A multi-channel switching method for smart water meter communication

Abstract

The present application relates to the field of digital information transmission technology, and in particular to a multi-channel switching method for smart water meter communication, the method comprising: obtaining round-trip time observation data of a water meter node during communication; if the deviation accumulation rate corresponding to the round-trip time observation data is greater than a preset communication anomaly threshold, requesting and obtaining a historical deviation accumulation rate sequence returned by multiple neighboring nodes; constructing a channel switching probability of the water meter node based on the consistency and trend strength of the change trends of the historical deviation accumulation rate sequences corresponding to the water meter node and multiple neighboring nodes; if the channel switching probability is greater than the preset switching threshold, performing channel switching on the water meter node. Through the solution of the present application, the current network status and the collaborative perception results of neighboring nodes can be utilized to dynamically evaluate regional congestion conditions, and some affected nodes can be intelligently selected to switch to alternative communication channels, effectively ensuring the real-time and continuity of key meter reading data and the transmission reliability of the overall communication network.

Description

Technical Field

[0001] The present application relates to the field of digital information transmission technology, and in particular to a multi-channel switching method for smart water meter communication. Background Art

[0002] In high-density residential areas, with the advancement of smart water management, smart water meters are being deployed on a large scale to enable remote reading and centralized management of water usage data. LoRa (Long Range) technology, due to its low power consumption, wide coverage, and ease of deployment, is widely used in the wireless communications of smart water meters. LoRa networks utilize an ALOHA-like random access mechanism and lack a strict collision avoidance strategy. This can easily lead to regional communication congestion, especially in high-density deployment scenarios, when a large number of nodes report data or perform periodic meter readings at similar times.

[0003] Regional congestion caused by LoRa technology will result in increased data reporting delays, increased packet loss rates, and energy waste due to frequent retries. Existing technologies mainly use communication quality optimization strategies that focus on a single node, such as increasing the number of data retransmissions, dynamically adjusting the transmission power or spreading factor, etc. to restore communication. These methods have the following shortcomings: single-node optimization cannot identify the spatial clustering characteristics of communication anomalies, and it is difficult to promptly determine whether systemic congestion has occurred in a local area. At the same time, such methods rely on frequent retransmissions and lack dynamic adaptive adjustment and rapid self-healing capabilities, making it difficult to achieve a rapid response and effective solution to regional congestion. Therefore, existing technologies lack effective identification and response mechanisms when faced with regional communication anomalies, and cannot achieve accurate diversion of some key nodes, limiting the scalability and operational stability of the network in high-density deployment scenarios. Summary of the Invention

[0004] In order to ensure the stable transmission of remote meter reading data and the overall performance of the network, the present application provides a multi-channel switching method for smart water meter communication.

[0005] In a first aspect, the present application provides a multi-channel switching method for smart water meter communication, which adopts the following technical solution:

[0006] A multi-channel switching method for smart water meter communication includes the following steps:

[0007] Obtain the round-trip time observation data of the water meter node during the communication process;

[0008] If the deviation accumulation rate corresponding to the round-trip time observation data is greater than a preset communication anomaly threshold, requesting and obtaining a historical deviation accumulation rate sequence returned by multiple neighboring nodes;

[0009] Constructing a channel switching probability of the water meter node based on the consistency and trend strength of the change trends of the historical deviation accumulation rate sequences corresponding to the water meter node and the plurality of neighboring nodes;

[0010] If the channel switching probability is greater than a preset switching threshold, performing channel switching on the water meter node;

[0011] Among them, constructing the channel switching probability of the water meter node includes: performing Pearson correlation calculation on the historical deviation accumulation rate sequence of the water meter node and the multiple neighboring nodes to obtain n Pearson correlation coefficients, and calculating the average correlation coefficient of the n Pearson correlation coefficients, performing linear fitting on the historical deviation accumulation rate sequence of each neighboring node and calculating the trend slope, and calculating the average trend strength of the multiple neighboring nodes based on the trend slope, and the channel switching probability of the water meter node is equal to the product of the average correlation coefficient and the average trend strength.

[0012] Optionally, calculating the deviation accumulation rate corresponding to the round-trip time observation data includes:

[0013] Obtain the communication round trip time when the water meter node sends data for the i-th time in the round trip time observation data;

[0014] Select the communication round-trip time values ​​of the water meter node M consecutive successful communications in history, and calculate the average of the communication round-trip time values ​​as the benchmark communication round-trip time;

[0015] The corresponding deviation accumulation rate is calculated by continuously monitoring the relative deviation between the communication round-trip time of the water meter node and the reference communication round-trip time.

[0016] Optionally, the calculating the corresponding deviation accumulation rate by continuously monitoring the relative deviation between the communication round-trip time of the water meter node and the reference communication round-trip time includes:

[0017] The first N successful communication round-trip time values ​​of the water meter node when sending data for the i-th time are obtained as a sliding observation window, and the cumulative deviation rate between the communication round-trip time corresponding to the sliding observation window and the benchmark communication round-trip time is calculated, where the window length is N.

[0018] Optionally, obtaining round-trip time observation data of a water meter node during communication includes:

[0019] Each water meter node records its own sending time and the time when the gateway returns the Ack message confirming receipt during communication;

[0020] Calculate the round trip time observation data of the water meter node a when sending data for the i-th time .

[0021] Optionally, requesting and obtaining a historical deviation accumulation rate sequence returned by multiple neighbor nodes includes:

[0022] According to the deviation accumulation rate of the current water meter node during the communication process, the communication quality abnormality judgment threshold is set ;

[0023] If the deviation accumulation rate of the current water meter node when sending data satisfies , it is determined that the communication of the current water meter node is in an abnormal state;

[0024] Based on the abnormal state, a broadcast request is initiated, and a collection request is sent to surrounding neighboring nodes to obtain a historical deviation accumulation rate sequence of the neighboring nodes.

[0025] Optionally, the slope of the linear fitting of the historical deviation accumulation rate sequence is the degree of change of the corresponding values ​​of the historical deviation accumulation rate sequence, and the degree of change of the corresponding values ​​of the historical deviation accumulation rate sequence reflects the degree of deterioration of the communication quality of each neighbor node.

[0026] Optionally, after calculating the average trend strength of the plurality of neighboring nodes, the method further includes:

[0027] The average trend strength is normalized to the range of (0, 1) by the inverse tangent normalization method to obtain the normalized average trend strength.

[0028] Optionally, if the channel switching probability is greater than a preset switching threshold, switching the channel of the water meter node includes:

[0029] When the channel switching probability is greater than a preset switching threshold, the communication mode of the water meter node is switched from LoRa to NB-IoT.

[0030] This application has the following technical effects: it can utilize the current network status and the collaborative perception results of neighboring nodes to dynamically evaluate regional congestion, and intelligently select some affected nodes to switch to alternative NB-IoT communication channels, effectively ensuring the real-time and continuity of key meter reading data and the transmission reliability of the overall communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of steps S1-S4 in a multi-channel switching method for smart water meter communication in the present application. DETAILED DESCRIPTION

[0032] The present application embodiment discloses a multi-channel switching method for smart water meter communication, referring to Figure 1 , including the following steps:

[0033] S1: Obtain the round-trip time observation data of the water meter node during the communication process.

[0034] It should be noted that this application specifically targets a remote meter reading system for smart water meters in high-density residential areas. In such environments, the widespread deployment of smart water meters and the need to upload large amounts of data in real time can lead to communication network congestion. In particular, when LoRa technology is used, the frequent data reporting by multiple smart water meter nodes can easily lead to localized network resource competition, resulting in communication bottlenecks, data loss, and transmission delays.

[0035] Therefore, in the wireless communication of smart water meters, the node communication quality is affected by factors such as channel interference and load changes, and the round-trip time observation data (round-trip time ) is a real-time, continuous communication status feedback signal, which will show an upward trend when communication is congested and communication quality is degraded. Fluctuations are easily affected by transient interference (such as temporary obstruction, environmental noise, and electromagnetic interference). Directly using a single value to judge communication anomalies is prone to false alarms. To this end, we introduce an observation window for continuous observation and calculate the actual communication times for multiple consecutive times. and benchmarks The relative deviation between the two nodes is calculated, and the average degree of increase in multiple consecutive communication times is defined as the deviation accumulation rate to determine whether the node is in an abnormal situation.

[0036] S2: If the deviation accumulation rate corresponding to the round-trip time observation data is greater than the preset communication anomaly threshold, request and obtain the historical deviation accumulation rate sequence returned by multiple neighboring nodes.

[0037] In one embodiment of the present application, the round trip time of a single water meter node The analysis process is as follows:

[0038] 1. Get the round trip time of each node communication ;

[0039] Each node records its own sending time and the time when the gateway returns the Ack message to confirm receipt during communication, and calculates the round-trip time of communication when node a sends data for the i-th time .

[0040] 2. By calculating the node history M times The mean is set as the benchmark ; 3. Through each communication before N times The value is used to calculate the cumulative rate of deviation;

[0041] Different nodes have different installation environments, which may cause problems in their normal communication. For each node, in the early stage of node deployment or when the communication status is good, select multiple communication The average value is used as a benchmark, which can more stably and objectively reflect the communication level of the node in a non-abnormal state, effectively reducing the single The risk of misjudgment caused by deviation. Select M consecutive successful communications Value, average as the benchmark , recorded as , M takes the experience value of 5.

[0042] 3. Through each communication before N times Calculate the cumulative rate of deviation ;

[0043] in, ,In the actual communication of remote meter reading of smart water meters, the round-trip time of the node ( ) will fluctuate due to the transmission conditions in the communication link. To judge whether the communication is abnormal, it is very susceptible to instantaneous interference and environmental noise, which may lead to false alarms. Therefore, an abnormal indicator based on the observation window is introduced. , in order to more stably reflect the changing trend of node communication quality. By continuously monitoring the actual communication quality of nodes and benchmarks The relative deviation between them is calculated by using the recent N communication data as the sliding window observation data, with the window length being N, and N taking the empirical value of 10. The average degree of increase in the communication time for multiple consecutive times is calculated and defined as the deviation accumulation rate, which measures whether the node's recent communication time has increased and reflects whether the communication quality of the link is abnormal. The first N successful communications when sending data for the i-th time Value as a sliding observation window , and calculate the sliding observation window The cumulative rate of deviation It serves as an indicator of whether the communication quality of the node is abnormal.

[0044]

[0045] Composition: max(0, single communication with the benchmark The difference between the two values ​​is the relative growth of the baseline value), max(0,) means to focus on the increase of communication round-trip time and ignore the short-term reduce.

[0046] Furthermore, when the communication quality of a node deteriorates and causes communication abnormality (the deviation accumulation rate exceeds the threshold), if the deviation accumulation rate of a single communication is directly used Direct channel switching can cause multiple neighboring nodes to switch channels simultaneously in a local area, leading to a sudden drop in channel utilization and impacting the stability of the transmission system. To mitigate the instability caused by multiple neighboring nodes switching simultaneously in a local area, this step, when a node communication anomaly is detected, broadcasts the historical deviation accumulation rate sequence of neighboring nodes to determine whether the anomaly is regionally consistent, allowing for the adoption of a more appropriate switching strategy.

[0047] In one embodiment of the present application, the specific implementation is as follows:

[0048] 1. By setting the communication abnormality threshold (Preset communication abnormality threshold) Determine whether a node has communication abnormality.

[0049] 2. When a communication anomaly occurs, a broadcast request is initiated to request multiple neighboring nodes to return the historical deviation accumulation rate sequence R.

[0050] The specific implementation is as follows:

[0051] (1) By setting the communication abnormality threshold Determine whether there is communication abnormality in the node;

[0052] According to the current node Cumulative deviation rate during communication , set the judgment threshold for abnormal communication quality , when sending data satisfy , it is determined that the communication of the current node is abnormal.

[0053] (2) When a communication anomaly occurs, a broadcast request is initiated to request multiple neighboring nodes to return a historical deviation accumulation rate sequence. ;

[0054] When a communication anomaly is detected, a broadcast request is initiated to send a collection request to the surrounding neighbor nodes to obtain the communication quality fluctuation data of the neighbor nodes, that is, the R value sequence of the neighbor nodes. The number of neighbor nodes n and the historical deviation accumulation rate sequence of the previous T communications of n neighbor nodes b are obtained. . T takes experience value 10.

[0055]

[0056] Neighbor nodes are other intelligent terminal nodes within the broadcast coverage area, with the same LoRa parameter configuration, that can directly receive and respond to the current node's broadcast signal. After broadcasting the response, the current node can collect the cumulative rate sequence of communication time deviations from multiple neighboring nodes (i.e., the R value sequence for each neighbor), which will be used in subsequent steps to determine whether the current communication anomaly has regional characteristics.

[0057] S3: Construct the channel switching probability of the water meter node based on the consistency and trend strength of the change trend of the historical deviation accumulation rate series corresponding to the water meter node and multiple neighboring nodes.

[0058] It should be noted that after obtaining the historical deviation accumulation rate sequence of the neighboring nodes, in order to prevent the entire local area from switching at the same time, which will cause the utilization rate of a single channel to drop sharply and affect the stability of use, the current node needs to determine whether there is a trend consistency in the deviation accumulation rate sequence of its own node and the neighboring nodes. If the communication change trend of multiple neighboring nodes is consistent with that of the current node, it can be determined that the abnormality of the current node is "regional". Prioritize switching the areas with consistent communication changes, so that when switching, the areas with consistent deviation accumulation rate sequence changes can be switched first to relieve the communication pressure of the channel and avoid causing the value of the deviation accumulation rate. If the magnitudes are consistent but the trends differ, subsequent channel switching will still fail to alleviate regional channel pressure, rendering the switch ineffective. Low-intensity changes can also lead to consistent trends. While considering consistency, the strength of the trend must also be considered. The slope of the straight line fitted to the deviation accumulation rate series can be used to determine the mean of the change, serving as an indicator of the intensity of the change. Furthermore, it is necessary to analyze the consistency and strength of the trend of the deviation accumulation rate series between neighboring nodes and the node itself to determine the switching probability for that node.

[0059] In one embodiment of the present application, the process of constructing the channel switching probability of the water meter node itself is as follows:

[0060] 1. Obtain the historical deviation accumulation rate sequence of the previous T communications of its own node a . T takes experience value 10.

[0061]

[0062] 2. Calculate the similarity of communication quality changes between the node itself and multiple neighboring nodes.

[0063] After obtaining the historical deviation accumulation rate series for the previous T communications between the node and multiple neighboring nodes, consider their correlation, that is, the consistency of their changing trends. A high correlation indicates that the changes in neighboring nodes are similar to those of the node itself, and both have experienced communication anomalies. This may be because the node and neighboring nodes use the same channel for transmission, which may be congested. Multiple nodes cannot receive ACK confirmation information for successful transmission, and repeatedly retransmit, causing channel congestion. A low correlation indicates that the problem is probably localized due to abnormal interference received by the current node. Switching at the node level can ensure transmission efficiency and stability.

[0064] The implementation steps are as follows:

[0065] 1) Perform Pearson correlation calculation on the historical deviation accumulation rate sequence between the node a and its n neighboring nodes b to obtain the values ​​of n Pearson correlation coefficients , and then normalize it using the normalization method to get ;

[0066]

[0067] In order to determine whether the communication changes between two nodes are consistent, if the correlation is high (close to 1), it means that the changes in the communication quality of the two nodes are similar, and they may both be affected by channel congestion; if the correlation is low (close to 0), it means that there is no common factor affecting them at present, and their changes may be affected by their own environment.

[0068] 2) Calculate the average correlation coefficient of all n neighbor nodes ;

[0069] Calculate the average correlation coefficient of node a's n neighboring nodes, and consider the correlation between all neighboring nodes b and node a. If the average correlation is high, it means that most of the neighboring nodes have poor communication quality, which may be related to the fact that they all use the same channel for transmission, resulting in channel congestion.

[0070] 3. Calculate the trend strength of the communication quality changes of multiple neighboring nodes b.

[0071] Detailed logic: Since correlation analysis only considers the degree of correlation between sequences, even low-intensity changes can lead to higher correlations between the node itself and multiple neighboring nodes. Therefore, we cannot simply consider the degree of correlation; we must also consider the intensity of the change in communication quality. By using the least squares method to linearly fit the cumulative deviation rate series of multiple neighboring nodes, we obtain the trend slope to quantify the intensity of the change. Since the R value calculation only considers deterioration, the slope reflects the degree of deterioration in communication quality. If the average slope of all neighboring nodes is high, it indicates that the deterioration in communication quality of all neighboring nodes is high, and one or more nodes need to switch channels to ensure real-time and stable transmission.

[0072] The implementation steps are as follows:

[0073] 1) Use the least squares method to perform linear fitting on the historical deviation accumulation rate sequence R of each neighbor node b to calculate the trend slope k.

[0074] The slope of the linear fitting of the historical deviation accumulation rate series is the degree of change of the R value, which reflects the degree of deterioration of the communication quality of each neighbor node b.

[0075] 2) Calculate the average trend strength of all n neighbor nodes .

[0076] Calculate the average correlation coefficient of the n neighboring nodes of node a, and consider the average of the communication quality deterioration trend strength of all neighboring nodes b. If the average trend strength is high, it means that the communication quality deterioration strength of most neighboring nodes is very high, which may be due to high channel congestion, resulting in high communication quality deterioration of multiple nodes.

[0077] 3) The average trend strength is normalized by the inverse tangent method Normalized to the range of (0,1) to obtain the normalized average trend strength .

[0078] Use the inverse tangent function Average Trend Strength Convert to angles. Normalize the angles to the range (0, 1) based on the range of the values. This not only preserves the relative magnitude of the slopes, but also makes the results more intuitive and easier to compare.

[0079] 4. Construct the switching probability S of the current node.

[0080] When considering the switching probability of a single node a, the consistency and intensity of the communication quality change trend must be comprehensively considered. Construct a switching probability index to evaluate the switching probability S of the current node. The switching probability S of the current node a is constructed as follows:

[0081]

[0082] S = correlation coefficient with the neighbor R value series * mean trend slope of the neighbor R value.

[0083] S4: If the channel switching probability is greater than the preset switching threshold, the channel of the water meter node is switched.

[0084] In one embodiment of the present application, a preset switching threshold is used to determine whether the current node needs to switch channels. When the node's channel switching probability exceeds the preset switching threshold, the node switches its communication mode from LoRa to NB-IoT to alleviate the load on the LoRa network and ensure the reliability of communication at key nodes. The threshold is set to 0.5 based on empirical values.

[0085] In summary, by implementing the solution of this application, it is possible to utilize the current network status and the collaborative perception results of neighboring nodes to dynamically evaluate regional congestion, and intelligently select some affected nodes to switch to alternative NB-IoT communication channels, effectively ensuring the real-time and continuity of key meter reading data and the transmission reliability of the overall communication network.

[0086] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A multi-channel switching method for smart water meter communication, characterized in that: The following steps are involved: Obtain the round-trip time observation data of the water meter node during the communication process; If the deviation accumulation rate corresponding to the round-trip time observation data is greater than a preset communication anomaly threshold, requesting and obtaining a historical deviation accumulation rate sequence returned by multiple neighboring nodes; Constructing a channel switching probability of the water meter node based on the consistency and trend strength of the change trends of the historical deviation accumulation rate sequences corresponding to the water meter node and the plurality of neighboring nodes; If the channel switching probability is greater than a preset switching threshold, performing channel switching on the water meter node; Wherein, constructing the channel switching probability of the water meter node includes: performing Pearson correlation calculation on the historical deviation accumulation rate sequence of the water meter node and the multiple neighboring nodes to obtain n Pearson correlation coefficients, and calculating the average correlation coefficient of the n Pearson correlation coefficients, performing linear fitting on the historical deviation accumulation rate sequence of each neighboring node and calculating the trend slope, and calculating the average trend strength of the multiple neighboring nodes based on the trend slope, and the channel switching probability of the water meter node is equal to the product of the average correlation coefficient and the average trend strength; Calculating the deviation accumulation rate corresponding to the round-trip time observation data includes: Obtain the communication round trip time when the water meter node sends data for the i-th time in the round trip time observation data; Select the communication round-trip time values ​​of the water meter node M consecutive successful communications in history, and calculate the average of the communication round-trip time values ​​as the benchmark communication round-trip time; Obtain the first N successful round-trip time values ​​of the water meter node when sending data for the i-th time as a sliding observation window, and calculate the cumulative deviation rate between the communication round-trip time corresponding to the sliding observation window and the reference communication round-trip time, where the window length is N; Among them, the deviation accumulation rate The calculation formula is: ， Where RTT is the round-trip time, N is the window length, and max(0,) is the relative increase of the difference between the RTT of a single communication and the benchmark RTT relative to the benchmark value. max(0,) means to focus on the increase in the round-trip time of communication and ignore the short-term reduction in RTT.

2. The method according to claim 1, characterized in that Obtaining round-trip time observation data of water meter nodes during communication includes: Each water meter node records its own sending time and the time when the gateway returns the Ack message confirming receipt during communication; Calculate the round trip time observation data of the water meter node a when sending data for the i-th time .

3. The method according to claim 1, characterized in that Requesting and obtaining the historical deviation accumulation rate sequence returned by multiple neighbor nodes includes: According to the deviation accumulation rate of the current water meter node during the communication process, the communication quality abnormality judgment threshold is set ; If the deviation accumulation rate of the current water meter node when sending data satisfies , it is determined that the communication of the current water meter node is in an abnormal state; Based on the abnormal state, a broadcast request is initiated, and a collection request is sent to surrounding neighboring nodes to obtain a historical deviation accumulation rate sequence of the neighboring nodes.

4. The method according to claim 1, wherein The slope of the linear fitting of the historical deviation accumulation rate sequence is the degree of change of the corresponding value of the historical deviation accumulation rate sequence, and the degree of change of the corresponding value of the historical deviation accumulation rate sequence reflects the degree of deterioration of the communication quality of each neighbor node.

5. The method according to claim 1, characterized in that After calculating the average trend strength of the plurality of neighbor nodes, the method further includes: The average trend strength is normalized to the range of (0, 1) by the inverse tangent normalization method to obtain the normalized average trend strength.

6. The method according to claim 1, characterized in that If the channel switching probability is greater than a preset switching threshold, performing channel switching on the water meter node includes: When the channel switching probability is greater than a preset switching threshold, the communication mode of the water meter node is switched from LoRa to NB-IoT.

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