Network node planning method, apparatus, and computer program product

Abstract

The application is suitable for the field of communication technology, and provides a network node planning method, device and computer program product. A plurality of network nodes are deployed in a network. The method comprises: obtaining environment information between each two network nodes, and obtaining weight range information and priority information of each influence factor; wherein, the influence factor represents a factor influencing communication effect in the network, and the priority information represents the importance of the influence factor; determining a target weight of the influence factor according to the weight range information and the priority information of the influence factor; determining node planning information of the network according to the target weight of each influence factor and the environment information between each two network nodes; wherein, the node planning information represents grouping of the network nodes in the network. Based on the communication environment and various influence factors, the weights of various factors are dynamically calculated, and the accuracy of node grouping is improved.

Description

Technical Field

[0001] This application belongs to the field of communication technology, and in particular relates to a method, apparatus and computer program product for planning network nodes. Background Technology

[0002] Current complex networks typically contain multiple nodes. By grouping the nodes, the master node within a group can communicate with the cloud and issue instructions to each slave node within the group, effectively reducing hardware costs.

[0003] However, in complex outdoor environments, how to reasonably group nodes in the network, plan the number of slave nodes attached to a single master node in each sub-network, maximize cost savings, and ensure stable network operation are technical challenges that urgently need to be solved. Summary of the Invention

[0004] This application provides a method, apparatus, and computer program product for planning network nodes, which can improve the planning accuracy of network nodes.

[0005] In a first aspect, embodiments of this application provide a method for planning network nodes, wherein multiple network nodes are deployed in the network; the method includes: For every two network nodes, obtain the environmental information between the two network nodes, and obtain the weight range information and priority information of each influencing factor; wherein, the influencing factor represents the factors that affect the communication effect in the network, and the priority information represents the importance of the influencing factor; The target weight of the impact factor is determined based on the weight range information and priority information of the impact factor; Based on the target weights of each of the aforementioned influencing factors and the environmental information between each pair of network nodes, the node planning information of the network is determined; wherein, the node planning information represents the grouping of network nodes in the network.

[0006] In one implementation, the weight range information includes an upper weight limit and a lower weight limit; Determining the target weight of the impact factor based on the weight range information and priority information of the impact factor includes: The amplitude information of the influence factor is determined based on the upper limit of the weight, the lower limit of the weight, and the priority information of the influence factor; wherein the amplitude information represents the magnitude of change of the influence factor based on the lower limit of the weight. The target weight of the influence factor is determined based on the magnitude information and the lower limit of the weight of the influence factor.

[0007] In one implementation, determining the magnitude information of the influence factor based on the upper weight limit, the lower weight limit, and the priority information of the influence factor includes: Determine the difference between the upper and lower weight limits of the influencing factor; The magnitude information of the influencing factor is determined based on the difference information and the priority information.

[0008] In one implementation, determining the target weight of the influence factor based on the amplitude information of the influence factor and the lower limit of the weight includes: The amplitude information of the influence factor and the lower limit of the weight are added together to obtain the initial weight of the influence factor; Obtain the scenario information and device information of the network; wherein, the scenario information represents the application scenario of the network, and the device information represents the device attributes of the network nodes in the network; Based on the network's scenario information and device information, the initial weights of the influencing factors are adjusted to obtain the target weights of the influencing factors.

[0009] In one implementation, the initial weights of the influencing factors are adjusted based on the network's scene information and device information to obtain the target weights of the influencing factors, including: Based on the scene information of the network, and based on a preset first association relationship, a first weight correction value corresponding to the scene information of the network is determined; based on the device information of the network, and based on a preset second association relationship, a second weight correction value corresponding to the device information of the network is determined; wherein, the preset first association relationship represents the association relationship between the scene information and the first weight correction value, and the preset second association relationship represents the association relationship between the device information and the second weight correction value; The initial weight of the influencing factor is adjusted based on the first weight correction value corresponding to the scene information of the network and the second weight correction value corresponding to the device information of the network to obtain the target weight of the influencing factor.

[0010] In one embodiment, the environmental information includes factor information for each influencing factor, wherein the factor information characterizes the information in the environmental information used to evaluate the influencing factors; The step of determining the node planning information of the network based on the target weights of each of the influencing factors and the communication environment information between each pair of nodes includes: Based on the factor information of the influencing factor between each pair of network nodes, the score information of the influencing factor between each pair of network nodes is determined; wherein, the score information characterizes the degree of influence of the influencing factor on the communication effect between each pair of network nodes; Based on the target weights of each of the aforementioned influencing factors and the scoring information of each of the aforementioned influencing factors between each pair of network nodes, the quality information corresponding to each pair of network nodes is determined; wherein, the quality information characterizes the communication quality between each pair of network nodes. Based on the quality information corresponding to each pair of network nodes, the node planning information of the network is determined.

[0011] In one implementation, determining the score information of the influence factor between every two network nodes based on the factor information of the influence factor between every two network nodes includes: The factor information of the influencing factor between every two network nodes is normalized to obtain the score information of the influencing factor between every two network nodes.

[0012] In one implementation, determining the quality information corresponding to each pair of network nodes based on the target weights of each of the influencing factors and the scoring information of each of the influencing factors between each pair of network nodes includes: For each of the aforementioned impact factors, a communication quality score between each pair of network nodes corresponding to the impact factor is determined based on the target weight of the impact factor and the scoring information of the impact factor between each pair of network nodes; wherein, the communication quality score characterizes the communication quality between each pair of network nodes when only the impact factor is considered. Based on the communication quality score between each pair of network nodes corresponding to each of the aforementioned influencing factors, the quality information corresponding to each pair of network nodes is determined.

[0013] In one implementation, determining the node planning information of the network based on the quality information corresponding to each pair of network nodes includes: A communication topology graph is generated based on the quality information corresponding to each pair of network nodes; wherein, the communication topology graph includes each network node in the network, and there are edge connections between different network nodes, and the edge connections represent the quality information between different network nodes; Clustering is performed on the communication topology graph to obtain node planning information in the network; wherein, the node planning information includes multiple clusters divided from the communication topology graph, each cluster includes a master node and multiple slave nodes, and the master node is used to communicate with each slave node.

[0014] In one implementation, it further includes: Based on the node planning information of the network, the network is simulated to obtain the network's performance indicators; wherein, the performance indicators include at least packet delivery rate and average latency. A performance evaluation report is generated based on the network's metrics information; wherein the performance evaluation report characterizes the predicted communication performance of the network after planning the network based on the node planning information.

[0015] In one implementation, it further includes: If the network's indicator information does not meet the preset indicator conditions, then the target weights of each of the influencing factors are adjusted. The node planning information of the network is updated based on the adjusted target weights of each of the aforementioned influencing factors and the communication environment information between each pair of network nodes.

[0016] In one implementation, the network is a lighting network.

[0017] Secondly, embodiments of this application provide a network node planning device, wherein multiple network nodes are deployed in the network; the device includes: The information acquisition module is used to acquire environmental information between every two network nodes, and to acquire the weight range information and priority information of each influencing factor; wherein, the influencing factor represents the factors that affect the communication effect in the network, and the priority information represents the importance of the influencing factor. The weight determination module is used to determine the target weight of the influence factor based on the weight range information and the priority information of the influence factor; The node planning module is used to determine the node planning information of the network based on the target weights of each of the influencing factors and the environmental information between each pair of network nodes; wherein the node planning information represents the grouping of network nodes in the network.

[0018] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0019] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0020] Fifthly, embodiments of this application provide a computer program product that, when run on an electronic device, causes the electronic device to execute the method described in the first aspect above.

[0021] In this embodiment, multiple influencing factors affecting communication performance are pre-set. For each pair of network nodes, environmental information between them can be obtained, along with the weight range and priority information of each influencing factor. Based on these information, the target weights of the influencing factors can be determined. The target weights of each influencing factor are dynamically calculated based on the communication environment and various influencing factors. This allows for node grouping by combining the target weights of each influencing factor with the environmental information between each pair of nodes, improving the flexibility and accuracy of node planning. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a network diagram provided in an embodiment of this application; Figure 2 This is a flowchart illustrating the network node planning method provided in an embodiment of this application; Figure 3 This is a flowchart illustrating the network node planning method provided in an embodiment of this application; Figure 4 This is a flowchart illustrating the network node planning method provided in an embodiment of this application; Figure 5 This is a schematic diagram of the communication topology provided in the embodiments of this application; Figure 6 This is a flowchart illustrating the network node planning method provided in an embodiment of this application; Figure 7 This is a flowchart of network evaluation provided in an embodiment of this application; Figure 8 This is a schematic diagram of the closed-loop iterative optimization process provided in the embodiments of this application; Figure 9 This is a schematic diagram of the structure of the network node planning device provided in the embodiments of this application; Figure 10 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0024] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0025] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0026] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0028] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0030] The methods provided in this application can be applied to terminal devices such as mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, robots, and personal digital assistants (PDAs). This application does not impose any restrictions on the specific type of terminal device.

[0031] Before providing a further detailed description of the embodiments of this application, the nouns and terms used in the embodiments of this application are explained, and the nouns and terms used in the embodiments of this application shall be interpreted as follows: Lighting network: Also known as intelligent interconnected lighting network, it is a dedicated Internet of Things (IoT) network composed of lighting fixtures as terminal nodes and equipped with wireless communication modules. It enables remote lighting control, equipment status collection, fault reporting, and other functions, and can also carry data transmission for smart cities such as environmental monitoring and traffic sensing. In this embodiment, the lighting network can be a mesh-like hybrid lighting network for urban streetlights, serving as the underlying IoT infrastructure for smart cities.

[0032] Node: A node is an independent hardware device with communication capabilities in a network. It is the most basic unit in the network, capable of sending and receiving data and participating in network formation. In this embodiment, a node can refer to a smart street light device; that is, each street light is a node.

[0033] Master Node: Acting as the "manager" and "gateway" of a group of nodes, it manages multiple subordinate slave nodes. All slave node data interacts with the backend business system through the master node's cellular interface. The number of master nodes is limited because cellular modules are costly; reducing the number of master nodes is key to cost reduction.

[0034] Slave nodes: Terminal lighting nodes managed by the master node, responsible for specific lighting control, data acquisition, etc., and communicating with their respective master nodes. There are a large number of slave nodes; a single master node can have multiple slave nodes attached.

[0035] Outdoor lighting systems can employ a hybrid architecture, where the master node in the network integrates both cellular and Bluetooth Mesh communication interfaces, while managing slave nodes equipped only with Bluetooth Mesh interfaces. This reduces hardware costs by decreasing the number of cellular communication modules used.

[0036] In other words, this hybrid architecture can effectively reduce hardware costs by decreasing the number of cellular communication modules used. Theoretically, the more slave nodes a single master node connects to, the more significant the cost reduction. However, an excessive number of slave nodes can lead to a decline in communication connection quality, resulting in instability in the lighting network.

[0037] In complex outdoor environments, how to rationally plan the number of slave nodes attached to a single master node and determine the number of slave nodes that a single master node can connect to, while maximizing cost savings and ensuring stable operation of the lighting network, has always been a challenge for the industry.

[0038] The current industry standard is to fix the number of slave nodes connected to a single master node, and the node grouping process is entirely manual. However, this fixed number of slave nodes and manual grouping approach has several problems: 1. It relies on manual operation and the experience of employees, resulting in high labor costs; 2. In areas with strong signal interference, poor communication connection quality is prone to occur; 3. Unable to achieve optimal cost: In some areas with low signal interference and simple environment, the master node can support more slave nodes without affecting communication performance, which the current solution does not fully utilize; 4. The overall networking solution lacks flexibility. This application's embodiments can dynamically determine the number of nodes that a single master node can manage by combining environmental information such as project maps, node locations, and professional rules. It also dynamically calculates the target weights of various influencing factors by considering factors such as radio frequency interference, regional scene attributes, road conditions, lighting application types, and lighting equipment characteristics, simulating the communication connectivity between nodes to complete node grouping.

[0039] This application's embodiments can be applied to scenarios such as urban street light mesh networks, smart city IoT networks, industrial mesh networks, agricultural sensor networks, and traffic management networks. Urban street light mesh networks are the primary application scenario, suitable for hybrid lighting networks with a master-slave architecture; smart city IoT networks refer to various large-scale outdoor IoT systems that require guaranteed operational stability; industrial mesh networks refer to factory and park networks subject to dynamic signal interference; agricultural sensor networks refer to outdoor monitoring networks affected by seasonal changes; and traffic management networks refer to roadside equipment and sensor networks with high operational reliability requirements.

[0040] The method provided in the embodiments of this application can be applied to, for example, Figure 1 The network shown includes multiple nodes, which may include at least one master node and at least one slave node. The master node is a gateway node, and the slave nodes are non-gateway nodes. The master node and slave nodes can communicate wirelessly, for example, via Bluetooth technology.

[0041] The network provided in this application embodiment can be applied to any system, such as a lighting system. Taking the network provided in this application embodiment as an example of a lighting system, both the master node and the slave node can be a lamp.

[0042] Both the master node and the slave node can have a first wireless communication module. This first wireless communication module is used to enable wireless communication between the master node and the slave node. For example, the first wireless communication module can refer to a Bluetooth Mesh network module. The master node and the slave node form a Bluetooth Mesh network and communicate through the Bluetooth Mesh network protocol.

[0043] The master node may also include a second wireless communication module. This second wireless communication module enables wireless communication between the master node and the server. For example, the second wireless communication module could be a cellular communication network module (equipped with a Subscriber Identity Module, SIM card), thus forming a cellular communication network between the master node and the server. The master node can then receive target information from the server through the second wireless communication module. Examples include the following commands: lighting switch, lighting off, and dimming.

[0044] General Description of Embodiments in this Application According to one embodiment of this application, a method for planning network nodes is provided.

[0045] Figure 2 A flowchart illustrating the planning method for network nodes, as shown below. Figure 2 As shown, a network node planning method according to one embodiment of this application may include: Step 210: For every two network nodes, obtain the environmental information between the two network nodes, and obtain the weight range information and priority information of each influencing factor; whereby the influencing factor represents the factors that affect the communication effect in the network, and the priority information represents the importance of the influencing factor. Step 220: Determine the target weights of the impact factors based on their weight range and priority information; Step 230: Determine the node planning information of the network based on the target weights of each influencing factor and the environmental information between each pair of network nodes; wherein, the node planning information represents the grouping of network nodes in the network.

[0046] Steps 210-230 are described in detail below.

[0047] In step 210, the network in this embodiment is a lighting network, and more specifically, a hybrid lighting network. Network nodes are terminal hardware units with wireless communication capabilities within the hybrid lighting network. Multiple network nodes can be deployed in batches to cover the entire lighting operation area. Network nodes can be divided into two types of functional units: one type is gateway nodes equipped with both cellular communication interfaces and Bluetooth mesh network communication interfaces, and the other type is terminal lighting nodes equipped only with Bluetooth mesh network communication interfaces. All network nodes together constitute a complete outdoor lighting network.

[0048] A variety of influencing factors are pre-set. These factors are environmental, equipment, and scenario-related elements that can have a positive or negative impact on the wireless communication connectivity between any two network nodes. For example, influencing factors may include node spacing, traffic density, surrounding building attributes, and obstacle conditions. The types of influencing factors can be flexibly expanded according to the project scenario, and there is no fixed number.

[0049] For every two network nodes, environmental information between them can be acquired in real time or periodically. This environmental information can be multi-source data collected from the project site, objectively reflecting the actual communication conditions between the two network nodes, including various interferences, obstructions, and distance-related factors. For example, environmental information may include electronic maps marking node locations, regional traffic conditions, surrounding building information, communication standards, and network configuration schemes.

[0050] Each impact factor has its own weight range and priority information, which can be obtained in real time or periodically. The weight range information is a pre-configured normalized value interval boundary for each type of impact factor, for example, a range from 0 to 1. The weight range information constrains the upper and lower limits of the weight values ​​of a single type of impact factor, providing basic boundary conditions for the calculation of weight values ​​and ensuring that the weight calculation results have a unified quantitative standard.

[0051] Priority information is a quantitative parameter used to distinguish the importance levels of different influencing factors, reflecting the strength of their impact on communication quality in the current lighting service scenario. Priority information participates in the calculation of subsequent target weights, enabling dynamic adjustment of weight values ​​for different service scenarios.

[0052] The weight range and priority information of the influencing factors can be adjusted at any time according to actual business needs. For example, the priority information can be set with different levels of quantified values ​​according to the data transmission requirements and communication latency requirements of the lighting business. The influencing factor representing wireless frequency band interference in high data transmission scenarios can be configured with higher priority information, and the influencing factor representing node spacing in low latency sensing lighting scenarios can be configured with higher priority information.

[0053] Table 1. Influence Factors under Different Scenarios

[0054] In step 220, for each influencing factor, the current target weight can be determined based on its current weight range and priority information. The target weight characterizes the degree of influence of the influencing factor on the communication quality between nodes at the current moment. The target weight is the final weight value of a single type of influencing factor, used to weight and summarize the environmental information scores corresponding to each type of influencing factor, thereby quantifying the communication quality level between any two network nodes.

[0055] For example, for each type of influencing factor, interpolation can be performed by combining weight range information and priority information, and then the interpolation results can be uniformly normalized to output the target weight corresponding to each type of influencing factor. Alternatively, the weights can be iteratively corrected based on actual measured data of communication conditions on-site to continuously optimize the target weights.

[0056] For example, a random value can be selected from the weight range information, multiplied by the priority information, and then the result of the multiplication can be normalized to obtain the target weight.

[0057] In step 230, the environmental information objectively reflects the actual communication conditions between two network nodes, including various interferences, obstructions, and distance-related factors. For each pair of network nodes, the communication quality between them can be determined based on the environmental information and the target weights of each influencing factor. For example, the communication quality can be calculated by weighting the various information in the environmental information according to the target weights of each influencing factor. After obtaining the communication quality between each pair of nodes, the network nodes can be grouped according to their communication quality to obtain node planning information. For example, network nodes with similar communication quality can be grouped together.

[0058] Node planning information is network topology planning content generated based on the calculation results of the communication quality of all network nodes. Node planning information can completely record the grouping results of all network nodes, and can also include the screening and judgment results of gateway-type nodes within each group. That is, it can determine the master node and slave nodes in each group. For example, if the communication quality of a certain node is similar to that of other nodes in the group, then that node is the master node, and the other nodes in the group are slave nodes.

[0059] Node planning information can be directly used for pre-sales planning and on-site deployment of lighting networks, and is also the input basis for network communication performance simulation evaluation.

[0060] In this embodiment, multi-dimensional environmental information corresponding to each pair of network nodes is collected. The target weights of various influencing factors are dynamically calculated by combining the preset weight range information of influencing factors with the priority information of scene adaptation. Then, network nodes are automatically grouped according to the target weights and standardized environmental information, eliminating the reliance on the experience of professionals in manual network planning. The number of terminal nodes that a single group of gateway nodes can support is dynamically adapted to different complex outdoor lighting scenarios, fully utilizing the communication carrying capacity of areas with low signal interference and minimal obstruction. This reduces the cost of cellular communication hardware deployment while ensuring the overall stability of the lighting network communication operation. The output standardized node planning information can directly support project bidding and on-site deployment, improving the flexibility of hybrid lighting network topology planning and the cost optimization of the overall network solution.

[0061] In this embodiment, multiple influencing factors affecting communication performance are pre-set. For each pair of network nodes, environmental information between them can be obtained, along with the weight range and priority information of each influencing factor. Based on these information, the target weights of the influencing factors can be determined. The target weights of each influencing factor are dynamically calculated based on the communication environment and various influencing factors. This allows for node grouping by combining the target weights of each influencing factor with the environmental information between each pair of nodes, improving the flexibility and accuracy of node planning.

[0062] Figure 3 A flowchart illustrating the planning method for network nodes, as shown below. Figure 3 As shown, a network node planning method according to one embodiment of this application may include: Step 310: For every two network nodes, obtain the environmental information between the two network nodes, and obtain the weight range information and priority information of each influencing factor; whereby, the influencing factor represents the factors that affect the communication effect in the network, and the priority information represents the importance of the influencing factor. Step 320: Determine the magnitude information of the impact factor based on the upper limit of the weight, the lower limit of the weight, and the priority information of the impact factor; wherein, the magnitude information represents the magnitude of change of the impact factor based on the lower limit of the weight. Step 330: Determine the target weight of the impact factor based on the magnitude information and lower limit of the weight of the impact factor; Step 340: Determine the node planning information of the network based on the target weights of each influencing factor and the environmental information between each pair of network nodes; wherein, the node planning information represents the grouping of network nodes in the network.

[0063] Steps 310 and 340 have been described in detail above and will not be repeated here. Steps 320-330 will be described in detail below.

[0064] In step 320, the weight range information is a pre-defined normalized value constraint range for a single type of influence factor, including two types of boundary parameters: upper weight limit and lower weight limit. The upper weight limit constrains the maximum value that the weight of a single type of influence factor can take, and the lower weight limit constrains the minimum value that the weight of a single type of influence factor can take.

[0065] For each influencing factor, the upper and lower weight limits are obtained from its weight range information. Based on these weight limits and priority information, the magnitude of the influencing factor can be calculated. The magnitude information reflects the range within which the influencing factor can fluctuate upwards relative to the lower weight limit; that is, it further restricts the magnitude of the target weight based on the weight range information. For example, linear interpolation logic can be used to map the priority information to the fluctuation range between the lower and upper weight limits.

[0066] In step 330, the amplitude information can be combined with the lower limit of the weight to obtain the original, unnormalized weight value of the single-class influence factor. Then, the original weight value is globally normalized to obtain the final weight value, i.e., the target weight. Alternatively, the amplitude information can be combined with the upper limit of the weight to obtain the original, unnormalized weight value of the single-class influence factor, and then the original weight value is globally normalized to obtain the final weight value, i.e., the target weight.

[0067] For example, the amplitude information is summed with the corresponding lower limit weight value of the impact factor to obtain the original weight value of a single type of impact factor. The original weight values ​​of all impact factors are then aggregated and normalized to obtain a unified and standardized target weight set. Alternatively, the amplitude information can be subtracted from the corresponding lower limit weight value of the impact factor to obtain the original weight value of a single type of impact factor. The original weight values ​​of all impact factors are then aggregated and normalized to obtain a unified and standardized target weight set. The target weight set includes the target weights of each impact factor.

[0068] In this embodiment, by using upper and lower weight limits to define the range of influencing factor weights, the magnitude information corresponding to the weight change range is solved based on priority information, and the target weight of the corresponding influencing factor is obtained by superimposing the lower weight limit. This allows for dynamic adjustment of the weight fluctuation range of various influencing factors according to different lighting business scenarios, improving the accuracy of target weight determination, and thus optimizing the rationality of network node grouping results.

[0069] In one implementation, the magnitude information of the influence factor is determined based on the upper limit of the weight, the lower limit of the weight, and the priority information of the influence factor, including: determining the difference information between the upper limit of the weight and the lower limit of the weight of the influence factor; and determining the magnitude information of the influence factor based on the difference information and the priority information.

[0070] Specifically, for each influencing factor, the difference between the upper and lower weight limits corresponding to that factor is calculated to obtain the difference information. Combining the difference information and priority information, the magnitude of the influencing factor is calculated.

[0071] The formula for calculating amplitude information can be: F i =P i ×(w) ui -w di ); Among them, F i P represents the magnitude information of the i-th influencing factor. i w represents the priority information of the i-th influence factor. ui w represents the upper limit of the weight of the i-th influence factor. di The lower limit of the weight of the i-th influence factor.

[0072] In this embodiment, the overall weight fluctuation range is locked by first calculating the difference between the upper and lower weight limits, and then the amplitude information is obtained by combining the difference information and priority information. This allows the weight fluctuation range to be allocated based on a unified weight range span and the importance of the scenario, ensuring that the calculation result of the amplitude information always falls within the constraint range of the weight range information. This reduces the probability of abnormal values ​​in the weight calculation, improves the accuracy of subsequent target weight and communication quality score calculations, and further optimizes the adaptability of the network node grouping scheme.

[0073] In one implementation, determining the target weight of the influence factor based on its amplitude information and lower weight limit includes: adding the amplitude information and lower weight limit of the influence factor to obtain the initial weight of the influence factor; acquiring network scene information and device information; wherein the scene information represents the application scenario of the network, and the device information represents the device attributes of network nodes in the network; and adjusting the initial weight of the influence factor based on the network scene information and device information to obtain the target weight of the influence factor.

[0074] Specifically, the amplitude information is a parameter calculated based on the upper and lower weight limits and priority information, which reflects the range in which the corresponding influence factor can fluctuate upwards relative to the lower weight limit. After obtaining the amplitude information, it is combined with the lower weight limit to obtain the original weight value.

[0075] The original formula for calculating the weight values ​​can be: w (i,raw) =w di +F i ; Among them, w (i,raw) This represents the original weight value of the i-th influence factor.

[0076] Based on the original weight values ​​of each influencing factor, the original weight values ​​are normalized to obtain the initial weights.

[0077] The formula for calculating the initial weights can be: W i = ; Among them, W i Let be the initial weight of the i-th influence factor, and n be the number of influence factors.

[0078] The initial weights are intermediate parameters obtained by adding the amplitude information to the lower limit of the weight. They are generated only based on the weight range and scene priority, without adapting or correcting for the differentiated characteristics of on-site lighting services and lighting hardware. The target weights, which can be used for communication quality calculations, need to be adjusted together with scene information and equipment information. Alternatively, the initial weights can also be intermediate parameters obtained by subtracting the lower limit of the weight from the amplitude information.

[0079] Network scene and device information can be acquired in real time or periodically. Scene information can characterize the overall network service usage type, i.e., various lighting application scenarios. By determining scene information, different service types can be distinguished, such as big data transmission lighting services and radar-sensored on-demand lighting services. Device information can characterize device attributes such as the hardware configuration characteristics of all network nodes. For example, device information can include the type of lamp, related hardware configuration parameters, and also the type of sensor mounted on the lamp, the lamp's installation height, antenna hardware parameters, etc.

[0080] The target weight is a standardized weight parameter obtained by correcting the initial weights based on both scene and device information. The initial weights can be adjusted by increasing or decreasing them according to the scene and device information to obtain the target parameters. For example, different lighting scenes may correspond to different adjustment step sizes, and different device types may also correspond to different adjustment step sizes. The initial weights can be adjusted according to the corresponding adjustment step sizes to obtain the target weights.

[0081] In this embodiment, an initial weight is generated by superimposing amplitude information and a lower limit of weight. Then, the initial weight is modified in layers by combining scene information representing the service type and device information representing the hardware attributes of the node to obtain the target weight. This can superimpose two types of actual project characteristics, namely lighting services and lamp hardware, on the basis of weight range and priority to complete weight adaptation, making the target weight more in line with the actual communication constraints of outdoor lighting site, and improving the matching degree between the network node grouping scheme and the project site environment.

[0082] In one implementation, the initial weights of the influencing factors are adjusted based on network scene information and device information to obtain target weights for the influencing factors. This includes: determining a first weight correction value corresponding to the network scene information based on a preset first correlation relationship; determining a second weight correction value corresponding to the network device information based on a preset second correlation relationship; wherein the preset first correlation relationship represents the correlation between scene information and the first weight correction value, and the preset second correlation relationship represents the correlation between device information and the second weight correction value; and adjusting the initial weights of the influencing factors based on the first weight correction value corresponding to the network scene information and the second weight correction value corresponding to the network device information to obtain target weights for the influencing factors.

[0083] Specifically, a first association relationship is pre-set, representing the correlation between scene information and a first weight correction value. The first weight correction value is a numerical adjustment made to the target weight based on the scene information. After obtaining the scene information of the current network, the first weight correction value corresponding to that scene information can be found according to the pre-set first association relationship. The initial weights are then adjusted based on the first weight correction value. For example, the initial weights can be added to the first weight correction value to obtain the adjusted weights.

[0084] A second association relationship is pre-set, representing the correlation between device information and a second weight correction value. The second weight correction value is a numerical adjustment made to the target weight based on the device information. After obtaining the device information of the current network, the second weight correction value corresponding to that device information can be found according to the pre-set second association relationship. The initial weight is then adjusted based on the second weight correction value. For example, the initial weight can be added to the second weight correction value to obtain the adjusted weight.

[0085] You can first adjust the initial weights based on the first weight correction value, and then make a second adjustment based on the second weight correction value to obtain the target weight. Alternatively, you can first adjust the initial weights based on the second weight correction value, and then make a second adjustment based on the first weight correction value to obtain the target weight. You can also add both the first and second weight correction values ​​to the initial weights to obtain the target weight.

[0086] For example, if the initial weight is 0.333, the first weight adjustment value is 0.05, and the second weight adjustment value is 0.1, then the target weight is 0.483.

[0087] In this embodiment, the first weight correction value corresponding to the scene information and the second weight correction value corresponding to the device information are obtained by independent correlation matching. The initial weight is then adjusted by combining the two types of weight correction values ​​to obtain the target weight. This can independently complete the weight correction of two dimensions: lighting business scene and lighting hardware device, accurately restore the differentiated constraints of the on-site environment on wireless communication, and further improve the planning accuracy of network nodes.

[0088] In this embodiment, multiple influencing factors affecting communication performance are pre-set. For each pair of network nodes, environmental information between them can be obtained, along with the weight range and priority information of each influencing factor. Based on these information, the target weights of the influencing factors can be determined. The target weights of each influencing factor are dynamically calculated based on the communication environment and various influencing factors. This allows for node grouping by combining the target weights of each influencing factor with the environmental information between each pair of nodes, improving the flexibility and accuracy of node planning.

[0089] Figure 4 A flowchart illustrating the planning method for network nodes, as shown below. Figure 4 As shown, a network node planning method according to one embodiment of this application may include: Step 410: For every two network nodes, obtain the environmental information between the two network nodes, and obtain the weight range information and priority information of each influencing factor; whereby the influencing factor represents the factors that affect the communication effect in the network, and the priority information represents the importance of the influencing factor. Step 420: Determine the target weights of the impact factors based on their weight range and priority information; Step 430: Based on the factor information of the influencing factor between each pair of network nodes, determine the score information of the influencing factor between each pair of network nodes; wherein, the score information characterizes the degree of influence of the influencing factor on the communication effect between each pair of network nodes. Step 440: Determine the quality information corresponding to each pair of network nodes based on the target weights of each influencing factor and the scoring information of each influencing factor between each pair of network nodes; wherein, the quality information characterizes the communication quality between each pair of network nodes. Step 450: Determine the node planning information of the network based on the quality information corresponding to each pair of network nodes.

[0090] Steps 410 and 420 have been described in detail above and will not be repeated here. Steps 430-450 will be described in detail below.

[0091] In step 430, the environmental information is the summary data of the on-site environment corresponding to all influencing factors between any two network nodes. The environmental information includes factor information specific to each type of influencing factor, and this factor information characterizes the information in the environmental information used to evaluate the influencing factors. That is, the factor information is the detailed data collected within the environmental information for a single influencing factor, recording the objective on-site state between two network nodes corresponding to a certain type of influencing factor. For example, influencing factors include node spacing and traffic density; the environmental information includes the specific distance values ​​for node spacing and the specific values ​​for traffic density.

[0092] For each influencing factor and each pair of network nodes, the factor information corresponding to that influencing factor is obtained from the environmental information of those two network nodes. Based on this factor information, the score information of that influencing factor for the two network nodes is determined. The score information can intuitively reflect the degree of interference or gain that this type of influencing factor causes to the wireless communication between the two network nodes.

[0093] For each impact factor, different rules for determining scoring information can be preset. Based on the corresponding rules, the scoring information of the impact factor can be determined. For example, the numerical range to which the factor information belongs can be determined, and the scoring results corresponding to that numerical range can be found as the scoring information.

[0094] In step 440, for every two network nodes, the score information of each influencing factor is determined. Combining the target weights and score information of each influencing factor, the quality information corresponding to these two network nodes is determined. This quality information characterizes the communication quality between the two network nodes.

[0095] For example, the target weights of each influencing factor can be mapped one-to-one with the scoring information to complete the weighted calculation and obtain the quality information.

[0096] The formula for calculating quality information can be: Q= ; Where Q represents quality information. S represents the target weight of influence factor i. iThis represents the scoring information of influence factor i between two network nodes.

[0097] Quality information is a comprehensive parameter obtained by weighting and summing all influencing factors between two network nodes with target weights and scoring information, which fully characterizes the overall wireless communication connectivity level between the two network nodes.

[0098] In step 450, the quality information corresponding to all pairwise network nodes is summarized to obtain the network's node planning information. That is, it can be determined which network nodes can be grouped together, and which network node in a group should be the master node. Quality information is the core input basis for dividing network node groups and selecting master nodes. The node planning information fully records the grouping affiliation of all network nodes and the selection results of master nodes within each group. For example, network nodes can be clustered based on quality information; the resulting clusters constitute a group, and the network node at the cluster center is the master node.

[0099] In this embodiment, environmental information is broken down to obtain factor information corresponding to various influencing factors and converted into standardized scoring information. This information is then combined with target weights to calculate quality information representing the communication level between each pair of network nodes. Based on all quality information across the entire network, network nodes are automatically divided and node planning information is generated. This allows for the unified quantification of complex and diverse field environmental data, objective determination of node grouping boundaries, and avoidance of subjective bias caused by manual grouping. This approach ensures the overall communication stability of the lighting network while controlling the cost of cellular communication hardware.

[0100] In one implementation, determining the score information of the influence factor between each pair of network nodes based on the factor information of the influence factor between each pair of network nodes includes: normalizing the factor information of the influence factor between each pair of network nodes to obtain the score information of the influence factor between each pair of network nodes.

[0101] Specifically, factor information refers to the detailed data collected from environmental information for a single influencing factor. For different influencing factors, factor information exhibits inconsistent dimensions and numerical ranges, making it unsuitable for direct weighted fusion calculations of multiple influencing factors. Therefore, factor information can be normalized, and the result of this normalization can be used as scoring information to eliminate computational obstacles caused by differences in data formats among different influencing factors.

[0102] Taking the influence factor of node spacing as an example, the normalization process, that is, the calculation formula for the scoring information, can be: S distance (i,j) = ; Among them, S distance(i,j) represents the score information of the distance between network nodes i and j, and d(i,j) represents the distance between network nodes i and j, which is the factor information of the influencing factor. max It can be a preset maximum distance value, or it can be the maximum distance value among all pairs of network nodes.

[0103] In this embodiment, standardized scoring information is obtained by uniformly normalizing the original factor information corresponding to various influencing factors. This can eliminate the differences in the dimensions and numerical ranges of different types of field environmental data and improve the calculation accuracy of node planning.

[0104] In one implementation, the quality information corresponding to each pair of network nodes is determined based on the target weights of each impact factor and the scoring information of each impact factor between each pair of network nodes. This includes: for each impact factor, determining the communication quality score between each pair of network nodes corresponding to the impact factor based on the target weights of the impact factor and the scoring information of the impact factor between each pair of network nodes; wherein the communication quality score characterizes the communication quality between each pair of network nodes when only the impact factor is considered; and determining the quality information corresponding to each pair of network nodes based on the communication quality score between each pair of network nodes corresponding to each impact factor.

[0105] Specifically, for each influencing factor and each pair of network nodes, after obtaining the score information of the influencing factor between these two network nodes, the communication quality score between the two network nodes corresponding to the influencing factor can be determined based on the score information and the target weight of the influencing factor. The communication quality score can characterize the communication quality between the two network nodes when only the influencing factor is considered. For example, the target weight can be multiplied by the score information to obtain the communication quality score.

[0106] Communication quality scores are quantitative indicators that quantify the quality of communication between two network nodes under the independent influence of a single influencing factor, reflecting only the communication impact of that single factor. The sum of communication quality scores corresponding to all influencing factors can be used to form a complete communication evaluation basis, thus obtaining the final quality information. In other words, the communication quality score serves as an intermediate calculation parameter used to fuse and solve for quality information that represents the overall communication level. For example, the quality information can be the sum of the communication quality scores of each influencing factor.

[0107] In this embodiment, by first combining the target weight and scoring information to solve the communication quality score corresponding to a single type of influencing factor, and then integrating all communication quality scores to obtain quality information representing the overall communication level, it is possible to independently retain the sub-item impact data of various environmental factors on node communication, thereby improving the matching degree between the network node grouping scheme and the complex communication environment of outdoor lighting sites.

[0108] In one implementation, determining the node planning information of the network based on the quality information corresponding to each pair of network nodes includes: generating a communication topology graph based on the quality information corresponding to each pair of network nodes; wherein the communication topology graph includes each network node in the network, and there are edge connections between different network nodes, and the edge connections represent the quality information between different network nodes; performing clustering processing on the communication topology graph to obtain the node planning information in the network; wherein the node planning information includes multiple clusters divided from the communication topology graph, each cluster including a master node and multiple slave nodes, and the master node is used to communicate with each slave node.

[0109] Specifically, the quality information is a comprehensive parameter obtained by integrating and calculating the communication quality scores corresponding to all influencing factors between two network nodes. It can fully characterize the overall wireless communication connectivity level between the two network nodes. Based on the quality information corresponding to all pairs of network nodes, a communication topology graph of the network can be generated. The quality information is represented on the edge connections between nodes within the communication topology graph, providing a unified quantitative basis for subsequent clustering and master node selection. That is, the communication topology graph is a weighted graph structure built based on the quality information between all network nodes and all pairs of nodes. The nodes in the communication topology graph are network nodes in the network, and there are edge connections between different network nodes. The edge connections represent the quality information between different network nodes. Figure 5 This is a schematic diagram of the communication topology.

[0110] By constructing a communication topology graph, discrete nodes can be integrated into a visualized, batch-processable graph structure. Based on the communication topology graph, algorithms such as clustering, community detection, and shortest path algorithms (e.g., Infographic algorithms, Dijkstra's algorithm, Leuven's algorithm, spectral clustering algorithms, etc.) are run to group nodes; simultaneously, a general algorithm is used to select master nodes and determine the identity of each node.

[0111] For example, during clustering, the affiliation relationship between nodes is determined by the quality information carried by edge connections. Network nodes with higher communication compatibility are grouped into the same group, and the clusters are directly output after the clustering process is completed. Each cluster is configured with a single master node and several slave nodes. The clusters correspond to Mesh subnets in the hybrid lighting network that are uniformly managed by the same master node. All clusters together constitute a complete network grouping system, and the clustering results are fully recorded in the node planning information. In other words, the node planning information can be the topology planning content generated based on the clustering results of the communication topology graph. The node planning information fully records the partitioning boundaries of all clusters and the affiliation relationship of the master and slave nodes within each cluster, which can be directly used for project pre-sales solution preparation and on-site lighting network deployment.

[0112] In this embodiment, a weighted edge-connected communication topology graph is constructed based on the quality information of all pairwise network nodes. Clustering is then performed on the communication topology graph to divide the network into clusters containing master and slave nodes, generating node planning information. This enables automatic subnet grouping based on the strength of actual communication relationships between nodes, dynamically adapting to the communication carrying capacity of different outdoor lighting areas, reducing the number of cellular communication hardware deployments to lower project hardware costs, avoiding communication stability defects caused by manual grouping, and improving the automation level of hybrid lighting network planning.

[0113] In this embodiment, multiple influencing factors affecting communication performance are pre-set. For each pair of network nodes, environmental information between them can be obtained, along with the weight range and priority information of each influencing factor. Based on these information, the target weights of the influencing factors can be determined. The target weights of each influencing factor are dynamically calculated based on the communication environment and various influencing factors. This allows for node grouping by combining the target weights of each influencing factor with the environmental information between each pair of nodes, improving the flexibility and accuracy of node planning.

[0114] Figure 6 A flowchart illustrating the planning method for network nodes, as shown below. Figure 6 As shown, a network node planning method according to one embodiment of this application may include: Step 610: Based on the network node planning information, perform simulation processing on the network to obtain network indicator information; among which, the indicator information includes at least packet delivery rate and average latency. Step 620: Generate a performance evaluation report based on the network's indicator information; wherein, the performance evaluation report represents the predicted network communication performance after planning the network based on node planning information.

[0115] Steps 610 and 620 are described in detail below.

[0116] In step 610, the node planning information is the topology planning content generated by clustering the quality information corresponding to all pairs of network nodes in the entire network. It includes the partition boundaries of all clusters and the master and slave node affiliations within each cluster. The node planning information can be used as input data for simulation processing to completely restore the node network structure of the hybrid lighting network to be deployed, providing a standardized network topology model for simulation calculations.

[0117] Simulation processing refers to building a virtual network operating environment based on node planning information to simulate the data transmission process of lighting services. It can reproduce the data transmission and reception interaction processes between master and slave nodes within different clusters. Simulation processing outputs quantitative indicator information, enabling communication performance prediction before the network deployment plan is implemented and avoiding communication defects that only surface after on-site deployment. In other words, a virtual lighting network is built based on node planning information, the network is run, and its indicator information is obtained. In this embodiment, the simulation processing procedure is not specifically limited.

[0118] The performance metrics can be a quantitative set of communication performance output after the simulation process is completed. These metrics include at least two core parameters: packet delivery rate and average latency. Packet delivery rate measures the integrity of data transmission, reflecting the network's ability to deliver lighting service data completely to the target node—that is, the overall level of lighting service data packets reaching the terminal node without loss during network transmission. Average latency measures the real-time performance of data transmission, reflecting the waiting time for the transmission of communication commands and sensor data between nodes—that is, the average time taken for service commands such as sensor acquisition and on-demand lighting control to be transmitted between the master and slave nodes. These metrics characterize the communication quality of the network scheme corresponding to the current node planning information and serve as the data basis for compiling performance evaluation reports.

[0119] In step 620, the packet delivery rate is used to assess whether the scale of slave nodes within a cluster exceeds the communication capacity limit of the master node, providing data support for network optimization. Average latency adapts to the performance evaluation requirements of low-latency lighting services such as radar sensing and human body sensing, and is used to identify cluster structures with high communication latency. Based on various indicators such as packet delivery rate and average latency obtained from simulation, a performance evaluation report can be generated. The format requirements for the performance evaluation report can be pre-set. The performance evaluation report is a standardized document compiled based on integrated indicator information, fully recording the network topology corresponding to the node planning information, the complete set of indicator information obtained from simulation, and the conclusions on the performance evaluation of the network scheme. It can intuitively demonstrate the expected communication performance of the network scheme to project operators and customers, and can also serve as a basis for adjusting node planning information.

[0120] For example, simulation processing can utilize general system simulation tools to build a virtual network environment, or it can be based on a customized wireless mesh communication simulation program to complete data transmission simulation. In addition to packet delivery rate and average latency, performance metrics can also simultaneously generate extended performance parameters such as subnet load and channel occupancy, enriching the data dimensions of the performance evaluation report. The performance evaluation report can output only textual performance judgment conclusions, or it can be supplemented with visual content such as indicator comparison charts and topology diagrams.

[0121] Figure 7 This is a flowchart for network evaluation. Figure 7 In this process, multi-source information such as environmental information, weight range information, and priority information is acquired. This multi-source information is processed to obtain quality information between every two network nodes. Quality information can be used as edges in the communication topology graph, thereby determining the communication topology graph. The communication topology graph is then subnetted, and for each subnet, the master and slave nodes are identified to obtain node planning information. Simulation processing is performed based on the node planning information to obtain a performance evaluation report.

[0122] In this embodiment, simulation processing based on node planning information is used to obtain indicator information including data packet delivery rate and average latency. Then, a performance evaluation report that can predict network communication performance is generated based on the indicator information. This allows for the performance verification of the networking scheme before the actual deployment of the lighting network, early identification of topological defects such as communication packet loss and excessive transmission latency, and output of standardized evaluation documents to support project bidding and deployment, thereby further improving the reliability and practicality of the hybrid lighting network node planning scheme.

[0123] In one implementation, the method further includes: if the network's indicator information does not meet the preset indicator conditions, adjusting the target weights of each influencing factor; and updating the network's node planning information based on the adjusted target weights of each influencing factor and the environmental information between each pair of network nodes.

[0124] Specifically, for each indicator, corresponding preset indicator conditions can be set. After obtaining the indicator information, it is determined whether the indicator information meets the corresponding preset indicator conditions. That is, the indicator information can be used to determine the performance qualification of the network scheme by comparing it with the preset indicator conditions. If any indicator information does not meet the preset indicator conditions, the weight iteration and correction process will be triggered to provide a performance basis for the readjustment of the target weights; if all indicator information meets the preset indicator conditions, then there is no need to adjust the target weights, that is, there is no need to adjust the node planning information.

[0125] The preset index conditions are pre-defined network communication performance qualification standards. They can define qualification criteria for performance parameters such as data packet delivery rate and average latency. This is used to identify whether there are communication performance defects in the network topology scheme corresponding to the current node planning information, and then initiate the iterative optimization process of weights and topology.

[0126] If one or more indicators are found to fail to meet the preset conditions, the target weights of each influencing factor need to be adjusted. For example, the target weights can be appropriately increased or decreased based on a preset step size. When simulation performance is substandard, adjusting the target weights can change the proportion of various environmental factors in the communication quality assessment, thereby reconstructing the communication topology and node grouping results.

[0127] After optimizing the target weights, the node planning information is redefined based on the new target weights and the environmental information between each pair of network nodes. That is, step 230 is executed again.

[0128] For example, the adjustment of target weights can be based on the defect type of the indicator information, adjusting the weight values ​​of the corresponding influencing factors accordingly, or it can be done by simultaneously fine-tuning the target weights of all types of influencing factors according to a unified correction rule. Weight adjustment and node planning information update can be completed in a single iteration, or it can be continuously iterated until the indicator information output by the simulation meets the preset indicator conditions.

[0129] Figure 8 This is a schematic diagram of the closed-loop iterative optimization process. Figure 8 In this process, initial weights can be determined based on multi-source data. A first weight correction value can be determined based on the description of the lighting business scenario. A second weight correction value can be determined based on the equipment information of the lighting fixtures and hardware configuration parameters. The target weight can then be obtained based on the initial weight, the first weight correction value, and the second weight correction value. Deployment and simulation are then performed based on the target weight, and the simulation results determine whether adjustments to the target weight are necessary. Adjustments can be made by increasing or decreasing the first and second weight correction values, thereby achieving closed-loop iterative optimization of the target weight.

[0130] In this embodiment, by adjusting the target weights of various influencing factors when the indicator information does not meet the preset indicator conditions, and then combining the adjusted target weights with the original communication environment information to update the node planning information, a closed-loop iterative optimization mechanism of topology simulation, weight correction, and topology regeneration can be formed. This continuously improves the communication performance indicators such as data packet delivery rate and average latency of the hybrid lighting network, and enhances the adaptability of the node grouping scheme to the complex communication environment on site.

[0131] It is understood that although the steps in the above flowcharts are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in this embodiment, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the above flowcharts may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.

[0132] It should be noted that in various specific embodiments of this application, when processing is required based on data related to the characteristics of the target object, such as target object attribute information or a set of attribute information, the permission or consent of the target object will be obtained first. Furthermore, the collection, use, and processing of this data will comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require obtaining target object attribute information, separate permission or consent from the target object will be obtained through pop-ups or redirection to a confirmation page. Only after obtaining the target object's separate permission or consent will the necessary target object-related data for the normal operation of the embodiments of this application be obtained.

[0133] Description of apparatus and devices in embodiments of this application Reference Figure 9 , Figure 9 This is a schematic diagram of the structure of a network node planning device 900 provided in an embodiment of this application. Multiple network nodes are deployed in the network, and the network node planning device 900 includes: The information acquisition module 901 is used to acquire environmental information between each pair of network nodes, and to acquire the weight range information and priority information of each influencing factor; wherein, the influencing factor represents the factors that affect the communication effect in the network, and the priority information represents the importance of the influencing factor. The weight determination module 902 is used to determine the target weight of the impact factor based on the weight range information and priority information of the impact factor; The node planning module 903 is used to determine the node planning information of the network based on the target weights of each influencing factor and the environmental information between each pair of network nodes; wherein, the node planning information represents the grouping of network nodes in the network.

[0134] In one example, the weight range information includes an upper weight limit and a lower weight limit; The weight determination module 902 includes: The amplitude determination unit is used to determine the amplitude information of the impact factor based on the upper limit of the weight, the lower limit of the weight, and the priority information of the impact factor; wherein, the amplitude information represents the magnitude of change of the impact factor based on the lower limit of the weight. The target determination unit is used to determine the target weight of the impact factor based on the magnitude information and the lower limit of the weight; or, based on the magnitude information and the upper limit of the weight of the impact factor, to determine the target weight of the impact factor.

[0135] In one example, the amplitude determination unit is specifically used for: Determine the difference between the upper and lower weight limits of the influencing factors; Based on the difference information and priority information, the magnitude information of the influencing factor is determined.

[0136] In one example, the target determination unit is specifically used for: The magnitude information of the impact factor and the lower limit of the weight are added together to obtain the initial weight of the impact factor; Obtain network scenario information and device information; where scenario information represents the application scenario of the network, and device information represents the device attributes of network nodes in the network; Based on the network's scenario and device information, the initial weights of the influencing factors are adjusted to obtain the target weights of the influencing factors.

[0137] In one example, the target determination unit is specifically used for: Based on the network scene information and a preset first association relationship, a first weight correction value corresponding to the network scene information is determined; based on the network device information and a preset second association relationship, a second weight correction value corresponding to the network device information is determined; wherein, the preset first association relationship represents the association relationship between the scene information and the first weight correction value, and the preset second association relationship represents the association relationship between the device information and the second weight correction value; Based on the first weight correction value corresponding to the network scenario information and the second weight correction value corresponding to the network device information, the initial weights of the influencing factors are adjusted to obtain the target weights of the influencing factors.

[0138] In one example, the environmental information includes factor information for each influencing factor, and the factor information represents the information in the environmental information used to assess the influencing factors. Node planning module 903 includes: The scoring determination unit is used to determine the scoring information of the influence factor between each pair of network nodes based on the factor information of the influence factor between each pair of network nodes; wherein, the scoring information characterizes the degree of influence of the influence factor on the communication effect between each pair of network nodes. The quality determination unit is used to determine the quality information corresponding to each pair of network nodes based on the target weight of each influencing factor and the scoring information of each influencing factor between each pair of network nodes; wherein, the quality information characterizes the communication quality between each pair of network nodes. The node planning unit is used to determine the node planning information of the network based on the quality information corresponding to each pair of network nodes.

[0139] In one example, the scoring unit is specifically used for: The factor information of the impact factor between every two network nodes is normalized to obtain the score information of the impact factor between every two network nodes.

[0140] In one example, the quality determination unit is specifically used for: For each impact factor, the communication quality score between each pair of network nodes is determined based on the target weight of the impact factor and the score information of the impact factor between each pair of network nodes; wherein, the communication quality score represents the communication quality between each pair of network nodes when only the impact factor is considered. Based on the communication quality score between each pair of network nodes corresponding to each influencing factor, the quality information corresponding to each pair of network nodes is determined.

[0141] In one example, the node planning unit is specifically used for: A communication topology graph is generated based on the quality information corresponding to every two network nodes. The communication topology graph includes each network node in the network, and there are edge connections between different network nodes. The edge connections represent the quality information between different network nodes. Clustering is performed on the communication topology graph to obtain node planning information in the network. The node planning information includes multiple clusters divided from the communication topology graph. Each cluster includes a master node and multiple slave nodes. The master node is used to communicate with each slave node.

[0142] In one example, it also includes: The network simulation module is used to simulate the network based on the network node planning information to obtain network performance information; among which, the performance information includes at least packet delivery rate and average latency. The report generation module is used to generate a performance evaluation report based on the network's indicator information. The performance evaluation report represents the predicted network communication performance after the network is planned based on node planning information.

[0143] In one example, it also includes: The weight adjustment module is used to adjust the target weights of each influencing factor if the network's indicator information does not meet the preset indicator conditions. The information update module is used to update the node planning information of the network based on the adjusted target weights of each influencing factor and the environmental information between each pair of network nodes.

[0144] In one example, the network is a lighting network.

[0145] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0147] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 10 As shown, the electronic device 1000 of this embodiment includes: at least one processor 1001 ( Figure 10 (Only one is shown in the diagram), memory 1002, and computer program 1003 stored in said memory 1002 and executable on said at least one processor 1001, which, when executing said computer program 1003, implements the steps in the above-described embodiments of the planning method for any of the network nodes.

[0148] Electronic device 1000 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This electronic device may include, but is not limited to, a processor 1001 and a memory 1002. Those skilled in the art will understand that... Figure 10 This is merely an example of electronic device 1000 and does not constitute a limitation on electronic device 1000. It may include more or fewer components than shown, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, etc.

[0149] The processor 1001 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0150] In some embodiments, the memory 1002 may be an internal storage unit of the electronic device 1000, such as a hard disk or memory of the electronic device 1000. In other embodiments, the memory 1002 may be an external storage device of the electronic device 1000, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1000. Further, the memory 1002 may include both internal and external storage units of the electronic device 1000. The memory 1002 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 1002 can also be used to temporarily store data that has been output or will be output.

[0151] This application also provides an electronic device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0152] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0153] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0154] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographic device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0155] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0156] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0157] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0158] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0159] The above-described 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, and should all be included within the protection scope of this application.

Claims

1. A method for planning network nodes, characterized in that, The network has multiple network nodes deployed; the method includes: For every two network nodes, obtain the environmental information between the two network nodes, and obtain the weight range information and priority information of each influencing factor; wherein, the influencing factor represents the factors that affect the communication effect in the network, and the priority information represents the importance of the influencing factor; The target weight of the impact factor is determined based on the weight range information and priority information of the impact factor; Based on the target weights of each of the aforementioned influencing factors and the environmental information between each pair of network nodes, the node planning information of the network is determined; wherein, the node planning information characterizes the grouping of network nodes in the network; The weight range information includes an upper weight limit and a lower weight limit; Determining the target weight of the impact factor based on the weight range information and priority information of the impact factor includes: The amplitude information of the influence factor is determined based on the upper limit of the weight, the lower limit of the weight, and the priority information of the influence factor; wherein the amplitude information represents the magnitude of change of the influence factor based on the lower limit of the weight. The target weight of the influence factor is determined based on the amplitude information and the lower limit of the weight; or, the target weight of the influence factor is determined based on the amplitude information and the upper limit of the weight.

2. The method according to claim 1, characterized in that, The step of determining the magnitude information of the influence factor based on the upper limit of the weight, the lower limit of the weight, and the priority information of the influence factor includes: Determine the difference between the upper and lower weight limits of the influencing factor; The magnitude information of the influencing factor is determined based on the difference information and the priority information.

3. The method according to claim 1, characterized in that, Determining the target weight of the influence factor based on the magnitude information and the lower limit of the weight of the influence factor includes: The amplitude information of the influence factor and the lower limit of the weight are added together to obtain the initial weight of the influence factor; Obtain the scenario information and device information of the network; wherein, the scenario information represents the application scenario of the network, and the device information represents the device attributes of the network nodes in the network; Based on the network's scenario information and device information, the initial weights of the influencing factors are adjusted to obtain the target weights of the influencing factors.

4. The method according to claim 3, characterized in that, The step of adjusting the initial weights of the influence factors based on the network's scene information and device information to obtain the target weights of the influence factors includes: Based on the scene information of the network, and based on a preset first association relationship, a first weight correction value corresponding to the scene information of the network is determined; based on the device information of the network, and based on a preset second association relationship, a second weight correction value corresponding to the device information of the network is determined; wherein, the preset first association relationship represents the association relationship between the scene information and the first weight correction value, and the preset second association relationship represents the association relationship between the device information and the second weight correction value; The initial weight of the influencing factor is adjusted based on the first weight correction value corresponding to the scene information of the network and the second weight correction value corresponding to the device information of the network to obtain the target weight of the influencing factor.

5. The method according to claim 1, characterized in that, The environmental information includes factor information for each influencing factor, and the factor information represents the information in the environmental information used to evaluate the influencing factors. The step of determining the node planning information of the network based on the target weights of each of the influencing factors and the communication environment information between each pair of nodes includes: Based on the factor information of the influencing factor between each pair of network nodes, the score information of the influencing factor between each pair of network nodes is determined; wherein, the score information characterizes the degree of influence of the influencing factor on the communication effect between each pair of network nodes; Based on the target weights of each of the aforementioned influencing factors and the scoring information of each of the aforementioned influencing factors between each pair of network nodes, the quality information corresponding to each pair of network nodes is determined; wherein, the quality information characterizes the communication quality between each pair of network nodes. Based on the quality information corresponding to each pair of network nodes, the node planning information of the network is determined.

6. The method according to claim 5, characterized in that, The step of determining the score information of the influence factor between every two network nodes based on the factor information of the influence factor between every two network nodes includes: The factor information of the influencing factor between every two network nodes is normalized to obtain the score information of the influencing factor between every two network nodes.

7. The method according to claim 5, characterized in that, The step of determining the quality information corresponding to each pair of network nodes based on the target weights of each of the influencing factors and the scoring information of each of the influencing factors between each pair of network nodes includes: For each of the aforementioned impact factors, a communication quality score between each pair of network nodes corresponding to the impact factor is determined based on the target weight of the impact factor and the scoring information of the impact factor between each pair of network nodes; wherein, the communication quality score characterizes the communication quality between each pair of network nodes when only the impact factor is considered. Based on the communication quality score between each pair of network nodes corresponding to each of the aforementioned influencing factors, the quality information corresponding to each pair of network nodes is determined.

8. The method according to claim 5, characterized in that, The step of determining the node planning information of the network based on the quality information corresponding to every two network nodes includes: A communication topology graph is generated based on the quality information corresponding to each pair of network nodes; wherein, the communication topology graph includes each network node in the network, and there are edge connections between different network nodes, and the edge connections represent the quality information between different network nodes; Clustering is performed on the communication topology graph to obtain node planning information in the network; wherein, the node planning information includes multiple clusters divided from the communication topology graph, each cluster includes a master node and multiple slave nodes, and the master node is used to communicate with each slave node.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: Based on the node planning information of the network, the network is simulated to obtain the network's performance indicators; wherein, the performance indicators include at least packet delivery rate and average latency. A performance evaluation report is generated based on the network's metrics information; wherein the performance evaluation report characterizes the predicted communication performance of the network after planning the network based on the node planning information.

10. The method according to claim 9, characterized in that, The method further includes: If the network's indicator information does not meet the preset indicator conditions, then the target weights of each of the influencing factors are adjusted. The node planning information of the network is updated based on the adjusted target weights of each of the aforementioned influencing factors and the environmental information between each pair of network nodes.

11. The method according to any one of claims 1-8, characterized in that, The network is a lighting network.

12. A network node planning device, characterized in that, The network has multiple network nodes deployed; the device includes: The information acquisition module is used to acquire environmental information between every two network nodes, and to acquire the weight range information and priority information of each influencing factor; wherein, the influencing factor represents the factors that affect the communication effect in the network, and the priority information represents the importance of the influencing factor. The weight determination module is used to determine the target weight of the influence factor based on the weight range information and the priority information of the influence factor; The node planning module is used to determine the node planning information of the network based on the target weights of each of the influencing factors and the environmental information between each pair of network nodes; wherein the node planning information represents the grouping of network nodes in the network; The weight range information includes an upper weight limit and a lower weight limit; The weight determination module includes: An amplitude determination unit is used to determine the amplitude information of the influence factor based on the upper limit of the weight, the lower limit of the weight, and the priority information of the influence factor; wherein the amplitude information characterizes the magnitude of change of the influence factor based on the lower limit of the weight. The target determination unit is configured to determine the target weight of the influence factor based on the amplitude information and the lower limit of the weight; or, to determine the target weight of the influence factor based on the amplitude information and the upper limit of the weight.

13. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method described in any one of claims 1 to 11.

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