IoT-based remote flow meter management system

Abstract

This invention discloses a remote flow meter management system based on the Internet of Things (IoT). The system includes: a data acquisition module for collecting flow data from multiple flow meters; a protocol conversion module for converting the flow data from each flow meter into target data in a unified protocol format according to protocol mapping rules; a communication transmission module for transmitting all target data via a wireless IoT network; a data integration module for performing time-series alignment and format standardization on all target data to generate standardized data; an intelligent diagnostic module for performing fault detection analysis and predictive maintenance calculations based on the standardized data to generate a status assessment result; and a control output module for generating remote control commands based on the status assessment result and sending them to designated flow meters to adjust their operating parameters or control their start / stop. This invention solves the problems of difficult data interoperability and slow operation and maintenance response among heterogeneous devices, improving the intelligence and operational reliability of centralized flow meter management.

Description

Technical Field

[0001] This invention relates to the fields of industrial automation and Internet of Things (IoT) technology, and in particular to a remote management system for flow meters based on the Internet of Things. Background Technology

[0002] With the acceleration of urbanization and the development of the Industrial Internet of Things (IIoT), flow meters, as core equipment for measuring energy such as water, gas, and heat, are experiencing a dramatic increase in management scale and complexity. Traditional flow meters rely on regular manual inspections and on-site readings, resulting in low maintenance efficiency and high costs. Especially in remote pipeline networks or underground deployment scenarios, fault detection is delayed and data reading errors are frequent, making it difficult to meet the modern management needs of real-time monitoring and accurate metering. Although some existing systems have achieved local data acquisition and wired transmission, the communication protocols of devices from different manufacturers are incompatible, leading to severe data silos and making cross-platform integration difficult, thus hindering the construction of a unified scheduling and intelligent analysis system. In addition, the lack of remote diagnostics and predictive maintenance capabilities means that most systems only respond passively after a fault occurs, resulting in resource waste and service interruption risks, which restricts the in-depth construction of smart cities and digital energy systems.

[0003] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a remote flow meter management system based on the Internet of Things (IoT). The technical solution of this system is as follows: The data acquisition module is used to collect flow data from multiple flow meters in real time. The protocol conversion module is used to convert the flow data of each flow meter into target data in a unified protocol format according to predefined protocol mapping rules. A communication transmission module, used to transmit all target data via a wireless Internet of Things (IoT) network; The data integration module is used to receive and store all target data, and to perform time-series alignment and format standardization on all target data to generate standardized data; The intelligent diagnostic module is used to perform fault detection analysis and predictive maintenance calculations based on the standardized data, and generate condition assessment results. The control output module is used to generate remote control commands based on the status assessment results, and send the remote control commands to the designated flow meter through the communication transmission module, for adjusting the operating parameters of the designated flow meter or controlling the start and stop of the designated flow meter.

[0005] Furthermore, the data acquisition module is specifically used for: The system sends data request commands to the multiple flow meters at periodic time intervals, receives response data packets from each flow meter, and parses the flow data from each response data packet.

[0006] Furthermore, the protocol conversion module is specifically used for: According to the predefined protocol mapping rules, identify the source communication protocol corresponding to the flow data of any flow meter, and match the corresponding data parsing template and field mapping relationship; The flow data of any flow meter is parsed according to the data parsing template, the metering parameters are extracted, and the metering parameters are mapped to the predefined fields of the unified protocol format based on the field mapping relationship to generate the target data corresponding to any flow meter. Return to the steps described in the predefined protocol mapping rules until the target data corresponding to each flow meter is generated.

[0007] Furthermore, the communication transmission module is specifically used for: Receive the target data corresponding to each flow meter, attach a transmission header containing the data source identifier and sequence number to each target data, and assemble them into an independent transmission data packet; Based on the data priority and network conditions of each transmitted data packet, a low-power wide area network or a cellular mobile network is dynamically selected as the transmission link, and each transmitted data packet is sent to the cloud server.

[0008] Furthermore, the data integration module is specifically used for: Receive each data packet from the cloud server and parse the target data and corresponding timestamp in each data packet; Target data belonging to the same acquisition period are grouped based on timestamps, and the grouped target data are time-series aligned based on a unified time reference. According to the predefined standard data format, the measurement values ​​in the time-aligned target data are converted into data values ​​in standard units and filled into database records with a unified field structure to generate the standardized data.

[0009] Furthermore, the intelligent diagnostic module is specifically used for: A dynamic threshold range is set for the standardized data, abnormal data points that exceed the dynamic threshold range are identified, and the flow meter identifier corresponding to each abnormal data point is recorded. Based on historical standardized data sequences, calculate the health index of each flow meter; Based on each abnormal data point, the corresponding flow meter identifier, and the health index, a status assessment result containing the fault level and maintenance recommendations is generated.

[0010] Furthermore, the health index is calculated using the following formula: ; In the formula, H represents the health index. This represents the preset attenuation coefficient, and n represents the number of selected characteristic parameters. This represents the weight coefficient of the i-th feature parameter. This represents the actual measured value of the i-th feature parameter. This represents the baseline value of the i-th feature parameter. This represents the historical standard deviation of the i-th feature parameter.

[0011] Furthermore, the control output module is specifically used for: Extract the health index and the fault level from the status assessment results; Calculate the control decision value based on the health index and the fault level; When the control decision value is greater than a first preset threshold, a remote control command is generated to adjust the operating parameters of the specified flow meter; when the control decision value is greater than a second preset threshold, a remote control command is generated to control the start and stop of the specified flow meter.

[0012] Furthermore, the control decision value is calculated using the following formula: ; In the formula, C represents the control decision value, H represents the health index, F represents the fault level, and α and β represent preset adjustment parameters.

[0013] Furthermore, the data integration module is also used for: The standardized data is divided into multiple data blocks according to a preset time window; Each data block is compressed using a compression algorithm based on linear predictive coding; The compressed data blocks and their corresponding metadata are stored together in the time-series database.

[0014] The system of this invention collects data from multiple flow meters in real time, performs protocol conversion and wireless transmission, and performs fault detection and predictive maintenance calculations after time alignment and standardization, generating remote control commands. This solves the problems of difficult data interoperability and slow operation and maintenance response of heterogeneous devices, and improves the intelligence level and operational reliability of centralized management and control of flow meters.

[0015] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of an embodiment of a flow meter remote management system based on the Internet of Things according to the present invention. Detailed Implementation

[0018] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0019] Figure 1 A schematic diagram of an embodiment of a flow meter remote management system based on the Internet of Things (IoT) provided by the present invention is shown. Figure 1 As shown, the IoT-based remote flow meter management system includes: The data acquisition module 110 is used to collect flow data from multiple flow meters in real time.

[0020] A flow meter is an instrument used to measure the flow rate of a fluid; for example, an ultrasonic flow meter installed in the heating system of an industrial park is used to measure the instantaneous and cumulative flow rate of hot water. Flow data refers to numerical information obtained from the flow meter that reflects the fluid flow state; for example, an ultrasonic flow meter outputs a set of data every 10 minutes containing an instantaneous flow rate of 25.8 m³ / s. 3 / h and cumulative flow value 38560m 3 Measurement data.

[0021] The protocol conversion module 120 is used to convert the flow data of each flow meter into target data in a unified protocol format according to predefined protocol mapping rules.

[0022] Protocol mapping rules refer to the defined correspondence between different communication protocol formats and standard formats; for example, the conversion rules that establish a correspondence between data types in the Profibus protocol and node types in the OPC UA protocol. A unified protocol format refers to the standard data exchange format agreed upon within the system; for example, using an OPC UA information model to encapsulate all flowmeter data, including a data structure with three basic attributes: device identifier, timestamp, and numerical value. Target data refers to data that conforms to the unified format requirements after protocol conversion; for example, standardized OPC UA data packets generated after converting raw data from different flowmeter models.

[0023] The communication transmission module 130 is used to transmit all target data through a wireless Internet of Things network.

[0024] Among them, wireless IoT network refers to: IoT device access network built based on wireless communication technology; for example, using LoRaWAN network to connect flow meter devices distributed in industrial parks.

[0025] The data integration module 140 is used to receive and store all target data, and to perform time-series alignment and format standardization processing on all target data to generate standardized data.

[0026] Time alignment refers to arranging data collected at different times according to a unified time reference; for example, data collected by multiple flow meters at 09:00:02, 09:00:05, and 09:00:03 are uniformly corrected to the reference time point of 09:00:00. Format standardization refers to converting data into a data processing procedure with a unified structure and units; for example, temperature data from different units (Celsius, Fahrenheit) are uniformly converted to the standard unit Kelvin. Standardized data refers to the standardized data generated after time alignment and format standardization; for example, all flow meter data is converted into a well-organized dataset containing device identifiers, standard timestamps, and standard unit values.

[0027] The intelligent diagnostic module 150 is used to perform fault detection analysis and predictive maintenance calculations based on the standardized data, and generate condition assessment results.

[0028] Fault detection and analysis refers to the process of identifying abnormal equipment states by analyzing data characteristics; for example, detecting that the instantaneous flow rate of a flow meter continuously exceeds the normal range of 0-50 m³ / h. 3 / h and reach 65m 3Predictive maintenance calculations refer to algorithmic calculations that predict the future health status of equipment based on historical data; for example, predicting the remaining service life of a flow meter based on the operating data trends of the last 90 days. Status assessment results refer to conclusions drawn from a comprehensive evaluation of the equipment's operating status; for example, generating an assessment report containing an equipment health score of 0.78 and a recommendation for maintenance in 15 days.

[0029] The control output module 160 is used to generate remote control commands based on the status assessment results, and send the remote control commands to the designated flow meter through the communication transmission module 130, for adjusting the operating parameters of the designated flow meter or controlling the start and stop of the designated flow meter.

[0030] Remote control commands refer to control commands sent to the flow meter remotely; for example, a command sent from the control center to a flow meter to adjust the sampling interval from 10 minutes to 2 minutes. Designated flow meter refers to a specific flow meter device selected as the control object; for example, the flow meter numbered FM-2024-001 located at the entrance of the production area. Operating parameters refer to the adjustable settings parameters of the flow meter during operation; for example, configurable parameters such as the flow meter's measurement range, output frequency, and filter time constant.

[0031] The technical solution of this embodiment collects data from multiple flow meters in real time, performs protocol conversion and wireless transmission, and performs fault detection and predictive maintenance calculations after time alignment and standardization, generating remote control commands. This solves the problems of difficult data interoperability between heterogeneous devices and slow operation and maintenance response, and improves the intelligence level and operational reliability of centralized flow meter management.

[0032] In an alternative embodiment, the data acquisition module 110 is specifically used for: The system sends data request commands to the multiple flow meters at periodic time intervals, receives response data packets from each flow meter, and parses the flow data from each response data packet.

[0033] The periodic time interval refers to the duration of operations performed at fixed intervals; for example, sending a data acquisition request to the flow meter every 15 minutes. The data request command refers to a command sent to the flow meter requesting the return of measurement data; for example, sending a command to the flow meter to read variable values ​​via the HART protocol. The response data packet refers to the data packet returned by the flow meter after receiving the request; for example, the flow meter returning a HART protocol data frame containing measurement values ​​and status information.

[0034] Among the above-mentioned optional methods, flow meter data can be obtained by periodically and proactively sending data request commands to ensure real-time collection and timely updates of flow data, thereby improving the initiative and timeliness of data collection.

[0035] In an alternative embodiment, the protocol conversion module 120 is specifically used for: Based on the predefined protocol mapping rules, the source communication protocol corresponding to the flow data of any flow meter is identified, and the corresponding data parsing template and field mapping relationship are matched.

[0036] Here, the source communication protocol refers to the communication protocol natively supported by the flow meter device; for example, the PROFIBUS DP protocol used by a certain model of flow meter. The data parsing template refers to the template definition used to parse data formats of a specific protocol; for example, the parsing rules defining the physical meaning of each data byte in the PROFIBUS protocol. The field mapping relationship refers to the correspondence between source protocol fields and target protocol fields; for example, mapping the values ​​of PROFIBUS input bytes 0-3 to the FlowRate field in OPC UA format.

[0037] The flow data of any flow meter is parsed according to the data parsing template, the metering parameters are extracted, and the metering parameters are mapped to the predefined fields of the unified protocol format based on the field mapping relationship to generate the target data corresponding to any flow meter. Metering parameters refer to the physical quantities measured by the flow meter; for example, instantaneous flow rate, cumulative flow rate, medium temperature, pipeline pressure, and other measured values. Predefined fields refer to data fields predefined in the unified protocol format; for example, DeviceID, Timestamp, Value, and other fields predefined in the OPC UA information model.

[0038] Return to the steps described in the predefined protocol mapping rules until the target data corresponding to each flow meter is generated.

[0039] In the above-mentioned optional methods, the communication protocols of each heterogeneous flow meter are further identified and the corresponding parsing templates are matched to map the metering parameters to a unified protocol format, thereby realizing data interoperability and standardized processing of multi-brand equipment.

[0040] In one alternative embodiment, the communication transmission module 130 is specifically used for: Receive the target data corresponding to each flow meter, attach a transmission header containing the data source identifier and sequence number to each target data, and assemble them into an independent transmission data packet.

[0041] Here, the data source identifier refers to a code used to uniquely identify the device from which the data originates; for example, a device identification code conforming to ISO standards assigned to each flow meter. The sequence number refers to a consecutive number assigned to a data packet; for example, a sequence number starting from 10001 assigned to each transmitted data packet. The transmission header refers to control information appended to the data packet; for example, adding header information containing the protocol version, data length, and checksum before the data. The transmitted data packet refers to the data unit prepared for transmission after adding the header; for example, encapsulating the target data into a 512-byte transmitted data packet.

[0042] Based on the data priority and network conditions of each transmitted data packet, a low-power wide area network or a cellular mobile network is dynamically selected as the transmission link, and each transmitted data packet is sent to the cloud server.

[0043] Data priority refers to the transmission priority level based on the importance of the data; for example, alarm data is defined as the highest priority, status data as medium priority, and historical data as ordinary priority. Network status refers to the real-time performance indicators of the communication network; for example, the signal strength, signal-to-noise ratio, and transmission rate of the current LoRaWAN network. Wide area network refers to a wireless communication network with a wide coverage area; for example, the LoRaWAN wide area IoT network deployed by operators. Cellular mobile network refers to a mobile communication network based on cellular technology; for example, 4G LTE or 5G NR mobile communication networks. Transmission link refers to the communication path traversed by data transmission; for example, choosing to transmit data to a cloud platform via the LoRaWAN network.

[0044] In the above-mentioned optional methods, a data source identifier and sequence number are further added to the transmitted data, and a low-power wide area network or cellular network is dynamically selected according to priority and network conditions to optimize data transmission efficiency and reliability.

[0045] In an alternative embodiment, the data integration module 140 is specifically used for: Receive each data packet from the cloud server and parse the target data and corresponding timestamp in each data packet.

[0046] In this context, "cloud server" refers to server equipment deployed in the cloud; for example, a data receiving and processing server cluster deployed on a cloud service provider's platform. "Time stamp" refers to a tag that records the time of data collection; for example, adding a time format marker conforming to the ISO 8601 standard to a data packet.

[0047] Target data belonging to the same acquisition period are grouped based on timestamps, and the grouped target data are time-series aligned based on a unified time reference.

[0048] The data acquisition period refers to the length of the data acquisition time cycle; for example, setting each 15-minute period as a complete data acquisition cycle. The target data after grouping refers to the data set grouped according to the time period; for example, grouping all flow meter data collected between 10:00 and 10:15 into one group.

[0049] According to the predefined standard data format, the measurement values ​​in the time-aligned target data are converted into data values ​​in standard units and filled into database records with a unified field structure to generate the standardized data.

[0050] Here, "standard data format" refers to the unified data format defined within the system; for example, specifying that all numerical data should be stored in the IEEE 754 double-precision floating-point format. "Data values ​​in standard units" refers to values ​​converted to international standard units; for example, pressure values ​​are uniformly converted to Pascals (Pa). "Database with a unified field structure" refers to a data storage system that uses a fixed field structure; for example, designing a time-series database table containing three fixed fields: DeviceID, Timestamp, and Value.

[0051] In the above optional methods, data from the same collection period are further grouped and aligned based on timestamps, converted to standard units, and filled into a unified field structure to ensure data spatiotemporal consistency.

[0052] In one alternative embodiment, the intelligent diagnostic module 150 is specifically used for: A dynamic threshold range is set for the standardized data, abnormal data points that exceed the dynamic threshold range are identified, and the flow meter identifier corresponding to each abnormal data point is recorded.

[0053] The dynamic threshold range refers to the threshold range that is dynamically adjusted according to actual conditions; for example, adjusting the flow rate threshold from 30-40m³ / h according to process requirements. 3 / h adjusted to 35-45m 3 / h. Abnormal data points refer to data points that are outside the normal range; for example, a flow meter is detected to have a 0m value during normal operation. 3 / h abnormal flow rate value. Flow meter identification refers to: a code used to uniquely identify the flow meter; for example, a unique identifier in the format FM-areaB-002 assigned to each flow meter.

[0054] The health index of each flow meter is calculated based on historical standardized data sequences.

[0055] Historical standardized data sequence refers to a set of historical data arranged in chronological order; for example, a standardized data sequence collected every 15 minutes over the past 60 days from a flow meter. Health index refers to a numerical indicator that quantitatively assesses the health status of equipment; for example, an algorithm might calculate the current health status of a flow meter as 0.85.

[0056] Based on each abnormal data point, the corresponding flow meter identifier, and the health index, a status assessment result containing the fault level and maintenance recommendations is generated.

[0057] Among these, fault level refers to the graded evaluation of the severity of a fault; for example, equipment faults are classified into three levels: Level 1 warning, Level 2 alarm, and Level 3 emergency. Maintenance recommendations refer to maintenance guidance based on the equipment status; for example, generating specific maintenance recommendations to calibrate sensors within 3 days.

[0058] Among the above optional methods, dynamic thresholds can be further set to identify abnormal data points, and a health index can be calculated by combining historical data to generate fault levels and maintenance suggestions, thereby achieving predictive intelligent operation and maintenance.

[0059] In one alternative approach, the health index is calculated using the following formula: ; In the formula, H represents the health index. This represents the preset attenuation coefficient, and n represents the number of selected characteristic parameters. This represents the weight coefficient of the i-th feature parameter. This represents the actual measured value of the i-th feature parameter. This represents the baseline value of the i-th feature parameter. This represents the historical standard deviation of the i-th feature parameter.

[0060] It should be noted that the health index formula is based on multivariate statistical process control theory. It calculates the weighted sum of squares of the standardized deviations of the actual measured values ​​of each characteristic parameter from the benchmark value, takes the square root, multiplies it by a decay coefficient, and finally applies an exponential function to map it to the zero-to-one interval. The function of this formula is to comprehensively assess the overall health status of the flowmeter. The decay characteristic of the exponential function ensures that large deviations lead to a rapid decrease in the health index, while the weighted summation considers the importance of different parameters, and the introduction of historical standard deviation makes the assessment results statistically significant.

[0061] Among the above-mentioned optional methods, the operating status of the flow meter is further quantitatively assessed by weighting and normalizing the deviation and exponential decay through the health index calculation formula, providing a scientific and accurate equipment health assessment model.

[0062] In an alternative embodiment, the control output module 160 is specifically used for: The health index and the fault level are extracted from the status assessment results.

[0063] The control decision value is calculated based on the health index and the fault level.

[0064] Among them, the control decision value refers to the quantitative value used to determine what control measures to take; for example, the control decision value of 0.68 is calculated by an algorithm.

[0065] When the control decision value is greater than a first preset threshold, a remote control command is generated to adjust the operating parameters of the specified flow meter; when the control decision value is greater than a second preset threshold, a remote control command is generated to control the start and stop of the specified flow meter.

[0066] The first preset threshold refers to a threshold used to determine whether to adjust the operating parameters; for example, adjusting the flow meter's operating parameters when the control decision value exceeds 0.5. The second preset threshold refers to a threshold used to determine whether to start or stop the equipment; for example, urgently stopping the flow meter when the control decision value exceeds 0.8.

[0067] In the above-mentioned optional methods, control decision values ​​are further calculated based on health index and fault level, and parameter adjustment or start / stop commands are automatically generated according to threshold to achieve automated closed-loop control.

[0068] In an alternative approach, the control decision value is calculated using the following formula: ; In the formula, C represents the control decision value, H represents the health index, F represents the fault level, and α and β represent preset adjustment parameters.

[0069] It should be noted that the control decision value formula combines the logistic function and the natural logarithmic transformation. The logistic part handles the product relationship between the health index and the fault level, simulating the saturation effect of the decision, while the square root part introduces nonlinear growth to cope with severe fault conditions. The function of the above formula is to generate a continuous control decision value to determine whether the flowmeter operating parameters need to be adjusted or a start / stop operation needs to be performed. The adjustment parameters α and β are used to balance the two contributions, ensuring smooth decision-making and timely response.

[0070] In the above-mentioned optional methods, the accuracy and response speed of the remote control strategy can be further optimized by comprehensively considering health status and fault level through the control decision value calculation formula.

[0071] In an alternative embodiment, the data integration module 140 is further configured to: The standardized data is divided into multiple data blocks according to a preset time window.

[0072] A data block refers to a set of data divided according to a time window; for example, traffic data collected within 4 hours is divided into a data block.

[0073] Each data block is compressed using a compression algorithm based on linear predictive coding.

[0074] Linear predictive coding refers to data compression algorithms based on the principle of linear prediction; for example, a compression method that uses the first 24 data points to predict the 25th data point. Compression algorithms refer to encoding methods that reduce data storage space; for example, using the LZW algorithm for lossless compression of traffic data.

[0075] The compressed data blocks and their corresponding metadata are stored together in the time-series database.

[0076] In this context, compressed data blocks refer to data sets that have undergone compression processing; for example, compressing original 48KB data into 16KB compressed data blocks. Metadata refers to data that describes the characteristics of the data; for example, information recording the data block creation time, compression ratio, and data quality indicators. A time-series database refers to a database specifically designed for storing time-series data; for example, using a specially designed time-series database to store timestamped traffic data.

[0077] In the above-mentioned optional methods, the standardized data is further divided into blocks and compressed using linear predictive coding, and then stored in a time-series database, which effectively reduces storage space usage and improves the efficiency of historical data query.

[0078] To better illustrate the technical solution of this embodiment, the following complete example is used for explanation, specifically: S10, the data acquisition module 110 sends data request commands to multiple flow meters at periodic time intervals, receives response data packets from each flow meter, and parses the flow data from each response data packet; S20, Protocol conversion module 120 identifies the source communication protocol corresponding to the flow data of each flow meter according to the predefined protocol mapping rules, matches the corresponding data parsing template and field mapping relationship, parses the flow data of each flow meter according to the data parsing template, extracts the metering parameters, and maps the metering parameters to the predefined fields of the unified protocol format based on the field mapping relationship, generating the target data corresponding to each flow meter. S30, the communication transmission module 130 receives the target data corresponding to each flow meter, adds a transmission header containing the data source identifier and sequence number to each target data, assembles it into an independent transmission data packet, and dynamically selects a low-power wide area network or cellular mobile network as the transmission link based on the data priority and network conditions of each transmission data packet, and sends each transmission data packet to the cloud server. S40, the data integration module 140 receives each transmission data packet from the cloud server, parses the target data and corresponding timestamp in each transmission data packet, groups the target data belonging to the same collection period based on the timestamp, and performs time-series alignment on the grouped target data based on a unified time reference. According to the predefined standard data format, the measurement values ​​in the time-series aligned target data are converted into data values ​​under standard units and filled into database records with a unified field structure to generate standardized data. The S50 and intelligent diagnostic module 150 set dynamic threshold ranges for standardized data, identify abnormal data points that exceed the dynamic threshold range, and record the flow meter identifier corresponding to each abnormal data point. Based on the historical standardized data sequence, they calculate the health index of each flow meter and generate a status assessment result containing fault level and maintenance recommendations based on each abnormal data point, the corresponding flow meter identifier, and the health index. S60, the control output module 160 extracts the health index and fault level from the status assessment results, calculates the control decision value based on the health index and fault level, generates a remote control command for adjusting the working parameters of the specified flow meter when the control decision value is greater than the first preset threshold, and generates a remote control command for controlling the start and stop of the specified flow meter when the control decision value is greater than the second preset threshold, and sends the remote control command to the specified flow meter through the communication transmission module 130.

[0079] Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above.

[0080] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0081] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0082] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A remote flow meter management system based on the Internet of Things, characterized in that, The system includes: The data acquisition module is used to collect flow data from multiple flow meters in real time. The protocol conversion module is used to convert the flow data of each flow meter into target data in a unified protocol format according to predefined protocol mapping rules. A communication transmission module, used to transmit all target data via a wireless Internet of Things (IoT) network; The data integration module is used to receive and store all target data, and to perform time-series alignment and format standardization on all target data to generate standardized data; The intelligent diagnostic module is used to perform fault detection analysis and predictive maintenance calculations based on the standardized data, and generate condition assessment results. The control output module is used to generate remote control commands based on the status assessment results, and send the remote control commands to the designated flow meter through the communication transmission module, for adjusting the operating parameters of the designated flow meter or controlling the start and stop of the designated flow meter.

2. The IoT-based remote flow meter management system according to claim 1, characterized in that, The data acquisition module is specifically used for: The system sends data request commands to the multiple flow meters at periodic time intervals, receives response data packets from each flow meter, and parses the flow data from each response data packet.

3. The IoT-based remote flow meter management system according to claim 2, characterized in that, The protocol conversion module is specifically used for: According to the predefined protocol mapping rules, identify the source communication protocol corresponding to the flow data of any flow meter, and match the corresponding data parsing template and field mapping relationship; The flow data of any flow meter is parsed according to the data parsing template, the metering parameters are extracted, and the metering parameters are mapped to the predefined fields of the unified protocol format based on the field mapping relationship to generate the target data corresponding to any flow meter. Return to the steps described in the predefined protocol mapping rules until the target data corresponding to each flow meter is generated.

4. The IoT-based remote flow meter management system according to claim 3, characterized in that, The communication transmission module is specifically used for: Receive the target data corresponding to each flow meter, attach a transmission header containing the data source identifier and sequence number to each target data, and assemble them into an independent transmission data packet; Based on the data priority and network conditions of each transmitted data packet, a low-power wide area network or a cellular mobile network is dynamically selected as the transmission link, and each transmitted data packet is sent to the cloud server.

5. The IoT-based remote flow meter management system according to claim 4, characterized in that, The data integration module is specifically used for: Receive each data packet from the cloud server and parse the target data and corresponding timestamp in each data packet; Target data belonging to the same acquisition period are grouped based on timestamps, and the grouped target data are time-series aligned based on a unified time reference. According to the predefined standard data format, the measurement values ​​in the time-aligned target data are converted into data values ​​in standard units and filled into database records with a unified field structure to generate the standardized data.

6. The IoT-based remote flow meter management system according to claim 5, characterized in that, The intelligent diagnostic module is specifically used for: A dynamic threshold range is set for the standardized data, abnormal data points that exceed the dynamic threshold range are identified, and the flow meter identifier corresponding to each abnormal data point is recorded. Based on historical standardized data sequences, calculate the health index of each flow meter; Based on each abnormal data point, the corresponding flow meter identifier, and the health index, a status assessment result containing the fault level and maintenance recommendations is generated.

7. The IoT-based remote flow meter management system according to claim 6, characterized in that, The health index is calculated using the following formula: ; In the formula, H represents the health index. This represents the preset attenuation coefficient, and n represents the number of selected characteristic parameters. This represents the weight coefficient of the i-th feature parameter. This represents the actual measured value of the i-th feature parameter. This represents the baseline value of the i-th feature parameter. This represents the historical standard deviation of the i-th feature parameter.

8. The IoT-based remote flow meter management system according to claim 7, characterized in that, The control output module is specifically used for: Extract the health index and the fault level from the status assessment results; Calculate the control decision value based on the health index and the fault level; When the control decision value is greater than a first preset threshold, a remote control command is generated to adjust the operating parameters of the specified flow meter; when the control decision value is greater than a second preset threshold, a remote control command is generated to control the start and stop of the specified flow meter.

9. The IoT-based remote flow meter management system according to claim 8, characterized in that, The control decision value is calculated using the following formula: ; In the formula, C represents the control decision value, H represents the health index, F represents the fault level, and α and β represent preset adjustment parameters.

10. The IoT-based remote flow meter management system according to claim 5, characterized in that, The data integration module is also used for: The standardized data is divided into multiple data blocks according to a preset time window; Each data block is compressed using a compression algorithm based on linear predictive coding; The compressed data blocks and their corresponding metadata are stored together in the time-series database.

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