A water meter data monitoring method and system based on NB-IoT wireless communication technology

Abstract

The application discloses a water meter data monitoring method and system based on NB-IoT wireless communication technology; the method comprises the following steps: using a water meter data acquisition terminal integrated with an NB-IoT wireless communication chip and a sensor, intelligently and adaptively collecting water meter data according to an adaptive collection strategy and an intelligent prediction strategy; constructing a water meter Mesh networking, and determining a water meter cluster head in the Mesh networking; collecting water meter data through the NB-IoT wireless communication chip, and triggering the water meter data to be reported to the water meter cluster head according to a triggering reporting strategy, and uploading the water meter data to the cloud by the water meter cluster head; using a cloud computing processing platform to monitor whether each water meter data is abnormal in real time, generating an abnormal warning and a data retransmission instruction to indicate that the abnormal water meter data is re-reported when the water meter data is monitored to be abnormal. The application can balance low-power wide coverage and real-time monitoring of water meter monitoring.

Description

Technical Field

[0001] This application relates to the technical field of data monitoring and communication, specifically to a water meter data monitoring method and system based on NB-IoT wireless communication technology. Background Technology

[0002] With the development of IoT technology, remote meter reading systems are widely used in smart water management, enabling real-time monitoring and management of urban water supply networks, and improving production efficiency and water resource utilization efficiency.

[0003] To address the issue of water meter data collection and transmission, various methods are commonly employed in this field. For data collection, the traditional timed collection method is typically used, collecting water meter data at fixed intervals. This method is simple and direct but lacks flexibility. For data transmission, traditional GPRS or 4G communication technologies are used for wireless transmission of water meter data. Regarding data processing and monitoring, the collected data is generally stored and analyzed on the server side. When data anomalies are detected, manual intervention is performed.

[0004] However, existing conventional methods have significant drawbacks. Traditional data acquisition and transmission methods are power-intensive and costly, making them unsuitable for battery-powered scenarios requiring long-term unattended operation. Especially in environments such as underground pipelines and remote areas, signal attenuation is severe, and GPRS and 4G signals may not provide effective coverage, leading to intermittent or even non-existent data transmission. To ensure data transmission, the device needs to continuously boost the signal, further increasing power consumption. Moreover, the instability of data transmission greatly affects the accuracy and real-time performance of data acquisition, failing to reflect the actual condition of the water meter in a timely and accurate manner. Therefore, existing technologies cannot achieve a balance between low power consumption and wide coverage for remote intelligent water meter monitoring, and are insufficient to meet the real-time monitoring needs of water resource management. Summary of the Invention

[0005] To achieve a balance between low power consumption and wide coverage in water meter data monitoring and real-time monitoring, this application provides a water meter data monitoring method and system based on NB-IoT wireless communication technology.

[0006] In a first aspect, this application provides a water meter data monitoring method based on NB-IoT wireless communication technology, comprising: using a water meter data acquisition terminal integrating an NB-IoT wireless communication chip and a sensor, and intelligently adaptively acquiring water meter data according to an adaptive acquisition strategy and an intelligent prediction strategy; the adaptive acquisition strategy includes: dynamically adjusting the acquisition frequency based on a combination of water load, signal strength, and device status; the intelligent prediction strategy includes: analyzing historical water usage data of users and predicting future water usage data to dynamically adjust the acquisition frequency;

[0007] A water meter Mesh network is constructed, and the water meter cluster heads in the Mesh network are determined. The water meter data collected by the water meter data acquisition terminal is transmitted to the water meter cluster heads through the NB-IoT wireless communication chip according to the triggering reporting strategy. The water meter cluster heads then upload the water meter data to the cloud. The triggering reporting strategy includes a periodic reporting strategy and a preset emergency event instant reporting strategy.

[0008] The cloud computing platform is used to monitor the data of each water meter in real time to detect any abnormalities. If an abnormality is detected, an abnormality warning is generated and a data retransmission instruction is generated to instruct the abnormal water meter data to be re-reported.

[0009] By adopting the above scheme, a water meter data acquisition terminal integrating NB-IoT wireless communication chips and sensors is used. This is combined with adaptive acquisition and intelligent prediction strategies for intelligent adaptive data acquisition. Simultaneously, the water meter mesh network is established, and cluster heads are determined. Data is uploaded to the cloud according to a trigger-based reporting strategy, enabling real-time monitoring of data anomalies by a cloud computing platform. Considering the advantages of NB-IoT wireless communication technology—low power consumption and wide coverage—this approach ensures real-time and effective data acquisition and orderly data transmission while reducing power consumption and expanding signal coverage, achieving a balance between low power consumption, wide coverage, and real-time monitoring.

[0010] Preferred options also include:

[0011] Data is collected in different modes based on signal strength, device status, and the physical location of the water meter. These different modes include: data collection according to a first collection strategy adapted to the energy-saving mode, data collection according to a second collection strategy adapted to the high-speed mode, and data collection according to a third collection strategy adapted to the strong signal mode.

[0012] The energy-saving mode refers to a mode adapted to conditions where the signal strength is less than a first preset signal strength, the device status is less than a first preset status, and the water meter is physically located in an underground pipeline or remote mountainous area. The first acquisition strategy includes extending the acquisition interval to the hour level and acquiring only preset core data. The high-speed mode refers to a mode adapted to conditions where the signal strength is greater than a second preset signal strength, the device status is greater than a second preset status, and the water meter is physically located in a commercial or industrial area. The second acquisition strategy includes shortening the acquisition interval to the minute level and adding water flow data acquired by the sensor. The strong signal mode refers to a mode adapted to conditions where the signal strength is greater than a third preset signal strength, the device status is greater than a third preset status, and the water meter is physically located in a residential area. The third acquisition strategy includes adjusting the acquisition interval to the half-hour level, supporting batch acquisition, and automatically switching to the acquisition strategy corresponding to the energy-saving mode when the signal strength drops to the first preset signal strength.

[0013] By adopting the above scheme, data is collected in different modes based on signal strength, equipment status, and the physical location of the water meter. The corresponding mode-adaptive collection strategy is used in different scenarios to reduce system power consumption, improve the real-time performance and accuracy of data collection, and automatically switch strategies to ensure stable system operation when the signal strength decreases.

[0014] Preferred options also include:

[0015] An enhanced NB-IoT wireless communication chip is pre-configured, with the water meter data acquisition terminal integrating the enhanced NB-IoT wireless communication chip serving as the enhanced node;

[0016] In determining the water meter cluster head in the Mesh network, the network is pre-clustered and divided into several groups. For each group, a water meter cluster head is determined, including: querying and directly using the water meter corresponding to the enhanced node as the water meter cluster head; when no enhanced node is found, selecting the water meter with the highest comprehensive index corresponding to the signal strength and device status in the network according to the preset comprehensive index rules as the water meter cluster head.

[0017] By adopting the above scheme, when determining the water meter cluster head, priority is given to selecting the water meter corresponding to the enhanced node. When there is no enhanced node, the water meter with the highest comprehensive index of signal strength and equipment status is selected. This can ensure that the signal quality of each group of water meter cluster heads is good, thereby solving the problem of limited signal coverage and realizing stable transmission of water meter data in remote areas or underground pipelines.

[0018] Preferred options also include:

[0019] The NB-IoT base station resource priority is set according to time and region, and each time and region combination has a corresponding NB-IoT base station resource priority setting; the NB-IoT base station resource priority matching the region and operating period of each water meter cluster is determined, and the NB-IoT base station time slot is allocated according to the matching NB-IoT base station resource priority to complete the uploading of water meter data to the cloud through the NB-IoT wireless communication chip;

[0020] For water meter clusters with the same NB-IoT base station resource priority, NB-IoT base station time slots are allocated according to the number of water meter connections in each water meter cluster. The more connections a water meter cluster has, the higher the proportion of NB-IoT base station time slots it is allocated.

[0021] By adopting the above scheme, the priority of NB-IoT base station resources was reasonably set according to time and region, and the base station time slots were scientifically allocated, which improved the efficiency and stability of water meter data uploading to the cloud.

[0022] Preferred options also include:

[0023] During the process of water meter clusters uniformly uploading water meter data to the cloud, for water meter clusters located in underground pipelines or remote mountainous areas, the corresponding water meter data is uploaded to edge servers deployed around the NB-IoT base station. The edge servers are used to replace the cloud computing processing platform to monitor in real time whether there are any data anomalies in the water meter data. If an anomaly is detected, an anomaly warning is generated and a data retransmission instruction is generated to instruct the re-reporting. Water meter data without anomalies is uploaded to the cloud for storage.

[0024] By adopting the above solution, for water meter clusters located in underground pipelines or remote mountainous areas with poor signal, edge servers are used to replace the cloud for real-time monitoring, which effectively reduces data transmission pressure, improves the response speed to abnormal data, and uploads data without abnormalities to the cloud for storage, ensuring data integrity and traceability.

[0025] Preferred options also include:

[0026] A layered detection approach is adopted to monitor the data of each water meter in real time using a cloud computing platform. This includes: before timeout, the cloud computing platform returns a response message for each received water meter data; for water meters that do not return a response message, a water meter data transmission anomaly is generated. For all received water meter data, consistency and conflict detection are performed according to the water meter data within the Mesh network cluster. Water meter data that fails the detection is considered to have abnormal data content. For water meter data that passes the detection, cross-validation is performed between regional water meter data based on the regional water pipe network topology. For regional water meter data that fails cross-validation, anomaly investigation is triggered, comparing with historical regional water meter data to obtain the water meter data with abnormal data.

[0027] By adopting the above scheme, a layered detection method is used to monitor whether water meter data is abnormal in real time. The detection is carried out from multiple levels, such as data transmission, data consistency and conflict, and cross-validation of regional water meter data, which can more comprehensively and accurately detect abnormal water meter data.

[0028] Preferred options also include:

[0029] If abnormal water meter data is still detected after generating a preset number of data retransmission commands, a water meter data acquisition terminal with an integrated LORA wireless communication chip is used as a supplementary communication method. The NLORA wireless communication chip is switched to allow the water meter data acquisition terminal to collect water meter data.

[0030] By adopting the above solution, when anomalies persist after multiple data retransmissions, the acquisition terminal with an integrated LoRa wireless communication chip can be used as a supplementary communication method to ensure that water meter data can still be transmitted normally under abnormal conditions, thus improving the reliability of data transmission.

[0031] Secondly, this application provides a water meter data monitoring system based on NB-IoT wireless communication technology, comprising: a water meter data acquisition module, used to intelligently and adaptively acquire water meter data using a water meter data acquisition terminal integrating an NB-IoT wireless communication chip and a sensor, according to an adaptive acquisition strategy and an intelligent prediction strategy; the adaptive acquisition strategy includes: dynamically adjusting the acquisition frequency based on a combination of water load, signal strength, and device status; the intelligent prediction strategy includes: analyzing historical water usage data of users and predicting future water usage data to dynamically adjust the acquisition frequency;

[0032] The water meter data transmission module is used to construct a water meter Mesh network and determine the water meter cluster heads in the Mesh network. It uses an NB-IoT wireless communication chip to transmit water meter data collected by the water meter data acquisition terminal to the water meter cluster heads according to a trigger reporting strategy. The water meter cluster heads then uniformly upload the water meter data to the cloud. The trigger reporting strategy includes a periodic reporting strategy and a preset emergency event instant reporting strategy.

[0033] The water meter data early warning module is used to monitor the data of each water meter in real time using a cloud computing processing platform to detect whether there are any data anomalies. When an anomaly is detected, an anomaly warning is generated and a data retransmission instruction is generated to instruct the abnormal water meter data to be re-reported.

[0034] By adopting the above scheme, the water meter data acquisition frequency is dynamically adjusted to achieve intelligent adaptive acquisition and effectively reduce power consumption; a water meter mesh network is constructed and the water meter cluster head is determined. The water meter data is reported to the water meter cluster head and then uniformly uploaded to the cloud with the help of the NB-IoT wireless communication chip, which improves the effectiveness and timeliness of data transmission; the cloud computing processing platform is used to monitor data anomalies in real time, and anomaly warnings and data retransmission instructions are generated in a timely manner to ensure the accuracy of data and the reliability of the system.

[0035] Thirdly, this application provides a computer-readable storage medium including a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to perform the method described above.

[0036] Fourthly, this application provides a computer device, the computer device including a memory, a processor and a program stored in the memory and executable thereon, the program being executed by the processor to implement the steps of the method described above.

[0037] In summary, this application has the following beneficial effects:

[0038] 1. By utilizing terminals integrating NB-IoT wireless communication chips and sensors, and combining adaptive acquisition strategies and intelligent prediction strategies to adjust the acquisition frequency, intelligent adaptive acquisition of water meter data is achieved; a water meter mesh network is constructed and cluster heads are determined, and data is uploaded according to the trigger reporting strategy; the cloud computing processing platform is used to monitor data in real time, and warnings and retransmission instructions are generated when anomalies are detected; together, the effectiveness of water meter data acquisition and transmission, the accuracy and reliability of monitoring are ensured, and a balance between low power consumption, wide coverage and real-time monitoring is achieved;

[0039] 2. Data is collected in different modes. The acquisition strategy is adjusted according to signal strength, equipment status and physical location of water meter, which can improve data acquisition efficiency and reduce power consumption.

[0040] 3. The cloud computing processing platform monitors water meter data anomalies in real time, adopts a layered detection method, promptly detects and handles anomalies, improves the real-time performance and accuracy of data collection, and meets the real-time monitoring needs of water resource management. Attached Figure Description

[0041] Figure 1 This is a flowchart of the water meter data monitoring method based on NB-IoT wireless communication technology described in a specific embodiment;

[0042] Figure 2 This is a schematic diagram of the water meter data monitoring system based on NB-IoT wireless communication technology described in a specific embodiment. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] like Figure 1 As shown in the figure, this application discloses a water meter data monitoring method based on NB-IoT wireless communication technology, including several key links: water meter data acquisition, water meter data transmission, and water meter data early warning; each link cooperates with each other to realize intelligent adaptive acquisition, stable transmission, and timely early warning of abnormal situations of water meter data; the content of each link will be described in detail below.

[0045] S1. Use the water meter data acquisition terminal to complete the intelligent adaptive acquisition of water meter data.

[0046] Specifically, water meter data is collected using a data acquisition terminal that integrates an NB-IoT wireless communication chip and sensors. Each water meter is equipped with a corresponding data acquisition terminal. The sensors include flow sensors installed inside the water meter's pipe to measure the flow rate of each meter; pipe pressure sensors can also be installed to measure the pressure within the water pipe; and device status sensors can be installed to obtain the device status of the water meter, such as the meter's power consumption. The NB-IoT wireless communication chip is responsible for subsequent data transmission, and modules supporting 3GPPR14 and above standards can be selected.

[0047] Considering the differences in user water demand under different water usage scenarios, and the tendency of traditional fixed-period data collection to lead to excessive redundant data and excessive power consumption, as well as the possibility of missing key data and resulting in inaccurate water meter data monitoring, an adaptive data collection strategy based on water usage scenario matching is designed. This adaptive strategy enables adaptive data collection from the water meter, specifically by dynamically adjusting the collection frequency based on a combination of water load, signal strength, and device status. In this embodiment, the water flow rate change rate ΔQ over the past 10 minutes, the current signal strength (RSRP value) of the NB-IoT network, and the remaining battery power (SOC) of the water meter are obtained through sensors. Different combinations of ΔQ, RSRP, and SOC are used to match different water usage scenarios, including peak water usage, stable water usage, and low water usage. Different collection strategies are set for different water usage scenarios, as shown in Table 1 below. The collection period gradually decreases from peak water usage to stable water usage and then to low water usage, meaning the sensor only wakes up during the collection period and enters a sleep state during non-collection periods.

[0048] Table 1

[0049] Peak water usage >5 ≥-100 >50 5min Water stability 0.5-5 -120～-100 30-50 30min Water off-peak <0.5 <-120 <30 2h

[0050] In addition to adjusting data collection from passive to adaptive, a smart prediction strategy is also set up for intelligent collection of water meter data to avoid missing key water usage data. The smart prediction strategy includes analyzing users' historical water usage data (water meter data) and predicting future water usage data to dynamically adjust the collection frequency. Specifically, a lightweight water usage data analysis algorithm is embedded locally in the water meter data collection terminal to analyze users' historical water usage data, identify the flow rate change rate of water meter data in future time periods, pre-determine the water usage scenario, pre-match the corresponding collection strategy, and then collect water meter data according to the pre-matched collection strategy.

[0051] S2. Construct a water meter Mesh network, determine the water meter cluster heads in the Mesh network, and transmit water meter data sequentially to the water meter cluster heads and the cloud through the NB-IoT wireless communication chip.

[0052] To further reduce the energy consumption of water meter data transmission, and addressing the network transmission obstruction issues in underground pipelines and remote areas, the concept of Mesh networking is introduced to improve coverage and data transmission reliability. Specifically, a water meter Mesh network is constructed, including: establishing data communication between adjacent water meters through NB-IoT wireless communication chips, generating a communication topology, and selecting a specific water meter as a cluster head to coordinate and manage the communication of other water meter nodes within its cluster, including data collection, forwarding, and communication with the cloud; determining the water meter cluster head in the Mesh network, primarily ensuring that the cluster head requires good NB-IoT communication, and prioritizing water meters with strong NB-IoT signal strength and sufficient battery power as cluster heads.

[0053] The NB-IoT wireless communication chip collects water meter data from the data acquisition terminal and triggers the reporting strategy to the water meter cluster head, which then uploads the data to the cloud. The triggering reporting strategy includes a periodic reporting strategy and a preset instant reporting strategy for emergencies. The periodic reporting strategy can be set to report data at regular intervals (e.g., 24 hours). The preset instant reporting strategy for emergencies involves immediately reporting data when there are sudden changes in flow (e.g., pipe rupture and leakage, abnormal water meter rotation), low battery power (below a preset power level), or abnormal signal strength (below a preset signal strength).

[0054] Furthermore, to further optimize the determination of water meter cluster heads, an enhanced NB-IoT wireless communication chip can be pre-configured, with the water meter data acquisition terminal integrating the enhanced NB-IoT wireless communication chip serving as the enhanced node. During the determination of water meter cluster heads in the Mesh network, the network is pre-clustered and divided into several groups. For each group, a water meter cluster head is determined, including: querying and directly identifying the water meter corresponding to the enhanced node as the water meter cluster head; if no enhanced node is found, selecting the water meter with the highest comprehensive index corresponding to signal strength and device status in the network, calculated according to preset comprehensive index rules, as the water meter cluster head. The preset comprehensive index rules include: setting different signal strength ranges for NB-IoT signal strength, with each range having a pre-defined first index value; setting different power ranges for water meter power consumption in device status, with each range having a pre-defined second index value, and calculating the comprehensive index value using weighted averages.

[0055] S3. Utilize a cloud computing platform to monitor the data of each water meter in real time to identify any abnormalities and issue early warnings based on the monitoring results.

[0056] Specifically, the system utilizes a data anomaly identification model built on a neural network within the cloud computing platform to identify anomalies in each water meter's data received in real time, including data failures and data content transmission errors. Upon detecting anomalies in the water meter data, an anomaly warning is generated, and a data retransmission command is generated to instruct the re-reporting of the abnormal water meter data.

[0057] In addition, to ensure that water meter data is not lost, that the data content is error-free, and that the water meter data is reliable and usable, this method also includes: using a cloud computing processing platform to monitor the data of each water meter in real time for data anomalies, and employing a layered detection approach, specifically divided into three layers of detection:

[0058] First, the first layer directly performs data transmission confirmation and detection for individual water meters.

[0059] Before the timeout (exceeding the preset time), the cloud computing platform returns an acknowledgment message for each received water meter data. For water meters that do not return an acknowledgment message, a water meter data transmission error is generated. Specifically, in a mesh network, the ACK acknowledgment mechanism exists between the member nodes (water meters) within the cluster and the cloud. After the cluster head node aggregates the data from the member nodes, it attaches the original source ID to each data packet. After receiving and parsing the aggregated packet, the cloud needs to send an ACK to the original member node. The cluster head node is responsible for relaying this ACK message.

[0060] Secondly, the second layer uses Mesh networking data for cross-validation.

[0061] For all received water meter data, consistency and conflict detection are performed according to the water meter data within the Mesh network cluster. Specifically, consistency detection for a cluster of water meter data within the Mesh network cluster includes: receiving multiple water meter data packets aggregated by a cluster head node in the cloud; considering the proximity of these water meters in physical location, their water usage behavior should be correlated over time; if the data of one meter significantly deviates from the quiescent state of all other meters, such as intermittent high flow rates late at night, the water meter data will be marked as "suspicious," and the cluster head will be bypassed, directly... A data verification command is sent to the water meter to obtain the corresponding water meter data, which is then compared with the corresponding water meter data uploaded by the cluster head. If they are inconsistent, there may be a risk that the cluster head has tampered with the data. In this case, a certain percentage (e.g., 30%) of the member nodes are randomly selected from the cloud, and data verification commands are sent directly to the corresponding water meters of these member nodes. If the consistency between the extracted data and the data uploaded by the cluster head is ≥95%, the data within the cluster is confirmed to be valid, and the water meter data marked as "suspicious" is identified as having incorrect content. If the consistency is <80%, it is determined that there is a risk of data tampering by the cluster head, and "cluster head replacement" is immediately triggered to replace the cluster head.

[0062] The process involves conflict detection of water meter data within a Mesh network cluster. This includes recording the cumulative water volume of each meter in the cloud. If the cumulative value reported in a particular instance is less than the previous value (except in cases where a zeroing operation has been performed), a logical conflict exists, the data is invalid, and the detection fails. For water meter data that fails the detection, the corresponding water meter data content is considered abnormal.

[0063] Finally, the third layer utilizes multi-dimensional mutual verification of regional water meter data.

[0064] For water meter data that passes the test, the area where the current network data is located is determined. Cross-validation between regional water meter data can be completed based on the regional water pipe network topology. For regional water meter data that fails cross-validation, anomaly investigation is triggered, and historical regional water meter data is compared to obtain water meter data with abnormal data.

[0065] Given the clear topological relationships of water pipe networks (e.g., community master meter - building meter - household meter or main pipe meter - branch pipe meter), the sum of the cumulative billing increments of downstream water meters should equal the increment of upstream water meters (considering the network leakage rate, usually calculated using historical leakage rate, e.g., leakage rate ≤ 15%). This verifies the accuracy of water meter data. Specifically, a water pipe network topology model is constructed in the cloud-based GIS system, marking the upstream and downstream relationships of each water meter. The cloud automatically calculates the sum of the upstream and downstream meter increments to verify whether it conforms to the rule that the sum of the cumulative billing increments of downstream water meters should equal the increment of upstream water meters. If the verification fails, an anomaly investigation is triggered, comparing the historical upstream and downstream water meter data for the corresponding area to initially locate the anomaly range. Then, combined with the second layer (data analysis within the Mesh), the focus is further narrowed down to specific suspicious water meters to obtain the water meter data with anomalies.

[0066] In a specific embodiment, to further optimize the data acquisition process, achieve a balance between low power consumption and efficient acquisition in different scenarios, and improve the system's adaptability and practicality, a multi-mode acquisition system can be set up to employ different acquisition strategies based on different environmental conditions and device statuses, better meeting the needs of water meter data acquisition in different environments. The method also includes:

[0067] Data is collected in different modes based on signal strength, device status, and the physical location of the water meter; these modes include: energy-saving mode, high-speed mode, and strong signal mode.

[0068] The energy-saving mode is applicable when the signal strength is less than the first preset signal strength (e.g., RSRP < -115dBm), the device status is less than the first preset status (SOC < 50%), and the water meter is physically located in an underground pipeline or remote mountainous area. In energy-saving mode, the first data acquisition strategy is adopted, which extends the acquisition interval to the hourly level, such as extending the original 1-2 hours to 3 hours. Only preset core data, such as flow rate, current, voltage, and signal strength, are collected to minimize power consumption and adapt to harsh environmental conditions.

[0069] The high-speed mode is applicable when the signal strength is greater than the second preset signal strength (e.g., RSRP > -90dBm), the device status is greater than the second preset status (SOC > 70%), and the water meter's physical location is in a commercial or industrial area. In high-speed mode, a second acquisition strategy is adopted, which shortens the acquisition interval to the minute level (e.g., 1-5 minutes), adds flow data collected by additional sensors, and obtains flow data through redundant sensors. This allows for more timely and comprehensive acquisition of water meter data, meeting the real-time water resource monitoring needs of commercial and industrial areas.

[0070] The strong signal mode is applicable when the signal strength is greater than the third preset signal strength (e.g., RSRP > -100dBm), the device status is greater than the third preset status (SOC > 60%), and the water meter is physically located in a residential area. In strong signal mode, a third acquisition strategy is adopted, adjusting the acquisition interval to the half-hour level (30 minutes), supporting batch acquisition (transmission every 3 acquisitions), and automatically switching to the energy-saving mode's corresponding acquisition strategy when the signal strength drops to the first preset signal strength, ensuring data acquisition accuracy while flexibly responding to signal changes.

[0071] In one specific embodiment, to further improve the efficiency and stability of data transmission and avoid resource waste and data transmission congestion, the utilization of base station resources is optimized by setting time-sharing and region-based base station resource priorities and allocating time slots according to the number of connections; the method further includes:

[0072] Given the limited resources of NB-IoT base stations, resource priorities need to be designed according to different regions and time periods to ensure that data transmission in key areas and during key periods is not congested. Different regions are divided into underground pipelines or remote mountainous areas, commercial areas or industrial areas, and residential areas. Different time periods are divided according to the time interval of each hour in 24 hours.

[0073] NB-IoT base station resource priority settings are implemented according to time and region. Each time and region combination has a corresponding NB-IoT base station resource priority setting. For example, the priority of commercial areas is high from 10:00 to 22:00, the priority of residential areas is medium from 10:00 to 18:00, and the priority of remote mountainous areas is low from 22:00 to 6:00.

[0074] The priority of NB-IoT base station resources matching the location and operating time of each water meter cluster is determined. NB-IoT base station time slots are allocated according to the matching NB-IoT base station resource priorities to complete the uploading of water meter data to the cloud via NB-IoT wireless communication chips. Specifically, 50% of the base station time slots are allocated to NB-IoT base station resources with high priority; 30% of the base station time slots are allocated to NB-IoT base station resources with medium priority; and 20% of the base station time slots are allocated to NB-IoT base station resources with high priority.

[0075] For water meter clusters with the same NB-IoT base station resource priority, NB-IoT base station time slots are allocated according to the number of water meter connections in each water meter cluster. The more connections a water meter cluster has, the higher the proportion of NB-IoT base station time slots it is allocated.

[0076] In one specific embodiment, by deploying edge servers around base stations, the problem of unstable data transmission caused by weak signals in underground pipelines and remote mountainous areas is solved. The edge servers can perform data monitoring and processing locally, promptly detect and handle anomalies, and reduce reliance on the cloud. The method also includes:

[0077] During the process of water meter clusters uniformly uploading water meter data to the cloud, for water meter clusters located in underground pipelines or remote mountainous areas, the corresponding water meter data is uploaded to edge servers deployed around the NB-IoT base station. The edge servers are used to replace the cloud computing processing platform to monitor in real time whether there are any data anomalies in the water meter data. If an anomaly is detected, an anomaly warning is generated and a data retransmission instruction is generated to instruct the re-reporting. Water meter data without anomalies is uploaded to the cloud for storage.

[0078] In addition, during the process of water meter clusters uniformly uploading water meter data to the cloud, the data types of water meter data transmitted by the water meter clusters are classified. For water meter data of sudden event types, edge servers are adaptively selected to replace the cloud computing platform for real-time monitoring of whether there are data anomalies in water meter data, so as to achieve rapid local response and long-term cloud storage.

[0079] In one specific embodiment, by introducing a LoRa wireless communication chip as a supplementary communication method, the system's communication capabilities in complex environments are enhanced; when NB-IoT communication encounters problems, it can promptly switch to LoRa communication, ensuring the normal transmission of water meter data. The method includes:

[0080] If abnormal water meter data is still detected after generating a preset number of data retransmission commands, it is determined that there may be a problem with data interaction through the NB-IoT wireless communication chip. The water meter data acquisition terminal with an integrated LORA wireless communication chip is used as a supplementary communication method, and the NB-IoT wireless communication chip is switched to collect water meter data from the water meter data acquisition terminal.

[0081] like Figure 2 As shown in the figure, this application discloses a water meter data monitoring system based on NB-IoT wireless communication technology, including:

[0082] The water meter data acquisition module 101 is used to intelligently and adaptively acquire water meter data using a water meter data acquisition terminal that integrates an NB-IoT wireless communication chip and a sensor, according to an adaptive acquisition strategy and an intelligent prediction strategy. The adaptive acquisition strategy includes dynamically adjusting the acquisition frequency based on a combination of water load, signal strength, and device status. The intelligent prediction strategy includes analyzing the user's historical water usage data and predicting future water usage data to dynamically adjust the acquisition frequency.

[0083] The water meter data transmission module 102 is used to construct a water meter Mesh network and determine the water meter cluster heads in the Mesh network. It uses an NB-IoT wireless communication chip to transmit water meter data collected by the water meter data acquisition terminal to the water meter cluster heads according to a triggering reporting strategy. The water meter cluster heads then upload the water meter data to the cloud. The triggering reporting strategy includes a periodic reporting strategy and a preset emergency event instant reporting strategy.

[0084] The water meter data early warning module 103 is used to monitor the data of each water meter in real time using a cloud computing processing platform to detect whether there are any data anomalies. When an anomaly is detected, an anomaly warning is generated and a data retransmission instruction is generated to instruct the abnormal water meter data to be re-reported.

[0085] In one specific embodiment, the system further includes:

[0086] The water meter data acquisition module 101 is also used to perform sub-mode acquisition based on signal strength, device status, and the physical location of the water meter; the sub-mode acquisition includes: acquisition according to a first acquisition strategy adapted to the energy-saving mode, acquisition according to a second acquisition strategy adapted to the high-speed mode, and acquisition according to a third acquisition strategy adapted to the strong signal mode.

[0087] In one specific embodiment, the system further includes:

[0088] The water meter data transmission module 102 is also used to pre-set an enhanced NB-IoT wireless communication chip, with the water meter data acquisition terminal integrating the enhanced NB-IoT wireless communication chip as the enhanced node; in the process of determining the water meter cluster head in the Mesh network, the network is pre-clustered and divided into several groups, and a water meter cluster head is determined for each group, including: querying and directly using the water meter corresponding to the enhanced node as the water meter cluster head, and when no enhanced node is found, selecting the water meter with the highest comprehensive index corresponding to the signal strength and device status in the network according to the preset comprehensive index rules as the water meter cluster head.

[0089] In one specific embodiment, the system further includes:

[0090] The water meter data transmission module 102 is also used to set NB-IoT base station resource priorities in a time-sharing and region-sharing manner. Each time-sharing and region-sharing combination has an NB-IoT base station resource priority setting that is adapted to it. It determines the NB-IoT base station resource priority that matches the region and operating period of each water meter cluster head, and allocates NB-IoT base station time slots according to the matched NB-IoT base station resource priority to complete the uploading of water meter data to the cloud through the NB-IoT wireless communication chip. For water meter cluster head sets with the same NB-IoT base station resource priority, NB-IoT base station time slots are allocated according to the number of water meter connections in each water meter cluster head. The more connections a water meter cluster head has, the higher the proportion of NB-IoT base station time slots allocated to it.

[0091] In one specific embodiment, the system further includes:

[0092] The water meter data early warning module 103 is also used to upload the corresponding water meter data of water meter clusters located in underground pipelines or remote mountainous areas to edge servers deployed around the NB-IoT base station during the process of water meter clusters uniformly uploading water meter data to the cloud. The edge server is used to replace the cloud computing platform to monitor whether there are data anomalies in the water meter data in real time. If water meter data anomalies are detected, an anomaly warning is generated and a data retransmission instruction is generated to instruct re-reporting. Water meter data without anomalies is uploaded to the cloud for storage.

[0093] In one specific embodiment, the system further includes:

[0094] The water meter data early warning module 103 is also used to monitor in real time whether there are data anomalies in each water meter data using a layered detection method, including: the cloud computing platform returns a response message for each received water meter data before the timeout, and generates a water meter data transmission anomaly for the corresponding water meter that does not return a response message; for all received water meter data, data consistency and conflict detection are performed according to the water meter data within the Mesh network cluster; for water meter data that fails the detection, the corresponding water meter data content is identified as abnormal; for water meter data that passes the detection, cross-validation between regional water meter data is completed according to the regional water pipe network topology, and anomaly investigation is triggered for regional water meter data that fails cross-validation, comparing with historical regional water meter data to obtain water meter data with abnormal data.

[0095] This application also discloses a computer-readable storage medium.

[0096] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executed, such as the water meter data monitoring method based on NB-IoT wireless communication technology described above. The computer-readable storage medium includes, for example, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] This application also discloses a computer device.

[0098] Specifically, the computer device includes a memory and a processor. The memory stores a computer program that can be loaded by the processor and executed to perform the aforementioned water meter data monitoring method based on NB-IoT wireless communication technology.

[0099] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A method for monitoring water meter data based on NB-IoT wireless communication technology, characterized in that, include: Using a water meter data acquisition terminal that integrates NB-IoT wireless communication chip and sensor, intelligent adaptive acquisition of water meter data is carried out according to adaptive acquisition strategy and intelligent prediction strategy. The adaptive data acquisition strategy includes: dynamically adjusting the acquisition frequency based on a combination of water load, signal strength, and device status; the intelligent prediction strategy includes: analyzing historical water usage data of users and predicting future water usage data to dynamically adjust the acquisition frequency; the device status includes water meter power consumption. A water meter Mesh network is constructed, and the water meter cluster heads in the Mesh network are determined. The water meter data collected by the water meter data acquisition terminal is transmitted to the water meter cluster heads through the NB-IoT wireless communication chip according to the triggering reporting strategy. The water meter cluster heads then upload the water meter data to the cloud. The triggering reporting strategy includes a periodic reporting strategy and a preset emergency event instant reporting strategy. The cloud computing platform is used to monitor the data of each water meter in real time to detect any abnormalities. If abnormalities are detected, an abnormality warning is generated and a data retransmission instruction is generated to instruct the abnormal water meter data to be re-reported. It also includes: collecting data in different modes based on signal strength, device status, and the physical location of the water meter; the different modes of data collection include: collecting data according to a first collection strategy adapted to the energy-saving mode, collecting data according to a second collection strategy adapted to the high-speed mode, and collecting data according to a third collection strategy adapted to the strong signal mode; The energy-saving mode refers to a mode adapted to conditions where the signal strength is less than a first preset signal strength, the device status is less than a first preset status, and the water meter is physically located in an underground pipeline or remote mountainous area. The first acquisition strategy includes extending the acquisition interval to the hourly level and acquiring only preset core data. The high-speed mode refers to a mode adapted to conditions where the signal strength is greater than a second preset signal strength, the device status is greater than a second preset status, and the water meter is physically located in a commercial or industrial area. The second acquisition strategy includes shortening the acquisition interval to the minute level and adding water flow data acquired by the sensor. The strong signal mode refers to a mode adapted to conditions where the signal strength is greater than a third preset signal strength, the device status is greater than a third preset status, and the water meter is physically located in a residential area. The third acquisition strategy includes adjusting the acquisition interval to the half-hour level, supporting batch acquisition, and automatically switching to the acquisition strategy corresponding to the energy-saving mode when the signal strength drops to the first preset signal strength. This also includes: setting NB-IoT base station resource priorities based on time and region, with each time-based and region-based combination having its own corresponding NB-IoT base station resource priority settings; determining the NB-IoT base station resource priorities matching the region and operating period of each water meter cluster, and allocating NB-IoT base station time slots according to the matching NB-IoT base station resource priorities to complete the uploading of water meter data to the cloud via the NB-IoT wireless communication chip; for water meter clusters with the same NB-IoT base station resource priorities, allocating NB-IoT base station time slots according to the number of water meter connections in each water meter cluster, with water meter clusters with more connections receiving a higher proportion of NB-IoT base station time slots.

2. The water meter data monitoring method based on NB-IoT wireless communication technology according to claim 1, characterized in that, Also includes: An enhanced NB-IoT wireless communication chip is pre-configured, with the water meter data acquisition terminal integrating the enhanced NB-IoT wireless communication chip serving as the enhanced node; In determining the water meter cluster head in the Mesh network, the network is pre-clustered and divided into several groups. For each group, a water meter cluster head is determined, including: querying and directly using the water meter corresponding to the enhanced node as the water meter cluster head; when no enhanced node is found, selecting the water meter with the highest comprehensive index corresponding to the signal strength and device status in the network according to the preset comprehensive index rules as the water meter cluster head.

3. The water meter data monitoring method based on NB-IoT wireless communication technology according to claim 1, characterized in that, Also includes: During the process of water meter clusters uniformly uploading water meter data to the cloud, for water meter clusters located in underground pipelines or remote mountainous areas, the corresponding water meter data is uploaded to edge servers deployed around NB-IoT base stations; Edge servers are used to replace cloud computing platforms for real-time monitoring of water meter data for anomalies. When anomalies are detected, an anomaly warning is generated and a data retransmission command is generated to instruct the data to be resubmitted. Water meter data without anomalies is uploaded to the cloud for storage.

4. The water meter data monitoring method based on NB-IoT wireless communication technology according to claim 1, characterized in that, Also includes: The system utilizes a cloud computing platform to monitor the data of each water meter in real time for anomalies, employing a layered detection approach, including: Before the timeout, the cloud computing platform returns a response message for each received water meter data, and generates a water meter data transmission error for the corresponding water meter that does not return a response message; For all received water meter data, consistency and conflict detection are performed according to the water meter data within the Mesh network cluster. Water meter data that fails the detection is considered abnormal. The consistency detection includes: when the cloud receives multiple water meter data packets aggregated by a cluster head node, if the data of one of the tables differs from the silent state of all other tables, the water meter data is marked as suspicious. The cluster head is bypassed, and a data verification command is directly sent to that water meter to obtain the corresponding water meter data. This data is then compared with the corresponding water meter data uploaded by the cluster head. If they are inconsistent, there is a risk that the cluster head has tampered with the data. A certain proportion of member nodes are randomly selected from the cloud to perform data verification on these... Some member nodes directly issue data verification commands to the corresponding water meters. If the consistency between the extracted data and the data uploaded by the cluster head is not less than 95%, the data within the cluster is confirmed to be valid, and the water meter data content marked as suspicious is identified as incorrect. If the consistency is less than 80%, it is determined that the cluster head has a risk of data tampering, and the cluster head is immediately replaced. The conflict detection includes: the cloud records the cumulative water volume of each meter. Except for the zeroing operation, if any reported cumulative value is less than its previous value, there is a logical conflict, the data is invalid, and the detection is deemed to have failed. For water meter data that fails the detection, the corresponding water meter data content is deemed to be abnormal. For water meter data that passes the test, cross-validation is performed between regional water meter data based on the regional water pipe network topology. For regional water meter data that fails cross-validation, anomaly investigation is triggered, and historical regional water meter data is compared to obtain water meter data with abnormal data.

5. The water meter data monitoring method based on NB-IoT wireless communication technology according to claim 1, characterized in that, Also includes: If abnormal water meter data is still detected after generating a preset number of data retransmission commands, a water meter data acquisition terminal with an integrated LORA wireless communication chip is used as a supplementary communication method. The NLORA wireless communication chip is switched to allow the water meter data acquisition terminal to collect water meter data.

6. A water meter data monitoring system based on NB-IoT wireless communication technology, characterized in that, include: The water meter data acquisition module is used to intelligently and adaptively acquire water meter data according to the adaptive acquisition strategy and the intelligent prediction strategy by utilizing the water meter data acquisition terminal that integrates NB-IoT wireless communication chip and sensor. The adaptive data acquisition strategy includes: dynamically adjusting the acquisition frequency based on a combination of water load, signal strength, and device status; the intelligent prediction strategy includes: analyzing historical water usage data of users and predicting future water usage data to dynamically adjust the acquisition frequency; the device status includes water meter power consumption. The water meter data acquisition module is also used to perform sub-mode acquisition based on signal strength, device status, and the physical location of the water meter; the sub-mode acquisition includes: acquisition according to a first acquisition strategy adapted to the energy-saving mode, acquisition according to a second acquisition strategy adapted to the high-speed mode, and acquisition according to a third acquisition strategy adapted to the strong signal mode. The water meter data transmission module is used to construct a water meter mesh network and determine the water meter cluster heads in the mesh network. It uses an NB-IoT wireless communication chip to transmit water meter data collected by the water meter data acquisition terminal to the water meter cluster heads according to a trigger reporting strategy. The water meter cluster heads then uniformly upload the water meter data to the cloud. The trigger reporting strategy includes a periodic reporting strategy and a preset instant reporting strategy for sudden events. The water meter data transmission module is also used to set NB-IoT base station resource priorities based on time and region. Each time-region combination has a corresponding NB-IoT base station resource priority setting. It determines the NB-IoT base station resource priority matching the region and operating period of each water meter cluster head, and allocates NB-IoT base station time slots according to the matching NB-IoT base station resource priorities to complete the upload of water meter data to the cloud via the NB-IoT wireless communication chip. For water meter cluster head sets with the same NB-IoT base station resource priority, NB-IoT base station time slots are allocated according to the number of water meter connections in each cluster head. Water meter cluster heads with more connections are allocated more NB-IoT base station time slots. The higher the base station time slot ratio, the better; the water meter data early warning module is used to monitor whether there are data anomalies in each water meter data in real time using the cloud computing processing platform. When an anomaly is detected, an anomaly warning is generated and a data retransmission instruction is generated to instruct the abnormal water meter data to be re-reported.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the method as described in any one of claims 1 to 5.

8. A computer device, characterized in that, The computer device includes a memory, a processor, and a program stored in and executable on the memory, the program being executed by the processor to implement the steps of the method as described in any one of claims 1 to 5.

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