Water affair data transmission method and device for intelligent water meter

Abstract

The invention discloses a water affair data transmission method and device for an intelligent water meter, and relates to the technical field of data transmission. Acquiring node information of a target area, initializing a data transmission strategy, and clustering each node according to the node information to obtain an optimal cluster list; dividing the area of the target area according to the optimal cluster list to obtain a sub-area set, and updating a data transmission strategy of the sub-area set; obtaining target data uploaded by each sub-region, determining a link score of the corresponding sub-region according to the target data, and if the link score is less than an abnormal threshold, determining that the transmission link is abnormal; the method is advantaged in that node residual energy and communication success rate are utilized to screen high-energy-efficiency cluster heads, dependence of frequent communication on low-electric-quantity nodes is reduced, equipment life is prolonged, low-efficiency paths are eliminated in real time according to data such as transmission times and energy consumption, redundant transmission is reduced, network overall efficiency is improved, water affair data transmission reliability is improved, and network life is prolonged.

Description

Technical Field

[0001] The present invention belongs to the technical field of data transmission, and particularly relates to a water service data transmission method and device for intelligent water meters. Background Art

[0002] The transmission of water meter data has undergone a transformation from manual meter reading to intelligent transmission. In the early days, mechanical water meters needed to be read manually, with low efficiency and prone to errors. With the development of electronic technology, electromechanical integrated water meters emerged, which can convert mechanical signals into electrical signals to achieve local storage and simple output. The development of communication technology has further promoted the development of remote water meters. From wired RS485 and M-Bus buses to wireless technologies such as GPRS, LoRa, and NB-IoT, the data transmission capacity has been greatly improved.

[0003] Patent No.: CN119155574A, which discloses a data transmission method and device for wireless remote water meters. When the preset cycle reporting time of the wireless remote water meter arrives, a reporting operation is triggered when the wireless network signal is normal to report the water meter data; if the reporting fails, a retransmission operation is triggered when the wireless network signal is normal to retransmit the water meter data; if the retransmission fails, the status of the water meter data is set to the status of pending supplementary reporting; when the reporting is successful or the retransmission is successful, a supplementary reporting operation is triggered when the wireless network signal is normal to supplement the water meter data in the pending supplementary reporting status; by triggering reporting, retransmission, and supplementary reporting only when the wireless network signal is normal, the technical problem of large power consumption in the data transmission of wireless remote water meters in the prior art is alleviated.

[0004] Existing Internet of Things water meters have the following problems: frequent communication leads to shortened device lifespan, low network efficiency, and redundant transmission caused by randomly selecting paths. Summary of the Invention

[0005] The object of the present invention is to solve the problems of shortened device lifespan caused by frequent communication, low network efficiency, and redundant transmission caused by randomly selecting paths, and to propose a water service data transmission method and device for intelligent water meters.

[0006] In the first aspect of the implementation of the present invention, a water service data transmission method for intelligent water meters is first proposed. The method includes:

[0007] Obtain node information of the target area and initialize the data transmission strategy, and cluster each node according to the node information to obtain an optimal cluster list;

[0008] Divide the area of the target area according to the optimal cluster list to obtain a subset of sub-areas, and update the data transmission strategy of the subset of sub-areas;

[0009] Obtain the target data uploaded by each sub-region, determine the link score of the corresponding sub-region according to the target data, and if the link score < the abnormal threshold, determine that the transmission link is abnormal; the water service data includes: water service data, transmission times, and transmission losses.

[0010] Optionally, cluster each node according to the node information to obtain an optimal cluster list, including:

[0011] Send verification data packets to each node so that each node receives the verification data packet and sends a reply data packet to the base station;

[0012] Determine a preset rule according to the reply data packet, screen each node to obtain a preset intermediate node set, and generate an optimal cluster list through the bee optimization algorithm;

[0013] Update the verification data packet according to the optimal cluster list to obtain an updated data packet, and send the updated data packet to the target area so that each node can be updated.

[0014] Optionally, generate an optimal cluster list through the bee optimization algorithm, including:

[0015] Determine a preset intermediate node set according to a preset rule, and determine the initial scale of the bee population according to the preset intermediate node set; the preset intermediate node set contains multiple preset intermediate nodes;

[0016] Randomly select a target intermediate node set according to the preset intermediate node set, calculate the target distance between any target node and any target intermediate node, and cluster each target node according to the target distance to obtain a target node cluster list; the target node cluster list contains multiple target node clusters, and each target node cluster is composed of a target intermediate node and any target node;

[0017] Iteratively update the target node clusters in the target node cluster list to obtain an optimal cluster list, and output the optimal cluster list; the optimal cluster list contains multiple optimal node clusters, and a bee represents a possible solution to a target node cluster.

[0018] Optionally, iteratively update the target node clusters in the target node cluster list to obtain an optimal cluster list, including:

[0019] Step 1, use the target node cluster list as the initial bee colony, execute the bee optimization algorithm to update the initial bee colony to obtain the first bee colony, and calculate the fitness through the fitness function;

[0020] Step 2, randomly select a dimension to update the employed bees in the first bee colony to obtain the second bee colony, calculate the swarm signal according to the second bee colony, and cluster the second bee colony according to the swarm signal to obtain the third bee colony;

[0021] Step 3: Calculate the target probability based on the fitness in the third bee colony, select the bees in the third bee colony according to the target probability, and repeat Step 2 for the unselected bees;

[0022] Step 4: If the preset maximum number of iterations is reached or the fitness value does not converge, select the bee with the lowest fitness value as the optimal cluster list.

[0023] Optionally, after determining that the transmission link is abnormal, it includes:

[0024] Then remove the central node, enable the backup path for data transmission, and reselect the central node for the sub-region.

[0025] Send a control instruction to the central node to make the central node retransmit the data, and broadcast the updated optimal cluster list to make the data transmission strategy updated twice.

[0026] In the second aspect of the implementation of the present invention, a water service data transmission device for an intelligent water meter is proposed, including: a node clustering module, a region division module, and a link judgment module:

[0027] The node clustering module is used to obtain the node information of the target region and initialize the data transmission strategy, and cluster each node according to the node information to obtain an optimal cluster list;

[0028] The region division module is used to divide the region of the target region according to the optimal cluster list to obtain a sub-region set, and update the data transmission strategy of the sub-region set;

[0029] The link judgment module is used to obtain the target data uploaded by each sub-region, determine the link score of the corresponding sub-region according to the target data, and if the link score < abnormal threshold, determine that the transmission link is abnormal; the water service data includes: water service data, transmission times, and transmission losses.

[0030] Optionally, the node clustering module includes: a data verification module, a node screening module, and a node update module:

[0031] The data verification module is used to send verification data packets to each node so that each node receives the verification data packets and sends reply data packets to the base station;

[0032] The node screening module is used to determine a preset rule according to the reply data packet, screen each node to obtain a preset intermediate node set, and generate an optimal cluster list through the bee optimization algorithm;

[0033] The node update module is used to update the verification data packet according to the optimal cluster list to obtain an updated data packet, and send the updated data packet to the target area to enable each node to be updated.

[0034] Optionally, the node screening module includes: a population initialization module, a second clustering module, and an iterative update module:

[0035] The population initialization module is used to determine a preset intermediate node set according to a preset rule, and determine the initial scale of the bee population according to the preset intermediate node set; the preset intermediate node set contains multiple preset intermediate nodes;

[0036] The second clustering module is used to randomly select a target intermediate node set according to the preset intermediate node set, calculate the target distance between any target node and any target intermediate node, and cluster each target node according to the target distance to obtain a target node cluster list; the target node cluster list contains multiple target node clusters, and the target node cluster is composed of a target intermediate node and any target node;

[0037] The iterative update module is used to iteratively update the target node clusters in the target node cluster list to obtain an optimal cluster list, and output the optimal cluster list; the optimal cluster list contains multiple optimal node clusters, and a bee represents a solution to a possible target node cluster.

[0038] Optionally, the iterative update module includes: a first execution module, a second execution module, a third execution module, and a fourth execution module:

[0039] The first execution module is used to use the target node cluster list as the initial bee colony, execute the bee optimization algorithm to update the initial bee colony to obtain a first bee colony, and calculate the fitness through a fitness function;

[0040] The second execution module is used to randomly select a dimension update for the employed bees in the first bee colony to obtain a second bee colony, calculate the swarm signal according to the second bee colony, and cluster the second bee colony according to the swarm signal to obtain a third bee colony;

[0041] The third execution module is used to calculate the target probability according to the fitness in the third bee colony, select the bees in the third bee colony according to the target probability, and repeat the execution of the second execution module for the unselected bees;

[0042] The fourth execution module is used to, if the preset maximum number of iterations is reached or the fitness value no longer converges, select the bee with the lowest fitness value as the optimal cluster list.

[0043] Optionally, the link judgment module includes: a node reselection module and a data retransmission module:

[0044] The node reselection module is used to eliminate the central node, enable an alternate path for data transmission, and reselect the central node for the sub-region;

[0045] The data retransmission module is used to send a control instruction to the central node to cause the central node to retransmit data, and broadcast an updated optimal cluster list to cause a secondary update of the data transmission strategy.

[0046] Advantages of the present invention:

[0047] The present invention proposes a water service data transmission method for an intelligent water meter. By obtaining node information of a target area and initializing a data transmission strategy, clustering each node according to the node information to obtain an optimal cluster list; dividing the area of the target area according to the optimal cluster list to obtain a sub-region set, and updating the data transmission strategy of the sub-region set; obtaining target data uploaded by each sub-region, determining a link score of the corresponding sub-region according to the target data, and if the link score < an abnormal threshold, determining that the transmission link is abnormal; screening high-energy clusters based on the remaining energy of the nodes and the communication success rate, reducing the dependence on low-power nodes for frequent communication, extending the device life, and eliminating inefficient paths in real time according to data such as the number of transmissions and energy consumption, reducing redundant transmissions, and improving the overall network efficiency, thereby improving the reliability of water service data transmission and the network life. Description of the Drawings

[0048] The present invention will be further described below with reference to the accompanying drawings.

[0049] Figure 1 It is a flowchart of a water service data transmission method for an intelligent water meter provided by an embodiment of the present invention;

[0050] Figure 2 It is a schematic structural diagram of a water service data transmission device provided by an embodiment of the present invention. Detailed Embodiments

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the descriptions such as "first" and "second" in the present invention are only for the purpose of description, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on what can be achieved by those of ordinary skill in the art. When the combination of technical solutions results in contradictions or cannot be achieved, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0052] Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0053] The embodiments of the present invention provide a water service data transmission method for an intelligent water meter. Refer to Figure 1 , Figure 1 which is a flowchart of a water service data transmission method for an intelligent water meter provided by the embodiments of the present invention. The method includes the following steps:

[0054] S101, obtain the node information of the target area and initialize the data transmission strategy, and cluster each node according to the node information to obtain an optimal cluster list;

[0055] S102, divide the area of the target area according to the optimal cluster list to obtain a sub-area set, and update the data transmission strategy of the sub-area set;

[0056] S103, obtain the target data uploaded by each sub-area, determine the link score of the corresponding sub-area according to the target data, and if the link score < abnormal threshold, it is determined that the transmission link is abnormal;

[0057] The water service data includes: water service data, transmission times, and transmission losses.

[0058] Based on a water service data transmission method for intelligent water meters provided by an embodiment of the present invention, high - energy - efficient cluster heads are screened through the remaining energy of nodes and communication success rate, reducing the dependence on low - power nodes for frequent communication, prolonging the device life, combining hierarchical or direct - transmission modes to reduce signal attenuation in complex environments, and real - time eliminating inefficient paths according to data such as transmission times and energy consumption, reducing redundant transmissions, improving the overall network efficiency, and achieving energy consumption balance, environmental adaptation, and topology optimization through three - step coordination, comprehensively improving the reliability of water service data transmission and network life.

[0059] In one implementation, the target area includes a general base station. The base station is used to aggregate the water service data of the target area and upload the aggregated data to the cloud (water service company). In addition to receiving data, the base station is also used to update the information of the nodes (intelligent water meters) in the target area (updates such as the data upload time of any water meter, water meter calibration, intermediate node update, etc.).

[0060] In one implementation, the target area is divided into a set of sub - areas according to the optimal cluster list. The optimal cluster list contains multiple optimal node clusters. The target area is divided according to the optimal node clusters. For example, an optimal node cluster is for data transmission by a single A node and multiple B nodes. The B nodes upload the water service data to the A node, and any two A nodes are not adjacent (there are multiple optimal node clusters, so there are multiple separate A nodes). Therefore, the target area is divided into multiple sub - areas; the data transmission direction can be from A1 to A2 to A n and finally transmitted to the base station, or it can be that A1, A2,..., A n are respectively transmitted to the base station; the target area is divided into multiple sub - area sets according to the optimal cluster list to achieve refined network management. Each sub - area is managed by an independent optimal node cluster (A node and associated B nodes), supporting two flexible transmission modes: Hierarchical transmission (A1→A2→…→base station): reducing the single - hop communication distance and signal attenuation. Direct - transmission mode (A1, A2… directly connected to the base station): shortening the end - to - end delay and improving the emergency data response speed. This step optimizes resource allocation through dynamic path selection to adapt to different scenario requirements.

[0061] In one implementation, the optimal cluster list is generated through dynamic clustering. High - reliability nodes are selected as cluster heads based on node information (such as remaining energy and communication success rate) to optimize the network topology structure. This step ensures the shortest communication distance within the cluster, energy consumption balance, reduces the rapid energy consumption of edge nodes due to long - distance transmission, and improves link stability and network life.

[0062] In one implementation, the link score is calculated in real time based on the water service data (transmission times, transmission loss) uploaded by sub-regions to accurately identify abnormal links (such as score < threshold). By dynamically removing low-quality links or triggering re-election of cluster heads, the reliability of data transmission is ensured. For example: abnormal transmission times (such as a single node frequently retransmitting > 3 times) indicate link interference; a sharp increase in transmission loss (such as a 30% increase in energy consumption) reflects node energy imbalance or environmental interference. This mechanism realizes real-time monitoring and self-healing of the network state, ensuring continuous and stable upload of water service data; the link score is obtained by weighted summation of the transmission times and transmission loss. For example: link score = weight A * transmission times + weight B * transmission loss, where the sum of weight A and weight B is 1, and both weight A and weight B are constant proportionality coefficients.

[0063] In one embodiment, each node is clustered according to node information to obtain an optimal cluster list, including:

[0064] Sending verification data packets to each node so that each node receives the verification data packets and sends reply data packets to the base station;

[0065] Determining preset rules according to the reply data packets, screening each node to obtain a preset intermediate node set, and generating an optimal cluster list through the bee optimization algorithm;

[0066] Updating the verification data packets according to the optimal cluster list to obtain updated data packets, and sending the updated data packets to the target area so that each node can be updated.

[0067] In one implementation, there are multiple nodes (smart water meters) included in the target area. The base station broadcasts verification data packets to the target area. The verification data packets include: the location of the base station itself, MAC address, network address, etc. The initial broadcast is used for network startup, and subsequent broadcasts are used to notify the start of a new round of transmission; when each node receives the verification data packets, it adjusts the transmission power and sends a reply data packet to the base station. The reply data packet includes: the unique identifier (ID) of the water meter, remaining energy, historical communication success rate (number of successful transmissions / total number of transmissions), etc. The base station screens the nodes based on the reply data packets to obtain target intermediate nodes; the cluster head assigns time slots to the target nodes and collects data in sequence to avoid conflicts; the base station re-performs clustering at a preset period (such as every 24 hours) to adapt to energy changes and environmental interference; dual-mode data transmission: 1. Low-power transmission at a fixed period (every hour), using the minimum energy path; 2. Trigger dynamic routing optimization, select a highly stable path or transmit through multi-path redundancy to ensure a delivery rate > 99%.

[0068] In one implementation, MOO-DGP solves the problems of uneven energy consumption, unstable links, and inefficient topologies in mobile or static IoT sensor networks through multi-objective optimization and dynamic clustering mechanisms. Its execution process, from network initialization, multi-objective clustering to steady-state data transmission, takes into account energy efficiency, link quality, and environmental adaptability throughout; determines preset rules based on the reply data packets, that is, determined according to the power and communication quality of each node, and gives priority to ensuring the power of the nodes to prevent the water meter from failing to transmit data or transmitting data unsuccessfully due to insufficient power.

[0069] In one implementation, an optimal cluster list is generated based on the bee optimization algorithm, and nodes with high remaining energy and high communication success rate are dynamically selected as cluster heads to balance the network load, reduce the energy consumption of long-distance communication of edge nodes, and extend the overall network life; through the dynamic distribution of updated data packets, ensure that nodes are synchronized with the latest network configuration (such as cluster head list, time slot allocation) in a timely manner, reduce data conflicts or losses caused by topological changes, and improve the real-time performance and reliability of data transmission; by comprehensively optimizing energy consumption, link stability, and environmental adaptability, solve the problems of uneven energy consumption, link fluctuations, and inefficient topologies in static or mobile networks, and achieve the coordinated improvement of global energy efficiency and local environmental adaptability.

[0070] In one embodiment, generating an optimal cluster list through the bee optimization algorithm includes:

[0071] Determine a preset intermediate node set according to preset rules, and determine the initial scale of the bee population according to the preset intermediate node set; the preset intermediate node set contains multiple preset intermediate nodes;

[0072] Randomly select a target intermediate node set according to the preset intermediate node set, calculate the target distance between any target node and any target intermediate node, and cluster each target node according to the target distance to obtain a target node cluster list; the target node cluster list contains multiple target node clusters, and each target node cluster is composed of a target intermediate node and any target node;

[0073] Iteratively update the target node clusters in the target node cluster list to obtain an optimal cluster list, and output the optimal cluster list; the optimal cluster list contains multiple optimal node clusters, and a bee represents a possible solution to a target node cluster.

[0074] In one implementation, there are many nodes (smart water meters) in the target area, and intermediate nodes (nodes with the ability to summarize local data between each water meter and the base station) that are eligible to become intermediate channels are selected from the numerous nodes as the preset intermediate node set, and the preset rules are the energy level and installation location of the water meter.

[0075] In one implementation, the initial size of the bee population is determined according to a preset intermediate node set, that is, the bee optimization algorithm is initialized; according to the number of intermediate nodes in the preset intermediate node set, the initial size of the bee population is determined. Each bee represents a solution to a possible target node cluster. The bees navigate in the search space to find the optimal cluster head configuration.

[0076] In one implementation, the target distance between any target node and any target intermediate node is calculated. Each Internet of Things sensor (smart water meter) node calculates its distance from each intermediate node in the current cluster list. The distance is calculated by performing a weighted sum calculation on the node density, data transmission frequency, physical distance, and communication cost between the target node and the target intermediate node. The target distance will be used for subsequent cluster clustering decisions.

[0077] In one implementation, 1. Optimize the selection of cluster heads (intermediate nodes): Filter out intermediate nodes with local data aggregation capabilities as cluster head candidates through preset rules to ensure a more reasonable selection of cluster heads and reduce energy consumption; 2. Dynamically adjust the cluster structure: Use the bee optimization algorithm to iteratively update the cluster structure, which can dynamically adjust the cluster heads (target intermediate nodes) and cluster members (target nodes) according to the network state, improving the adaptability and stability of the network; 3. Improve data transmission efficiency: By calculating the target distance and performing clustering, ensure that each node is assigned to the most suitable cluster head, thereby optimizing the data transmission path and reducing communication costs; 4. Enhance the network lifespan: By optimizing the selection of cluster heads and the cluster structure, reduce the energy consumption of nodes and extend the service life of the entire network.

[0078] In one embodiment, iteratively updating the target node clusters in the target node cluster list to obtain an optimal cluster list includes:

[0079] Step 1, Take the target node cluster list as the initial bee colony, execute the bee optimization algorithm to update the initial bee colony to obtain the first bee colony, and calculate the fitness through the fitness function;

[0080] Step 2, Randomly select dimension updates for the employed bees in the first bee colony to obtain the second bee colony, calculate the swarm signal according to the second bee colony, and cluster the second bee colony according to the swarm signal to obtain the third bee colony;

[0081] Step 3, Calculate the target probability according to the fitness in the third bee colony, select the bees in the third bee colony according to the target probability, and repeat Step 2 for the unselected bees;

[0082] Step 4, If the preset maximum number of iterations is reached or the fitness value no longer converges, select the bee with the lowest fitness value as the optimal cluster list.

[0083] In one implementation, before the bee optimization algorithm is executed, parameters need to be initialized, including the input population size and the maximum number of iterations. In the first stage: for any individual (bee, employed bee), it searches for food and updates the position of the individual, θ ij = B ij + Φ ij *(B ij - B kj ), where θ ij is the updated position of the individual, B ij is the j-th dimensional value of the current solution B i Φ ij is a randomly generated perturbation factor, with a value range of [-1, 1], B kj is the j-th dimensional value of another randomly selected solution B k (k ≠ i); calculate the fitness value of the updated individual, compare the fitness values of the new individual and the old individual, replace the original (old) individual, and update it with the fitness value of the new individual.

[0084] In one implementation, the fitness function of the bee optimization algorithm:

[0085]

[0086] where α and β are constant proportionality coefficients, E z is the z-th target intermediate node, d(x i , y i ) is the Euclidean distance from the target node x i to the target intermediate node y i , M is the total number of target intermediate nodes, Z cc is the swarm signal;

[0087] Calculate the target probability based on the fitness in the third bee colony, P i is the target probability, G(B i ) is the fitness of the solution B i .

[0088] In one implementation, an attraction-repulsion mechanism is added to the bee optimization algorithm to dynamically adjust the direction of the bee optimization algorithm's scheme (bee, individual, solution); in the bee optimization algorithm, after the employed bee or the observing bee generates a new solution through random perturbation, the swarm chemotaxis operation of BFO is immediately executed to traverse the scheme, through the formula:

[0089]

[0090] where Z ccis the group signal, q and p are the depth (intensity) and width (range of action) of the attraction signal respectively to control bacterial aggregation, n and m are the height (intensity) and width (range of action) of the repulsion signal respectively to prevent excessive concentration of bacteria, and θ r is the coordinate of the current bacterium in the r-th dimension, is the coordinate of other bacterium i in the r-th dimension, N represents the size of the bacterial population, and D represents the dimension of θ; calculating the group signal is used to simulate the attraction-repulsion behavior between bacteria. The attraction signal (q): guides the solution to aggregate in the high fitness region and accelerates convergence; the repulsion signal (n): avoids excessive concentration of solutions and maintains population diversity; after the bee optimization algorithm generates a new solution through random perturbation, it calls the group signal formula of the bacterial optimization algorithm (Z cc ), and finely tunes other dimensions of the solution; for example: after generating a new cluster head candidate solution, adjust the distribution of cluster heads through the attraction-repulsion signal to avoid excessive density of local cluster heads. The bee optimization algorithm is responsible for global exploration (generating new solutions), and the group signal of the bacterial optimization algorithm is responsible for local fine-tuning. The two cooperate to improve the convergence speed and accuracy.

[0091] In one implementation, the group signal mechanism (attraction-repulsion) of the bacterial optimization algorithm breaks the limitation of the single-dimensional optimization of the bee optimization algorithm and guides the solution to jump out of the local optimal region; by embedding the bacterial chemotaxis of the bacterial optimization algorithm into the local search stage of the bee optimization algorithm, multi-dimensional cooperative optimization and group cooperation guidance are realized, effectively solving the problems of local optimality, slow convergence, and poor environmental adaptability faced by traditional meta-heuristic algorithms in IoT sensor networks. This fusion mechanism provides an efficient and stable data collection solution for complex scenarios such as water meters.

[0092] In one embodiment, after determining that the transmission link is abnormal, it includes:

[0093] Then remove this central node, enable the backup path for data transmission, and reselect the central node for this sub-region;

[0094] Send a control instruction to this central node to make this central node retransmit data, and broadcast the updated optimal cluster list to make the data transmission strategy updated for the second time.

[0095] In one implementation, by removing abnormal central nodes and enabling backup paths, data loss or delay caused by unstable links is avoided. The data retransmission mechanism ensures the complete delivery of key water service information. The backup path dynamically selects high-stability nodes based on link scoring, reducing the impact of environmental interference (such as signal attenuation in metal pipes) on transmission, and ensuring the real-time and reliability of emergency data.

[0096] In one implementation, central node reselection and optimal cluster list update achieve rapid recovery of the fault area. The new cluster head is dynamically elected based on indicators such as remaining energy and communication success rate, optimizing the local topology structure, shortening the abnormal recovery time from minutes to seconds. Broadcasting the updated cluster list ensures that all network nodes synchronize the latest configuration, supports dynamic routing switching (such as flexible adaptation of hierarchical transmission and direct transmission modes), and improves the network robustness in complex environments.

[0097] In one implementation, abnormal node removal and cluster head reselection reduce the excessive energy consumption of inefficient links. The standby path preferentially selects short-distance and low-energy-consuming nodes, combined with priority scheduling of data retransmission (such as only retransmitting critical data), reducing redundant transmission and improving bandwidth utilization. After the global policy is updated, idle nodes can enter the sleep mode in a timely manner, extending the device life.

[0098] Based on the same inventive concept, the embodiments of the present invention also provide a water service data transmission device for an intelligent water meter. Refer to Figure 2 , Figure 2 which is a schematic structural diagram of a water service data transmission device for an intelligent water meter provided by the embodiments of the present invention, including: a node clustering module, a region division module, and a link judgment module:

[0099] The node clustering module is used to obtain the node information of the target area and initialize the data transmission strategy, and cluster each node according to the node information to obtain an optimal cluster list;

[0100] The region division module is used to divide the region of the target area according to the optimal cluster list to obtain a sub-region set, and update the data transmission strategy of the sub-region set;

[0101] The link judgment module is used to obtain the target data uploaded by each sub-region, determine the link score of the corresponding sub-region according to the target data, and if the link score < abnormal threshold, it is determined that the transmission link is abnormal; the water service data includes: water service data, transmission times, and transmission loss.

[0102] Based on the water service data transmission device for an intelligent water meter provided by the embodiments of the present invention, high-energy-efficient cluster heads are screened through the remaining energy of the nodes and the communication success rate, reducing the dependence on low-power nodes for frequent communication and extending the device life. Short-distance communication is managed by independent cluster heads, combined with hierarchical or direct transmission modes to reduce signal attenuation in complex environments. Inefficient paths are removed in real time according to data such as transmission times and energy consumption, reducing redundant transmission and improving the overall network efficiency. The three steps cooperate to achieve energy consumption balance, environment adaptation, and topology optimization, comprehensively improving the reliability of water service data transmission and the network life.

[0103] In one embodiment, the node clustering module includes: a data verification module, a node screening module, and a node update module:

[0104] A data verification module, configured to send verification data packets to each node so that each node receives the verification data packets and sends reply data packets to the base station;

[0105] A node screening module, configured to determine a preset rule according to the reply data packets, screen each node to obtain a preset intermediate node set, and generate an optimal cluster list through a bee optimization algorithm;

[0106] A node update module, configured to update the verification data packets according to the optimal cluster list to obtain updated data packets, and send the updated data packets to the target area so that each node can be updated.

[0107] In one embodiment, the node screening module includes: a population initialization module, a second clustering module, and an iterative update module:

[0108] The population initialization module is configured to determine a preset intermediate node set according to a preset rule, and determine the initial scale of the bee population according to the preset intermediate node set; the preset intermediate node set includes multiple preset intermediate nodes;

[0109] The second clustering module is configured to randomly select a target intermediate node set according to the preset intermediate node set, calculate the target distance between any target node and any target intermediate node, and cluster each target node according to the target distance to obtain a target node cluster list; the target node cluster list includes multiple target node clusters, and a target node cluster is composed of a target intermediate node and any target node;

[0110] The iterative update module is configured to iteratively update the target node clusters in the target node cluster list to obtain an optimal cluster list, and output the optimal cluster list; the optimal cluster list includes multiple optimal node clusters, and a bee represents a solution to a possible target node cluster.

[0111] In one embodiment, the iterative update module includes: a first execution module, a second execution module, a third execution module, and a fourth execution module:

[0112] The first execution module is configured to use the target node cluster list as an initial bee colony, execute the bee optimization algorithm to update the initial bee colony to obtain a first bee colony, and calculate the fitness through a fitness function;

[0113] The second execution module is configured to randomly select a dimension update for the employed bees in the first bee colony to obtain a second bee colony, calculate the swarm signal according to the second bee colony, and cluster the second bee colony according to the swarm signal to obtain a third bee colony;

[0114] The third execution module is configured to calculate the target probability according to the fitness in the third bee colony, select the bees in the third bee colony according to the target probability, and repeat the execution of the second execution module for the unselected bees;

[0115] A fourth execution module, configured to select the bee with the lowest fitness value as the optimal cluster list if the preset maximum number of iterations is reached or the fitness value no longer converges.

[0116] In one embodiment, the link judgment module includes: a node reselection module and a data retransmission module:

[0117] The node reselection module is configured to remove the central node, enable an alternative path for data transmission, and reselect the central node for the sub-region;

[0118] The data retransmission module is configured to send a control instruction to the central node to cause the central node to retransmit data, and broadcast the updated optimal cluster list to cause the data transmission strategy to be updated twice.

[0119] The above has described an embodiment of the present invention in detail, but the content is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A water service data transmission method for an intelligent water meter, characterized in that, The method includes: Obtain the node information of the target area and initialize the data transmission strategy, and cluster each node according to the node information to obtain an optimal cluster list; Divide the area of the target area according to the optimal cluster list to obtain a subset of sub-areas, and update the data transmission strategy of the subset of sub-areas; Obtain the target data uploaded by each sub-area, determine the link score of the corresponding sub-area according to the target data, and if the link score < abnormal threshold, determine that the transmission link is abnormal; The water service data includes: water service data, transmission times, and transmission losses.

2. The water service data transmission method for an intelligent water meter according to claim 1, wherein, Clustering each node according to the node information to obtain an optimal cluster list includes: Send verification data packets to each node so that each node receives the verification data packets and sends reply data packets to the base station; Determine preset rules according to the reply data packets, screen each node to obtain a preset intermediate node set, and generate an optimal cluster list through the bee optimization algorithm; Update the verification data packets according to the optimal cluster list to obtain updated data packets, and send the updated data packets to the target area so that each node can be updated.

3. A water service data transmission method for an intelligent water meter according to claim 2, characterized in that Generating an optimal cluster list through the bee optimization algorithm includes: Determine a preset intermediate node set according to preset rules, and determine the initial scale of the bee population according to the preset intermediate node set; The preset intermediate node set contains multiple preset intermediate nodes; Randomly select a target intermediate node set according to the preset intermediate node set, calculate the target distance between any target node and any target intermediate node, and cluster each target node according to the target distance to obtain a target node cluster list; The target node cluster list contains multiple target node clusters, and each target node cluster is composed of a target intermediate node and any target node; Iteratively update the target node clusters in the target node cluster list to obtain an optimal cluster list, and output the optimal cluster list; The optimal cluster list contains multiple optimal node clusters, and a bee represents a possible solution to a target node cluster.

4. A water service data transmission method for an intelligent water meter according to claim 3, characterized in that, Iteratively updating the target node clusters in the target node cluster list to obtain an optimal cluster list includes: Step 1, use the target node cluster list as the initial bee colony, execute the bee optimization algorithm to update the initial bee colony to obtain a first bee colony, and calculate the fitness through the fitness function; Step 2, randomly select a dimension to update the employed bees in the first bee colony to obtain a second bee colony, calculate the swarm signal according to the second bee colony, and cluster the second bee colony according to the swarm signal to obtain a third bee colony; Step 3, calculate the target probability according to the fitness in the third bee colony, select the bees in the third bee colony according to the target probability, and repeat Step 2 for the unselected bees; Step 4, if the preset maximum number of iterations is reached or the fitness value no longer converges, select the bee with the lowest fitness value as the optimal cluster list.

5. A water service data transmission method for an intelligent water meter according to claim 1, characterized in that, After determining that the transmission link is abnormal, it includes: Then remove this central node, enable the backup path for data transmission, and reselect the central node for this sub-area; Send a control instruction to the central node to cause the central node to retransmit data, and broadcast the updated optimal cluster list to cause a secondary update of the data transmission strategy.

6. A water service data transmission device for an intelligent water meter, characterized in that, The device includes: a node clustering module, a region division module, and a link judgment module: The node clustering module is used to obtain node information of a target region and initialize a data transmission strategy, and cluster each node according to the node information to obtain an optimal cluster list; The region division module is used to divide the region of the target region according to the optimal cluster list to obtain a sub-region set, and update the data transmission strategy of the sub-region set; The link judgment module is used to obtain target data uploaded by each sub-region, determine a link score of the corresponding sub-region according to the target data, and if the link score < an abnormal threshold, determine that the transmission link is abnormal; The water service data includes: water service data, number of transmissions, and transmission loss.

7. The water service data transmission device for an intelligent water meter according to claim 6, characterized in that, The node clustering module includes: a data verification module, a node screening module, and a node update module: The data verification module is used to send verification data packets to each node so that each node receives the verification data packets and sends reply data packets to the base station; The node screening module is used to determine a preset rule according to the reply data packets, screen each node to obtain a preset intermediate node set, and generate an optimal cluster list through a bee optimization algorithm; The node update module is used to update the verification data packets according to the optimal cluster list to obtain updated data packets, and send the updated data packets to the target region so that each node is updated.

8. A water service data transmission device for an intelligent water meter according to claim 7, characterized in that, The node screening module includes: a population initialization module, a second clustering module, and an iterative update module: The population initialization module is used to determine a preset intermediate node set according to a preset rule, and determine an initial scale of a bee population according to the preset intermediate node set; The preset intermediate node set includes multiple preset intermediate nodes; The second clustering module is used to randomly select a target intermediate node set according to the preset intermediate node set, calculate a target distance between any target node and any target intermediate node, and cluster each target node according to the target distance to obtain a target node cluster list; The target node cluster list includes multiple target node clusters, and each target node cluster is composed of a target intermediate node and any target node; The iterative update module is used to iteratively update the target node clusters in the target node cluster list to obtain an optimal cluster list, and output the optimal cluster list; The optimal cluster list includes multiple optimal node clusters, and a bee represents a solution of a possible target node cluster.

9. The water service data transmission device for an intelligent water meter according to claim 8, characterized in that, The iterative update module includes: a first execution module, a second execution module, a third execution module, and a fourth execution module: The first execution module is used to use the target node cluster list as an initial bee colony, execute a bee optimization algorithm to update the initial bee colony to obtain a first bee colony, and calculate a fitness through a fitness function; The second execution module is used to randomly select dimension updates for the employed bees in the first bee colony to obtain a second bee colony, calculate a population signal based on the second bee colony, and cluster the second bee colony according to the population signal to obtain a third bee colony; The third execution module is used to calculate a target probability based on the fitness in the third bee colony, select the bees in the third bee colony according to the target probability, and repeatedly execute the second execution module for the unselected bees; The fourth execution module is used to, if the preset maximum number of iterations is reached or the fitness value does not converge, select the bee with the lowest fitness value as the optimal cluster list.

10. The water service data transmission device for an intelligent water meter according to claim 6, characterized in that, The link judgment module includes: a node reselection module and a data retransmission module: The node reselection module is used to remove the central node, enable an alternative path for data transmission, and reselect the central node for the sub-region; The data retransmission module is used to send a control instruction to the central node to cause the central node to retransmit data, and broadcast the updated optimal cluster list to cause a secondary update of the data transmission strategy.

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