Remote automatic meter reading method and system for smart water meter based on NB-IoT

Abstract

The application relates to the technical field of intelligent water meter monitoring, and particularly discloses a remote automatic meter reading method and system of an intelligent water meter based on NB-IoT. The application constructs a communication link quality evaluation model by sensing water meter installation environment data, determines a communication reliability level, carries out time series analysis based on water metering data to extract user water change characteristics, identifies abnormal water use behaviors according to the user water change characteristics, establishes a meter reading frequency adjustment model according to the user water change characteristics and communication environment quality indexes, and realizes dynamic sampling scheduling of water metering data. The application realizes stable remote automatic meter reading of the intelligent water meter in complex environments such as basements and tube wells, and improves meter reading success rate and battery service life.

Description

Technical Field

[0001] This invention relates to the field of smart water meter monitoring technology, and more specifically, to a method and system for remote automatic meter reading of smart water meters based on NB-IoT. Background Technology

[0002] With the increasing demand for refined urban water supply management, traditional manual meter reading methods are no longer sufficient to meet the requirements of modern water management. Manual meter reading not only requires significant manpower but also faces challenges such as difficulty in accessing homes, long reading cycles, and susceptibility to data entry errors. Furthermore, it cannot achieve real-time monitoring and dynamic analysis of water consumption. While existing remote meter reading technologies have addressed some of the drawbacks of manual reading, they still have several shortcomings in practical applications. These include weak signal penetration in complex installation environments such as basements, pipe shafts, and metal meter boxes, leading to unstable meter reading success rates; high power consumption of existing remote water meter communication modules resulting in frequent battery replacements and increased maintenance costs; and a lack of intelligent data acquisition strategies making it difficult to balance communication resource waste with data timeliness, hindering the ability to dynamically adjust meter reading frequencies based on actual water usage for refined water monitoring and management.

[0003] Therefore, it is necessary to provide a method and system for remote automatic meter reading of smart water meters based on NB-IoT to solve the above-mentioned technical problems. In order to solve the above problems, a technical solution is provided. Summary of the Invention

[0004] To overcome the aforementioned shortcomings of existing technologies, this invention provides a remote automatic meter reading method and system for smart water meters based on NB-IoT, addressing the problems of low efficiency in existing manual meter reading methods and insufficient signal coverage and excessive power consumption leading to short battery life in complex environments in existing remote meter reading technologies.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The method for remote automatic meter reading of smart water meters based on NB-IoT includes the following steps: By setting up an environmental detection device in the remote water meter terminal, the water meter installation environment data is sensed in real time, and the water meter installation environment data is preprocessed to form a communication environment dataset. A communication link quality assessment model is constructed based on a communication environment dataset to output a communication reliability index, and the communication reliability level is determined based on the communication reliability index. An adaptive communication mode switching mechanism is developed for the communication reliability level to dynamically adjust the data communication method, and a data forwarding path is established by establishing a node collaborative relay communication mechanism within the monitoring area; Time series analysis is performed based on water metering data to extract user water usage change characteristics, and abnormal water usage behavior is identified based on these characteristics. A meter reading frequency adjustment model is established based on user water usage variation characteristics and communication environment quality indicators to achieve dynamic sampling and scheduling of water metering data. Meter reading quality is evaluated by constructing a meter reading task scheduling and supplementary reading management mechanism, and a low-power sleep and fast wake-up collaborative control mechanism is designed to monitor battery power consumption in real time.

[0006] As a further aspect of the present invention, a communication node quality assessment model is constructed based on a communication environment dataset to output a communication node reliability index. The reliability level of the communication node is then determined based on the communication node reliability index. The specific steps are as follows: Preprocessed water meter installation environment data is extracted from the communication environment dataset. A communication node quality assessment model is constructed based on the water meter installation environment data, and the communication reliability index is calculated through weighted fusion. The reliability level of a communication node is determined by comparing its reliability index with a preset reliability threshold range.

[0007] As a further aspect of the present invention, the reliability level of the communication node is determined as follows: when the reliability index of the communication node exceeds the upper limit of the preset reliability threshold range, the communication node is at level one communication reliability; when the reliability index of the communication node is within the preset reliability threshold range, the communication node is at level two communication reliability; when the reliability index of the communication node exceeds the lower limit of the preset reliability threshold range, the communication node is at level three communication reliability.

[0008] As a further aspect of the present invention, a data forwarding path is established by setting up a node-cooperative relay communication mechanism within the monitoring area, and the specific steps are as follows: A short-range wireless communication module is configured in the remote water meter terminal to build a local communication network for the water meter terminal to realize communication between remote water meter nodes. Based on the local communication network of the water meter terminal, periodic broadcasting and discovery of neighboring nodes are performed to form a list of neighboring nodes. When a communication node is at level 2 or level 3 communication reliability, the node with the highest link quality index and level 1 communication reliability is selected from the list of neighboring nodes as the relay node, and a data forwarding path is established.

[0009] As a further aspect of the present invention, time series analysis is performed based on water metering data to extract user water usage change characteristics, and abnormal water usage behavior is identified based on these characteristics. The specific steps are as follows: The remote water meter terminal collects water metering data according to a preset monitoring time interval, and constructs a water metering time series dataset by arranging the continuously collected water metering data in chronological order. The change in water consumption per unit time is calculated based on the cumulative difference in water consumption over consecutive time periods. A sequence of user water consumption changes is constructed by calculating the water consumption changes over all time intervals. Based on the user water usage change sequence, user water usage change features are extracted, and abnormal water usage discrimination rules are constructed based on the user water usage change features to identify abnormal water usage behavior.

[0010] As a further aspect of the present invention, the user water usage change characteristics include water usage change rate characteristics, water usage fluctuation characteristics, and continuous water usage duration.

[0011] As a further aspect of the present invention, abnormal water use discrimination rules are constructed based on user water use change characteristics to identify abnormal water use behavior. The specific steps of the abnormal water use discrimination rules are as follows: When the change in water consumption at any given moment exceeds the preset water consumption change benchmark value, it is determined to be an abnormal surge in water consumption. When the duration of continuous water use exceeds the preset water use threshold during the monitoring period, it is determined to be an abnormal continuous water use behavior. When the water usage fluctuation characteristics exceed the preset water usage fluctuation threshold during the monitoring period, it is determined to be an abnormal water usage fluctuation behavior.

[0012] As a further aspect of the present invention, a meter reading frequency adjustment model is established based on user water usage variation characteristics and communication environment quality indicators to achieve dynamic sampling and scheduling of water metering data. The specific steps are as follows: The water use change rate characteristics, water use fluctuation characteristics, and continuous water use duration are extracted, weighted and fused, and the water use change intensity index is calculated. Construct communication environment adjustment factors based on communication environment quality indicators; A meter reading frequency adjustment model was built based on water usage change intensity index and communication environment adjustment factor to calculate dynamic sampling period; The sampling frequency of user water metering data is dynamically adjusted based on the meter reading frequency adjustment model.

[0013] The NB-IoT-based smart water meter remote automatic meter reading system includes: a communication environment data acquisition module, a communication quality assessment module, a dynamic communication adjustment module, an abnormal water usage identification module, a data dynamic sampling and scheduling module, and a water meter monitoring and management module. The communication environment data acquisition module is used to sense the water meter installation environment data in real time by setting up an environmental detection device in the remote water meter terminal, and to preprocess the water meter installation environment data to form a communication environment dataset. The communication quality assessment module is used to build a communication link quality assessment model based on the communication environment dataset and output a communication reliability index, and determine the communication reliability level based on the communication reliability index; The dynamic communication adjustment module is used to formulate an adaptive communication mode switching mechanism based on the communication reliability level, dynamically adjust the data communication mode, and establish a data forwarding path by establishing a node collaborative relay communication mechanism within the monitoring area. The abnormal water usage identification module is used to extract user water usage change characteristics based on time series analysis of water metering data, and to identify abnormal water usage behavior based on user water usage change characteristics. The dynamic data sampling and scheduling module is used to establish a meter reading frequency adjustment model based on the characteristics of user water usage changes and communication environment quality indicators, so as to realize dynamic sampling and scheduling of water metering data. The water meter monitoring and management module is used to evaluate meter reading quality by building a meter reading task scheduling and supplementary reading management mechanism, and to design a low-power sleep and fast wake-up collaborative control mechanism to monitor battery power consumption in real time.

[0014] A readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of the NB-IoT-based smart water meter remote automatic meter reading method as described above.

[0015] The technical effects and advantages of the NB-IoT-based remote automatic meter reading method and system for smart water meters are as follows: This invention constructs a communication link quality assessment model by sensing water meter installation environment data to determine the communication reliability level, extracts user water usage change characteristics by performing time series analysis based on water metering data, identifies abnormal water usage behavior based on user water usage change characteristics, and establishes a meter reading frequency adjustment model based on user water usage change characteristics and communication environment quality indicators to achieve dynamic sampling and scheduling of water metering data.

[0016] This invention can effectively improve the communication stability and meter reading success rate of remote water meters in complex communication environments, and reduce data loss caused by signal blockage. At the same time, it reduces the communication frequency and energy consumption of the device through dynamic meter reading scheduling and low power consumption control mechanism, and extends the battery life of the terminal. In addition, the abnormal water use identification function can detect water leakage or abnormal water use in a timely manner, improve the precision and intelligence of water management, thereby improving the overall operating efficiency of the smart water system and reducing the later operation and maintenance costs. Attached Figure Description

[0017] Figure 1 A flowchart of a remote automatic meter reading method for smart water meters based on NB-IoT provided in an embodiment of the present invention; Figure 2 This is a system block diagram of a remote automatic meter reading system for smart water meters based on NB-IoT, provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described technical solutions are only a part of this invention, and not all of it. All other technical solutions obtained by those skilled in the art based on the technical solutions of this invention without inventive effort are within the scope of protection of this invention.

[0019] Example 1: As Figure 1 The diagram shown is a flowchart of a remote automatic meter reading method for smart water meters based on NB-IoT provided in an embodiment of the present invention. Figure 1 The execution entity of the method shown can be a software and / or hardware device. The execution entity of this application can include, but is not limited to, at least one of the following: user equipment, network equipment, etc. User equipment can include, but is not limited to, computers, smartphones, personal digital assistants (PDAs), and the aforementioned electronic devices. Network equipment can include, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers. Cloud computing is a type of distributed computing, consisting of a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. Steps S1 to S6 are detailed as follows: Step S1: By setting up an environmental detection device in the remote water meter terminal, the water meter installation environment data is sensed in real time, and the water meter installation environment data is preprocessed to form a communication environment dataset. Step S2: Construct a communication link quality assessment model based on the communication environment dataset and output a communication reliability index; determine the communication reliability level based on the communication reliability index. Step S3: Develop an adaptive communication mode switching mechanism for the communication reliability level, dynamically adjust the data communication method, and establish a data forwarding path by establishing a node collaborative relay communication mechanism within the monitoring area; Step S4: Perform time series analysis based on water metering data to extract user water usage change characteristics, and identify abnormal water usage behavior based on user water usage change characteristics; Step S5: Establish a meter reading frequency adjustment model based on user water usage change characteristics and communication environment quality indicators to realize dynamic sampling and scheduling of water metering data. Step S6: The meter reading quality is evaluated by constructing a meter reading task scheduling and supplementary reading management mechanism, and a low-power sleep and fast wake-up collaborative control mechanism is designed to monitor battery power consumption in real time.

[0020] Preferably, an environmental detection device is installed in the remote water meter terminal to sense the water meter installation environment data in real time, specifically including: signal reception strength, signal-to-noise ratio, data packet loss rate, communication delay, and historical meter reading success rate. Preprocessing of water meter installation environment data includes time synchronization, outlier removal, and normalization.

[0021] Preferably, a communication node quality assessment model is constructed based on the communication environment dataset to output a communication node reliability index. The reliability level of the communication node is determined based on the communication node reliability index. The specific steps are as follows: Preprocessed water meter installation environment data was extracted from the communication environment dataset. A communication node quality assessment model was constructed based on the water meter installation environment data, and the communication reliability index was calculated using a weighted fusion method. The calculation formula for the communication node quality assessment model is as follows: ; In the formula: This is a reliability index for communication nodes. The weighting coefficient for signal received strength. For signal reception strength, This is the weighting coefficient for the signal-to-noise ratio. For signal-to-noise ratio, This is the weighting coefficient for the data packet loss rate. For data packet loss rate, This is a weighting factor for communication delay. Due to communication delay, The weighting coefficient for historical meter reading success rate. For historical meter reading success rate; The reliability level of a communication node is determined by comparing its reliability index with a preset reliability threshold range. Specifically: when the reliability index exceeds the upper limit of the preset reliability threshold range, the communication node is at Level 1 reliability, and the current communication state is stable; when the reliability index is within the preset reliability threshold range, the communication node is at Level 2 reliability, and the current communication state is in need of enhanced communication; when the reliability index exceeds the lower limit of the preset reliability threshold range, the communication node is at Level 3 reliability, and the current communication state is unreachable.

[0022] In one specific embodiment of the present invention, some water meters in the residential community are installed in underground pipe shafts, metal meter boxes in corridors, and underground equipment rooms, etc., where the communication environment is complex and the signal is easily blocked by metal structures and building walls, resulting in unstable communication between some water meter terminals and the concentrator. To solve the above problems, an environmental detection device is installed in each remote water meter terminal to sense the water meter installation environment data in real time. The environmental detection device continuously collects communication-related parameters during the operation of the water meter communication module, specifically including signal reception strength, signal-to-noise ratio, data packet loss rate, communication delay, and historical meter reading success rate. For example, when a water meter terminal in an underground pipe shaft communicates with the concentrator, the detected signal reception strength is −95dBm, the signal-to-noise ratio is 8dB, and the data packet loss rate is 0.18 and the average communication delay is 2.4s during multiple communication processes. The meter reading success rate in the most recent meter reading cycle is recorded as 0.82. The above data was used as water meter installation environment data, and the collected data was preprocessed, including time synchronization processing through a unified timestamp, removal of abnormal mutation values ​​through a sliding window method, and conversion of various indicators into a unified dimension using a normalization method, thereby forming a communication environment dataset.

[0023] After obtaining the communication environment dataset, a communication node quality assessment model is further constructed based on the dataset to quantitatively evaluate the communication reliability of each water meter terminal. Specifically, preprocessed water meter installation environment data is extracted from the communication environment dataset and calculated according to the communication node quality assessment model. The communication node reliability index is obtained through a weighted fusion method. For example, calculated within a certain detection cycle. .

[0024] Subsequently, the calculated communication node reliability index is compared with a preset reliability threshold range to determine the reliability level of the communication node. In this embodiment, the preset reliability threshold range is 0.5 to 0.8. When the communication node reliability index is higher than 0.8, the communication node is determined to be at Level 1 communication reliability, indicating that the communication link between the water meter terminal and the concentrator is stable and the current communication state is stable, with data collection and uploading performed according to the normal meter reading cycle. When the communication node reliability index is between 0.5 and 0.8, the communication node is determined to be at Level 2 communication reliability, indicating that the communication link has some fluctuations. An enhanced communication strategy is automatically activated, such as increasing the communication transmission power or increasing the number of data retransmissions, to ensure that the meter reading data can be uploaded stably. When the communication node reliability index is lower than 0.5, the communication node is determined to be at Level 3 communication reliability, indicating that the communication link is in an unreachable communication state. The water meter terminal is marked as a communication anomaly node, and a relay communication path is established through a nearby water meter node or the meter reading task is delayed to achieve data retransmission and communication recovery.

[0025] Based on the above, this invention can evaluate the reliability of communication nodes in real time according to the communication characteristics of the actual installation environment of the water meter, and dynamically adjust the communication strategy according to the evaluation results. This effectively improves the communication stability and meter reading success rate of remote water meters in complex environments such as basements, pipe wells and metal meter boxes, while reducing repeated meter reading and maintenance costs caused by communication instability.

[0026] Preferably, an adaptive communication mode switching mechanism is established based on the reliability level of the communication nodes to dynamically adjust the data communication method. The specific steps are as follows: When the communication reliability is determined to be Level 1, the water meter terminal uses a conventional low-power communication mode to periodically upload data. When the communication reliability is determined to be level 2, it automatically switches to enhanced communication mode, which improves the communication success rate by increasing the transmission power, increasing the number of signal retransmissions, or using stronger error correction coding methods. When the communication reliability is determined to be Level 3, the node collaborative relay communication mechanism is activated to establish a relay link through a nearby water meter or building concentrator to achieve data forwarding.

[0027] In one specific embodiment of this invention, remote water meter reading is performed in a residential community. The water meter terminals in this community are installed in pipe shafts in building corridors, underground equipment rooms, and inside metal meter boxes, where the communication environment varies significantly. First, communication environment data of the water meter terminals is collected using an environmental monitoring device, and a communication node reliability index is calculated using a communication node quality assessment model to determine the reliability level of the communication nodes. Based on this, an adaptive communication mode switching mechanism is developed for each communication node's reliability level to dynamically adjust the data communication method of the water meter terminals, thereby reducing communication energy consumption while ensuring meter reading success rate.

[0028] When a water meter terminal is determined to be at Level 1 communication reliability, it indicates that the communication link between the water meter terminal and the concentrator is stable. This is typically achieved when the water meter is installed in an open area of ​​a building or near a communication gateway, resulting in high signal strength and low packet loss. In this case, the water meter terminal uses a conventional low-power communication mode for periodic data uploads. This involves collecting water metering data periodically according to a preset reading cycle and directly uploading the data to the building concentrator or gateway device via a low-power wireless communication module. This communication mode maintains low transmission power and fewer communication cycles, thereby reducing terminal power consumption and extending battery life.

[0029] When a water meter terminal is determined to be at level two communication reliability, it indicates that the current communication link experiences some fluctuations. For example, if the water meter is installed in a semi-enclosed pipe well or at a remote location, signal attenuation or occasional packet loss may occur during communication. In this case, the system automatically switches to enhanced communication mode. This improves data transmission reliability by increasing the transmission power of the wireless module, increasing the number of signal retransmissions, and employing stronger error correction coding. For instance, during a meter reading, the water meter terminal may send the same data packet two to three times consecutively, while simultaneously using forward error correction coding to perform redundant verification of the data, reducing the impact of communication interference on data transmission and thus improving the meter reading success rate.

[0030] When the system determines that a water meter terminal is at level three communication reliability, it indicates that the communication link between the water meter terminal and the concentrator is severely obstructed. For example, the water meter may be installed deep in a basement or inside a metal meter box, making it difficult for the signal to be directly transmitted to the concentrator. In this case, a node collaborative relay communication mechanism is activated to establish a relay communication link through nearby water meter nodes or building concentrators to achieve data forwarding. Specifically, the water meter terminal at level three communication reliability first scans surrounding nodes, selects a nearby water meter with better communication quality as a relay node, and sends its collected water metering data to that relay node. Subsequently, the relay node forwards the data to the concentrator or gateway device, thus forming a multi-hop communication path. In this way, even if the water meter terminal is in a signal-blocked area, it can still complete data transmission with the help of nearby nodes.

[0031] The above implementation method can automatically select the appropriate communication mode according to the reliability level of the communication node, realize dynamic switching between low power communication, enhanced communication and cooperative relay communication, thereby improving the communication stability and meter reading success rate of remote water meters in complex installation environments, while effectively reducing equipment energy consumption and improving the overall operating efficiency of the smart water system.

[0032] Preferably, a data forwarding path is established by setting up a node-coordinated relay communication mechanism within the monitoring area. The specific steps are as follows: The remote water meter terminal is equipped with a short-range wireless communication module to build a local communication network for the water meter terminal to realize communication between remote water meter nodes. Based on the local communication network of the water meter terminal, the terminal performs periodic broadcasting and discovery of neighboring nodes to form a list of neighboring nodes. The remote water meter terminal sends node broadcast signals at preset time intervals. The node broadcast signals include node ID, remaining power, current communication status and node location identification information. After receiving the node broadcast signals, the neighboring remote water meter terminals record the communication node reliability index, thereby forming a list of neighboring nodes. When a communication node is at level 2 or level 3 communication reliability, the node with the highest link quality index and level 1 communication reliability is selected from the list of neighboring nodes as a relay node, and a data forwarding path is established. If the relay node still cannot communicate directly with the concentrator, it continues to forward data to its neighboring nodes, establishing multi-hop communication paths step by step until a relay node that can communicate stably with the concentrator is reached. When some water meters cannot transmit signals directly due to metal meter boxes or underground structures, data can be forwarded step by step through neighboring nodes, thereby effectively bypassing signal shielding areas, improving the success rate of remote meter reading and communication stability, and reducing the cost of repeated meter reading and maintenance caused by communication failures.

[0033] Preferably, time series analysis is performed based on water metering data to extract user water usage change characteristics, and abnormal water usage behavior is identified based on these characteristics. The specific steps are as follows: The remote water meter terminal collects water metering data according to a preset monitoring time interval, and constructs a water metering time series dataset by arranging the continuously collected water metering data in chronological order. ,in, For the i-th data acquisition time, For a moment Water metering data, This represents the total number of data collection moments. It should be noted that the water metering time series data is preprocessed, including time synchronization, outlier removal, missing data interpolation, and data smoothing, to form standardized water use time series data. Calculate the change in water consumption per unit time based on the cumulative difference in water consumption over consecutive time points. ,in, for Water usage data at any given time Time interval Changes in water consumption within the area; By calculating the water consumption changes across all time intervals, a user water consumption change sequence is constructed. ,in, Time interval Water consumption changes within the area Time interval Changes in water consumption within the area; Based on the user water usage change sequence, user water usage change features are extracted, and abnormal water usage discrimination rules are constructed based on the user water usage change features to identify abnormal water usage behavior.

[0034] Preferably, user water usage change features are extracted based on user water usage change sequences. These user water usage change features include water usage change rate features, water usage fluctuation features, and continuous water usage duration. The formula for calculating the rate of change of water usage is as follows: ; In the formula: for Characteristics of the rate of change in water usage at any given time Time interval Water consumption changes within the area For time intervals.

[0035] Water usage fluctuation characteristics are obtained by calculating the standard deviation of water usage changes during the monitoring period.

[0036] Preferably, abnormal water use discrimination rules are constructed based on the characteristics of user water use changes to identify abnormal water use behavior. The specific steps of the abnormal water use discrimination rules are as follows: When the change in water consumption at any given moment exceeds the preset water consumption change benchmark value, it is determined to be an abnormal surge in water consumption. When the duration of continuous water use exceeds the preset water use threshold during the monitoring period, it is determined to be an abnormal continuous water use behavior. When the water usage fluctuation characteristics exceed the preset water usage fluctuation threshold during the monitoring period, it is determined to be an abnormal water usage fluctuation behavior.

[0037] In one specific embodiment of this invention, it is necessary to upgrade the remote water meters in a residential community. Some water meters in this community are installed inside underground pipe shafts, metal meter boxes, and building equipment rooms, resulting in a complex communication environment and significant signal obstruction between some water meter terminals and the concentrator. To ensure stable operation of remote meter reading, a node collaborative relay communication mechanism is established within the monitoring area to construct a stable data forwarding path. Specifically, a short-range wireless communication module is configured inside each remote water meter terminal, enabling the water meter terminal to communicate not only with the concentrator but also with neighboring water meter terminals, thereby constructing a local communication network for the water meter terminals. In this local communication network, each remote water meter terminal periodically sends a node broadcast signal at preset time intervals. The broadcast signal includes information such as node ID, remaining power, current communication status, and node location identifier. After receiving the broadcast signal, neighboring remote water meter terminals record the node communication quality and, combined with the aforementioned communication node quality assessment model, obtain the corresponding communication node reliability index, thus forming a list of neighboring nodes. When a water meter terminal detects that its communication node is at level two or three communication reliability, it will select the node with the highest link quality index and level one communication reliability from the list of neighboring nodes as a relay node and establish a data forwarding path. If the relay node still cannot communicate directly with the concentrator, it will continue to forward data to its neighboring nodes, building a multi-hop communication path step by step until it reaches a node that can communicate stably with the concentrator. For example, when a water meter terminal in an underground pipe well cannot communicate directly with the concentrator due to the obstruction of a metal manhole cover, the water usage data it collects is first sent to a neighboring water meter terminal in the corridor with better communication quality, and then forwarded by that terminal to the building concentrator, thus achieving reliable data transmission. In this way, the signal shielding area caused by metal meter boxes or underground structures can be effectively bypassed, improving the success rate of remote meter reading and communication stability, while reducing the cost of repeated meter reading and maintenance caused by communication failures.

[0038] Building upon the aforementioned communication mechanism, this embodiment further utilizes water metering data collected by remote water meter terminals for time series analysis to identify abnormal user water usage behavior. Specifically, the remote water meter terminal collects water metering data according to preset monitoring time intervals and constructs a water metering time series dataset by sequentially arranging the continuously collected data. To ensure the accuracy of data analysis, the water metering time series data is first preprocessed, including time synchronization using a unified timestamp, outlier removal using a sliding window algorithm, missing data supplementation using interpolation, and random noise elimination using a smoothing filter, thereby forming standardized water usage time series data. Subsequently, the change in water usage per unit time is calculated based on the difference between cumulative water usage at consecutive times. By calculating the change in water usage across all time intervals, a user water usage change sequence is constructed, reflecting the user's water usage changes over different time periods.

[0039] After obtaining the user water consumption change sequence, further characteristics of user water consumption change are extracted, including water consumption change rate characteristics, water consumption fluctuation characteristics, and continuous water consumption duration. Among them, the water consumption change rate characteristic is used to reflect the trend of water consumption change. The water consumption fluctuation characteristic is obtained by calculating the standard deviation of water consumption change values ​​within the monitoring period, and is used to reflect the stability of user water consumption behavior. The continuous water consumption duration is obtained by statistically analyzing the duration of water consumption change values ​​greater than zero within a continuous time interval, and is used to identify long-term continuous water consumption.

[0040] Based on the aforementioned water usage variation characteristics, abnormal water usage discrimination rules are constructed to identify abnormal water usage behavior. Specifically, when the water usage change value at any given time exceeds a preset water usage change benchmark value, it is judged as an abnormal surge in water usage, which may correspond to a pipe rupture or a sudden large flow of water usage; when the duration of continuous water usage within the monitoring period exceeds a preset water usage threshold, it is judged as abnormal continuous water usage behavior, for example, prolonged continuous water usage at night may indicate a pipe leak; when the water usage fluctuation characteristics within the monitoring period exceed a preset water usage fluctuation threshold, it is judged as abnormal fluctuation in water usage behavior. Through these discrimination rules, potential leaks or abnormal water usage can be identified in a timely manner, and relevant information can be uploaded to the water management platform for early warning, thereby achieving real-time monitoring and anomaly identification of user water usage behavior and improving the management efficiency and service capabilities of the smart water system.

[0041] Preferably, a meter reading frequency adjustment model is established based on user water usage variation characteristics and communication environment quality indicators to achieve dynamic sampling and scheduling of water metering data. The specific steps are as follows: The water use change rate characteristics, water use fluctuation characteristics, and continuous water use duration are extracted, weighted and fused, and the water use change intensity index is calculated. A communication environment adjustment factor is constructed using communication environment quality indicators. The calculation formula for the communication environment adjustment factor is as follows: ; In the formula: As a communication environment adjustment factor, This is a communication environment adjustment coefficient; A meter reading frequency adjustment model is built based on water usage change intensity index and communication environment adjustment factor to calculate dynamic sampling period. The calculation formula is as follows: ; In the formula: For dynamic sampling period, Based on the sampling period, This is an indicator of the intensity of water use change; The sampling frequency of user water metering data is dynamically adjusted based on the meter reading frequency adjustment model.

[0042] It should be noted that the dynamic adjustment of the sampling frequency of user water metering data is achieved based on the meter reading frequency adjustment model, specifically as follows: When water usage changes drastically, that is When the water usage is relatively high, the sampling period is reduced, thereby increasing the meter reading frequency; when water usage is stable, i.e. Smaller sampling rates lead to larger sampling periods, thus reducing communication frequency; when the communication environment is poor, i.e. The sampling period is relatively low, and the sampling period should be appropriately extended to reduce communication conflicts and energy consumption.

[0043] In one specific embodiment of this invention, a large number of remote water meter terminals are installed in the residential community. These terminals interact with the building concentrator via wireless communication. To reduce communication energy consumption and network congestion while ensuring data timeliness, this embodiment establishes a meter reading frequency adjustment model based on user water usage change characteristics and communication environment quality indicators to achieve dynamic sampling and scheduling of water metering data. Specifically, firstly, user water usage change characteristics are extracted based on the aforementioned time series analysis method, including indicators such as water usage change rate characteristics, water usage fluctuation characteristics, and continuous water usage duration. These indicators are then weighted and fused to calculate a water usage change intensity index, which comprehensively reflects the user's water usage activity during a certain monitoring period. For example, when residents engage in concentrated water usage during peak morning and evening water usage periods, the water usage change rate and fluctuation amplitude increase significantly, resulting in a relatively high water usage change intensity index. Conversely, water usage changes are smaller at night or when residents are out, leading to a lower corresponding water usage change intensity index.

[0044] After obtaining the water usage change intensity index, the meter reading frequency is further adjusted by combining it with the communication environment quality index of the water meter terminal. Specifically, the communication node reliability index is calculated through the communication node quality assessment model, and a communication environment adjustment factor is constructed based on this index. When the communication environment is good, the communication environment quality index is close to 1, and the communication environment adjustment factor is also close to 1, indicating that the communication environment has little impact on the sampling period. When the communication environment is poor, the communication environment quality index value is low, and the communication environment adjustment factor is increased accordingly. This allows for an appropriate extension of the sampling period in subsequent calculations to reduce conflicts and energy consumption caused by frequent communication.

[0045] Based on this, a meter reading frequency adjustment model is constructed according to the water usage change intensity index and the communication environment adjustment factor, and the dynamic sampling period is calculated. The dynamic sampling period calculated by this model will be used as the data acquisition and upload interval for the remote water meter terminal in the next cycle, thereby realizing the dynamic adjustment of the water usage metering data sampling frequency.

[0046] In actual operation, when a resident's water usage changes drastically, such as during family bathing or concentrated water usage periods, the calculated water usage change intensity index is high. In this case, the dynamic sampling period will be reduced accordingly, and the remote water meter terminal will increase the data acquisition and upload frequency to record water usage changes more precisely. When a user's water usage behavior is relatively stable, such as at night or during unoccupied periods, the water usage change intensity index is low, and the sampling period will be appropriately increased to reduce communication frequency and terminal power consumption. Furthermore, when the water meter terminal is located in a poor communication environment, such as a basement or metal meter box, the communication environment quality index is low, and the communication environment adjustment factor will be increased, resulting in a slightly longer dynamic sampling period. This reduces the impact of frequent communication on network stability and device battery life.

[0047] The above-mentioned ability to adaptively adjust the meter reading frequency based on the user's actual water usage behavior and changes in the communication environment enables dynamic sampling and scheduling of water metering data. This not only improves the precision of water usage monitoring but also effectively reduces the energy consumption and network load of remote water meter communication, thereby enhancing the overall operational efficiency and reliability of the smart water management remote meter reading system.

[0048] It should be noted that the meter reading quality is evaluated by constructing a meter reading task scheduling and supplementary reading management mechanism, and a low-power sleep and fast wake-up collaborative control mechanism is designed to monitor battery power consumption in real time. The specific steps are as follows: In traditional remote meter reading systems, manual troubleshooting is typically required when remote water meter terminals fail to report data for extended periods, resulting in low management efficiency. This is addressed by establishing a meter reading task scheduling and supplementary reading management mechanism on a cloud platform. The cloud platform periodically analyzes the reported data of each water meter. When a water meter fails to upload data within a preset time frame, it automatically sends an active meter reading command to the remote water meter terminal, triggering the terminal to immediately perform a data upload. If multiple supplementary readings fail, the remote water meter terminal is marked as an offline device, and a maintenance and troubleshooting work order is automatically generated. Simultaneously, statistical analysis of the meter reading success rate for different regions and batches of water meters is performed to generate a meter reading quality assessment report, providing data support for network coverage optimization and equipment maintenance.

[0049] A low-power sleep and fast wake-up coordinated control mechanism is designed to monitor battery power consumption in real time. Specifically, when it is detected that the user is in a period of inactivity when water usage is low, the sampling frequency of the non-magnetic sensor acquisition unit is automatically reduced, and the communication module enters a low-power sleep state. When a change in the water meter counter is detected, the fast wake-up mechanism is immediately triggered to restore normal sampling and communication functions. In this way, the system power consumption can be significantly reduced while ensuring real-time monitoring capabilities, thereby extending the battery life of the smart water meter.

[0050] Example 2: This embodiment of the invention applies the NB-IoT-based remote automatic meter reading method for smart water meters in a residential community. The remote water meters in this community are distributed in various complex environments such as pipe shafts in stairwells, underground equipment rooms, and inside metal meter boxes. Traditional wireless communication methods are easily affected by building structures and metal obstructions, resulting in a low meter reading success rate for some water meters. To improve the communication stability and management efficiency of the remote meter reading system, an environmental detection device is installed in each remote water meter terminal to perceive real-time water meter installation environment data, such as signal reception strength, signal-to-noise ratio, communication latency, data packet loss rate, and historical meter reading success rate. The collected water meter installation environment data is preprocessed, including time synchronization, outlier removal, and normalization, thereby forming a communication environment dataset, providing basic data for subsequent communication quality assessment.

[0051] After obtaining the communication environment dataset, a communication link quality assessment model is constructed based on this dataset. A communication reliability index is calculated using a weighted fusion method, and the communication status is classified according to this index to determine whether the current communication environment is stable. When the communication reliability of a remote water meter terminal is high, it is determined to be a stable communication state; when the communication reliability is at a medium level, it is determined to be a state requiring enhanced communication; and when the communication reliability is low, it is determined to be a communication obstruction state. Based on this, an adaptive communication mode switching mechanism is formulated for different communication reliability levels to dynamically adjust the data communication mode of the remote water meter terminal. For example, a low-power periodic communication mode is used when the communication reliability is high; when the communication reliability is medium, the transmission power is automatically increased or the number of data retransmissions is increased to improve the communication success rate; when the communication reliability is low, a node collaborative relay communication mechanism is established within the monitoring area. Multi-hop data forwarding paths are constructed through neighboring water meter nodes, forwarding water meter data that cannot be directly communicated to building concentrators or gateway devices step by step, thereby bypassing signal shielding areas caused by underground structures or metal meter boxes.

[0052] Based on stable communication operation, the water consumption data collected by the remote water meter is further analyzed. Specifically, the remote water meter terminal collects user water consumption data at preset time intervals and constructs water consumption time series data. Time series analysis methods are used to extract user water consumption change characteristics, including indicators such as the rate of change, the degree of fluctuation, and the duration of continuous water use. Based on these water consumption change characteristics, abnormal water consumption identification rules are constructed to identify behaviors such as abnormally high water consumption, abnormally continuous water use, or abnormally fluctuating water use. For example, when a resident uses water continuously for an extended period at night, it can be determined that there may be a pipe leak, and the abnormal information is uploaded to the water management platform for early warning.

[0053] Furthermore, a meter reading frequency adjustment model is established based on user water usage variation characteristics and communication environment quality indicators to dynamically calculate and adjust the data sampling period of the water meter terminal. When user water usage changes drastically, the sampling period is automatically shortened, and the data acquisition and upload frequency is increased to achieve more precise water usage monitoring. When user water usage is relatively stable, the sampling period is appropriately extended, and the communication frequency is reduced to decrease energy consumption. Simultaneously, when the water meter is located in an area with poor communication, the sampling period is appropriately extended to reduce conflicts and battery consumption caused by frequent communication.

[0054] During operation, the platform also assesses overall meter reading quality by constructing a meter reading task scheduling and supplementary reading management mechanism. When it detects that some water meter terminals have failed to complete data upload on time, a supplementary reading task is automatically triggered. By adjusting the communication time window or calling relay nodes, data transmission is retried to ensure the integrity and reliability of the meter reading data. Simultaneously, a low-power sleep and fast wake-up collaborative control mechanism is designed within the remote water meter terminal. When the device is not in a communication period, it automatically enters a low-power sleep state and quickly wakes up to execute data acquisition and transmission tasks when sampling or communication time arrives, thereby achieving real-time monitoring and optimized control of battery power consumption. Through the above implementation methods, the communication stability and meter reading success rate of remote water meters can be significantly improved in complex installation environments, while reducing system operating energy consumption and enhancing the intelligence and refinement of smart water management.

[0055] Example 3: A remote automatic meter reading system for smart water meters based on NB-IoT. The system includes: a communication environment data acquisition module, a communication quality assessment module, a dynamic communication adjustment module, an abnormal water usage identification module, a data dynamic sampling and scheduling module, and a water meter monitoring and management module. The communication environment data acquisition module is connected to the communication quality assessment module, the communication quality assessment module is connected to the dynamic communication adjustment module, the dynamic communication adjustment module is connected to the abnormal water usage identification module, the abnormal water usage identification module is connected to the data dynamic sampling and scheduling module, and the data dynamic sampling and scheduling module is connected to the water meter monitoring and management module. The communication environment data acquisition module is used to sense the water meter installation environment data in real time by setting up an environmental detection device in the remote water meter terminal, and to preprocess the water meter installation environment data to form a communication environment dataset. The communication quality assessment module is used to build a communication link quality assessment model based on the communication environment dataset and output a communication reliability index, and determine the communication reliability level based on the communication reliability index; The dynamic communication adjustment module is used to formulate an adaptive communication mode switching mechanism based on the communication reliability level, dynamically adjust the data communication mode, and establish a data forwarding path by establishing a node collaborative relay communication mechanism within the monitoring area. The abnormal water usage identification module is used to extract user water usage change characteristics based on time series analysis of water metering data, and to identify abnormal water usage behavior based on user water usage change characteristics. The dynamic data sampling and scheduling module is used to establish a meter reading frequency adjustment model based on the characteristics of user water usage changes and communication environment quality indicators, so as to realize dynamic sampling and scheduling of water metering data. The water meter monitoring and management module is used to evaluate meter reading quality by building a meter reading task scheduling and supplementary reading management mechanism, and to design a low-power sleep and fast wake-up collaborative control mechanism to monitor battery power consumption in real time.

[0056] like Figure 2 The diagram shown is a system block diagram of a remote automatic meter reading system for smart water meters based on NB-IoT, according to an embodiment of the present invention. This system can be used to execute... Figure 1 The steps in the method embodiments shown are implemented in a similar manner and have similar technical effects, and will not be repeated here.

[0057] A readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of the above-described NB-IoT-based smart water meter remote automatic meter reading method.

[0058] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0059] The present invention also provides a program product including executable instructions stored in a readable storage medium. At least one processor of the device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the device to implement the methods provided in the various embodiments described above.

[0060] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0061] Through the above embodiments, this invention, by installing an environmental monitoring device within the remote water meter terminal, collects real-time data on the water meter installation environment and forms a communication environment dataset through time synchronization, outlier removal, and normalization. Subsequently, a communication link quality assessment model is constructed based on this dataset to calculate a communication reliability index, and communication reliability levels are classified according to this index. An adaptive communication mode switching mechanism is established based on different communication reliability levels to dynamically adjust the data communication method. Simultaneously, a node collaborative relay communication mechanism is established within the monitoring area, constructing multi-hop data forwarding paths through neighboring water meter nodes, thereby achieving stable data transmission in signal-obstructed environments such as basements and metal meter boxes. Time series analysis is performed using the water metering data collected by the remote water meter to extract features such as the rate of change in user water usage, water usage fluctuations, and duration of continuous water usage. Abnormal water usage behaviors, such as sudden increases in water usage, continuous water usage, or abnormal fluctuations, are identified based on these features. Furthermore, a meter reading frequency adjustment model is constructed based on user water usage change characteristics and communication environment quality indicators to dynamically adjust the water meter data sampling period, achieving adaptive control of the meter reading frequency. Meter reading quality is evaluated by constructing a meter reading task scheduling and supplementary reading management mechanism, and battery power consumption is monitored and optimized in real time by combining a low-power sleep and fast wake-up collaborative control mechanism.

[0062] The embodiments of the present invention can effectively improve the communication stability and meter reading success rate of remote water meters in complex communication environments, and reduce data loss caused by signal blockage. At the same time, the device communication frequency and energy consumption are reduced through dynamic meter reading scheduling and low power consumption control mechanisms, extending the battery life of the terminal. In addition, the abnormal water use identification function can promptly detect water leakage or abnormal water use, improve the precision and intelligence of water management, thereby improving the overall operating efficiency of the smart water system and reducing the later maintenance costs.

[0063] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

[0064] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A remote automatic meter reading method for smart water meters based on NB-IoT, characterized in that, Includes the following steps: By setting up an environmental detection device in the remote water meter terminal, the water meter installation environment data is sensed in real time, and the water meter installation environment data is preprocessed to form a communication environment dataset. A communication link quality assessment model is constructed based on a communication environment dataset to output a communication reliability index, and the communication reliability level is determined based on the communication reliability index. An adaptive communication mode switching mechanism is developed for the communication reliability level to dynamically adjust the data communication method, and a data forwarding path is established by establishing a node collaborative relay communication mechanism within the monitoring area; Time series analysis is performed based on water metering data to extract user water usage change characteristics, and abnormal water usage behavior is identified based on these characteristics. A meter reading frequency adjustment model is established based on user water usage variation characteristics and communication environment quality indicators to achieve dynamic sampling and scheduling of water metering data. Meter reading quality is evaluated by constructing a meter reading task scheduling and supplementary reading management mechanism, and a low-power sleep and fast wake-up collaborative control mechanism is designed to monitor battery power consumption in real time.

2. The remote automatic meter reading method for smart water meters based on NB-IoT according to claim 1, characterized in that, A communication node quality assessment model is constructed based on a communication environment dataset to output a communication node reliability index. The reliability level of the communication node is determined based on the communication node reliability index. The specific steps are as follows: Preprocessed water meter installation environment data is extracted from the communication environment dataset. A communication node quality assessment model is constructed based on the water meter installation environment data, and the communication reliability index is calculated through weighted fusion. The reliability level of a communication node is determined by comparing its reliability index with a preset reliability threshold range.

3. The remote automatic meter reading method for smart water meters based on NB-IoT according to claim 2, characterized in that, The reliability level of a communication node is determined as follows: when the reliability index of a communication node exceeds the upper limit of the preset confidence threshold range, the communication node is at level one communication reliability; when the reliability index of a communication node is within the preset confidence threshold range, the communication node is at level two communication reliability. When the reliability index of a communication node exceeds the lower limit of the preset reliability threshold range, the communication node is at level three communication reliability.

4. The remote automatic meter reading method for smart water meters based on NB-IoT according to claim 1, characterized in that, By establishing a node-coordinated relay communication mechanism within the monitoring area, a data forwarding path is established. The specific steps are as follows: A short-range wireless communication module is configured in the remote water meter terminal to build a local communication network for the water meter terminal to realize communication between remote water meter nodes. Based on the local communication network of the water meter terminal, periodic broadcasting and discovery of neighboring nodes are performed to form a list of neighboring nodes. When a communication node is at level 2 or level 3 communication reliability, the node with the highest link quality index and level 1 communication reliability is selected from the list of neighboring nodes as the relay node, and a data forwarding path is established.

5. The remote automatic meter reading method for smart water meters based on NB-IoT according to claim 1, characterized in that, The following steps are taken to extract user water usage change characteristics based on time series analysis of water metering data, and to identify abnormal water usage behavior based on these characteristics: The remote water meter terminal collects water metering data according to a preset monitoring time interval, and constructs a water metering time series dataset by arranging the continuously collected water metering data in chronological order. The change in water consumption per unit time is calculated based on the cumulative difference in water consumption over consecutive time periods. A sequence of user water consumption changes is constructed by calculating the water consumption changes over all time intervals. Based on the user water usage change sequence, user water usage change features are extracted, and abnormal water usage discrimination rules are constructed based on the user water usage change features to identify abnormal water usage behavior.

6. The remote automatic meter reading method for smart water meters based on NB-IoT according to claim 5, characterized in that, User water usage change characteristics include water usage change rate characteristics, water usage fluctuation characteristics, and continuous water usage duration.

7. The remote automatic meter reading method for smart water meters based on NB-IoT according to claim 6, characterized in that, Based on the characteristics of user water usage changes, abnormal water usage discrimination rules are constructed to identify abnormal water usage behavior. The specific steps of the abnormal water usage discrimination rules are as follows: When the change in water consumption at any given moment exceeds the preset water consumption change benchmark value, it is determined to be an abnormal surge in water consumption. When the duration of continuous water use exceeds the preset water use threshold during the monitoring period, it is determined to be an abnormal continuous water use behavior. When the water usage fluctuation characteristics exceed the preset water usage fluctuation threshold during the monitoring period, it is determined to be an abnormal water usage fluctuation behavior.

8. The remote automatic meter reading method for smart water meters based on NB-IoT according to claim 1, characterized in that, A meter reading frequency adjustment model is established based on user water usage variation characteristics and communication environment quality indicators to achieve dynamic sampling and scheduling of water metering data. The specific steps are as follows: The water use change rate characteristics, water use fluctuation characteristics, and continuous water use duration are extracted, weighted and fused, and the water use change intensity index is calculated. Construct communication environment adjustment factors based on communication environment quality indicators; A meter reading frequency adjustment model was built based on water usage change intensity index and communication environment adjustment factor to calculate dynamic sampling period; The sampling frequency of user water metering data is dynamically adjusted based on the meter reading frequency adjustment model.

9. A remote automatic meter reading system for smart water meters based on NB-IoT, applied to the remote automatic meter reading method for smart water meters based on NB-IoT as described in any one of claims 1-8, characterized in that, The system includes: a communication environment data acquisition module, a communication quality assessment module, a dynamic communication adjustment module, an abnormal water usage identification module, a data dynamic sampling and scheduling module, and a water meter monitoring and management module. The communication environment data acquisition module is used to sense the water meter installation environment data in real time by setting up an environmental detection device in the remote water meter terminal, and to preprocess the water meter installation environment data to form a communication environment dataset. The communication quality assessment module is used to build a communication link quality assessment model based on the communication environment dataset and output a communication reliability index, and determine the communication reliability level based on the communication reliability index; The dynamic communication adjustment module is used to formulate an adaptive communication mode switching mechanism based on the communication reliability level, dynamically adjust the data communication mode, and establish a data forwarding path by establishing a node collaborative relay communication mechanism within the monitoring area. The abnormal water usage identification module is used to extract user water usage change characteristics based on time series analysis of water metering data, and to identify abnormal water usage behavior based on user water usage change characteristics. The dynamic data sampling and scheduling module is used to establish a meter reading frequency adjustment model based on the characteristics of user water usage changes and communication environment quality indicators, so as to realize dynamic sampling and scheduling of water metering data. The water meter monitoring and management module is used to evaluate meter reading quality by building a meter reading task scheduling and supplementary reading management mechanism, and to design a low-power sleep and fast wake-up collaborative control mechanism to monitor battery power consumption in real time.

10. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it is used to implement the steps of the remote automatic meter reading method for smart water meters based on NB-IoT as described in any one of claims 1-8.

Images