A water meter data collection method based on open source Hongmeng

Abstract

The present invention relates to the field of Internet of Things technology, specifically a water meter data collection method based on open source Hongmeng, comprising the following steps: based on the open source Hongmeng distributed architecture, collecting water meter sensor and communication data, analyzing flow fluctuations, marking inefficient nodes, optimizing communication and energy consumption matching, screening abnormal links, identifying over-limit nodes, and generating abnormal and switching control tables. In the present invention, by analyzing the flow change trend within a unit time, the intelligent division of data fluctuation intervals is realized. On this basis, the operating cycle and power distribution are combined to match the data collection tasks with the energy consumption status. At the same time, a comprehensive evaluation is performed on the signal strength and delay in the transmission path. By continuously tracking the flow output changes, the abnormal operating nodes are identified and the switching control signal is triggered, thereby improving the stability and accuracy of data collection in complex environments, enhancing the coordination and execution efficiency of the whole process, reducing the risk of information loss caused by communication failures, and improving the level of intelligent response.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things technology, and in particular to a water meter data collection method based on open source Hongmeng. Background Art

[0002] The Internet of Things (IoT) encompasses physical devices, sensors, and embedded systems interconnected via a network. It aims to seamlessly connect physical devices with the digital world through data collection, transmission, and processing. IoT technology is widely used in various fields, including smart homes, industrial automation, agriculture, and energy management. Its core technologies include sensing, communications, embedded systems, and data processing and analysis. The development of IoT technology has driven the intelligentization, networking, and automation of devices, significantly improving the efficiency and reliability of various systems. Traditional water meter data collection methods rely on manual meter reading or a single communication method. These methods suffer from slow data transmission speeds, high data collection costs, and poor information accuracy. Traditional water meter data collection systems transmit data through wired or wireless communication protocols, but in complex environments, they face challenges such as signal interference and transmission delays. To address these technical issues, this paper proposes a water meter data collection method based on the open source Hongmeng operating system. By applying the open source Hongmeng operating system, this method aims to optimize the data collection process and improve the system's real-time performance, stability, and accuracy. This method leverages the multi-device collaboration, low power consumption, and efficient data transmission mechanisms of the Hongmeng operating system, providing a new water meter data collection solution.

[0003] Existing technologies rely on manual meter reading or a single communication method to obtain data. In areas with multiple devices or unstable signals, there are problems with low data collection efficiency and high latency. Due to the relatively fixed communication protocol, it is difficult to make real-time adjustments when the data link is interfered or blocked, resulting in interruption or failure of information transmission. For example, in densely populated buildings or underground facilities, signal transmission is easily hindered by physical structure and reduced quality. At the same time, traditional solutions do not jointly optimize energy consumption status and data tasks, causing low-power devices to still undertake high-frequency transmission tasks, resulting in uneven energy consumption distribution and frequent node disconnection. These problems make the equipment less stable and responsive in dynamic environments, affecting the continuity and effectiveness of the data collection process. Summary of the Invention

[0004] The purpose of this invention is to solve the shortcomings of the existing technology and propose a water meter data collection method based on open source Hongmeng.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a water meter data collection method based on open source Hongmeng, comprising the following steps:

[0006] S1: Based on the open source Hongmeng distributed architecture, it calls the sensor module and communication module data of the water meter terminal, analyzes the flow change trend within a unit time, and generates the flow fluctuation segment division result;

[0007] S2: Based on the flow fluctuation segment division results, extract the energy consumption status and communication link quality of the water meter terminal, detect the data packet transmission success rate in the segment, mark inefficient communication nodes, and generate communication optimization adjustment nodes;

[0008] S3: According to the communication optimization and adjustment node, the operating time period data and the remaining power value of the water meter terminal are extracted, the matching relationship between the device energy consumption and the data transmission volume per unit time is identified, and the priority is sorted to generate an energy consumption adaptation list;

[0009] S4: Based on the energy consumption adaptation list, call the transmission path data of the water meter terminal and the gateway node, record the signal strength and transmission delay of each path, filter abnormal links and synchronously update the path configuration to obtain the link optimization synchronization set.

[0010] As a further solution of the present invention, the traffic fluctuation segment division results include the traffic change rate, fluctuation interval duration, and traffic peak distribution; the communication optimization adjustment node includes the communication link quality threshold, data packet transmission success rate, inefficient node identification, and optimization trigger conditions; the energy consumption adaptation list includes device energy consumption intensity, data transmission efficiency, and energy consumption priority order; the link optimization synchronization set includes path signal strength data, transmission delay duration, and abnormal link mark.

[0011] As a further solution of the present invention, the steps for obtaining the flow fluctuation segment division result are specifically as follows:

[0012] S111: Based on the open source Hongmeng distributed architecture, the sensor module data of the water meter terminal is called, combined with the flow value corresponding to the time point, the time series is divided into multiple segments according to the flow change rate, and the flow fluctuation trend within each segment is analyzed to obtain the mapping value between the flow change rate and the time interval;

[0013] S112: Extracting flow extreme value points at adjacent time points based on the flow change rate and time interval mapping value, analyzing the flow change amplitude sequence, comparing the fluctuation amplitude with the duration reference value, filtering out the segment numbers that are out of range, and obtaining a flow fluctuation offset segment number sequence;

[0014] S113: Based on the flow fluctuation offset segment number sequence, extract segment flow change data, calculate the duration difference between flow growth and attenuation, divide the fluctuation segments based on the flow change rate and segment frequency, and generate a flow fluctuation segment division result.

[0015] As a further solution of the present invention, the step of acquiring the communication optimization adjustment node is specifically as follows:

[0016] S211: Extracting communication link quality data of the water meter terminal based on the flow fluctuation segment division result, calling the data packet transmission success rate, signal strength feedback and terminal location information within the segment, identifying the communication link status change within a unit time, and generating a segment communication link quality sequence;

[0017] S212: According to the segment communication link quality sequence, the signal strength change rate, data packet loss rate and link delay coefficient of each communication node are collected, and by comparing the relationship between the parameters and the preset link quality threshold, the change amplitude value of the inefficient node is calculated, and compared with the communication benchmark fluctuation amplitude point by point to obtain the communication optimization adjustment node.

[0018] As a further solution of the present invention, the steps of obtaining the energy consumption adaptation list are specifically as follows:

[0019] S311: Extracting the operating time period data and remaining power value of the water meter terminal according to the communication optimization and adjustment node, identifying the relationship between the device energy consumption and the data transmission volume per unit time, analyzing the matching degree between the energy consumption and the transmission volume in the continuous segment, and obtaining the unit energy consumption transmission matching interval;

[0020] S312: calling the unit energy consumption transmission matching interval, calculating the energy consumption deviation index value by differentiating the matching interval value of the device in the self-operation period, and sorting the index values ​​to generate an energy consumption adaptation list.

[0021] As a further solution of the present invention, the step of acquiring the link optimization synchronization set is specifically as follows:

[0022] S411: Based on the energy consumption adaptation list, call the transmission path data between the water meter terminal and the gateway node, record the signal strength of the starting node, transit node and terminal node of each path, identify the signal strength change and transmission delay of each path, and generate a path node set;

[0023] S412: For the path node set, identify the path signal strength and delay duration, calculate the link fluctuation index, compare it with a preset link fluctuation threshold, screen out paths with signal strength below the threshold or with a sudden change in delay, and generate an abnormal link identifier;

[0024] S413: Extract the node coordinates and signal strength data of the abnormal link according to the abnormal link identifier, update the filtered path data to the path configuration table in timestamp order, analyze the signal correlation between nodes, and obtain the link optimization synchronization set.

[0025] As a further embodiment of the present invention, the method further comprises step S5:

[0026] S5: Based on the link optimization synchronization set, extract the flow output value of the water meter terminal, analyze the number of zero flow segments in the continuous cycle, compare it with the standard abnormal interval, identify the over-limit node and abnormal mark and partition switching signal, and obtain the water meter terminal abnormality and switching control table;

[0027] The water meter terminal abnormality and switching control table includes the number of zero flow cycles, over-limit abnormal nodes, abnormal marking status, and partition switching identification.

[0028] As a further solution of the present invention, the steps for obtaining the water meter terminal abnormality and switching control table are specifically as follows:

[0029] S511: Based on the link optimization synchronization set, extract the flow output value of the water meter terminal, divide the time period into consecutive periods, determine whether the node flow value in each period is zero, count the number of time periods in which the node has zero value in the period, and obtain the number of zero flow segments of the node period;

[0030] S512: Call the number of zero flow segments in the node cycle, determine whether the node exceeds the normal range based on the difference between the number of zero flow segments of the node and the set standard abnormal interval, bind the index of the exceeding node to the corresponding cycle number, filter the abnormal nodes, record the cycle performance of the exceeding node, and obtain the exceeding node location value;

[0031] S513: Based on the over-limit node positioning value, call the partition switching threshold, compare the number of consecutive abnormal cycles with the threshold, mark the partition status of the node that meets the switching conditions, summarize the current flow value of the node and the switching status parameters, and obtain the water meter terminal abnormality and switching control table.

[0032] Compared with the prior art, the advantages and positive effects of the present invention are:

[0033] In the present invention, by analyzing the flow change trend of water meter data in unit time, the intelligent division of data fluctuation range can be realized, and then the dynamic identification and adjustment of data transmission efficiency and communication quality can be realized. On this basis, combined with the equipment operation cycle and power distribution, the accurate matching of data acquisition tasks and energy consumption allocation can be achieved. At the same time, the signal strength and transmission time efficiency are comprehensively evaluated in the communication link path to form an automatically correctable path configuration mechanism. By continuously tracking the flow output changes, it can dynamically identify abnormal operating nodes and trigger adaptive switching strategies, thereby improving the stability and accuracy of water meter data acquisition in complex environments, enhancing the coordination and execution efficiency of the entire data acquisition process, reducing the risk of information loss caused by communication failures, and improving the level of intelligent response in water meter data acquisition operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0035] Figure 2 This is a flow chart for obtaining the flow fluctuation segment division results in the present invention;

[0036] Figure 3 This is a flow chart for obtaining communication optimization and adjustment nodes in the present invention;

[0037] Figure 4 This is a flow chart for obtaining the energy consumption adaptation list in the present invention;

[0038] Figure 5 This is a flowchart of obtaining a link optimization synchronization set in the present invention;

[0039] Figure 6 This is a flow chart for obtaining the water meter terminal abnormality and switching control table in the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0041] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0042] Example 1: Please refer to Figure 1 , the present invention provides a technical solution: a water meter data collection method based on open source Hongmeng, comprising the following steps:

[0043] S1: Based on the open source Hongmeng distributed architecture, it calls the sensor module and communication module data of the water meter terminal, analyzes the flow change trend within a unit time, and generates the flow fluctuation segment division result;

[0044] S2: Based on the flow fluctuation segment division results, the energy consumption status and communication link quality of the water meter terminal are extracted, the data packet transmission success rate within the segment is detected, inefficient communication nodes are marked, and communication optimization adjustment nodes are generated;

[0045] S3: Based on the communication optimization and adjustment nodes, the operating time data and remaining power value of the water meter terminal are extracted, the matching relationship between the device energy consumption and the data transmission volume per unit time is identified, and the priority is sorted to generate an energy consumption adaptation list;

[0046] S4: Based on the energy consumption adaptation list, call the transmission path data between the water meter terminal and the gateway node, record the signal strength and transmission delay of each path, filter out abnormal links, and synchronously update the path configuration to obtain the link optimization synchronization set;

[0047] S5: Based on the link optimization synchronization set, the flow output value of the water meter terminal is extracted, the number of zero flow segments in the continuous cycle is analyzed, and compared with the standard abnormal interval, the over-limit nodes and abnormal marks and partition switching signals are identified to obtain the water meter terminal abnormality and switching control table.

[0048] The results of traffic fluctuation segment division include traffic change rate, fluctuation interval duration, and traffic peak distribution. Communication optimization adjustment nodes include communication link quality threshold, data packet transmission success rate, inefficient node identification, and optimization trigger conditions. The energy consumption adaptation list includes equipment energy consumption intensity, data transmission efficiency, and energy consumption priority order. The link optimization synchronization set includes path signal strength data, transmission delay duration, and abnormal link mark. The water meter terminal abnormality and switching control table includes the number of zero flow cycles, over-limit abnormal nodes, abnormal mark status, and partition switching mark.

[0049] See also Figure 2 ,The specific steps for obtaining the flow fluctuation segment division results are:

[0050] S111: Based on the open source Hongmeng distributed architecture, the sensor module data of the water meter terminal is called, combined with the flow value corresponding to the time point, the time series is divided into multiple segments according to the flow change rate, and the flow fluctuation trend within each segment is analyzed to obtain the mapping value between the flow change rate and the time interval;

[0051] Based on the distributed architecture of open source Hongmeng and combined with a smart water meter project in a certain community, the data of sensor modules such as pressure sensors and flow sensors deployed on the water meter terminals were called to obtain time series water consumption data from May 1 to May 7 with a time accuracy of minutes. The time series was divided into multiple segments according to the flow change rate, and the flow fluctuation trend within each segment was analyzed. For example, 0:00 to 6:00 on a certain day was divided into a stable water consumption period, 6:00 to 8:00 was divided into a peak water consumption period, and 8:00 to 12:00 was divided into a regular water consumption period. By analyzing the fluctuations of water consumption data in different time segments, the water consumption patterns were grasped, providing data support for the subsequent optimization of water resource scheduling strategies. For example, if the flow change rate threshold is set to 0.5L / min, when the flow difference between two adjacent minutes is greater than 0.5L / min, it is considered that the flow change rate is high, and new segments need to be divided to obtain the mapping value of flow change rate and time interval.

[0052] S112: Extracting flow extreme points at adjacent time points based on the flow change rate and time interval mapping value, analyzing the flow change amplitude sequence, comparing the fluctuation amplitude with the duration reference value, filtering out the segment numbers that are out of range, and obtaining a flow fluctuation offset segment number sequence;

[0053] According to the mapping value of flow change rate and time interval, for example, by comparing the flow values ​​of three consecutive time points, if the flow value of the middle time point is greater than the flow values ​​of the two previous and next time points, then this point is the flow maximum point, otherwise, it is the flow minimum point, analyze the flow change amplitude sequence, compare the fluctuation amplitude and the duration benchmark value, and filter out the segment numbers that are out of range. Specifically, set the fluctuation amplitude benchmark value to 2L and the duration benchmark value to 10 minutes. If the flow change amplitude of a segment is greater than 2L and the duration exceeds 10 minutes, then the segment is considered to be a flow fluctuation offset segment, and it is numbered and recorded, for example, numbered A001, and the number sequence is stored in the database for subsequent analysis to obtain the flow fluctuation offset segment number sequence.

[0054] S113: Based on the flow fluctuation offset segment number sequence, extract the segment flow change data, calculate the duration difference between flow growth and decay, divide the fluctuation segment based on the flow change rate and segment frequency, and generate the flow fluctuation segment division result;

[0055] Based on the flow fluctuation offset segment numbering sequence, for example, A001, A002, and A003, the segment flow change data is extracted, the duration difference between flow growth and attenuation is calculated, the duration of flow growth is set to T1, and the duration of flow attenuation is set to T2, and the difference between T1 and T2 is calculated. The fluctuation segments are divided based on the flow change rate and segment frequency. For example, the segment with fast flow growth and slow attenuation is divided into a sudden increase in water use segment, the segment with slow growth and fast attenuation is divided into a sudden decrease in water use segment, and the segment with both fast growth and attenuation is divided into a frequent water use fluctuation segment. The flow fluctuation segment division results provide data support for the refined management of the water supply network and help to timely discover abnormal water use. When the absolute value of the duration difference between flow growth and attenuation is greater than 5 minutes, it is determined to be an obvious fluctuation segment. Segment division is performed according to this standard to provide a data basis for subsequent water use analysis and generate flow fluctuation segment division results.

[0056] See also Figure 3 ,The specific steps for obtaining the communication optimization and adjustment nodes are:

[0057] S211: Based on the flow fluctuation segment division results, the communication link quality data of the water meter terminal is extracted, the data packet transmission success rate, signal strength feedback and terminal location information within the segment are called, the communication link status changes within a unit time are identified, and a segment communication link quality sequence is generated;

[0058] Based on the flow fluctuation segmentation results, for example, for segment A001, communication data from all water meter terminals within the segment is obtained to identify changes in the communication link status within a unit time. Communication link status changes include sudden changes in signal strength and increased packet loss rates. These changes reflect the stability and reliability of the communication link. When the signal strength is lower than -90dBm or the packet loss rate is higher than 10%, the communication link quality is considered poor and requires optimization and adjustment. This generates a segment communication link quality sequence.

[0059] S212: Based on the segment communication link quality sequence, the signal strength change rate, packet loss rate, and link delay coefficient of each communication node are collected. By comparing the parameters with the preset link quality threshold, the formula is used:

[0060] ;

[0061] Calculate the change amplitude of the inefficient node and compare it with the communication benchmark fluctuation amplitude point by point to obtain the communication optimization adjustment node;

[0062] in, Represents the change amplitude value of the inefficient node, Representative The signal strength change rate of each communication node, Represents the set signal strength threshold, represents the packet loss rate of the jth communication node, represents the link delay coefficient of the jth communication node, Represents the total number of communication nodes participating in the calculation, Represents the maximum delay tolerance of the communication link;

[0063] The inefficient node change amplitude value is used to measure the degree of change in the number of inefficient nodes in the network over a period of time, or the volatility of performance indicators (such as latency and packet loss rate). Inefficient nodes are nodes that fail to meet preset performance standards and exhibit problems such as high latency, significant packet loss, or low bandwidth utilization. Network administrators set a performance threshold, and nodes exceeding this threshold are considered inefficient nodes. When obtaining the inefficient node change amplitude, it is first necessary to define what constitutes an "inefficient node." Then, network monitoring tools are used to collect data in real time to determine the number of inefficient nodes or their specific performance indicators at each point in time. Next, the change amplitude is calculated between different time periods. This can be an absolute change, a relative rate of change (percentage change), or a smoothing process based on a sliding average. A high change amplitude value indicates a large change in the number of inefficient nodes in the network, indicating network instability, performance bottlenecks, or hardware failures. Therefore, by analyzing the change amplitude, network administrators can identify network health, help locate potential problems, and evaluate the effectiveness of network optimization measures.

[0064] Set the signal strength threshold according to the segment communication link quality sequence is -85dBm, link delay coefficient The unit is ms, packet loss rate The unit is %. Set to 500ms. Assuming there are three communication nodes, the signal strength change rates are -2dBm / s, -5dBm / s, and -3dBm / s, respectively; the packet loss rates are 5%, 8%, and 6%, respectively; and the link delay coefficients are 100ms, 150ms, and 120ms, respectively. The change amplitude of the inefficient node can be calculated as follows:

[0065] ;

[0066] Assuming that the communication benchmark fluctuation amplitude is 0.1, since 0.169>0.1, the node is determined to be a communication optimization adjustment node;

[0067] The benefit of the formula is that by comprehensively considering the signal strength change rate, packet loss rate and link delay coefficient, it can comprehensively evaluate the performance of communication nodes, thereby more accurately identifying inefficient nodes and avoiding the deviation caused by single indicator judgment. The innovation of this formula is that by introducing the signal strength threshold E, it can exclude nodes with low signal strength but little change, and pay more attention to nodes with obvious signal strength drops. By introducing the square root of the link delay coefficient, the impact of delay on the final result can be reduced, because high delay is caused by network congestion, not a problem with the node itself. In the calculation of V, the signal strength is calculated first. The absolute value of the difference between the change rate and the threshold reflects the degree of signal strength degradation. The packet loss rate is divided by the square root of the link delay coefficient to weight the packet loss rate and reduce the impact of delay. The weighted packet loss rate and signal strength difference of all nodes are summed up and divided by the maximum delay tolerance plus 1 to obtain the change amplitude value of the inefficient node. The higher this value, the worse the node performance and the more optimization adjustment is needed. As shown in Table 1, the parameter values ​​of different communication nodes and the calculated change amplitude values ​​of inefficient nodes are displayed. Nodes can be sorted according to this value, and nodes with higher change amplitude values ​​can be optimized first.

[0068] Table 1: Communication node parameters and inefficient node change range values

[0069] ;

[0070] As shown in Table 1, the inefficient node change amplitude value of node 1 is the highest, indicating that this node is most in need of optimization adjustment, for example, adjusting its antenna direction, replacing the signal amplifier, and other measures.

[0071] See also Figure 4 , the specific steps for obtaining the energy consumption adaptation list are:

[0072] S311: Based on the communication optimization and adjustment nodes, the operating time period data and the remaining power value of the water meter terminal are extracted, the relationship between the device energy consumption and the data transmission volume per unit time is identified, the matching degree between the energy consumption and the transmission volume in the continuous segment is analyzed, and the transmission matching interval per unit energy consumption is obtained;

[0073] Adjust nodes based on communication optimization. For example, for node 1, obtain its energy consumption and data transmission volume in different time periods, and analyze the matching degree of energy consumption and transmission volume in continuous segments. For example, if a device has high energy consumption but low data transmission volume during peak water use, it is considered that the energy consumption and transmission volume matching degree of the device is low and needs to be optimized and adjusted. If the energy consumption of a device is still high during idle periods, there is abnormal power consumption and further inspection is required. In actual applications, the unit energy consumption transmission matching interval can be divided into several levels, such as excellent, good, qualified, and unqualified. Different energy consumption management strategies are adopted according to the matching interval level of the device to obtain the unit energy consumption transmission matching interval.

[0074] S312: Call the unit energy consumption transmission matching interval, and use the matching interval value of the differentiated device in the self-operation period to use the formula:

[0075] ;

[0076] Calculate the energy consumption deviation index value, sort the index values, and generate an energy consumption adaptation list;

[0077] in, Represents the energy consumption deviation index value, Represents the actual energy consumption of the i-th device during the self-operation period, represents the target energy consumption of the i-th device during the self-operation period, represents the energy efficiency coefficient of the i-th device, Represents the weight of the i-th device in the calculation, Represents the total number of devices involved in the calculation;

[0078] The energy consumption deviation index (EOD) refers to the difference between the actual and expected energy consumption of network devices. A high EOD indicates poor energy efficiency or abnormal operation. To obtain the index, an energy consumption model must be established based on the device's technical specifications, historical energy consumption data, ambient temperature, and other factors to predict the device's expected energy consumption under different workloads. Then, power monitoring devices (such as smart sockets and power meters) are used to monitor the device's actual energy consumption in real time. EOD can be calculated by comparing the difference between actual and expected energy consumption. There are both absolute deviation (actual energy consumption minus expected energy consumption) and relative deviation (the ratio of deviation to expected energy consumption). EOD can be statistically analyzed over a period of time to calculate the average, maximum, and standard deviation of the deviations, which serve as the EOD index. Excessive EOD on certain devices indicates a malfunction or inefficient operation. Energy-saving measures should be implemented by adjusting the device's operating mode, optimizing traffic scheduling, and replacing outdated equipment. This can reduce energy consumption, lower operating costs, and improve the overall green performance of the network.

[0079] Call the unit energy consumption transmission matching interval and set the energy efficiency coefficient The value range is 0.5 to 1.5, and the weight The value range is 0.1 to 1. Assuming there are 5 devices, the actual energy consumption is 10Wh, 12Wh, 8Wh, 11Wh, and 9Wh respectively, the target energy consumption is 8Wh, 10Wh, 7Wh, 9Wh, and 8Wh respectively, the energy efficiency coefficient is 1.0, 1.2, 0.8, 0.9, and 1.1 respectively, and the weight is 0.2, 0.3, 0.25, 0.15, and 0.1 respectively. The energy consumption deviation index value can be calculated as follows:

[0080] ;

[0081] According to the calculation results, the energy consumption deviation index value is obtained, and then the index values ​​are sorted to generate an energy consumption adaptation list, such as device 3, device 5, device 1, device 4, and device 2. The sorting results can be used to guide the energy consumption optimization strategy, giving priority to optimizing devices with higher energy consumption deviation index values. For example, the operating time of the device can be adjusted, the data transmission strategy can be optimized, and other measures can be taken. The result shows that the higher the energy consumption deviation index value, the greater the deviation between the energy consumption of the device and the target energy consumption, and the more optimization adjustment is needed.

[0082] See also Figure 5 The specific steps for obtaining the link optimization synchronization set are as follows:

[0083] S411: Based on the energy consumption adaptation list, call the transmission path data between the water meter terminal and the gateway node, record the signal strength of the starting node, transit node, and terminal node of each path, identify the signal strength change and transmission delay of each path, and generate a path node set;

[0084] Based on the energy consumption adaptation list, for the devices ranked high in the energy consumption adaptation list, their data transmission paths are analyzed, and the signal strength and transmission delay of each node in the path are monitored. For example, if the signal strength of a path continues to decline and the transmission delay gradually increases, it is considered that the communication quality of the path is poor and the data transmission strategy needs to be adjusted. The path node set contains the coordinate information, signal strength, transmission delay and other data of all nodes in the path, which can be used to evaluate the communication quality of the path. In practical applications, a path quality assessment model can be established based on the data in the path node set to conduct a comprehensive evaluation of the path and generate a path node set.

[0085] S412: For the path node set, identify the path signal strength and delay time using the formula:

[0086] ;

[0087] Calculate the link fluctuation index, compare it with the preset link fluctuation threshold, filter out paths with signal strength below the threshold or with sudden delay changes, and generate abnormal link identification;

[0088] in, represents the link fluctuation index, Represents the difference between the maximum and minimum signal strengths detected by path k in multiple time windows, represents the average value of the signal strength change in the time window of path k, Represents the number of time periods when path k experiences a sudden change in signal strength within the detection period. represents the sum of the total delay duration of path k during the detection period, represents the number of delay measurements recorded for path k during the detection period;

[0089] The link fluctuation index measures the degree of change in the quality of a network link over a period of time. It evaluates the stability of the link through multiple quality indicators such as latency, packet loss rate, bandwidth utilization, and jitter. The greater the link fluctuation, the more unstable the network connection, which will affect the reliability and real-time performance of data transmission. In order to calculate the link fluctuation index, you first need to select appropriate link quality indicators and monitor the link quality in real time through network monitoring tools (such as Ping, Traceroute, SNMP, etc.). The link fluctuation index is obtained by calculating the standard deviation, coefficient of variation, sliding window range, or weighted average fluctuation of the quality indicator over a period of time. A high fluctuation index indicates that the quality of the network link is unstable, which is caused by reasons such as excessive link load, equipment failure, and external interference. Through the link fluctuation index, network administrators can evaluate the stability of the current network, identify unstable links, and optimize them, such as selecting more stable routes, predicting link failures, or adjusting link configurations, thereby ensuring the efficient operation of network service quality.

[0090] For the path node set, the link fluctuation threshold is set to 10. Assuming that the maximum signal strength detected by a path in multiple time windows is -70dBm and the minimum is -80dBm, then , assuming that the average value of the signal strength change in the time window of the path is -2dBm / s, then , assuming that the number of time periods during which the path experiences a sudden change in signal strength within the detection period is 2, then , assuming that the total delay of the path in the detection period is 500ms and the number of delay measurements is 100, then , , the link fluctuation index can be calculated as follows:

[0091] ;

[0092] Since 40.3>10, the path is determined to be an abnormal link and an abnormal link identifier is generated;

[0093] The benefit of the formula is that by comprehensively considering the signal strength change, signal strength mutation and delay time, it can comprehensively evaluate the stability of the link, thereby more accurately identifying abnormal links and avoiding the deviation caused by the judgment of a single indicator. The innovation of the formula is that by introducing the difference between the maximum and minimum signal strength values, it reflects the fluctuation range of the signal strength. By introducing the average value of the signal strength change, it reflects the trend of the signal strength change. By introducing the number of time periods with signal strength mutation, it reflects the frequency of signal strength change. By introducing the ratio of the sum of delay time and the number of delay measurements, it reflects the average delay of the link. In the calculation of Δθ, the signal strength difference and The square of the sum of the average signal strength changes, divided by the square root of the number of time periods with sudden signal strength changes, reflects the impact of signal strength fluctuations on the link. The sum of the delay durations, divided by the number of delay measurements plus 1, reflects the average delay of the link. Finally, the impact of signal strength fluctuations minus the average delay of the link is used to obtain the link fluctuation index. A higher value indicates greater link fluctuations and a greater likelihood of anomalies. Table 2 shows the parameter values ​​of different paths and the calculated link fluctuation index. Paths can be sorted based on this value, with paths with higher fluctuation indices being prioritized. This result indicates that the higher the link fluctuation index, the worse the communication quality of the path, and the greater the need for optimization and adjustment.

[0094] Table 2: Path parameters and link fluctuation index

[0095] ;

[0096] As shown in Table 2, path 1 has the highest link fluctuation index, indicating that this path is most prone to anomalies and needs to be optimized first. For example, measures such as adjusting node positions and adding signal amplifiers can be used.

[0097] S413: Extract the node coordinates and signal strength data of the abnormal link based on the abnormal link identifier, update the filtered path data to the path configuration table in timestamp order, analyze the signal correlation between the nodes, and obtain the link optimization synchronization set;

[0098] According to the abnormal link identification, the node coordinates and signal strength data of the abnormal link are extracted from the database. The data can be used to analyze the link topology and signal coverage. The filtered path data is updated to the path configuration table in timestamp order to maintain the real-time and accuracy of the path information. By analyzing the signal correlation between nodes, areas with weak signal strength can be found and targeted optimization can be performed, such as adding relay nodes and adjusting node positions. The link optimization synchronization set contains all link information that needs to be optimized, which can be used to guide link optimization strategies and can be optimized in a targeted manner, such as adding relay nodes and adjusting node positions. This process can be completed through automated scripts or manual intervention to obtain a link optimization synchronization set.

[0099] See also Figure 6 The specific steps for obtaining the water meter terminal abnormality and switching control table are as follows:

[0100] S511: Based on the link optimization synchronization set, the flow output value of the water meter terminal is extracted, and the time period is divided into continuous periods. It is determined whether the node flow value in each period is zero. The number of time periods in which the node has a zero value in the period is counted to obtain the number of zero flow periods of the node period.

[0101] Based on the link optimization synchronization set, for the water meter terminals in the link optimization synchronization set, their flow output values ​​are extracted, and the time periods are divided into continuous periods such as hours and days. It is determined whether the flow value of the node in each time period is zero, and the number of time periods in which the node has a zero value in the period is counted. The flow output value of the water meter terminal is set. If a node has a flow value of zero for multiple hours in a day, it is considered that the node is abnormal and needs further inspection. Counting the number of zero flow segments in the node period can reflect the water use of the node and provide data support for anomaly detection. Assuming that a day is divided into 24 time periods, if a node has a flow value of zero in more than 12 time periods, it is considered that the node is abnormal, and the number of zero flow segments in the node period is obtained.

[0102] S512: Call the number of zero flow segments in the node cycle, determine whether the node exceeds the normal range based on the difference between the number of zero flow segments of the node and the set standard abnormal interval, bind the index of the exceeding node to the corresponding cycle number, filter out the abnormal nodes, record the cycle performance of the exceeding node, and obtain the exceeding node location value;

[0103] The number of zero flow segments in the node cycle is called, and the standard abnormal interval is set to [0,2]. If the number of zero flow segments of a node is less than 0 or greater than 2, the node is considered to be out of the normal range. Different standard abnormal intervals are set for different types of water meters. For example, for residential water meters, the standard abnormal interval can be set to [0,2], and for industrial water meters, the standard abnormal interval can be set to [0,5]. Binding the index of the exceeded node with the corresponding cycle number can easily track the historical performance of the abnormal node and analyze it. Recording the cycle performance of the exceeded node, for example, recording the number of zero flow segments and flow fluctuations of the exceeded node, helps to more accurately determine the type and cause of the node abnormality. Nodes that are out of the normal range for three consecutive cycles are set as abnormal nodes. The result shows that the exceeded node positioning value can be used to locate the abnormal node, providing a basis for subsequent maintenance and management.

[0104] S513: Based on the location value of the exceeded node, the partition switching threshold is called, the number of consecutive abnormal cycles is compared with the threshold, the partition status of the node that meets the switching condition is marked, the current flow value of the node and the switching status parameter are summarized, and the water meter terminal abnormality and switching control table is obtained;

[0105] Based on the over-limit node location value, we can determine which nodes are abnormal. The partition switching threshold is called to determine whether the abnormal node needs to be switched to the backup partition. For example, if a node exceeds the normal range for three consecutive cycles, it is considered that the node needs to be switched to the backup partition. The number of consecutive abnormal cycles is compared with the threshold to determine whether the node meets the switching conditions and mark the partition status of the node that meets the switching conditions. If a node meets the switching conditions, its partition status is marked as "switching in progress". If the switch is successful, its partition status is marked as "switched". The current node flow value and the switching status parameters are summarized to obtain the water meter terminal abnormality and switching control table. The switching status parameters include switching time, switching reason, switching result, etc. For example, the water meter terminal abnormality and switching control table can be used to monitor the status of water meter terminals and promptly detect and handle abnormal situations. The partition switching threshold can be adjusted, and the partition switching strategy can be adjusted according to actual conditions. For example, for important water meter terminals, a lower switching threshold can be set to ensure timely switching. This result shows that the water meter terminal abnormality and switching control table can be used to achieve automatic switching of water meter terminals, improving the reliability and stability of the water meter system.

[0106] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A water meter data collection method based on open source Hongmeng, characterized in that: The following steps are involved: S1: Based on the open source Hongmeng distributed architecture, it calls the sensor module and communication module data of the water meter terminal, analyzes the flow change trend within a unit time, and generates the flow fluctuation segment division result; The steps for obtaining the flow fluctuation segment division result are specifically as follows: S111: Based on the open source Hongmeng distributed architecture, the sensor module data of the water meter terminal is called, combined with the flow value corresponding to the time point, the time series is divided into multiple segments according to the flow change rate, and the flow fluctuation trend within each segment is analyzed to obtain the mapping value between the flow change rate and the time interval; S112: Extracting flow extreme value points at adjacent time points based on the flow change rate and time interval mapping value, analyzing the flow change amplitude sequence, comparing the fluctuation amplitude with the duration reference value, filtering out the segment numbers that are out of range, and obtaining a flow fluctuation offset segment number sequence; S113: Based on the flow fluctuation offset segment number sequence, extracting segment flow change data, calculating the difference in duration between flow growth and decay, dividing the fluctuation segments based on the flow change rate and segment frequency, and generating a flow fluctuation segment division result; S2: Based on the flow fluctuation segment division results, extract the energy consumption status and communication link quality of the water meter terminal, detect the data packet transmission success rate in the segment, mark inefficient communication nodes, and generate communication optimization adjustment nodes; The steps for obtaining the communication optimization adjustment node are specifically as follows: S211: Extracting communication link quality data of the water meter terminal based on the flow fluctuation segment division result, calling the data packet transmission success rate, signal strength feedback and terminal location information within the segment, identifying the communication link status change within a unit time, and generating a segment communication link quality sequence; S212: Based on the segment communication link quality sequence, the signal strength change rate, packet loss rate, and link delay coefficient of each communication node are collected. By comparing the relationship between the parameters and the preset link quality threshold, the change amplitude value of the inefficient node is calculated, and the value is compared point by point with the communication benchmark fluctuation amplitude to determine the communication optimization adjustment node; S3: According to the communication optimization and adjustment node, the operating time period data and the remaining power value of the water meter terminal are extracted, the matching relationship between the device energy consumption and the data transmission volume per unit time is identified, and the priority is sorted to generate an energy consumption adaptation list; S4: Based on the energy consumption adaptation list, call the transmission path data of the water meter terminal and the gateway node, record the signal strength and transmission delay of each path, filter abnormal links and synchronously update the path configuration to obtain the link optimization synchronization set.

2. The water meter data collection method based on open source Hongmeng according to claim 1 is characterized in that: The traffic fluctuation segment division results include the traffic change rate, fluctuation interval duration, and traffic peak distribution; the communication optimization adjustment node includes the communication link quality threshold, data packet transmission success rate, inefficient node identification, and optimization trigger conditions; the energy consumption adaptation list includes device energy consumption intensity, data transmission efficiency, and energy consumption priority order; the link optimization synchronization set includes path signal strength data, transmission delay duration, and abnormal link mark.

3. The water meter data collection method based on open source Hongmeng according to claim 1 is characterized in that: The steps for obtaining the energy consumption adaptation list are specifically as follows: S311: Extracting the operating time period data and remaining power value of the water meter terminal according to the communication optimization and adjustment node, identifying the relationship between the device energy consumption and the data transmission volume per unit time, analyzing the matching degree between the energy consumption and the transmission volume in the continuous segment, and obtaining the unit energy consumption transmission matching interval; S312: calling the unit energy consumption transmission matching interval, calculating the energy consumption deviation index value by differentiating the matching interval value of the device in the self-operation period, and sorting the index values ​​to generate an energy consumption adaptation list.

4. The water meter data collection method based on open source Hongmeng according to claim 3 is characterized in that: The steps for obtaining the link optimization synchronization set are specifically as follows: S411: Based on the energy consumption adaptation list, call the transmission path data between the water meter terminal and the gateway node, record the signal strength of the starting node, transit node and terminal node of each path, identify the signal strength change and transmission delay of each path, and generate a path node set; S412: For the path node set, identify the path signal strength and delay duration, calculate the link fluctuation index, compare it with a preset link fluctuation threshold, screen out paths with signal strength below the threshold or with a sudden change in delay, and generate an abnormal link identifier; S413: Extract the node coordinates and signal strength data of the abnormal link according to the abnormal link identifier, update the filtered path data to the path configuration table in timestamp order, analyze the signal correlation between nodes, and obtain the link optimization synchronization set.

5. The water meter data collection method based on open source Hongmeng according to claim 1 is characterized in that: The method further comprises step S5: S5: Based on the link optimization synchronization set, extract the flow output value of the water meter terminal, analyze the number of zero flow segments in the continuous cycle, compare it with the standard abnormal interval, identify the over-limit node and abnormal mark and partition switching signal, and obtain the water meter terminal abnormality and switching control table; The water meter terminal abnormality and switching control table includes the number of zero flow cycles, over-limit abnormal nodes, abnormal marking status, and partition switching identification.

6. The water meter data collection method based on open source Hongmeng according to claim 5 is characterized in that: The steps for obtaining the water meter terminal abnormality and switching control table are specifically as follows: S511: Based on the link optimization synchronization set, extract the flow output value of the water meter terminal, divide the time period into consecutive periods, determine whether the node flow value in each period is zero, count the number of time periods in which the node has zero value in the period, and obtain the number of zero flow segments of the node period; S512: Call the number of zero flow segments in the node cycle, determine whether the node exceeds the normal range based on the difference between the number of zero flow segments of the node and the set standard abnormal interval, bind the index of the exceeding node to the corresponding cycle number, filter the abnormal nodes, record the cycle performance of the exceeding node, and obtain the exceeding node location value; S513: Based on the over-limit node positioning value, call the partition switching threshold, compare the number of consecutive abnormal cycles with the threshold, mark the partition status of the node that meets the switching conditions, summarize the current flow value of the node and the switching status parameters, and obtain the water meter terminal abnormality and switching control table.

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