Metering device online detection method, system and equipment based on Internet of Things

Abstract

The invention discloses a metering device online detection method, system and device based on the Internet of Things, and relates to the technical field of metering device detection. The metering instrument online detection method based on the Internet of Things comprises the steps of obtaining a first data set of a target instrument in a preset time period and a second data set of the target instrument in a historical time period corresponding to the preset time; according to the first data set, obtaining whether first abnormal data exists in the first data set or not; if the first abnormal data does not exist, judging whether second abnormal data exists in the first data set or not according to the first data set and the second data set; if the first abnormal data or the second abnormal data exist, acquiring a third data set of at least one associated instrument associated with the target instrument in a preset time period; judging the source of the first abnormal data according to the first data set and the third data set; and adjusting a maintenance strategy according to the source of the first abnormal data.

Description

Technical Field

[0001] The present application relates to the field of measuring instrument detection, and in particular to an Internet of Things-based online detection method, system, and device for measuring instruments. Background Art

[0002] With the rapid development of the Internet of Things (IoT), online instrument detection technology has gained widespread application. Traditional instrument detection methods often rely on regular manual inspections and data collection, which suffer from low efficiency, high costs, and poor real-time performance. The application of IoT technology has automated and remotely managed data collection, transmission, and analysis for measuring instruments, significantly improving detection efficiency and accuracy.

[0003] Currently, online meter detection systems based on the Internet of Things (IoT) are capable of real-time monitoring and data collection for various types of meters, such as water and gas meters. These systems utilize sensor networks and wireless communication technologies to transmit meter data, including instantaneous flow, accumulated flow, pressure, and temperature, to management platforms in real time for analysis and decision-making.

[0004] However, existing IoT-based online metering instrument detection technologies still have some limitations. For example, in data acquisition, due to factors such as sensor accuracy and network transmission stability, the collected data may contain errors and loss. In terms of anomaly detection, existing methods often rely on simple threshold judgments and have difficulty accurately identifying complex anomalies. Summary of the Invention

[0005] The main purpose of this application is to provide an online detection method for metering instruments based on the Internet of Things, aiming to solve the technical problem that the methods in the prior art often rely on simple threshold judgments and are difficult to accurately identify complex abnormal situations.

[0006] To achieve the above objectives, in a first aspect, the present application provides an online detection method for a meter based on the Internet of Things, comprising: Acquire a first data set of the target meter in a preset time period, and a second data set of the target meter in a historical time period corresponding to the preset time; According to the first data set, obtaining whether the first data set contains first abnormal data; If the first abnormal data does not exist, determining whether the first data set contains second abnormal data based on the first data set and the second data set; If the first abnormal data or the second abnormal data exists, obtaining a third data set of at least one associated meter associated with the target meter in a preset time period; determining a source of the first abnormal data based on the first data set and the third data set; Adjust the maintenance strategy according to the source of the first abnormal data.

[0007] Optionally, the step of obtaining a first data set of the target meter in a preset time period and a second data set of the target meter in a historical time period corresponding to the preset time includes: The first data set and the second data set both include instantaneous flow data, accumulated flow data, pressure value data, and temperature value data.

[0008] Optionally, the step of obtaining, based on the first data set, whether the first data set contains first abnormal data includes: Determining whether the instantaneous flow data and / or pressure data of the target meter exceeds a first preset threshold value based on a plurality of instantaneous flow data and pressure value data of the target meter in a preset time period; If there is instantaneous flow data and / or pressure data exceeding a first preset threshold, it is determined that the first data set contains first abnormal data; If there is no instantaneous flow data and pressure data exceeding the first preset threshold, it is determined whether the first data set contains first abnormal data.

[0009] Optionally, if there is no instantaneous flow data and pressure data exceeding the first preset threshold, the step of determining whether the first data set contains first abnormal data includes: If there is no instantaneous flow data and pressure data exceeding the first preset threshold, obtaining the change rate of the instantaneous flow data and pressure data; According to the change rate, determining whether the change rate exceeds a second preset threshold; If the change rate exceeds a second preset threshold, it is determined that the first data set contains first abnormal data; If the change rate does not exceed the second preset threshold, it is determined that the first data set does not contain first abnormal data.

[0010] Optionally, if the first abnormal data does not exist, the step of determining whether the first data set contains second abnormal data based on the first data set and the second data set includes: Acquire first accumulated flow data of the target meter at a preset time, and at least one second accumulated flow data of the target meter in a historical time period corresponding to the preset time period; Preprocessing the at least one second accumulated flow data and obtaining average accumulated flow data; determining whether the first data set contains second abnormal data according to a ratio of the first accumulated flow data to the average accumulated flow data; If the ratio exceeds the target threshold, the first data set contains second abnormal data; If the ratio does not exceed the target threshold, then there is no second abnormal data in the first data set.

[0011] Optionally, if the first abnormal data or the second abnormal data exists, the step of obtaining a third data set of at least one associated meter associated with the target meter in a preset time period includes: Acquire, according to the attribute information of the target instrument, at least one associated instrument that is logically associated and / or IoT-associated with the target instrument; A third data set of at least one associated instrument is obtained, wherein the third data set includes associated flow data, pressure value data, temperature data of the associated instrument in a preset time period, and historical flow data of the associated instrument corresponding to the preset time period.

[0012] Optionally, the step of determining the source of the first abnormal data based on the first data set and the third data set includes: determining, based on the third data set, whether the third data set contains third abnormal data; If so, determining that the source of the first abnormal data belongs to the system where the target instrument and the at least one associated instrument are located; If not, it is determined that the source of the first abnormal data belongs to the target instrument.

[0013] Optionally, the step of adjusting the maintenance strategy according to the source of the first abnormal data includes: When the source of the first abnormal data belongs to the target instrument, determining a first maintenance level; When the source of the first abnormal data belongs to the system where the target instrument and the at least one associated instrument are located, determining a second maintenance level; The maintenance strategy is adjusted according to the priorities of the first maintenance level and the second maintenance level.

[0014] In a second aspect, the present application provides an online detection system for metering instruments based on the Internet of Things, comprising a management platform, a sensor network platform, and an object platform that establish communication in sequence; The management platform is configured to: Acquire a first data set of the target meter in a preset time period, and a second data set of the target meter in a historical time period corresponding to the preset time; According to the first data set, obtaining whether the first data set contains first abnormal data; If the first abnormal data does not exist, determining whether the first data set contains second abnormal data based on the first data set and the second data set; If the first abnormal data or the second abnormal data exists, obtaining a third data set of at least one associated meter associated with the target meter in a preset time period; determining a source of the first abnormal data based on the first data set and the third data set; Adjust the maintenance strategy according to the source of the first abnormal data.

[0015] In a third aspect, the present application provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.

[0016] Beneficial effects that this application can achieve: The embodiments of the present application propose an online detection method, system, and device for measuring instruments based on the Internet of Things. By synchronously acquiring the real-time operating data (first data set) and historical data from the same period (second data set) of the target instrument, a longitudinal comparative analysis in the time dimension is achieved. This can identify hidden anomalies caused by performance degradation or environmental cycle changes of the target instrument, and significantly improves the sensitivity and coverage of anomaly detection compared to single real-time detection. By comparing the first data set of a preset time period with the second data set of a historical time period, and combining the third data set of the associated instrument, the method can more accurately determine the source of the abnormal data, thereby improving the accuracy and reliability of the data. By online real-time monitoring of the target instrument's data set, abnormal data can be discovered and processed in a timely manner. Compared with traditional manual inspection methods, this significantly improves detection efficiency and reduces labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of an online detection method for a metering instrument according to an embodiment of the present application; Figure 2 This is a schematic diagram of the framework of the service platform involved in this application; Figure 3 This is a schematic diagram of the framework of the management platform involved in this application; Figure 4 This is a schematic diagram of the framework of the sensor network platform involved in this application.

[0018] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0021] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0022] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme in which A and B are satisfied at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0023] Example 1 Reference Figure 1-Figure 4 The first embodiment of the present application provides an online detection method for a meter based on the Internet of Things, comprising the following steps: S10: Acquire a first data set of the target meter in a preset time period, and a second data set of the target meter in a historical time period corresponding to the preset time.

[0024] Optionally, the target meter can be a water meter or a gas meter, etc. The target meter can be used in residential areas, commercial buildings, and industrial parks. By performing online detection on the target meter, the normal metering of water and gas consumption can be ensured, abnormal consumption can be discovered in time, and safety hazards such as water leakage and gas leakage can be prevented to ensure the smooth progress of the production process. Select a specific water meter or gas meter as the target meter. Set a time range, such as the past 24 hours, or a preset frequency, such as collecting data from the target meter every hour or every 4 hours. The first data set includes several data for evaluating whether the target meter is abnormal. Collect the usage data of the target meter within the preset time period, such as water or gas volume readings. Select a historical time range corresponding to the preset time period, such as the time period that is the same as the preset time period every day in the past month as the historical time period. Collect the usage data of the target meter within the historical time period.

[0025] S20. According to the first data set, determine whether the first data set contains first abnormal data.

[0026] Optionally, set criteria for abnormal data, such as a sudden large increase or decrease, or data that is stable but suddenly jumps.

[0027] S30: If the first abnormal data does not exist, determine whether the first data set contains second abnormal data based on the first data set and the second data set.

[0028] Optionally, if the first dataset is normal, the first dataset is compared with the second dataset to analyze whether the usage trends are consistent. If the usage trend is significantly inconsistent with historical data or there are periodic abnormal fluctuations, it is considered second abnormal data.

[0029] S40: If the first abnormal data or the second abnormal data exists, obtain a third data set of at least one associated meter associated with the target meter in a preset time period.

[0030] Optionally, other water or gas meters in the same area or for the same user as the target meter can be selected as associated meters. Associated meters and the target meter can be IoT-linked, such as upstream and downstream equipment in the same process chain or meters on the same floor. They can also be logically linked, such as by clustering analysis to identify groups of meters with high data correlation. Usage data for the associated meters is collected over a preset time period to assess whether the associated meters are faulty.

[0031] S50: Determine the source of the first abnormal data according to the first data set and the third data set.

[0032] Optionally, compare the first and third datasets to analyze whether the abnormal data occurs only in the target meter or across multiple associated meters. If the abnormal data occurs only in the target meter, it may indicate a fault in the meter itself. If the abnormal data occurs across multiple associated meters, it may indicate a regional water / gas usage anomaly or data transmission issue.

[0033] S60: Adjust the maintenance strategy according to the source of the first abnormal data.

[0034] Optionally, if the instrument itself is judged to be faulty, maintenance personnel are arranged to conduct on-site inspections or replace the instrument. If it is judged to be a regional water / gas consumption anomaly or a data transmission problem, further investigation of the cause is carried out, such as checking whether the pipeline is leaking or whether the data transmission equipment is faulty, and appropriate repair measures are taken. Based on the source of the abnormal data, this method can adjust the maintenance strategy in a targeted manner, avoid unnecessary maintenance work, reduce resource waste, and improve maintenance efficiency and quality. By promptly detecting and processing abnormal data, this method helps prevent equipment failures, improve equipment reliability and stability, and extend the service life of the equipment. Based on Internet of Things technology, this method realizes the intelligent management of metering instruments, improves management efficiency, and reduces management costs.

[0035] Example 2 As an optional implementation, based on Example 1, this example provides an online detection method for a meter based on the Internet of Things, including the following steps: S10: Acquire a first data set of the target meter in a preset time period, and a second data set of the target meter in a historical time period corresponding to the preset time.

[0036] Optionally, the first data set and the second data set both include instantaneous flow data, accumulated flow data, pressure value data and temperature value data.

[0037] Specifically, based on monitoring needs, a specific time range is set as a preset time period. For example, the preset time period can be the past 24 hours, the past week, or daily from 6:00 PM to midnight. During the preset time period, various data from the target meter is collected in real time. The flow rate value of the target meter is recorded at each instant in time to reflect real-time flow rate changes. Each instant in time can be set every second, every 10 seconds, every minute, or every 5 minutes, depending on specific needs. The total flow rate of the target meter during the preset time period is recorded for use in calculating usage or consumption. For water meters or certain types of gas meters, pressure data may also be included to reflect pressure conditions within the pipeline. For meters that need to account for temperature effects, such as some gas meters, temperature data may also be included to calibrate flow rate measurements. A historical time range corresponding to the preset time period is selected. For example, if the preset time period is the past 24 hours, a historical time range could be selected from a single day during the same period last year, or a period in the past with similar usage characteristics. During the historical time period, various data from the target meter are retrieved from the IoT system's data storage. It also includes instantaneous flow data, cumulative flow data, pressure value data and temperature value data, so as to be compared and analyzed with the first data set.

[0038] S20. According to the first data set, determine whether the first data set contains first abnormal data.

[0039] Optionally, the step of obtaining, based on the first data set, whether the first data set contains first abnormal data includes: S201: Determine whether the instantaneous flow data and / or pressure data of the target meter exceeds a first preset threshold value based on a plurality of instantaneous flow data and pressure value data of the target meter in a preset time period; Specifically, all instantaneous flow rate and pressure data for the target meter within a preset time period are extracted from the first data set. A first preset threshold is set based on the target meter's normal operating range and historical data. This threshold can be the maximum instantaneous flow rate, the upper or lower limit of the pressure value, or a combination thereof. The extracted instantaneous flow rate and pressure data are compared with the first preset threshold. Simple comparison operations (such as greater than, less than, or equal to) can be used to determine whether the data exceeds the threshold.

[0040] S202: If there is instantaneous flow data and / or pressure data exceeding a first preset threshold, determine that the first data set contains first abnormal data; Specifically, if any instantaneous flow data or pressure value data exceeds a first preset threshold, it is considered that the first data set contains first abnormal data. The time point, value, and degree of exceeding the threshold of the abnormal data are recorded for subsequent analysis and processing.

[0041] S203: If there is no instantaneous flow data and pressure data exceeding the first preset threshold, determine whether the first data set contains first abnormal data.

[0042] Specifically, if all instantaneous flow data and pressure value data are within the first preset threshold range, it is considered that the first data set does not contain first abnormal data. After confirming that the first data set is normal, the operating status of the target instrument can be continuously monitored and abnormality detection can be performed regularly.

[0043] Optionally, if there is no instantaneous flow data and pressure data exceeding the first preset threshold, the step of determining whether the first data set contains first abnormal data includes: S2031. If there is no instantaneous flow data and pressure data exceeding the first preset threshold, obtain the change rate of the instantaneous flow data and pressure data; Specifically, after confirming that both the instantaneous flow data and pressure data do not exceed the first preset threshold, further analysis of the rate of change of these data is often necessary to more comprehensively detect possible anomalies in the first dataset. This approach aims to identify potential anomalies by calculating the rate of change of the instantaneous flow data and pressure data and determining whether it exceeds the second preset threshold.

[0044] For instantaneous flow and pressure data, perform necessary data cleaning and preprocessing to ensure data continuity and accuracy. Calculate the rate of change of instantaneous flow and pressure data between adjacent time points. The rate of change can be calculated by dividing the difference between adjacent data points by the time interval.

[0045] S2032. Determine, based on the change rate, whether the change rate exceeds a second preset threshold; Specifically, a second preset threshold is set based on the normal operating characteristics and historical data of the target instrument. This threshold is used to determine whether the rate of change is abnormal. The calculated rate of change is compared with the second preset threshold to determine whether the rate of change exceeds the threshold.

[0046] S2033: If the change rate exceeds a second preset threshold, determine that first abnormal data exists in the first data set; Specifically, if the rate of change of any instantaneous flow data or pressure value data exceeds a second preset threshold, it is considered that the first data set contains first abnormal data. Further analysis is performed on the abnormal change rate, such as checking the time point of the abnormality, the duration of the abnormality, and the possible cause.

[0047] S2034: If the change rate does not exceed a second preset threshold, determine that the first data set does not contain first abnormal data.

[0048] Specifically, if the change rates of all instantaneous flow data and pressure value data are within the second preset threshold range, it is considered that the first data set does not contain the first abnormal data. After confirming that the first data set is normal, the operating status of the target instrument can continue to be monitored and abnormality detection can be performed regularly.

[0049] S30: If the first abnormal data does not exist, determine whether the first data set contains second abnormal data based on the first data set and the second data set.

[0050] Optionally, if the first abnormal data does not exist, the step of determining whether the first data set contains second abnormal data based on the first data set and the second data set includes: S301, obtaining first accumulated flow data of a target meter at a preset time, and at least one second accumulated flow data of the target meter in a historical time period corresponding to the preset time period; Specifically, first accumulated flow data of the target meter within a preset time period is extracted from the first data set. At least one second accumulated flow data within a historical time period corresponding to the preset time period is extracted from the second data set. The historical data can be selected from the same year, the same month, or a time period with similar usage characteristics.

[0051] S302: Preprocess the at least one second accumulated flow data and obtain average accumulated flow data; Specifically, the extracted second accumulated flow data is cleaned as necessary to remove outliers or missing values. The average value of at least one second accumulated flow data is calculated to obtain average accumulated flow data. This average value can be used as a benchmark for normal operation.

[0052] S303: Determine whether the first data set contains second abnormal data based on the ratio of the first accumulated flow data to the average accumulated flow data; Specifically, the ratio of the first accumulated flow data to the average accumulated flow data is calculated, and a ratio range is set to determine whether the first accumulated flow data deviates from a normal range.

[0053] S304: If the ratio exceeds the target threshold, the first data set contains second abnormal data; Specifically, a target threshold is set based on the normal operating characteristics and historical data of the target instrument. If the ratio exceeds the target threshold, it is considered that the first data set contains second abnormal data, which may indicate that the operating status of the target instrument is significantly different from the historical normal status.

[0054] S305: If the ratio does not exceed the target threshold, the first data set does not contain second abnormal data.

[0055] Specifically, if the ratio does not exceed the target threshold, it is considered that there is no second abnormal data in the first data set, and the operating status of the target instrument is consistent with the historical normal state. After confirming that the first data set is normal, the operating status of the target instrument can continue to be monitored and anomaly detection can be performed regularly.

[0056] S40: If the first abnormal data or the second abnormal data exists, obtain a third data set of at least one associated meter associated with the target meter in a preset time period.

[0057] Optionally, if the first abnormal data or the second abnormal data exists, the step of obtaining a third data set of at least one associated meter associated with the target meter in a preset time period includes: S401. Acquire at least one associated instrument that is logically associated and / or IoT-associated with the target instrument based on attribute information of the target instrument. Specifically, after detecting the presence of first abnormal data or second abnormal data in a target meter, in order to more comprehensively understand the cause of the problem and possibly perform troubleshooting, it is usually necessary to obtain a third data set of at least one associated meter associated with the target meter within a preset time period.

[0058] Analyze the target meter's attribute information, including its installation location, purpose, and connection relationships, to identify its possible associated meters. Based on this attribute information, identify associated meters that are logically associated with the target meter (e.g., within the same water / gas / electricity system) and / or IoT-linked (e.g., connected via an IoT system).

[0059] S402. Acquire a third data set of at least one associated instrument, wherein the third data set includes associated flow data, pressure value data, and temperature data of the associated instrument in a preset time period, and historical flow data of the associated instrument corresponding to the preset time period.

[0060] Specifically, the associated flow rate data, pressure data, and temperature data for the associated instrument within a preset time period are extracted from the IoT system or related database. This data helps understand the operating status and potential problems of the associated instrument. Simultaneously, historical flow rate data for the associated instrument corresponding to the preset time period is extracted. This historical data can be used to compare and analyze operating trends and abnormal changes in the associated instrument. The extracted associated flow rate data, pressure data, temperature data, and historical flow rate data are integrated into a third dataset for subsequent analysis and processing.

[0061] S50: Determine the source of the first abnormal data according to the first data set and the third data set.

[0062] Optionally, the step of determining the source of the first abnormal data based on the first data set and the third data set includes: S501: Determine, based on the third data set, whether the third data set contains third abnormal data. Specifically, after obtaining the first dataset of the target instrument and the third dataset of at least one associated instrument, to accurately determine the source of the first abnormal data, it is necessary to analyze the third dataset to determine whether third abnormal data exists. This detailed step aims to accurately determine whether the first abnormal data originated from the target instrument itself or the system in which it resides (including the target instrument and its associated instruments) through this analysis process.

[0063] Perform necessary preprocessing on the third dataset, including data cleaning, denoising, and normalization, to ensure data accuracy and consistency. Apply appropriate anomaly detection algorithms or methods to analyze the associated flow, pressure, and temperature data in the third dataset to determine whether any third anomaly data exists. Based on the normal operating range and historical data of the associated instruments, establish anomaly determination criteria to accurately identify third anomaly data.

[0064] S502: If the third data set contains third abnormal data, determine that the source of the first abnormal data belongs to the system where the target meter and the at least one associated meter are located; Specifically, if the third data set contains third abnormal data, this indicates that the problem may not be limited to the target instrument itself, but rather involves the operating status of the entire system (including the target instrument and its associated instruments). Therefore, the source of the first abnormal data can be determined to be the system containing the target instrument and at least one associated instrument. Further investigation is needed into the overall system operation and the interactions between the various instruments.

[0065] S503: If the third abnormal data does not exist in the third data set, determine that the source of the first abnormal data belongs to the target instrument.

[0066] Specifically, if the third abnormal data does not exist in the third data set, it indicates that the problem is likely limited to the target instrument itself and is unrelated to other related instruments. Therefore, it can be determined that the source of the first abnormal data is the target instrument, and further troubleshooting and repair of the target instrument is required.

[0067] S60: Adjust the maintenance strategy according to the source of the first abnormal data.

[0068] Optionally, the step of adjusting the maintenance strategy according to the source of the first abnormal data includes: S601: When the source of the first abnormal data belongs to the target instrument, determine a first maintenance level; Specifically, after accurately determining the source of the first abnormal data, in order to efficiently resolve the problem and ensure the normal operation of the system, it is necessary to adjust the maintenance strategy based on the source of the abnormality. This is done by setting different maintenance levels and formulating corresponding maintenance plans based on the priorities of these levels.

[0069] Confirm that the first abnormal data originates only from the target instrument, indicating that the problem is limited to that instrument. Determine the first level of repair based on the importance of the target instrument, the severity of the abnormal data, and the difficulty of repair. This level is typically lower because the problem is relatively localized and easier to resolve. Based on the first level of repair, develop a repair plan for the target instrument, including repair time, required resources, and repair steps.

[0070] S602: When the source of the first abnormal data belongs to the system where the target instrument and the at least one associated instrument are located, determine a second maintenance level; Specifically, confirming that the first abnormal data originates from the system where the target instrument and associated instruments reside indicates that the problem may involve multiple instruments or the overall operational status of the system. Given the complexity and scope of the problem, a second maintenance level is assigned. This level is typically higher, requiring a more comprehensive inspection and possible system-wide repairs. Based on the second maintenance level, a maintenance plan for the entire system is developed, including system inspections, troubleshooting, repair procedures, and necessary system upgrades or modifications.

[0071] S603: Adjust the maintenance strategy according to the priorities of the first maintenance level and the second maintenance level.

[0072] Specifically, the priorities of the first and second maintenance levels are compared, taking into account factors such as the urgency of the issue, the impact on system operations, and the availability of maintenance resources. Based on the priority evaluation results, the maintenance strategy is adjusted. If the first maintenance level has a higher priority, the target instrument issue is prioritized; if the second maintenance level has a higher priority, system-level issues are prioritized. Based on the adjusted maintenance strategy, maintenance resources are allocated appropriately to ensure the smooth progress of maintenance work.

[0073] Example 3 Based on Example 1, this embodiment provides an online detection system for measuring instruments based on the Internet of Things, including a management platform, a sensor network platform, and an object platform that establish communication in sequence; The management platform is configured to: Acquire a first data set of the target meter in a preset time period, and a second data set of the target meter in a historical time period corresponding to the preset time; According to the first data set, obtaining whether the first data set contains first abnormal data; If the first abnormal data does not exist, determining whether the first data set contains second abnormal data based on the first data set and the second data set; If the first abnormal data or the second abnormal data exists, obtaining a third data set of at least one associated meter associated with the target meter in a preset time period; determining a source of the first abnormal data based on the first data set and the third data set; Adjust the maintenance strategy according to the source of the first abnormal data.

[0074] The user platform is configured to provide front-end services to users; users obtain the required perception service information through the user platform, process the perception service information, and convert it into user perception information; users analyze the user perception information and make corresponding decisions based on their own wishes, and convert the user perception information into user control information through the corresponding information system and send it to the service platform, thereby expressing the user's corresponding service demand intention.

[0075] The physical entities of the user platform include various user terminals, such as mobile phones, computers, dedicated terminals, etc., which realize user-end services through integration with user information system software.

[0076] The service platform is configured as an API server or other server used to establish communication between the management platform and the user platform to implement corresponding functions; the physical entity of the service platform includes various servers.

[0077] The management platform is configured to perform at least one of equipment operation status monitoring and management, data monitoring and management, equipment parameter management, and life cycle management; the management platform is the operation coordination platform of the Internet of Things, which may include various management sub-platforms, and different management sub-platforms perform different management services; the physical entities of the management platform include various servers.

[0078] The sensor network platform is configured to perform at least one of the following: network management, command management, device status management, data protocol management, data parsing, data classification, data transmission monitoring, and data transmission security management. The sensor network platform provides data communication, parsing, identification, and classification, preventing data from various object platforms from being directly aggregated on the management platform, which would result in data redundancy and inefficient data processing. The physical entities of the object platforms include various gateways, edge computing devices, and other devices.

[0079] The object platform is configured to perform specific production tasks such as production control, detection, and measurement; the physical entities in the object platform include various production equipment, sensors, etc.

[0080] Optionally, the sensor network platform includes a master database in communication with the management platform, and at least two sensor network sub-platforms in communication with the master database; Optionally, each sensor network sub-platform corresponds to an API function or API server.

[0081] Example 4 This embodiment provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement any of the above methods.

[0082] Example 5 This embodiment provides a computer-readable storage medium, on which a computer program is stored. A processor executes the computer program to implement any of the above methods.

[0083] In some embodiments, the computer-readable storage medium may be a memory device such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface mount memory, optical disk, or CD-ROM; or various devices including any one or any combination of the above memories. The computer may be various computing devices including smart terminals and servers.

[0084] In the above embodiments of the present disclosure, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0085] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0086] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0087] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0088] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a non-volatile storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program code.

[0089] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for online detection of measuring instruments based on the Internet of Things, characterized in that: include: Acquire a first data set of the target meter in a preset time period, and a second data set of the target meter in a historical time period corresponding to the preset time; According to the first data set, obtaining whether the first data set contains first abnormal data; If the first abnormal data does not exist, determining whether the first data set contains second abnormal data based on the first data set and the second data set; If the first abnormal data or the second abnormal data exists, obtaining a third data set of at least one associated meter associated with the target meter in a preset time period; determining a source of the first abnormal data based on the first data set and the third data set; Adjust the maintenance strategy according to the source of the first abnormal data.

2. The online detection method for measuring instruments based on the Internet of Things according to claim 1, characterized in that: The step of obtaining a first data set of the target meter in a preset time period and a second data set of the target meter in a historical time period corresponding to the preset time includes: The first data set and the second data set both include instantaneous flow data, accumulated flow data, pressure value data, and temperature value data.

3. The online detection method for measuring instruments based on the Internet of Things according to claim 1, characterized in that: The step of obtaining, based on the first data set, whether the first data set contains first abnormal data includes: Determining whether the instantaneous flow data and / or pressure data of the target meter exceeds a first preset threshold value based on a plurality of instantaneous flow data and pressure value data of the target meter in a preset time period; If there is instantaneous flow data and / or pressure data exceeding a first preset threshold, it is determined that the first data set contains first abnormal data; If there is no instantaneous flow data and pressure data exceeding the first preset threshold, it is determined whether the first data set contains first abnormal data.

4. The online detection method for measuring instruments based on the Internet of Things according to claim 3, characterized in that: If there is no instantaneous flow data and pressure data exceeding the first preset threshold, the step of determining whether the first data set contains first abnormal data includes: If there is no instantaneous flow data and pressure data exceeding the first preset threshold, obtaining the change rate of the instantaneous flow data and pressure data; According to the change rate, determining whether the change rate exceeds a second preset threshold; If the change rate exceeds a second preset threshold, it is determined that the first data set contains first abnormal data; If the change rate does not exceed the second preset threshold, it is determined that the first data set does not contain first abnormal data.

5. The online detection method for measuring instruments based on the Internet of Things according to claim 1, characterized in that: If the first abnormal data does not exist, the step of determining whether the first data set contains second abnormal data based on the first data set and the second data set includes: Acquire first accumulated flow data of the target meter at a preset time, and at least one second accumulated flow data of the target meter in a historical time period corresponding to the preset time period; Preprocessing the at least one second accumulated flow data and obtaining average accumulated flow data; determining whether the first data set contains second abnormal data according to a ratio of the first accumulated flow data to the average accumulated flow data; If the ratio exceeds the target threshold, the first data set contains second abnormal data; If the ratio does not exceed the target threshold, then there is no second abnormal data in the first data set.

6. The online detection method for measuring instruments based on the Internet of Things according to claim 1, characterized in that: If the first abnormal data or the second abnormal data exists, the step of obtaining a third data set of at least one associated meter associated with the target meter in a preset time period includes: Acquire, according to the attribute information of the target instrument, at least one associated instrument that is logically associated and / or IoT-associated with the target instrument; A third data set of at least one associated instrument is obtained, wherein the third data set includes associated flow data, pressure value data, temperature data of the associated instrument in a preset time period, and historical flow data of the associated instrument corresponding to the preset time period.

7. The online detection method for measuring instruments based on the Internet of Things according to claim 6, characterized in that: The step of determining the source of the first abnormal data based on the first data set and the third data set includes: determining, based on the third data set, whether the third data set contains third abnormal data; If so, determining that the source of the first abnormal data belongs to the system where the target instrument and the at least one associated instrument are located; If not, it is determined that the source of the first abnormal data belongs to the target instrument.

8. The online detection method for measuring instruments based on the Internet of Things according to claim 7, characterized in that: The step of adjusting the maintenance strategy according to the source of the first abnormal data includes: When the source of the first abnormal data belongs to the target instrument, determining a first maintenance level; When the source of the first abnormal data belongs to the system where the target instrument and the at least one associated instrument are located, determining a second maintenance level; The maintenance strategy is adjusted according to the priorities of the first maintenance level and the second maintenance level.

9. An online detection system for measuring instruments based on the Internet of Things, characterized in that: It includes establishing a management platform, a sensor network platform and an object platform for communication in sequence; The management platform is configured to: Acquire a first data set of the target meter in a preset time period, and a second data set of the target meter in a historical time period corresponding to the preset time; According to the first data set, obtaining whether the first data set contains first abnormal data; If the first abnormal data does not exist, determining whether the first data set contains second abnormal data based on the first data set and the second data set; If the first abnormal data or the second abnormal data exists, obtaining a third data set of at least one associated meter associated with the target meter in a preset time period; determining a source of the first abnormal data based on the first data set and the third data set; Adjust the maintenance strategy according to the source of the first abnormal data.

10. A computer device, characterized in that: The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 8.

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