A method and system for remote meter reading and water usage information management for smart water meters.

Abstract

This invention provides a method and system for remote meter reading and water usage information management for smart water meters. The method mainly includes: receiving raw data frames containing information such as cumulative flow values ​​and collection timestamps, and performing multi-dimensional quality checks to generate collection status identifiers; dividing the water usage interval into valid collection intervals and pending confirmation intervals based on the identifiers; calculating the measured water usage in the valid intervals and extracting abnormal features to generate initial abnormal events; generating a completion sequence for the pending confirmation intervals based on boundary flow values, historical water usage characteristics, and regional balance constraints; determining a supplementary reading control strategy and issuing supplementary reading instructions based on the completion sequence and other information; performing secondary verification of the initial abnormal events based on the supplementary data, and outputting the final abnormal result and meter reading data. This method improves the accuracy of water usage anomaly detection and the reliability of meter reading data through data quality grading, interval hierarchical management, and closed-loop verification of abnormal events.

Description

Technical Field

[0001] This application relates to the fields of smart water management and Internet of Things technology, and in particular to a method and system for remote meter reading and water usage information management for smart water meters. Background Technology

[0002] With the deepening of smart water management construction, smart water meters have become the mainstream device for water metering for residential, commercial, and industrial users. Smart water meters can periodically upload data such as cumulative flow and device status to a cloud management platform through wireless communication networks such as NB-IoT and LoRa or concentrators. Water supply companies can then use this data to achieve remote meter reading management, water usage analysis, and anomaly monitoring.

[0003] In related technologies, remote meter reading systems typically adopt the following technical approach: smart water meters upload data frames containing water meter identification, cumulative flow value, and collection timestamps at preset intervals; after receiving the data, the cloud platform uses the cumulative flow difference between adjacent times as the interval water consumption and performs statistical analysis based on the water consumption time series; when water consumption is detected to deviate significantly from the normal range, the system generates an abnormal alarm and pushes it to the operation and maintenance personnel.

[0004] However, in practice, due to complex communication environments, unstable power supply, and network transmission interference, uploaded raw data frames often exhibit packet loss, timestamp offsets, sudden spikes in cumulative traffic, or regressions. Related technologies typically process this raw data by directly performing statistical analysis or anomaly identification on the raw reported data. This leads to data loss caused by communication failures being mistakenly interpreted as water outages, and sudden spikes in traffic caused by clock drift being mistaken for water leaks, thus generating false alarms and reducing the reliability of remote meter reading systems. Summary of the Invention

[0005] This application provides a method and system for remote meter reading and water usage information management for smart water meters, which is used to improve the reliability of remote meter reading systems.

[0006] Firstly, this application provides a method and system for remote meter reading and water usage information management for smart water meters. The method includes: receiving uploaded raw data frames, which at least include cumulative flow value, collection timestamp, device status code, communication quality parameters, device power parameters, and collection path information; based on the raw data frames, detecting field integrity, monotonicity of the cumulative flow value, synchronization status of the collection timestamp, consistency of the device status code, and consistency of the collection path information, and generating a collection status identifier; dividing the water usage interval corresponding to the raw data frames into valid collection intervals and unconfirmed intervals according to the collection status identifier; calculating the measured water usage in the valid collection interval based on adjacent raw data frames, and extracting nighttime continuous flow and water usage transition amplitude based on the measured water usage in the valid collection interval. The system generates initial abnormal events based on the characteristics of degree, number of reverse changes, and duration of zero values. It then generates a supplementary sequence based on the cumulative flow value at the boundary of the interval to be confirmed, historical water usage characteristics within the same period, and preset related water meter or regional balance constraints. This supplementary sequence is used to determine the supplementary reading time window, reporting cycle, retransmission count, and acquisition path. Based on the acquisition status identifier, communication quality parameters, device power parameters, supplementary sequence, and initial abnormal events, a supplementary reading control strategy is determined. Supplementary reading control commands are issued to smart water meters or concentrators according to the supplementary reading control strategy. The system receives supplementary data frames corresponding to the supplementary reading control commands and, based on the supplementary data frames and the original data frames within the effective acquisition interval, confirms or eliminates the initial abnormal events, outputting the final abnormality type, abnormality level, abnormal alarm record, and remote meter reading results.

[0007] In the above embodiments, the system constructs a set of relevant water meters based on the water supply topology and introduces regional balance constraints as boundary conditions for generating the completion sequence. By acquiring the actual collected data of relevant water meters within the same zone and the zone inlet metering data, the single-meter data completion problem is transformed into an optimization problem that satisfies the regional water balance. This ensures that the completion sequence not only conforms to the historical patterns of single meters but is also subject to the physical constraint of the conservation of total water volume in the zone. This multi-meter collaborative constraint elevates the completion process to a spatial-temporal joint inference that conforms to the physical laws of the water supply network, reducing completion deviations caused by historical data biases of single meters or sudden changes in water usage patterns, and improving the rationality of water consumption estimation for the area to be confirmed.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the preset related water meter or area balance constraint includes: determining a set of related water meters that are in the same building, unit, branch road, or zone as the current water meter based on the water supply topology; obtaining the actual collected data of the related water meter set in the corresponding time period of the interval to be confirmed, as well as the inlet metering data of the zone to which the interval to be confirmed belongs; determining the actual water consumption change trend and zone summary metering data of the related water meter set based on the actual collected data, and using the difference between the actual water consumption change trend and inlet metering data and the zone summary metering data not exceeding a preset threshold as the preset related water meter or area balance constraint.

[0009] In the above embodiment, the system uses the cumulative flow difference at the boundary of the interval to be confirmed as the total amount to be allocated, and generates an initial allocation sequence based on historical water usage characteristics of the same period. This initial allocation is then corrected based on the actual water usage trends of the relevant water meters. The initial allocation of the total amount to be allocated using historical water usage characteristics of the same period captures typical fluctuation patterns in water usage over a day or week. Subsequently, the actual water usage trends of the relevant water meter set in the corresponding time period are introduced as a basis for real-time correction, combining historical patterns with the current situation. Finally, under the condition of satisfying regional balance constraints, a supplementary sequence is output, ensuring that the supplementary result conforms to historical statistical patterns and is consistent with the actual water usage behavior of neighboring water meters during the same period. This hierarchical correction transforms the supplementary process from a single time series prediction to a comprehensive inference based on multi-source information integrating historical patterns, real-time trends, and physical constraints, reducing the deviation between the supplementary sequence and the actual water usage process.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, for the interval to be confirmed, a completion sequence is generated based on the cumulative flow value at the boundary of the interval to be confirmed, historical water consumption characteristics in the same period, and preset relevant water meter or regional balance constraints. Specifically, this includes: determining the starting boundary and the ending boundary of the interval to be confirmed; using the difference between the cumulative flow value at the starting boundary and the cumulative flow value at the ending boundary as the total amount to be allocated; allocating the total amount to be allocated based on the historical water consumption characteristics in the same period to generate an initial allocation sequence; correcting the initial allocation sequence based on the actual water consumption change trend of the relevant water meter set in the corresponding time period, and outputting the completion sequence under the condition of satisfying the regional balance constraints.

[0011] In the above embodiments, the system performs consistency checks on five dimensions of the original data frame and generates corresponding acquisition status identifiers based on different check results. By setting five detection dimensions—field integrity, cumulative traffic monotonicity, timestamp synchronization status, device status code consistency, and acquisition path consistency—the system classifies the original data frame into valid acquisition status, missing item status, clock out-of-sync status, path conflict status, and device abnormal status, achieving fine-grained labeling of data quality. This multi-dimensional data quality grading enables the system to identify and mark unreliable data at the forefront of data processing, avoiding the misinterpretation of communication-level or device-level faults as real changes in data behavior, and providing a reliable data quality foundation for subsequent interval division and anomaly identification.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, based on the original data frame, the integrity of fields, the monotonicity of cumulative traffic values, the synchronization status of collection timestamps, the consistency of device status codes, and the consistency of collection path information are detected to generate collection status identifiers. Specifically, this includes: generating a valid collection status identifier when the integrity of fields, the monotonicity of cumulative traffic values, the synchronization status of collection timestamps, the consistency of device status codes, and the consistency of collection path information all meet the corresponding preset conditions; generating a missing item status identifier when the integrity of fields does not meet the preset conditions; generating a clock out-of-synchronization status identifier when the synchronization status of collection timestamps does not meet the preset conditions; generating a path conflict status identifier when the consistency of collection path information does not meet the preset conditions; and generating a device abnormal status identifier when the consistency of device status codes does not meet the preset conditions.

[0013] In the above embodiments, the system divides water usage intervals into valid collection intervals and pending confirmation intervals based on the collection status identifier, and makes a decision based on communication quality parameters and device status code consistency when path conflicts exist. Dividing the water usage timeline into valid collection intervals that can be directly used for calculation and pending confirmation intervals that require further confirmation through collection status identifiers achieves spatial management of data reliability. When multiple reporting paths exist within the same time window, communication quality parameters and device status codes are introduced as the basis for decision-making, prioritizing data frames whose communication quality meets the threshold and whose device status is normal. This interval layering based on data quality transforms data processing from undifferentiated full-volume calculation to a layered strategy where reliable intervals are directly used and suspicious intervals require confirmation, avoiding errors in water usage calculation and anomaly identification bias caused by the introduction of unreliable data.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the water usage interval corresponding to the original data frame is divided into a valid acquisition interval and a pending confirmation interval based on the acquisition status identifier. Specifically, this includes: determining the water usage interval between two adjacent original data frames corresponding to valid acquisition status identifiers as a valid acquisition interval; determining the water usage interval corresponding to an original data frame containing a missing item status identifier, a clock out-of-synchronization status identifier, a path conflict status identifier, or a device abnormal status identifier as a pending confirmation interval; and when there are original data frames uploaded through different acquisition paths within the same time window, determining the valid acquisition interval using original data frames whose communication quality parameters meet a preset threshold and whose device status code consistency meets a preset condition.

[0015] In the above embodiments, the system comprehensively determines the data replenishment control strategy based on the distribution of water consumption for replenishment in each time period of the replenishment sequence, the duration of the interval to be confirmed, the type of the initial abnormal event, equipment power parameters, and communication quality parameters. This multi-parameter joint decision-making replenishment control transforms the replenishment process from coarse-grained full retransmission to targeted and precise replenishment, reducing the amount of invalid data transmission and equipment energy consumption, and improving the matching degree between replenishment data and abnormal confirmation requirements.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, a supplementary data collection control strategy is determined based on the data collection status identifier, communication quality parameters, device power parameters, supplementary sequence, and initial abnormal event. Specifically, this includes: determining the supplementary data collection time window based on the supplementary water consumption distribution corresponding to each time period in the supplementary sequence; determining the reporting cycle and retransmission count based on the duration of the interval to be confirmed and the number of time slices corresponding to the supplementary sequence; determining a short-cycle supplementary data collection strategy when the initial abnormal event is generated by nighttime continuous flow or water consumption jump amplitude characteristics, and the device power parameters are higher than a preset power threshold; determining a path switching strategy when the communication quality parameters are lower than a preset communication threshold; determining a segmented supplementary data collection strategy when the duration of the interval to be confirmed is greater than a preset duration; and writing the determined supplementary data collection time window, reporting cycle, retransmission count, and data collection path into the determined supplementary data collection control strategy.

[0017] In the above embodiments, the system merges the supplementary data frame into the original data frame corresponding to the time period of the initial abnormal event, recalculates the measured water consumption of the interval, and re-extracts the abnormal features. Based on the re-extraction results, it decides whether to confirm the initial abnormal event. This secondary verification of anomalies based on data completion transforms anomaly identification from a single judgment to a closed-loop process of "initial judgment - supplementary data - re-judgment," reducing the false alarm rate caused by missing data and improving the credibility of anomaly alarms.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the initial abnormal event is confirmed or excluded based on the supplementary data frame and the original data frame within the effective acquisition interval. Specifically, this includes: merging the supplementary data frame into the original data frame of the time period corresponding to the initial abnormal event, and recalculating the corresponding measured water consumption of the interval; re-extracting the nighttime continuous flow, water consumption jump amplitude, number of reverse changes, and zero value duration features based on the re-calculated measured water consumption of the interval; when the initial abnormal event is generated again based on the re-extracted nighttime continuous flow, water consumption jump amplitude, number of reverse changes, and zero value duration features, the initial abnormal event is confirmed, and the final abnormality type, abnormality level, and abnormal alarm record are output; otherwise, the initial abnormal event is excluded and the remote meter reading result is output.

[0019] In the above embodiment, after receiving the original data frame, the system first performs multi-dimensional data quality detection and generates a collection status identifier. Based on the identifier, the water usage area is divided into a valid collection area and a pending confirmation area. For the valid collection area, abnormal features are extracted to generate initial abnormal events. For the pending confirmation area, a supplementary sequence is generated based on boundary cumulative flow, historical same-period characteristics, and regional balance constraints. Then, a supplementary reading control strategy is determined and a supplementary reading instruction is issued. Finally, the initial abnormal events are verified a second time based on the supplementary data frame. This embodiment constructs a complete technical chain from data quality detection, interval hierarchical management, initial anomaly judgment, intelligent supplementary reading, to anomaly re-judgment. It transforms the remote meter reading system into a closed-loop management process that proactively assesses data quality, supplements missing data as needed, and verifies anomalies based on complete data. This reduces misjudgments caused by data acquisition layer failures and improves the reliability of remote meter reading results and anomaly alarms.

[0020] In a second aspect, embodiments of this application provide a remote meter reading and water usage information management device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the remote meter reading and water usage information management device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a remote meter reading and water information management device, cause the remote meter reading and water information management device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a remote meter reading and water usage information management device, cause the remote meter reading and water usage information management device to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the remote meter reading and water usage information management equipment provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. This application divides the water usage area into a valid collection area and a pending confirmation area by performing multi-dimensional quality inspection on the original data frame and generating a collection status identifier. This avoids mistaking data loss caused by communication failure as water outage, reduces misjudgment caused by data collection failure, and improves the reliability of the remote meter reading system.

[0026] 2. This application generates a supplementary sequence by integrating the cumulative flow at the boundary of the interval to be confirmed, the historical water consumption characteristics of the same period, and the regional balance constraints. This transforms the inference of single-meter data into a multi-meter collaborative spatial-temporal joint inference that satisfies the physical conservation law of the water supply network. This reduces the supplementary deviation caused by the bias of historical data of single meters and improves the rationality of water consumption estimation.

[0027] 3. This application transforms anomaly identification from a single judgment to a "preliminary judgment-supplementary data-re-judgment" mode through a closed-loop process of secondary verification of initial abnormal events based on supplementary data frames. This effectively reduces false alarms such as water leakage caused by sudden flow jumps due to clock drift, and improves the reliability of abnormal alarms and the accuracy of remote meter reading results. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a remote meter reading and water usage information management method in an embodiment of this application.

[0029] Figure 2 This is a schematic diagram of the physical device structure of a remote meter reading and water information management device in the embodiments of this application. Detailed Implementation

[0030] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0031] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0032] The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a remote meter reading and water usage information management method in an embodiment of this application.

[0033] S101. Receive the uploaded raw data frame. The raw data frame includes at least the cumulative traffic value, collection timestamp, device status code, communication quality parameters, device power parameters, and collection path information.

[0034] The raw data frame refers to the data packet that the smart water meter or concentrator periodically uploads to the cloud management platform according to a preset communication protocol, encapsulating snapshot information of the water meter's operating status. The cumulative flow value represents the total flow measured by the water meter since it left the factory or was reset, and is recorded in a monotonically increasing manner; the acquisition timestamp is used to identify the time when the data frame was generated and is controlled by the water meter's internal clock; the device status code is an encoded identifier of the device's operating status, used to indicate device-level information such as valve status, low battery voltage, and sensor failure; communication quality parameters include network layer indicators such as signal strength RSSI, signal-to-noise ratio SNR, or number of retransmissions; the device power parameter represents the water meter's current remaining power percentage or voltage value; the acquisition path information records the communication path identifier through which the data frame travels, such as direct connection to a base station or relay via a specific concentrator.

[0035] In scenarios where smart water meters periodically report data or cloud platforms actively query data, the system needs to receive and parse the uploaded raw data frames. Specifically, the cloud management platform receives the raw data frames through the NB-IoT network interface or LoRa gateway, extracts the device identifier from the protocol header, unpacks each field in the data frame according to a predefined format, and stores it in a time-series database. For example, a residential user's water meter uploads a raw data frame at 2:00 AM containing a cumulative flow value of 123.45 cubic meters, a collection timestamp of 2026-03-19 02:00:00, a device status code of 0x00 (normal), an RSSI value of -85dBm, a remaining power of 78%, and a path identifier pointing to concentrator number C001. This complete reception mechanism provides the system with the original proof of water usage measurement. However, at this point, the data frame has not yet undergone quality verification and may have missing fields due to communication interference or timestamp deviations caused by clock drift. Therefore, subsequent testing is required to assess the reliability of the received raw data frames to avoid misinterpreting data acquisition layer faults as changes in actual water usage behavior.

[0036] S102. Based on the original data frame, the integrity of the fields, the monotonicity of the cumulative traffic value, the synchronization status of the collection timestamp, the consistency of the device status code, and the consistency of the collection path information are checked, and a collection status identifier is generated.

[0037] Among these, field integrity refers to the detection dimension of whether all required fields in the original data frame exist and are not empty; monotonicity detection targets the cumulative flow value, requiring the current value to be no less than the historical maximum value, used to identify flow decline or sudden jumps; synchronization status detection assesses whether the deviation between the acquisition timestamp and the server clock is within the allowable range, preventing timing chaos caused by clock drift; device status code consistency detection judges whether there are unreasonable jumps in status codes compared to historical states, such as jumping directly from normal to serious fault; acquisition path consistency detection verifies whether the path identifier matches the historical communication path of the water meter, identifying abnormal path switching. The acquisition status identifier is an enumerated value label of the original data frame quality assessment result, used for subsequent interval division and supplementary reading decisions.

[0038] Upon receiving the raw data frame, the system must perform quality checks and assign status tags before data is entered into the database. Specifically, the system first checks the completeness of fields; if the cumulative flow value or the collection timestamp field is missing, a missing item status flag is generated. Secondly, the system performs a monotonicity check on the cumulative flow value, comparing the current value with the water meter's historical maximum cumulative flow; if a regression occurs, an anomaly is marked. Next, the system compares the collection timestamp with the server's UTC time; if the deviation exceeds 5 minutes, a clock out-of-synchronization status flag is generated. For device status codes, the system checks whether the transition between the status code and the previous frame conforms to the state machine rules. Finally, the system verifies the collection path information; if the water meter has historically reported stably via concentrator C001, but this time suddenly shows a direct connection to the base station, a path conflict status flag is generated. For example, if an industrial user's water meter uploads a data frame with the cumulative flow jumping from 456.78 cubic meters to 455.20 cubic meters, the monotonicity check fails, and the system generates a pending confirmation flag instead of directly recording the water consumption. This multi-dimensional quality inspection places data credibility assessment at the forefront, enabling the system to identify communication layer or equipment layer faults at the very beginning of data processing, providing a reliable quality grading basis for subsequent interval division.

[0039] In some preferred embodiments, the above steps specifically include: generating a valid collection status identifier when the field integrity, the monotonicity of the cumulative traffic value, the synchronization status of the collection timestamp, the consistency of the device status code, and the consistency of the collection path information all meet the corresponding preset conditions.

[0040] When the field integrity does not meet the preset conditions, a missing item status flag is generated; when the synchronization status of the collected timestamp does not meet the preset conditions, a clock out-of-synchronization status flag is generated; when the consistency of the collected path information does not meet the preset conditions, a path conflict status flag is generated; when the consistency of the device status code does not meet the preset conditions, a device abnormal status flag is generated.

[0041] Specifically, the system performs five checks in a preset detection order: First, it checks whether required fields such as cumulative flow value and collection timestamp exist and are not empty. If any field is missing, it is immediately marked as missing and subsequent checks are terminated. After passing the check, it proceeds to monotonicity detection, comparing the current cumulative flow value with the maximum cumulative flow recorded in the water meter's historical database. If there is a regression or an increase exceeding the physically possible range (such as an increase of 100 cubic meters per hour), it is marked as abnormal. The third step is to calculate the deviation between the collection timestamp and the server's UTC time. If the deviation exceeds a preset threshold (usually 5 minutes), a clock out-of-synchronization flag is generated. The fourth step is to retrieve the historical change records of the device status code to verify whether the current status code has changed from a reasonable predecessor status. For example, a direct change from "normal" to "serious fault" is an unreasonable transition. Finally, it checks the collection path information. If the water meter has been reporting stably through the concentrator C001 for the past 30 days but suddenly shows a direct connection to the base station, a path conflict flag is generated.

[0042] S103. Based on the data collection status identifier, the water usage interval corresponding to the original data frame is divided into a valid data collection interval and a pending confirmation interval.

[0043] The effective acquisition interval refers to the time interval between two adjacent raw data frames where both acquisition status indicators are valid. The cumulative flow difference within this interval can be directly used for water consumption calculation. The pending confirmation interval refers to the time interval corresponding to data frames containing non-valid acquisition status indicators. The water consumption within this interval cannot be directly determined from the raw data frames and needs to be obtained through padding or inference. The water consumption interval refers to the time period between the acquisition times of any two adjacent raw data frames, representing the time window in which actual water consumption occurs.

[0044] After completing data quality checks, the system needs to divide the continuous water usage timeline into reliable and suspicious intervals based on the acquisition status identifiers. Specifically, the system traverses the original data frame sequence in chronological order. When two consecutive frames are marked as valid acquisition states, the water usage interval between these two frames is defined as the valid acquisition interval. When a frame is marked as missing, clock out of sync, path conflict, or equipment malfunction, the water usage intervals before and after that frame are defined as the unconfirmed interval. In cases where multiple paths are transmitting within the same time window, the system prioritizes data frames with communication quality parameters satisfying RSSI > -90dBm and a normal equipment status code to determine the valid acquisition interval.

[0045] For example, a commercial user's water meter uploaded 24 hours of data frames between 8:00 on March 18 and 8:00 on March 19. Among them, four data frames from 22:00 to 2:00 the next day were marked as pending confirmation due to clock synchronization issues. The system divided the period from 8:00 to 22:00 and from 2:00 to 8:00 into two valid collection intervals, and the period from 22:00 to 2:00 into the pending confirmation interval.

[0046] In some preferred embodiments, the above steps specifically include: determining the water usage interval between two adjacent raw data frames corresponding to valid acquisition status identifiers as valid acquisition intervals; determining the water usage interval corresponding to raw data frames containing missing item status identifiers, clock out-of-synchronization status identifiers, path conflict status identifiers, or device abnormal status identifiers as intervals to be confirmed; when there are raw data frames uploaded through different acquisition paths within the same time window, determining the valid acquisition interval using raw data frames whose communication quality parameters meet a preset threshold and whose device status codes meet preset consistency conditions.

[0047] Specifically, the system sorts the raw data frames according to the acquisition timestamp and then performs a sequential scan: when two adjacent frames both have valid acquisition status identifiers, the time period between the two frames is designated as a valid acquisition interval; when a data frame marked as missing item, clock out of sync, path conflict, or device malfunction is scanned, the entire time period between the preceding and following valid frames is designated as a pending confirmation interval. For cases of path conflicts within the same time window, the system triggers a path adjudication mechanism: extracting the RSSI value and device status code of the data frames reported by each path, prioritizing data frames with RSSI > -90dBm and a status code of 0x00 (normal) as valid data, designating the interval corresponding to that path as a valid acquisition interval, and designating the intervals corresponding to other conflicting paths as pending confirmation intervals. For example, a business user uploads 24 frames of data within 24 hours. Four frames from 22:00 to 2:00 the next day are marked due to clock synchronization issues. The system divides 8:00-22:00 and 2:00-8:00 the next day into two valid collection intervals, and 22:00-2:00 into an interval to be confirmed.

[0048] S104. Calculate the measured water consumption in the effective acquisition interval based on adjacent raw data frames, and extract the characteristics of nighttime continuous flow, water consumption jump amplitude, number of reverse changes and duration of zero value based on the measured water consumption in the effective acquisition interval to generate the initial abnormal event.

[0049] Among them, the measured water consumption within a specific interval refers to the difference in cumulative flow values ​​between two adjacent raw data frames within an effective acquisition interval, representing the actual water consumption during that time interval; the nighttime continuous flow characteristic indicates a persistent low flow rate during nighttime hours (e.g., 23:00 to 5:00 the next day), which may indicate leakage or equipment dripping; the water consumption jump amplitude characteristic refers to a significant abrupt change in water consumption between adjacent intervals, exceeding several times the standard deviation of the historical average; the reverse change frequency characteristic counts the number of times the cumulative flow value decreases, which should normally be zero; the zero value duration characteristic records the duration for which water consumption is zero in multiple consecutive intervals, which may indicate meter malfunction or long-term vacancy. The initial abnormal event is a record of suspicious water consumption behavior initially identified based on the above characteristics, including the abnormality type, the time of occurrence, and the characteristic parameter value.

[0050] Once the effective data collection interval is determined, the system needs to calculate water consumption and extract anomaly features based on reliable data. Specifically, the system sorts the original data frames within the effective data collection interval by collection timestamp and calculates the difference in cumulative flow values ​​between adjacent data frames to obtain the measured water consumption sequence for the interval. Subsequently, the system extracts four types of anomaly features: detecting whether there is a continuous low flow rate during the nighttime period (such as 0.05-0.2 cubic meters of water consumption per hour for 4 consecutive hours), if so, generating a nighttime continuous flow anomaly event; calculating the coefficient of variation of water consumption in each interval, if the water consumption of a certain interval exceeds the historical average for the same period plus 3 times the standard deviation, generating a water consumption jump anomaly event; traversing the cumulative flow sequence to check whether there is a situation where the current value is less than the previous value, if so, generating a reverse change anomaly event; and counting the duration of consecutive zero-value intervals, if it exceeds 7 days, generating a zero-value continuous anomaly event.

[0051] In one example, a residential user's hourly water consumption was 0.08, 0.09, and 0.08 cubic meters between 2:00 AM and 5:00 AM on March 18th. The system identified this as a continuous nighttime flow characteristic and generated an initial anomaly event. This anomaly feature extraction based on the effective data collection interval ensures the reliability of the data foundation for anomaly identification. However, since the interval to be confirmed is not yet complete, the initial anomaly event may be a false alarm caused by missing data. Therefore, secondary verification is required after the data is supplemented.

[0052] S105. Based on the cumulative flow value at the boundary of the interval to be confirmed, the historical water use characteristics of the same period, and the preset relevant water meter or regional balance constraints, generate a supplementary sequence. The supplementary sequence is used to determine the supplementary reading time window, reporting cycle, number of retransmissions, and collection path.

[0053] Among them, the completion sequence refers to the time-water consumption inference sequence generated for the interval to be confirmed, used to fill in the water consumption estimation for the missing data period; historical water consumption characteristics refer to the typical water consumption distribution pattern of the water meter in the same historical period (such as weekday night or weekend day); related water meters refer to other water meters at the same water supply topology level as the current water meter, such as the same building, unit, or branch; regional balance constraint refers to the physical constraint that the sum of water consumption of all water meters in a zone should be consistent with the inlet metering value of the zone. The supplementary reading time window indicates the time range within which the smart water meter or concentrator needs to re-collect data; the reporting cycle refers to the data upload frequency required by the supplementary reading command; the retransmission count is the maximum number of times the supplementary reading data frame is allowed to be repeatedly sent; and the acquisition path specifies the communication path identifier that should be used for supplementary reading data.

[0054] Once the interval to be confirmed is identified, the system needs to generate a completion sequence based on the known boundary cumulative flow and multi-source information. Specifically, the system extracts the cumulative flow values ​​of the starting and ending boundaries of the interval to be confirmed, and calculates the difference between the two as the total amount to be allocated for that interval. Then, it retrieves the water consumption distribution curves of the water meter for the same time period over the past four weeks (e.g., all weekdays or all weekends) from the historical database, and performs a preliminary allocation of the total amount to be allocated according to the historical typical distribution ratio, generating an initial allocation sequence. Next, it determines the relevant water meter set based on the water supply topology, obtains the actual collected data of these water meters for the corresponding time period of the interval to be confirmed, extracts the actual water consumption trend as a correction basis, and adjusts the initial allocation sequence. Finally, it introduces regional balance constraints to ensure that the sum of the current water meter completion sequence and the actual water consumption of the relevant water meters does not exceed the reasonable range of the zone inlet metering value.

[0055] In a specific example, a residential user's water meter recorded a cumulative flow difference of 0.8 cubic meters at the boundary of the interval to be confirmed between 22:00 on March 18th and 2:00 the following day. Based on historical nighttime water usage patterns, the system allocated this to four hours at 20%, 15%, 10%, and 5%. Then, considering the decreasing water usage trend of three other households in the same building during the same period, the allocation sequence was adjusted to 0.24, 0.20, 0.18, and 0.18 cubic meters. This result was verified to have a metering deviation within 5% of the zone's inlet measurement. This mechanism for generating a completion sequence, integrating historical patterns, real-time trends, and physical constraints, transforms single-meter data inference into a multi-meter collaborative spatial-temporal joint inference, reducing completion deviations and providing a time window and periodic basis for subsequent accurate meter readings.

[0056] In some preferred embodiments, the preset related water meter or area balance constraints include: determining the related water meter set that is in the same building, unit, branch road or zone as the current water meter according to the water supply topology; obtaining the actual collected data of the related water meter set in the time period corresponding to the interval to be confirmed, as well as the inlet metering data of the zone to which the interval to be confirmed belongs;

[0057] Based on actual collected data, the actual water usage trend and zone summary metering data of the relevant water meter set are determined, and the difference between the actual water usage trend and the inlet metering data and the zone summary metering data does not exceed a preset threshold as a preset balance constraint for the relevant water meters or regions.

[0058] Among them, the water supply topology refers to the physical connection structure and subordinate relationship between water meters in the water supply network, which is usually organized in a hierarchical form of zone-branch-unit-building; the actual collected data refers to the valid data frames successfully uploaded by the relevant water meters in the corresponding time period of the interval to be confirmed and the water consumption calculated therefrom; the zone inlet metering data is the total water volume entering the zone recorded by the main meter or flow meter set at the inlet of the water supply zone; the actual water consumption change trend is obtained by fitting the water consumption time series of the relevant water meter set, reflecting the overall water consumption behavior pattern of the area in a specific time period; the zone summary metering data is the theoretical total amount of the actual water consumption of the relevant water meter set and the current water meter completion sequence; the preset threshold is usually set to 5%-10% of the zone inlet metering data to allow for reasonable network leakage and metering error.

[0059] When generating the completion sequence, the system needs to construct multi-meter collaborative constraints to improve inference accuracy. Specifically, the system first queries the water supply topology database for the building, unit, branch, and zone information to which the current water meter belongs, and then filters the relevant water meter set according to the topological hierarchy. For example, if water meter No. 3 in Unit 2 of a 6-story residential building has an unconfirmed interval, the system extracts 6 water meters from No. 1 to No. 6 in the same unit as the relevant water meter set. Subsequently, the system obtains the actual collected data of these 6 water meters during the corresponding time period of the unconfirmed interval (e.g., from 22:00 on March 18 to 2:00 the next day), calculates the water consumption of each household, and sums them to obtain the zone summary metering data of the relevant water meter set. At the same time, it obtains the water inflow of 1.2 cubic meters during this period from the zone inlet master meter.

[0060] The system uses the actual water usage trends of the relevant water meter set (e.g., 60% of water usage from 22:00 to 24:00 and 40% from 0:00 to 2:00) as the basis for correction, adjusting the initial allocation sequence of water meter No. 3. After adjustment, the system verifies whether the deviation between the total water consumption of 6 households (including the supplementary value of No. 3) and the inlet meter reading of 1.2 cubic meters is less than 0.1 cubic meters (8.3%). If the deviation is too large, the system iteratively adjusts the supplementary sequence until the regional balance constraint is met. This multi-meter collaborative constraint based on topological relationships transforms the single-meter data supplementation problem into an optimization problem that satisfies the physical conservation law of the water supply network. This makes the supplementation result subject to the triple constraints of historical patterns, real-time behavior of neighboring water meters, and total volume conservation in the zone, which can reduce the supplementation deviation caused by historical deviations of single meters or sudden changes in water usage patterns.

[0061] In some preferred embodiments, step S105 specifically includes: determining the starting boundary and the ending boundary of the interval to be confirmed; taking the difference between the cumulative flow value at the starting boundary and the cumulative flow value at the ending boundary as the total amount to be allocated; allocating the total amount to be allocated according to the historical water use characteristics of the same period to generate an initial allocation sequence; correcting the initial allocation sequence according to the actual water use change trend of the relevant water meter set in the corresponding time period, and outputting the supplementary sequence under the condition of satisfying the regional balance constraint.

[0062] The starting and ending boundaries represent the times corresponding to the valid data frames collected before and after the interval to be confirmed, respectively; the total amount to be allocated is the difference between the cumulative flow at the starting and ending boundaries of the interval to be confirmed, representing the total water consumption that actually occurred in the interval but was not recorded in each time period; the initial allocation sequence is a preliminary result of decomposing the total amount to be allocated in time dimension according to the historical water consumption characteristics of the same period, and has not yet considered the current real-time trend.

[0063] After determining the interval to be confirmed, the system needs to convert the cumulative flow at the boundary into a time-resolved completion sequence. Specifically, the system first identifies the start and end times of the interval to be confirmed and extracts the corresponding cumulative flow values. For example, a water meter has a cumulative flow of 123.40 cubic meters at 22:00 on March 18 (the previous valid point of the termination boundary) and a cumulative flow of 124.20 cubic meters at 2:00 on March 19 (the next valid point of the start boundary), with a total amount to be allocated of 0.80 cubic meters. The system retrieves the hourly water consumption distribution of this water meter from the historical database for the past four weeks during the weekday nighttime hours of 22:00-2:00, calculates the typical distribution ratios as 30%, 25%, 25%, and 20%, and generates an initial allocation sequence of {0.24, 0.20, 0.20, 0.16} cubic meters.

[0064] Then, the actual water consumption data of the other 5 households in the same unit from 22:00 to 2:00 on March 18th was obtained. The actual trend showed a decreasing pattern (hourly percentages of 35%, 30%, 20%, and 15%). Based on this, the system corrected the initial allocation sequence to {0.28, 0.24, 0.16, 0.12} cubic meters. Finally, the corrected sequence was added to the actual water consumption of the 5 households. The deviation between the total and the 1.2 cubic meter metered at the zone inlet was verified to be 0.08 cubic meters (6.7%), meeting the preset threshold requirement. The final completed sequence was then output.

[0065] S106. Determine the data acquisition control strategy based on the data acquisition status identifier, communication quality parameters, device power parameters, completion sequence, and initial abnormal events.

[0066] Among them, the supplementary data collection control strategy is a set of data supplementary collection instructions generated for the interval to be confirmed, including control parameters such as supplementary data collection time window, reporting cycle, number of retransmissions and collection path; the short-cycle supplementary data collection strategy refers to shortening the reporting cycle to the minute or hour level, which is used to quickly verify anomalies such as continuous flow at night or water use jumps; the path switching strategy is to instruct the water meter or concentrator to switch to the backup communication path when the quality of the current communication path is poor; the segmented supplementary data collection strategy is to divide the long interval to be confirmed into multiple short time periods and issue supplementary data collection instructions in batches to reduce the burden of single transmission and equipment energy consumption.

[0067] After generating the completion sequence and identifying initial abnormal events, the system needs to formulate targeted data transfer control strategies. Specifically, the system first analyzes the water consumption distribution for each time period in the completion sequence, identifying periods with significant water consumption changes as key time windows for data transfer. For example, if the completion sequence shows that water consumption from 22:00 to 23:00 is 0.28 cubic meters, significantly higher than other time periods, the system includes this period in the data transfer time window. Subsequently, based on the duration of the interval to be confirmed (e.g., 4 hours) and the number of time slices in the completion sequence (4), the reporting cycle is determined to be 1 hour, and the number of retransmissions is set to 3 to cope with communication instability.

[0068] When the initial abnormal event is continuous flow at night and the device battery level is above 50%, the system activates a short-cycle data collection strategy, shortening the reporting cycle to 15 minutes to capture fine-grained flow changes. If the communication quality parameter RSSI is below -95dBm, the system triggers a path switching strategy, specifying the water meter to switch to the backup concentrator path in the data collection control command. If the duration of the interval to be confirmed exceeds 24 hours, the system adopts a segmented data collection strategy, dividing the interval into six 4-hour segments and issuing data collection commands six times. Finally, the determined data collection time window [22:00-2:00], reporting cycle of 1 hour, retransmission count of 3, and acquisition path C002 are written into the data collection control strategy and prepared for issuance.

[0069] In some preferred embodiments, the above steps determine the supplementary data collection time window based on the supplementary water consumption distribution corresponding to each time period in the supplementary sequence; and determine the reporting cycle and retransmission number based on the duration of the interval to be confirmed and the number of time slices corresponding to the supplementary sequence.

[0070] When the initial abnormal event is generated by the continuous flow at night or the amplitude characteristics of water usage transition, and the device power parameter is higher than the preset power threshold, a short-cycle re-reading strategy is determined; when the communication quality parameter is lower than the preset communication threshold, a path switching strategy is determined; when the duration of the interval to be confirmed is longer than the preset duration, a segmented re-reading strategy is determined; the determined re-reading time window, reporting cycle, retransmission count, and acquisition path are written into the determined re-reading control strategy.

[0071] Among them, water consumption distribution supplementation refers to supplementing the value and trend of water consumption corresponding to each time slice in the sequence, which is used to identify the time period that needs to be focused on; the number of time slices refers to the number of segments after dividing the interval to be confirmed into fixed time intervals (such as hours or 15 minutes); the short cycle supplementation strategy shortens the reporting cycle to 15 minutes or 30 minutes, which is suitable for anomaly types that require high time resolution verification; the path switching strategy instructs the water meter to switch from the current communication path to the backup path, such as switching from the concentrator relay to the direct connection base station; the segmented supplementation strategy divides the long interval to be confirmed into multiple sub-intervals and issues supplementation instructions segment by segment to distribute the communication load; the preset power threshold is usually set to 50%, and high-frequency supplementation should be avoided when it is below this threshold to extend the equipment life; the preset communication threshold is RSSI=-90dBm or SNR=5dB, and the communication success rate drops significantly when it is below this threshold; the preset duration is usually set to 24 hours, and the interval to be confirmed exceeding this duration needs to be segmented to reduce the amount of data in a single supplementation.

[0072] After generating the completion sequence, the system needs to formulate differentiated data transfer control strategies based on multi-dimensional information. Specifically, the system first analyzes the water consumption distribution of the completion sequence, identifies time slots where water consumption is significantly higher than the average or shows abrupt changes, and uses the time periods corresponding to these time slots as data transfer time windows. For example, if a completion sequence shows that water consumption is 0.28 cubic meters from 22:00 to 23:00, significantly higher than the 0.12-0.16 cubic meters in other time periods, the system marks 22:00 to 23:00 as a key data transfer period. Based on the fact that the interval to be confirmed lasts for 4 hours and the completion sequence contains 4 time slots, the reporting cycle is determined to be 1 hour, and the number of retransmissions is set to 3 to cope with possible transmission failures.

[0073] The system checks the initial abnormal event type. If it is a continuous flow anomaly at night and the device battery level is 78%, higher than the 50% threshold, a short-cycle data collection strategy is activated, shortening the reporting cycle to 15 minutes to capture fine-grained changes in the continuous flow. The communication quality parameter RSSI is checked and found to be -92dBm, lower than the -90dBm threshold. A path switching strategy is triggered, specifying in the data collection control command that the water meter switches from concentrator C001 to the direct-connected base station path. The system checks that the duration of the pending confirmation interval is 4 hours, which does not exceed the 24-hour threshold, so there is no need to activate the segmented data collection strategy. Finally, the data collection time window [22:00-2:00], reporting cycle of 15 minutes, retransmission count of 3, and data collection path of direct-connected base station are written into the data collection control strategy.

[0074] S107. Issue supplementary reading control instructions to smart water meters or concentrators according to the supplementary reading control strategy.

[0075] The concentrator is an intermediate device responsible for aggregating data from multiple water meters and communicating with the cloud. It can forward cloud-based data reading instructions to subordinate water meters. The distribution process involves the collaboration of the cloud management platform, wireless communication network, and terminal devices to ensure that the data reading instructions are accurately delivered to the target devices.

[0076] After determining the data transfer control strategy, the system needs to translate the strategy into executable instructions and send them to the target device. Specifically, the cloud management platform generates a data packet of data transfer control instructions that conforms to the communication protocol based on the data transfer control strategy. This data packet includes fields such as device identifier, instruction type (data transfer), start and end times of the time window, reporting cycle, number of retransmissions, and specified acquisition path.

[0077] For water meters directly connected to a base station, the system directly pushes the supplementary reading control command to the target water meter via the NB-IoT network. For water meters relayed via a concentrator, the system first sends the command to the concentrator, which then forwards it to the subordinate water meters via LoRa or RS485 bus. For example, for a residential user's water meter, the system generates a supplementary reading control command {Device ID: WM12345, Time Window: [2026-03-18 22:00, 2026-03-19 02:00], Period: 15 minutes, Retransmission: 3 times, Path: Direct connection to base station}, and sends it to the water meter via the NB-IoT downlink channel.

[0078] After receiving the command, the water meter retrieves historical data collection records for the corresponding time period from local storage or re-collects current data based on the time window parameters, and reports it every 15 minutes. If the reporting fails, it will retransmit up to 3 times. This mode of proactively issuing supplementary reading control commands from the cloud transforms the remote meter reading system from passively receiving data to actively collecting data on demand. Through precise time windows and flexible periodic control, the data supplementation process is highly targeted and timely, providing a timely and reliable supplementary data foundation for subsequent anomaly verification.

[0079] S108. Receive the supplementary data frame corresponding to the supplementary meter reading control command, and based on the supplementary data frame and the original data frame in the effective acquisition range, confirm or eliminate the initial abnormal event, and output the final abnormal type, abnormal level, abnormal alarm record and remote meter reading result.

[0080] The final anomaly types include categories such as continuous nighttime flow, water usage jumps, reverse changes, or persistent zero values; the anomaly levels are divided into three levels: high, medium, and low, based on the degree of deviation of the characteristic parameters; the anomaly alarm records include the anomaly type, the time of occurrence, the characteristic parameter value, the level, and the handling suggestions; the remote meter reading results are water consumption statistics reports and trend analysis results generated based on complete data.

[0081] After the supplementary data frames are uploaded to the cloud, the system needs to perform secondary verification of the initial abnormal event based on the complete data. Specifically, the system first inserts the received supplementary data frames into the original data frame sequence according to the collection timestamp. For example, if a water meter uploads 16 data frames with a 15-minute granularity during the unconfirmed interval of 22:00-2:00, the system merges these supplementary frames with the original frames from the preceding and following valid collection intervals to form a complete time series. After this, the water consumption of each interval is recalculated, and the water consumption of intervals that were previously marked as unconfirmed due to missing data can now be accurately calculated.

[0082] Based on the updated water consumption sequence, four types of abnormal features are re-extracted: The system checks whether a continuous low flow rate still exists between 22:00 and 2:00 at night. If supplementary data shows water consumption of 0.02-0.05 cubic meters every 15 minutes, and the continuous flow characteristic persists for a cumulative 4 hours, the system confirms the initial abnormal event, outputting the abnormality type as "continuous flow at night," the abnormality level as "medium," and the alarm record including the occurrence time and characteristic parameters. Maintenance personnel are advised to investigate on-site for leaks. Conversely, if supplementary data shows normal intermittent water consumption at night (e.g., a concentrated water consumption of 0.3 cubic meters at 23:30 followed by zero water consumption at other times), the system determines the initial abnormal event as a false alarm, excludes the event, and outputs remote meter reading results, including the user's total water consumption for March 18-19, water consumption distribution by time period, and water consumption trend chart.

[0083] In some preferred embodiments, the above steps specifically include: incorporating the supplementary data frame into the original data frame corresponding to the time period of the initial abnormal event, recalculating the corresponding measured water consumption of the interval; and re-extracting the nighttime continuous flow, water consumption jump amplitude, number of reverse changes, and zero value duration features based on the recalculated measured water consumption of the interval.

[0084] When an initial abnormal event is generated again based on the re-extracted nighttime continuous flow, water usage transition amplitude, number of reverse changes, and duration of zero value, the initial abnormal event is confirmed, and the final abnormal type, abnormal level, and abnormal alarm record are output; otherwise, the initial abnormal event is excluded and the remote meter reading results are output.

[0085] After receiving supplementary data frames, the system needs to complete the closed loop of anomaly verification through data fusion and feature recalculation. The system sorts the supplementary data frames by collection timestamp and inserts them into the original data frame sequence. For example, if a water meter has missing original data frames in the confirmation interval of 22:00-2:00, after re-reading, 16 supplementary data frames with a granularity of 15 minutes are obtained. These frames are then inserted in chronological order between the valid data frames before and after 22:00 to form a continuous time series.

[0086] Then, the cumulative flow difference between adjacent data frames is recalculated to obtain the updated measured water consumption sequence. Based on the updated sequence, four types of abnormal features are re-extracted: For the nighttime continuous flow feature, the system checks whether there is a continuous small flow during the period from 22:00 to 5:00. If the supplementary data shows that the water consumption is consistently between 0.02 and 0.05 cubic meters every 15 minutes, and the cumulative 4-hour continuous flow feature still meets the abnormal judgment condition (continuous flow > 0 and < 0.1 cubic meters / 15 minutes), the initial abnormal event is confirmed, and the abnormality type "nighttime continuous flow", the abnormality level "medium" (based on the duration and flow size), the alarm record including the occurrence time [22:00-2:00], the average flow of 0.035 cubic meters / 15 minutes, and the suggestion "suspected leak, on-site investigation recommended".

[0087] Conversely, if supplementary data shows that nighttime water usage is concentrated in a single normal session of 0.3 cubic meters at 23:30, with zero water usage at other times, the re-extracted nighttime continuous flow characteristics no longer meet the abnormal conditions. The system then excludes the initial abnormal event and outputs the remote meter reading results, including the user's total water usage of 1.2 cubic meters on March 18th, nighttime water usage of 0.3 cubic meters, and a distribution map of water usage periods.

[0088] The remote meter reading and water information management device in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 2 This is a schematic diagram of the physical device structure of a remote meter reading and water information management equipment in the embodiments of this application.

[0089] It should be noted that, Figure 2 The structure of the remote meter reading and water information management device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0090] like Figure 2 As shown, the remote meter reading and water information management device includes a Central Processing Unit (CPU) 201, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 202 or programs loaded from storage section 208 into Random Access Memory (RAM) 203, such as performing the methods described in the above embodiments. The RAM 203 also stores various programs and data required for system operation. The CPU 201, ROM 202, and RAM 203 are interconnected via a bus 204. An Input / Output (I / O) interface 205 is also connected to the bus 204.

[0091] The following components are connected to I / O interface 205: input section 206 including audio input devices, push-button switches, etc.; output section 207 including liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 208 including hard disks, etc.; and communication section 209 including network interface cards such as LAN (Local Area Network) cards, modems, etc. Communication section 209 performs communication processing via a network such as the Internet. Drive 210 is also connected to I / O interface 205 as needed. Removable media 211, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 210 as needed so that computer programs read from them can be installed into storage section 208 as needed.

[0092] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 209, and / or installed from removable medium 211. When the computer program is executed by central processing unit (CPU) 201, it performs the various functions defined in the present invention.

[0093] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0094] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0095] Specifically, the remote meter reading and water usage information management device in this embodiment includes a processor and a memory. The memory stores a computer program, and when the computer program is executed by the processor, it implements the remote meter reading and water usage information management method provided in the above embodiment.

[0096] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the remote meter reading and water usage information management device described in the above embodiments; or it may exist independently and not assembled into the remote meter reading and water usage information management device. The storage medium carries one or more computer programs, which, when executed by a processor of the remote meter reading and water usage information management device, cause the remote meter reading and water usage information management device to implement the remote meter reading and water usage information management method provided in the above embodiments.

[0097] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0098] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0099] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method and system for remote meter reading and water usage information management of smart water meters, characterized in that, The method includes: Receive uploaded raw data frames, which include at least the cumulative traffic value, collection timestamp, device status code, communication quality parameters, device power parameters, and collection path information; Based on the original data frame, the integrity of the fields, the monotonicity of the cumulative traffic value, the synchronization status of the collection timestamp, the consistency of the device status code, and the consistency of the collection path information are detected to generate a collection status identifier. Based on the collection status identifier, the water usage interval corresponding to the original data frame is divided into a valid collection interval and a pending confirmation interval; The measured water consumption is calculated based on the adjacent raw data frames within the effective acquisition interval, and features such as nighttime continuous flow, water consumption jump amplitude, number of reverse changes, and duration of zero value are extracted based on the measured water consumption to generate an initial abnormal event. Based on the cumulative flow value at the boundary of the interval to be confirmed, the historical water consumption characteristics of the same period, and the preset relevant water meter or regional balance constraints, a supplementary sequence is generated. The supplementary sequence is used to determine the supplementary reading time window, reporting cycle, retransmission number and collection path. The data acquisition control strategy is determined based on the data acquisition status identifier, the communication quality parameters, the device power parameters, the completion sequence, and the initial abnormal event. According to the aforementioned data replenishment control strategy, a data replenishment control command is issued to the smart water meter or concentrator; The system receives the supplementary data frame corresponding to the supplementary meter reading control command, and based on the supplementary data frame and the original data frame within the effective acquisition range, confirms or eliminates the initial abnormal event, and outputs the final abnormality type, abnormality level, abnormal alarm record and remote meter reading result.

2. The method according to claim 1, characterized in that, The preset related water meter or area balance constraints include: Based on the water supply topology, determine the set of related water meters that are in the same building, unit, branch road, or zone as the current water meter; Obtain the actual collected data of the relevant water meter set in the time period corresponding to the interval to be confirmed, as well as the inlet metering data of the partition to which the interval to be confirmed belongs; Based on the actual collected data, the actual water usage change trend and zone summary metering data of the relevant water meter set are determined, and the difference between the actual water usage change trend and the inlet metering data and the zone summary metering data does not exceed a preset threshold is used as the preset relevant water meter or zone balance constraint.

3. The method according to claim 2, characterized in that, For the interval to be confirmed, a completion sequence is generated based on the cumulative flow value at the boundary of the interval, the historical water consumption characteristics of the same period, and the preset relevant water meter or regional balance constraints. Specifically, this includes: Determine the start and end boundaries of the interval to be confirmed; The difference between the cumulative flow value at the starting boundary and the cumulative flow value at the ending boundary is taken as the total amount to be allocated; The total amount to be allocated is allocated based on the historical water usage characteristics of the same period, generating an initial allocation sequence; The initial allocation sequence is corrected based on the actual water usage trend of the relevant water meter set in the corresponding time period, and the supplementary sequence is output under the condition of satisfying the regional balance constraint.

4. The method according to claim 1, characterized in that, Based on the original data frame, the integrity of the fields, the monotonicity of the cumulative traffic value, the synchronization status of the collection timestamp, the consistency of the device status code, and the consistency of the collection path information are checked to generate a collection status identifier, specifically including: A valid collection status identifier is generated when the field integrity, the monotonicity of the cumulative traffic value, the synchronization status of the collection timestamp, the consistency of the device status code, and the consistency of the collection path information all meet the corresponding preset conditions. When the completeness of the field does not meet the preset condition, a missing item status identifier is generated; When the synchronization status of the collected timestamp does not meet the preset condition, a clock out-of-synchronization status flag is generated. When the consistency of the collected path information does not meet the preset condition, a path conflict status identifier is generated. When the consistency of the device status code does not meet the preset conditions, an abnormal device status identifier is generated.

5. The method according to claim 4, characterized in that, The step of dividing the water usage interval corresponding to the original data frame into a valid collection interval and a pending confirmation interval based on the collection status identifier specifically includes: The water usage interval between two adjacent raw data frames corresponding to the valid collection status identifier is determined as the valid collection interval; The water usage interval corresponding to the original data frame containing the missing item status identifier, the clock out-of-synchronization status identifier, the path conflict status identifier, or the equipment abnormal status identifier is determined as the interval to be confirmed. When there are raw data frames uploaded from different acquisition paths within the same time window, the effective acquisition interval is determined by the raw data frames whose communication quality parameters meet the preset threshold and whose device status codes meet the preset conditions.

6. The method according to claim 1, characterized in that, The step of determining the data acquisition control strategy based on the acquisition status identifier, the communication quality parameters, the device power parameters, the completion sequence, and the initial abnormal event specifically includes: The supplementary recording time window is determined based on the supplementary water consumption distribution corresponding to each time period in the supplementary sequence; The reporting period and the number of retransmissions are determined based on the duration of the interval to be confirmed and the number of time slices corresponding to the completion sequence. When the initial abnormal event is generated by the nighttime continuous flow or the water usage jump amplitude characteristics, and the device power parameter is higher than the preset power threshold, a short-cycle re-reading strategy is determined. When the communication quality parameter is lower than a preset communication threshold, a path switching strategy is determined; When the duration of the interval to be confirmed exceeds the preset duration, a segmented copying strategy is determined; The determined time window for data re-copying, the reporting cycle, the number of retransmissions, and the acquisition path are written into the determined data re-copying control strategy.

7. The method according to claim 1, characterized in that, The process of confirming or eliminating the initial abnormal event based on the supplementary data frame and the original data frame within the effective acquisition interval specifically includes: The supplementary data frame is incorporated into the original data frame corresponding to the time period of the initial abnormal event, and the water consumption of the corresponding measured interval is recalculated. Based on the recalculated measured water consumption in the interval, the nighttime continuous flow, the water consumption transition amplitude, the number of reverse changes, and the duration of the zero value are re-extracted as features. When the initial abnormal event is generated again based on the re-extracted features of the nighttime continuous flow, the water use transition amplitude, the number of reverse changes, and the duration of the zero value, the initial abnormal event is confirmed, and the final abnormal type, the abnormal level, and the abnormal alarm record are output. Otherwise, exclude the initial abnormal event and output the remote meter reading result.

8. A remote meter reading and water usage information management device, characterized in that, The remote meter reading and water usage information management device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the remote meter reading and water usage information management device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the remote meter reading and water information management device, the remote meter reading and water information management device performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the remote meter reading and water information management device, the remote meter reading and water information management device performs the method as described in any one of claims 1-7.

Images