodiment of the present application; Figure 4 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] Figure 1 This is one of the flow charts of the monitoring network organization method provided in the embodiment of the present application. Figure 1 , the embodiment of the present application provides a monitoring network organization method, which may include: 101. Based on the target path search algorithm, the optimal positions and optimal connection paths of multiple target monitoring nodes in the monitoring network are obtained; 102. Based on the optimal position and the optimal connection path, adjust the current position and the current connection path of the plurality of target monitoring nodes; 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 monitoring 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 safety function value of the path in real time based on the real-time feature data of the path obstacles in the monitoring network.

[0020] In step 101, the target path search algorithm may be an improved ant colony algorithm based on a target pheromone update algorithm and a target path security update algorithm formed on the basis of a traditional ant colony algorithm. The improved ant colony algorithm may be used to determine the optimal position and optimal connection path of the target monitoring node.

[0021] The monitoring network organization method provided in this embodiment obtains the optimal positions and optimal connection paths of multiple target monitoring nodes in the monitoring network based on the target path search algorithm, and adjusts the current positions and current connection paths of multiple target monitoring nodes based on the optimal positions and optimal connection paths. Since 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 for real-time updating of pheromone concentration based on the real-time feature data of the monitoring target in the monitoring network and the real-time feature data of the monitoring environment in which the monitoring network is located, and the target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on the real-time feature data of the path obstacles in the monitoring network, therefore, the acquisition of the optimal position and optimal connection path of the target monitoring node fully considers the real-time changes of the monitoring target, the monitoring environment and the path obstacles, so that the current position adjustment and the current connection path adjustment of the monitoring node are carried out in accordance with the changes of the monitoring target, the monitoring environment and the path obstacles, thereby realizing the rapid adjustment of the monitoring node position and the connection path in the face of the real-time changes of the monitoring target, the monitoring environment and the path obstacles, and improving the monitoring effect.

[0022] Furthermore, due to the relatively fixed positions and connection paths of monitoring nodes in the traditional static organization method of monitoring networks, once public emergencies such as traffic accidents and natural disasters occur, monitoring nodes are often unable to adjust monitoring resources in real time and focus on the incident area, which reduces the accuracy of the data to be monitored, and directly affects the efficiency of event processing and the actual application value of the monitoring network. Since the method of this embodiment can adjust the position and connection path of the monitoring node based on the real-time changes of the monitoring target, it can ensure that the monitoring network can quickly respond to and adjust the monitoring resources when an emergency occurs, focus on the actual incident area, and improve the accuracy of the data to be monitored, thereby improving the efficiency of event processing and the actual application value of the monitoring network, and effectively supporting safety management and emergency response.

[0023] Figure 2 This is the second flow chart of the monitoring network organization method provided in the embodiment of the present application. Figure 2 In one embodiment, the optimal location of any target monitoring node can be determined based on the following method: 201. Based on target data between a target monitoring node and a plurality of other monitoring nodes, obtain a plurality of current selection probabilities corresponding to a plurality of connection paths between the target monitoring node and the plurality of other monitoring nodes; 202. Determine a 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.

[0024] 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 safety function value of the connection path; Among them, 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; the current safety function value is obtained based on the current position of the path obstacle on the connection path and the current sensitivity of the monitored environment.

[0025] In step 201, the current time Target monitoring node With other monitoring nodes The current selection probability corresponding to the connection path It can be expressed as follows: ; (2-1) in, It is the current moment Target monitoring node With other monitoring nodes The current pheromone concentration of the connecting path between It is the current moment Target monitoring node With other monitoring nodes The current inverse distance between It is the current moment Target monitoring node The position vector With other monitoring nodes The position vector The current security function value of the connection path between them; 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 Middle The current pheromone concentration of the connection path between other monitoring nodes, It is the current moment Target monitoring node and Middle The current inverse distance between other monitoring nodes, It is the current moment Target monitoring node The position vector and Middle The position vectors of other monitoring nodes The current security function value of the connection path between them; , , is the adjustment coefficient.

[0026] Further, It can be obtained based on the following formula: ; (2-2) in, It was the last moment Target monitoring node With other monitoring nodes The historical pheromone concentrations of the connecting paths, It was the last moment No. The monitoring target is in the target monitoring node With other monitoring nodes The amount of pheromone left on the connecting path between It was the last moment No. The monitoring target is in the target monitoring node With other monitoring nodes The velocity vector moving on the connecting path between It is The monitoring target is in the target monitoring node With other monitoring nodes The maximum speed of movement on the connecting path between It was the last moment The historical sensitivity of the monitoring environment, It was the last moment Target monitoring node The position vector of It was the last moment No. The position vector of the monitored target, is the pheromone decay rate, which controls the decay rate of pheromones over time, ensuring that in the process of path selection, old pheromones gradually lose their effect and new pheromones gain greater weight. , , are adjustment coefficients, which respectively control the influence of the moving speed of the monitored target on the amount of pheromone left by the monitored target, the influence of the sensitivity of the monitoring environment on the amount of pheromone left by the monitored target, and the influence of the distance between the target monitoring node and the monitored target on the amount of pheromone left by the monitored target.

[0027] As can be seen from the above, the updating mechanism of pheromone concentration has been given special attention and improvement in this embodiment. Taking into account the real-time movement of the monitoring target and the dynamic changes of the monitoring environment, the updating of the pheromone concentration not only reflects the path selection results of the monitoring target group, but also incorporates the real-time changes of the monitoring target and the monitoring environment, such as the speed and position changes of the monitoring target, and the sensitivity changes of the monitoring environment. Through this dynamic adjustment, the distribution of pheromones can more truly reflect the status of the monitoring network, thereby improving the accuracy and efficiency of path selection.

[0028] In addition, the initial time Target monitoring node With other monitoring nodes Initial pheromone concentration of the connecting path between It can be obtained based on the following formula: ; in, It is the initial moment Target monitoring node The position vector of It is the initial moment Other monitoring nodes The position vector of is the spatial distance scaling factor, It is the initial moment No. The position vector of the monitored target, is the adjustment coefficient.

[0029] in, It can be obtained based on the following formula: ; in, yes The three-dimensional coordinates of is the initial deployment density of monitoring nodes, Is the target monitoring node The initial azimuth in the horizontal direction, Is the target monitoring node The initial azimuth in the vertical direction, is the initial height adjustment function value based on the terrain data. The same logic can be applied, so I will not go into details here.

[0030] In the initialization stage of the improved ant colony algorithm, different from the uniform initial pheromone distribution commonly used in the traditional ant colony algorithm, the initial layout of pheromones in this embodiment reflects the movement trend of the monitored target and the influence of environmental factors such as terrain, so that the improved ant colony algorithm can 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 depends on the dynamic characteristics of the monitored target and the initial data of the monitoring node to ensure that the initial pheromone reflects the actual monitoring needs.

[0031] At the same time, during the initialization phase, the communication range and sensing range can be configured for each monitoring node, and these ranges can be dynamically adjusted through an adaptive adjustment algorithm based on the physical location of the monitoring node and the expected monitoring area. In order to optimize the monitoring efficiency and coverage of the network, the target monitoring node The communication range and sensing range can be dynamically adjusted according to the following formula, taking into account environmental adaptability and monitoring requirements: ; ; in, It is the current moment Target monitoring node The communication range, is the target monitoring node at the initial moment The communication range, It is the current moment Environmental factors The measured value reflects the comprehensive influence of environmental sensitive factors such as temperature, humidity, and light intensity of the monitoring environment; It is the current moment Target monitoring node The sensing range, is the target monitoring node at the initial moment The sensing range, is the adjustment coefficient, It is the current moment No. The position vector of the monitored target.

[0032] in, The update can be based on the following formula: ; in, It was the last moment Environmental factors The measured value of yes and Environmental factors The measured difference, It is an environmental factor The rate of change is used to better reflect the dynamic changes of the monitoring environment. The larger the value, the greater the fluctuation of the environmental factor.

[0033] It should be noted that the communication range refers to the maximum distance that each monitoring node can transmit and receive signals, that is, the maximum range of data transmission between monitoring nodes. Within this range, monitoring nodes can exchange information with each other, thus 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 that each monitoring node can detect the monitoring target. Within this range, the monitoring node can sense and detect the existence and dynamic information (such as location, speed, etc.) of the monitoring target and transmit this information to the monitoring network.

[0034] Furthermore, combined with formula (2-2), at the current moment Monitor the current sensitivity of the environment It can be expressed as follows: ; in, is the basic environmental sensitivity, It is an environmental factor The weight of the monitoring environment sensitivity formula that integrates the weights of environmental factors can improve the overall adaptability and responsiveness of the monitoring network under changes in the monitoring environment.

[0035] Furthermore, combined with formula (2-1), Used to evaluate the corresponding connection path The security of the moment is taken into account, and the influence of the current position of the path obstacles on the corresponding connection path and the current sensitivity of the monitoring environment is integrated. Path obstacles on any connecting path The current position vector It can be expressed as follows: ; in, It was the last moment Same path obstacles on this connection path The historical position vector of It is the current moment Obstacles on the path The velocity vector of the movement, It was the last moment With the current moment time difference.

[0036] In addition to relying on the pheromone concentration of the connection path and the inverse distance between the monitoring nodes, this embodiment also evaluates the safety of the connection path based on the location of the path obstacles of the connection path and the sensitivity of the monitoring environment when selecting the optimal position. In the dynamic change process of the monitoring target and the monitoring environment, it can effectively avoid the potential risks of the connection path selection and enhance the reliability of the connection path selection, thereby obtaining the accurate current selection probability of each connection path, and then obtaining the optimal position. In this process, this embodiment optimizes the collection, processing and utilization of various real-time data, and improves the accuracy and response speed of data processing by integrating the real-time position, speed, location changes of path obstacles and other environmentally sensitive data of the monitoring target.

[0037] In one embodiment, the target data also includes the current energy consumption and task priority of the monitoring node.

[0038] Among them, any monitoring node at the current time Operational status Current energy consumption under It can be expressed as follows: ; Among them, the operating status Including standby, active and sleep, The monitoring node at the initial time Energy consumption, , and is the adjustment coefficient.

[0039] The current energy consumption of the monitoring node is taken into consideration in the connection path selection. The current energy consumption of the monitoring node can be used as a constraint or optimization target to obtain the current selection probability corresponding to the connection path, avoid selecting a connection path with too high energy consumption, and thus improve the overall energy efficiency of the monitoring network.

[0040] Among them, the task of any monitoring node Prioritize tasks It can be expressed as follows: ; in, It's a task The inherent priority coefficient of This monitoring node To the task Contribution to service capabilities, is the total number of monitoring nodes in the monitoring network.

[0041] Taking the task priority of the monitoring node into consideration when selecting the connection path, the selection weight of the connection path can be adjusted according to the task priority of different tasks, so as to obtain the current selection probability corresponding to the connection path, so that the connection path where the monitoring node with a high task priority is located obtains a greater current selection probability and can be selected first.

[0042] On the one hand, changes in monitoring tasks, such as switching from daily monitoring to large-scale event security, are often accompanied by changes in task priorities, requiring the monitoring network to be quickly reconfigured to respond to new security threats and monitoring priorities. On the other hand, without changing the network configuration, the monitoring system often needs to continue working in an all-weather operation state, resulting in a large amount of unnecessary calculations and data transmission, which not only increases the load on the monitoring network, but may also cause premature aging of the monitoring equipment.

[0043] This embodiment takes into account the current energy consumption and task priority of the monitoring node in the connection path selection. On the one hand, it can obtain the optimal position based on the task priority and complete the adjustment of the monitoring node, so as to quickly respond to new security threats and monitoring priorities. It has high flexibility and scalability, and can adjust the monitoring strategy and network layout according to different application scenarios, and adapt to the monitoring needs in various complex scenarios from urban security monitoring to environmental monitoring, traffic management, etc.; on the other hand, it can obtain the optimal position based on the current energy consumption and complete the adjustment of the monitoring node, so as to avoid a large amount of unnecessary calculation data transmission, reduce the monitoring network load, extend the service life of the monitoring equipment, and thereby extend the overall operation time of the monitoring network and reduce maintenance costs. That is, this implementation can maximize the resource utilization efficiency and the execution quality of monitoring tasks while ensuring the monitoring efficiency of the monitoring network.

[0044] In one embodiment, adjusting the current positions and current connection paths of multiple target monitoring nodes based on the optimal positions and the optimal connection paths may include: Adjusting the current positions of the multiple target monitoring nodes to the corresponding optimal positions, activating the optimal connection path among the current connection paths of the multiple target monitoring nodes, and closing the non-optimal connection paths among the current connection paths of the multiple target monitoring nodes; The optimal connection path is the connection path whose historical pheromone concentration is greater than the concentration threshold.

[0045] On the target monitoring node The amount of position adjustment from the current position to its optimal position It can be obtained according to the following formula: ; in, Is the target monitoring node The optimal position vector of Is the target monitoring node The current position vector of Is to adjust the step size coefficient, used to control the target monitoring node Adjust the speed and amplitude of the position.

[0046] In addition, a connection path whose pheromone concentration at the previous moment is greater than the concentration threshold may be determined as an optimal connection path, and the optimal connection path may be activated at the current moment, while other connection paths may be closed.

[0047] It should be noted that the position adjustment and connection path adjustment of the target monitoring node not only include the physical position movement of the target monitoring node, but also include the adjustment of the routing table and connection strategy in the monitoring network.

[0048] This embodiment can ensure 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.

[0049] In one embodiment, after adjusting the current positions and current connection paths of the multiple target monitoring nodes, the following steps may be included: Monitor the performance data of the monitoring network in real time, and evaluate and optimize the monitoring network based on the performance data; The performance data includes the coverage, data transmission delay and energy consumption of multiple target monitoring nodes.

[0050] Among them, the coverage range of any target monitoring node is determined based on the sensing range of the target monitoring node to the monitoring target, 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 monitoring target collected by the target monitoring node.

[0051] Specifically, the current moment Target monitoring node Coverage It can be obtained according to the following formula: ; in, is the adjustment coefficient.

[0052] In the calculation formula, The exponent of is -1, which means that the coverage changes with the inverse of the distance between the monitoring target and the target monitoring node, which means that the closer the monitoring target is to the target monitoring node, the more significant the adjustment of the coverage. This is a linear adjustment relationship for precise dynamic adjustment of the coverage.

[0053] In the calculation formula, The exponent is -2, which means that the change in the distance between the monitoring target and the target monitoring node has a greater impact on the sensing range of the target monitoring node, and is mainly used for the optimization adjustment of the sensing range.

[0054] The above coverage range 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, so that the calculation of the coverage range can effectively respond to rapidly changing monitoring needs and improve the spatial coverage capability and response sensitivity of the monitoring network.

[0055] Specifically, the current moment Target monitoring node The collected The actual speed of the monitored target It can be expressed as follows: ; Current moment Target monitoring node The collected The expected speed of the monitored target It can be expressed as follows: ; in, It is The speed baseline of the monitored target, is a natural constant, It is with The attenuation coefficient associated with each monitored target is It is the current moment No. Monitoring targets and target monitoring nodes The distance between It is the current moment No. The direction change rate of a monitored target.

[0056] In the expected speed formula, ; in, It is The angular velocity of the monitored target is It is The initial phase of a monitored target.

[0057] and The deviation between them may be caused by the data transmission delay of the target monitoring node or the failure of the monitoring target to move 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 monitoring target conforms to the expected motion trajectory, so as to timely adjust the tracking strategy for the monitoring target and improve the tracking accuracy.

[0058] 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 conditions can be evaluated, thereby optimizing the overall energy efficiency.

[0059] When the real-time characteristic data of the monitoring network changes due to various reasons, the relevant steps of this application are repeated to continuously optimize the monitoring network.

[0060] Traditional methods usually lack systematic evaluation and optimization mechanisms in the process of achieving self-organization of monitoring networks, which makes it difficult to guarantee the performance of the network after adjustment and lacks the necessary flexibility and scalability to cope with more complex environments and demand changes that may occur in the future.

[0061] This embodiment ensures that the entire monitoring network continues to operate in the optimal state by continuously monitoring and evaluating network performance and performing adjustments and optimizations based on actual monitoring results, effectively improving the overall robustness of the monitoring network and enabling it to resist the impact of environmental interference and equipment failures.

[0062] The monitoring network organization device provided in the embodiment of the present application is described below. The monitoring network organization device described below and the monitoring network organization method described above can be referenced to each other.

[0063] Figure 3 Schematic diagram of the structure of the monitoring network organization device provided in the embodiment of the present application. Figure 3 , the embodiment of the present application provides a monitoring network organization device, which may include: The path search module 301 is used to obtain the optimal position and optimal connection path of multiple target monitoring nodes in the monitoring network based on the target path search algorithm; The network organization module 302 is used to: adjust the current positions and current connection paths of the multiple 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 update algorithm and a target path safety update algorithm. The target pheromone update algorithm is an algorithm for real-time updating of pheromone concentration based on real-time feature data of monitoring targets in the monitoring network and real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on real-time feature data of path obstacles in the monitoring network.

[0064] The monitoring network organization device provided in this embodiment obtains the optimal position and optimal connection path of multiple target monitoring nodes in the monitoring network based on the target path search algorithm, and adjusts the current position and current connection path of multiple target monitoring nodes based on the optimal position and optimal connection path. Since 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 for real-time updating of pheromone concentration based on the real-time feature data of the monitoring target in the monitoring network and the real-time feature data of the monitoring environment in which the monitoring network is located, and the target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on the real-time feature data of the path obstacles in the monitoring network, therefore, the acquisition of the optimal position and optimal connection path of the target monitoring node fully considers the real-time changes of the monitoring target, the monitoring environment and the path obstacles, so that the current position adjustment and the current connection path adjustment of the monitoring node are carried out in accordance with the changes of the monitoring target, the monitoring environment and the path obstacles, thereby realizing the rapid adjustment of the monitoring node position and the connection path in the face of the real-time changes of the monitoring target, the monitoring environment and the path obstacles, and improving the monitoring effect.

[0065] In one embodiment, the path search module 301 is specifically used to: Based on the target data between the target monitoring node and the plurality of other monitoring nodes, obtaining a plurality of current selection probabilities corresponding to a plurality of connection paths between the target monitoring node and the plurality of other monitoring nodes; Determine a maximum current selection probability from the 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; 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 safety function value of the connection path; The current pheromone concentration is obtained by updating the historical pheromone concentration of the connection path based on the historical motion data of the monitoring target and the historical sensitivity of the monitoring environment; The current safety 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.

[0066] In one embodiment, the target data also includes the current energy consumption and task priority of the monitoring node.

[0067] In one embodiment, the network organization module 302 is specifically configured to: Adjusting the current positions of the multiple target monitoring nodes to corresponding optimal positions; activating an optimal connection path among the current connection paths of the multiple target monitoring nodes, and closing a non-optimal connection path among the current connection paths of the multiple target monitoring nodes; The optimal connection path is a connection path whose historical pheromone concentration is greater than a concentration threshold.

[0068] In one embodiment, a post-adjustment evaluation module (not shown in the figure) is further included, which is used to: Monitoring the performance data of the monitoring network in real time, and performing performance evaluation and optimization on the monitoring network based on the performance data; The performance data includes coverage, data transmission delay and energy consumption of the multiple target monitoring nodes.

[0069] In one embodiment, the coverage range of any target monitoring node is determined based on the sensing range of the target monitoring node to the monitoring 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 monitoring target collected by the target monitoring node.

[0070] FIG4 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. 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 through the communication bus 440. The processor 410 may call a computer program in the memory 430 to execute the steps of the monitoring network organization method, for example including: Based on the target path search algorithm, the optimal position and optimal connection path of multiple target monitoring nodes in the monitoring network are obtained; Based on the optimal position and the optimal connection path, adjusting the current positions and current connection paths of the multiple target monitoring nodes; 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 is an algorithm for real-time updating of pheromone concentration based on real-time feature data of monitoring targets in the monitoring network and real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on real-time feature data of path obstacles in the monitoring network.

[0071] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0072] On the other hand, an embodiment of the present application further provides a computer program product, the computer program product including a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, and 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, for example, including: Based on the target path search algorithm, the optimal position and optimal connection path of multiple target monitoring nodes in the monitoring network are obtained; Based on the optimal position and the optimal connection path, adjusting the current positions and current connection paths of the multiple target monitoring nodes; 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 is an algorithm for real-time updating of pheromone concentration based on real-time feature data of monitoring targets in the monitoring network and real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on real-time feature data of path obstacles in the monitoring network.

[0073] On the other hand, an embodiment of the present application further provides a non-transitory computer-readable storage medium on which a computer program is stored, wherein the computer program is used to enable a processor to execute the steps of the monitoring network organization method provided in the above embodiments, for example, including: Based on the target path search algorithm, the optimal position and optimal connection path of multiple target monitoring nodes in the monitoring network are obtained; Based on the optimal position and the optimal connection path, adjusting the current positions and current connection paths of the multiple target monitoring nodes; 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 is an algorithm for real-time updating of pheromone concentration based on real-time feature data of monitoring targets in the monitoring network and real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on real-time feature data of path obstacles in the monitoring network.

[0074] The non-transitory computer-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)), etc.

[0075] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0076] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A monitoring network organization method, characterized in that: include: Based on the target path search algorithm, the optimal position and optimal connection path of multiple target monitoring nodes in the monitoring network are obtained; Based on the optimal position and the optimal connection path, adjusting the current positions and current connection paths of the multiple target monitoring nodes; 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 is an algorithm for real-time updating of pheromone concentration based on real-time feature data of monitoring targets in the monitoring network and real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on real-time feature data of path obstacles in the monitoring network.

2. The monitoring network organization method according to claim 1, characterized in that: The optimal location of any target monitoring node is determined based on the following method: Based on the target data between the target monitoring node and the plurality of other monitoring nodes, obtaining a plurality of current selection probabilities corresponding to a plurality of connection paths between the target monitoring node and the plurality of other monitoring nodes; Determine a maximum current selection probability from the 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; 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 safety function value of the connection path; The current pheromone concentration is obtained by updating the historical pheromone concentration of the connection path based on the historical motion data of the monitoring target and the historical sensitivity of the monitoring environment; The current safety 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.

3. The monitoring network organization method according to claim 2, characterized in that: The target data also includes the current energy consumption and task priority of the monitoring node.

4. The monitoring network organization method according to claim 1, characterized in that: The 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 comprises: Adjusting the current positions of the multiple target monitoring nodes to corresponding optimal positions; activating an optimal connection path among the current connection paths of the multiple target monitoring nodes, and closing a non-optimal connection path among the current connection paths of the multiple target monitoring nodes; The optimal connection path is a connection path whose historical pheromone concentration is greater than a concentration threshold.

5. The monitoring network organization method according to claim 1, characterized in that: After the current positions and current connection paths of the plurality of target monitoring nodes are adjusted, the method further comprises: Monitoring the performance data of the monitoring network in real time, and performing performance evaluation and optimization on the monitoring network based on the performance data; The performance data includes coverage, data transmission delay and energy consumption of the multiple target monitoring nodes.

6. The monitoring network organization method according to claim 5, characterized in that: The coverage range of any target monitoring node is determined based on the sensing range of the target monitoring node to the monitoring 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 monitoring target collected by the target monitoring node.

7. A monitoring network organization device, characterized in that: include: The path search module is used to obtain the optimal position and optimal connection path of multiple target monitoring nodes in the monitoring network based on the target path search algorithm; A network organization module, used to: adjust 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 update algorithm and a target path safety update algorithm. The target pheromone update algorithm is an algorithm for real-time updating of pheromone concentration based on real-time feature data of monitoring targets in the monitoring network and real-time feature data of the monitoring environment in which the monitoring network is located. The target path safety update algorithm is an algorithm for real-time updating of the safety function value of the path based on real-time feature data of path obstacles in the monitoring network.

8. An electronic device comprising a processor and a memory storing a computer program, characterized in that: When the processor executes the computer program, the steps of the monitoring network organization method described in any one of claims 1 to 6 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the monitoring network organization method according to any one of claims 1 to 6 are implemented.

10. 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, the steps of the monitoring network organization method according to any one of claims 1 to 6 are implemented.

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