Valve remote monitoring method and system based on internet of things

Abstract

The application provides a valve remote monitoring method and system based on the Internet of Things, which comprises the following steps: first, effective valve opening and closing cycles are identified through range normalization processing to filter invalid data interference; then, a single-point state criterion containing position, load and maintenance marks is constructed, and continuous data streams are divided into state sections and running stages with physical semantics; further, sequence reconstruction is performed with maintenance events as boundaries, and segmented state values are obtained through aggregation calculation; finally, early warning of faults is realized based on stage difference and monitoring evaluation values. The application can accurately analyze the valve running state, quantitatively evaluate the maintenance effect, and improve the intelligent level and reliability of remote monitoring.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things and industrial valve monitoring technology, and particularly relates to a method and system for remote valve monitoring based on the Internet of Things. Background Technology

[0002] As key control components in pipeline networks of petrochemical, natural gas, hydrogen energy, thermal power and process industries, valves operate under complex conditions such as high temperature, high pressure, high pressure difference, frequent opening and closing, strong corrosion or particulate media for a long time. Their opening and closing performance, sealing reliability and operational stability are directly related to the regulation effect of the pipeline system, equipment safety and maintenance costs.

[0003] With the development of industrial site digitalization and IoT technology, existing technologies have emerged that connect signals such as valve position, pressure, temperature, and current to remote platforms for online display, threshold alarms, and ledger management of valves. Some systems can also link maintenance records to enable basic file queries. While these technologies have some application value in general valve or coarse-grained equipment management scenarios, existing methods still have significant shortcomings for high-pressure special valves, especially those with special opening and closing structures, obvious end-tightening processes, and replaceable valve seats or maintainable sealing components. Firstly, existing solutions typically focus on whether a single signal exceeds its limit. Although various data are collected, there is a lack of organization around the valve opening and closing process itself. It is difficult to convert continuous sampling signals into opening and closing cycle information corresponding to actual mechanical actions, thus making it difficult to promptly identify early signs such as start-up lag, abnormal main motion, and end-tightening imbalance. Secondly, existing solutions generally treat valves as simple on / off or regulating actuators, lacking the ability to express the phased characteristics of the opening and closing process. They fail to unify the relationships between displacement propulsion, actuator load changes, and action phases within the same monitoring logic, resulting in monitoring results often remaining at the level of surface signal changes, making it difficult to form judgments consistent with the valve's structural mechanism. Thirdly, for industrial valves requiring maintenance intervention, such as those with replaceable seats or high-sealing pairs, existing systems typically only store maintenance records as separate ledgers, lacking a way to directly embed maintenance events into the state evolution process. This leads to the inability to accurately delineate the operating states before and after maintenance within the same time series, easily resulting in problems such as mixed calculations before and after maintenance and distorted state evaluations after maintenance. Fourthly, in high-risk scenarios such as high-pressure hydrogen, hydrogen-containing media, and high-sealing requirements, valve performance degradation often first manifests as subtle behavioral changes and phased stress changes during the opening and closing process, rather than immediately as complete leakage or complete jamming. If remote monitoring remains at the level of general data acquisition and simple threshold judgment, it will be difficult to meet the monitoring requirements of these valves for continuity, interpretability, and post-maintenance traceability. Summary of the Invention

[0004] This invention discloses a valve remote monitoring method and system based on the Internet of Things (IoT) to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the first aspect of the present invention provides a valve remote monitoring method based on the Internet of Things, the method comprising: Valve opening and closing stroke data, actuator load data, and maintenance event identifier data are collected. Based on the range normalization result of the valve opening and closing stroke data, the effective opening and closing cycle is identified. Based on the effective opening and closing cycle, the actuator load data is synchronously extracted to obtain the valve monitoring input. The valve monitoring input includes time-synchronized valve opening and closing stroke data and actuator load data within the effective opening and closing cycle, as well as maintenance event identifiers aligned with the sampling points. The valve opening and closing stroke data and actuator load data in the valve monitoring input are normalized within the cycle. A single-point state criterion is constructed based on the positive enhancement of position ratio, the negative suppression of load ratio, and the superposition of local markers of maintenance events. Based on the continuity of the single-point state criterion, adjacent sampling points of the same type are merged into a state segment to generate a valve opening and closing cycle state sequence including the start-up state segment, the main motion state segment, and the end-pressing state segment. Based on the maintenance event identifier, the continuous valve opening and closing cycle state sequence is reconstructed. The arithmetic mean of each state segment is calculated and the segment-level state value is corrected for the beginning and end trends. Adjacent state segments are aggregated into an operation stage with the state segment containing the maintenance event identifier as the boundary. The segmented state values ​​of the state segments within the operation stage are calculated to form the valve segmented state result. Based on the valve segmentation status results, combined with the difference between the segmentation status value of the current operating stage and the segmentation status value of the previous operating stage, and the maintenance event boundary correction, a monitoring evaluation value is calculated. Based on the monitoring evaluation value, a remote valve monitoring conclusion including the current stage status, change direction, and maintenance priority is output.

[0006] Furthermore, the step of collecting valve opening and closing stroke data, actuator load data, and maintenance event identifier data, and identifying the effective opening and closing cycle based on the range normalization result of the valve opening and closing stroke data, includes: Read the valve opening and closing stroke data output by the position sensor, and calculate the difference between the maximum and minimum stroke values ​​within the candidate opening and closing segment as the actual stroke change range; Divide the actual stroke variation by the total designed stroke range of the valve to obtain the stroke variation ratio; When the stroke change ratio is greater than a preset ratio threshold, the candidate opening / closing segment is confirmed as a valid opening / closing cycle.

[0007] Furthermore, the step of merging adjacent similar sampling points into a state segment based on the continuity of the single-point state criterion includes: When the single-point state criteria of a group of adjacent sampling points are all less than the first state threshold, and the normalized position ratio of the valve opening and closing stroke data of the sampling points is all less than the first position threshold, the group of sampling points is merged into a starting state segment. When the rate of change of the single-point state criterion of a group of adjacent sampling points is greater than zero, or the single-point state criterion is greater than the second state threshold, the group of sampling points is merged into the main motion state segment. When the normalized position ratio of a group of adjacent sampling points is greater than the second position threshold, and the rate of change of the single-point state criterion is less than zero, the group of sampling points is merged into the end-compression state segment.

[0008] Furthermore, the step of calculating the segment-level state value by adding the arithmetic mean and first-to-last trend correction for each state segment includes: Calculate the arithmetic mean of all single-point state criteria within the state segment; The difference between the single-point state criterion of the last sampling point and the single-point state criterion of the first sampling point in this state segment is used as the beginning and end trend. The weighted value of the arithmetic mean and the first and last trends is used as the segment-level state value of this state segment.

[0009] Furthermore, the step of aggregating adjacent state segments into an operational phase based on the state segment containing the maintenance event identifier as the boundary includes: Read the status segments of all start-up and stop cycles in chronological order and check whether each status segment contains a maintenance event identifier. When a maintenance event identifier first appears within a certain state segment, the starting position of that state segment is taken as the boundary for the operation phase switching. The state segments within the continuous time range preceding the boundary are classified into the same operating phase, and the state segments within the continuous time range following the boundary are classified into the next operating phase.

[0010] Furthermore, the step of calculating the segmented state values ​​of the state segments within the operation phase includes: Calculate the average value of the segment-level state values ​​for all state segments within this operational phase; Calculate the difference between the segment-level state value of the last state segment and the segment-level state value of the first state segment in this operation phase; The weighted sum of the average value and the difference is used as the segmented state value for this operating phase.

[0011] Furthermore, the step of calculating the monitoring evaluation value based on the valve segmentation status results, combined with the difference between the segmentation status value of the current operating stage and the segmentation status value of the previous operating stage, and the maintenance event boundary correction, includes: Obtain the segmented state value and comparison benchmark of the current running stage, wherein the comparison benchmark is the segmented state value of the previous running stage or the segmented state value of the initial stable stage. Calculate the difference between the segmented state value of the current operating stage and the comparison benchmark to obtain the stage change. If the current operational phase begins after a maintenance event, a maintenance boundary correction factor is introduced; The monitoring evaluation value is obtained by weighting the segmented state value of the current operating stage, the stage change amount, and the maintenance boundary correction coefficient.

[0012] Furthermore, the step of outputting a remote valve monitoring conclusion based on the monitoring evaluation value, including the current stage status, direction of change, and maintenance priority, includes: The monitoring evaluation value is compared with a preset interval threshold, which includes a high interval, a middle interval, and a low interval. If the monitoring evaluation value is in the high range, a state category of recovery to stability or stable operation is generated, along with maintenance and operation recommendations for maintaining periodic inspections. If the monitoring evaluation value is in the middle range and the change is less than the preset value, then a normal monitoring status category is generated; If the monitoring evaluation value is in a low range or the monitoring evaluation value continues to decline for multiple consecutive operating phases, a status category of performance degradation or maintenance recommendation will be generated, along with maintenance handling suggestions for scheduling maintenance windows.

[0013] Furthermore, the steps of collecting valve opening and closing stroke data, actuator load data, and maintenance event identification data include: For electric actuators, the actuator load data is read through a current sampling module; for pneumatic actuators, the actuator load data is read through an air source pressure feedback module or a thrust feedback module. Maintenance event identifier data, including valve equipment identifier, event time, and event type, is written through the work order system, on-site handheld terminal, or maintenance confirmation interface. The event type includes valve seat replacement completed and maintenance completed.

[0014] A second aspect of the invention provides an Internet of Things (IoT)-based remote valve monitoring system, the system comprising: The data acquisition and organization module is used to collect valve opening and closing stroke data, actuator load data, and maintenance event identifier data. Based on the range normalization result of the valve opening and closing stroke data, it identifies the effective opening and closing cycle. Based on the effective opening and closing cycle, it synchronously extracts the actuator load data to obtain the valve monitoring input. The valve monitoring input includes time-synchronized valve opening and closing stroke data and actuator load data within the effective opening and closing cycle, as well as maintenance event identifiers aligned with the sampling points. The valve opening and closing cycle state sequence generation module is used to perform intra-cycle normalization processing on the valve opening and closing stroke data and actuator load data in the valve monitoring input. Based on the positive enhancement of position ratio, the negative suppression of load ratio, and the superposition of local markers of maintenance events, a single-point state criterion is constructed. Based on the continuity of the single-point state criterion, adjacent sampling points of the same type are merged into a state segment to generate a valve opening and closing cycle state sequence including the start-up state segment, the main motion state segment, and the end-pressing state segment. The segmented state result reconstruction module is used to reconstruct multiple consecutive valve opening and closing cycle state sequences based on the maintenance event identifier, calculate the arithmetic mean of each state segment plus the beginning and end trend correction for the segment-level state value, aggregate adjacent state segments into an operating stage with the state segment containing the maintenance event identifier as the boundary, calculate the segmented state value of the state segment within the operating stage, and form the valve segmented state result. The monitoring conclusion output module is used to calculate the monitoring evaluation value based on the valve segment status results, combined with the difference between the segment status value of the current operating stage and the segment status value of the previous operating stage, and the maintenance event boundary correction, and output the valve remote monitoring conclusion containing the current stage status, change direction and maintenance priority according to the monitoring evaluation value.

[0015] The beneficial technical effects of the present invention are at least as follows: To address the aforementioned issues, this invention provides a valve remote monitoring method and system based on the Internet of Things (IoT). First, range normalization is used to filter valid opening and closing cycles to eliminate invalid data interference and ensure data purity. Then, by constructing single-point state criteria, the raw data is transformed into semantic states with physical meaning, such as "start," "main movement," and "end-point clamping," achieving refined analysis of the valve's operational stages. Based on this, maintenance event identifiers are used as time anchors to reconstruct sequences and quantitatively evaluate performance differences before and after maintenance. Combined with stage difference calculations, performance degradation trends are accurately captured, achieving a shift from passive alarm to proactive prediction. Simultaneously, segment-level and stage-level aggregation processing strategies significantly reduce data transmission volume, improving response speed in the industrial IoT environment while effectively ensuring the intelligence and precision of valve operation and maintenance. Attached Figure Description

[0016] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0017] Figure 1 This is a flowchart of the valve remote monitoring method based on the Internet of Things of the present invention.

[0018] Figure 2 This is a framework diagram of the valve remote monitoring system based on the Internet of Things of this invention. Detailed Implementation

[0019] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0020] In one or more embodiments, such as Figure 1 As shown, a method for remote valve monitoring based on the Internet of Things is disclosed, the method comprising the following: S1: Collect valve opening and closing stroke data, actuator load data, and maintenance event identifier data. Identify the effective opening and closing cycle based on the range normalization result of the valve opening and closing stroke data. Synchronously extract the actuator load data based on the effective opening and closing cycle to obtain the valve monitoring input. The valve monitoring input includes time-synchronized valve opening and closing stroke data and actuator load data within the effective opening and closing cycle, as well as maintenance event identifiers aligned with the sampling points.

[0021] Specifically, step one revolves around organizing basic data for remote monitoring of the valve opening and closing process. The processing objects are limited to three types of information: opening and closing stroke data, actuator load data, and maintenance event identification data. These three types of data originate from the position detection component of the valve actuator, the load detection component within the actuator drive unit, and the event recording interface of the maintenance management terminal, respectively. For electric actuators, the opening and closing stroke data can be provided by a rotary encoder or linear displacement sensor. The controller reads the current position at fixed sampling intervals and forms a stroke record sorted by time. The actuator load data can be provided by a current sampling module. The controller reads the change in drive current at the same sampling moment and forms a load record. For pneumatic actuators, the opening and closing stroke data also comes from the position detection component, while the load data is provided by an air source pressure feedback module or thrust feedback module. Maintenance event identification data is written by the work order system, on-site handheld terminal, or maintenance confirmation interface, and includes at least the valve equipment identification, event time, and event type. The event type preferably includes valve seat replacement completion and maintenance completion. After the three types of data are uniformly time-stamped at the collection end, they are uploaded to the remote monitoring system, so that the trip records, load records and maintenance event records share the same time reference, laying the foundation for subsequent organization according to the start and stop cycle.

[0022] After entering the remote monitoring system, candidate opening / closing segments are first identified along the stroke change trajectory. This employs a segmentation approach commonly used in time series processing: from a stable segment to a changing segment and back to a stable segment. When the stroke value fluctuates within a small range over a period of time, the corresponding valve is in a relatively stable position. When the stroke value begins to change continuously in one direction and then re-enters a new stable range after the change ends, this continuous changing range can be considered a candidate opening / closing segment. To determine whether this candidate opening / closing segment is sufficient to represent an effective opening / closing action, this step introduces the range normalization approach to construct the stroke change ratio. This approach originates from the interval range representation method in mathematics, which uses the difference between the maximum and minimum values ​​within a sample interval to characterize the change amplitude of that sample interval. Considering that different valves have different total stroke ranges, directly comparing the change amplitude would be affected by equipment specifications. Therefore, a normalization process is further adopted, dividing the actual change amplitude of the candidate interval by the designed total stroke range of the valve to obtain a uniform ratio value. This results in the following formula: ; In the formula, Indicates the first The percentage change in the stroke of each candidate opening and closing segment; This indicates the maximum travel value collected within the candidate opening / closing segment, and its value comes from the sampling results of the position sensor within this time interval; This represents the minimum travel value collected within the candidate opening / closing segment, and its value also comes from the position sensor sampling results within this time interval; This represents the total design stroke range of the valve, and its value is derived from the equipment's factory parameter sheet, on-site commissioning records, or initial calibration results after installation. The derivation of this formula is straightforward: first use... Extract the actual stroke variation of the candidate opening and closing segment, and then divide it by the total stroke range of the valve. This method maps the stroke variation range of valves of different specifications to the same proportional scale. Since both the numerator and denominator represent the same type of stroke quantity, the result obtained after proportionalization is dimensionless, and therefore can be directly used for unified judgment between different valves. When the preset ratio threshold is reached, the system confirms the candidate opening / closing segment as a valid opening / closing cycle; when When the value is significantly below this threshold, the system categorizes it as a local fine-tuning or disturbance segment. Taking a ball valve with a total stroke range of 100 as an example, if the maximum stroke value within a candidate segment is 94 and the minimum stroke value is 12, then... This ratio indicates that this segment has covered the main opening and closing strokes and can be considered as an effective opening and closing cycle; if the maximum stroke value in another candidate segment is 48 and the minimum stroke value is 42, then... This ratio value only corresponds to a small range of fluctuations, and the system retains it as an original record but does not write it into the set of valid opening and closing cycles. This calculation process is also easy to verify during actual debugging: by replaying a certain stroke curve on site, as long as you observe whether the maximum stroke and the minimum stroke have crossed the main opening and closing intervals, you can find a one-to-one correspondence with the above ratio judgment result.

[0023] After identifying valid start-stop cycles, the system synchronously extracts travel records and load records within the original time series, using the start and end times of the cycle as boundaries, forming two sets of synchronized sequences within the same cycle unit. Here, "synchronization" originates from the aforementioned unified time stamp; therefore, extraction only requires extracting corresponding data within the same time range. Subsequently, the system reads the event times from the maintenance event records and compares them with the start and end times of each valid start-stop cycle. If a maintenance event time falls within the coverage area of ​​a valid start-stop cycle, the system further locates the sampling interval adjacent to that event time within that cycle unit and generates an event location identifier sequence within that cycle. Among them, the first Within the first effective start-stop cycle Each sampling point corresponds to The value of a sampling point is 1 when the time window corresponding to the maintenance event time is covered; otherwise, the value is 0. For example, if a valve seat is replaced at 14:32:18, and the adjacent sampling times within this effective opening and closing cycle are 14:32:17.8, 14:32:18.0, and 14:32:18.2, then the sampling point corresponding to 14:32:18.0 is written as... All other sampling points within the same period remain at 0. The processed result is recorded as the valve monitoring input. ,in This represents a set of valid start-stop cycle data units arranged in chronological order; each data unit contains a sequence of travel records corresponding to that start-stop cycle, a sequence of load records synchronized with it, and a sequence of event position identifiers aligned with the sampling point. After this processing, subsequent steps no longer deal with long-term, continuous, and mixed data streams, but with structured inputs that already have periodic boundaries, synchronization relationships, and maintain event attribution relationships.

[0024] S2: Perform intra-cycle normalization processing on the valve opening and closing stroke data and actuator load data in the valve monitoring input. Construct a single-point state criterion based on the positive enhancement of position ratio, the reverse suppression of load ratio, and the superposition of local markers of maintenance events. Based on the continuity of the single-point state criterion, merge adjacent sampling points of the same type into a state segment to generate a valve opening and closing cycle state sequence including the start-up state segment, the main motion state segment, and the end-pressing state segment.

[0025] Specifically, step two processes the valve monitoring input output from step one. . It is not a long, raw data stream, but a set of periodic data units that have been organized according to the start and end cycles; take any of them... Each periodic data unit contains travel records arranged synchronously by time within that period. Load records and the event location identifier aligned to the sampling point within that period. Step two's task is not to further divide the cycle, but to transform this cycle data unit into a "valve opening and closing cycle state sequence," that is, to output... Here It's not a single calculated value, but a sequence of state segments arranged chronologically; each state segment in the sequence corresponds to a specific mechanical action stage, and retains information about whether that cycle is associated with a maintenance event. In other words, The formation process consists of two layers: first, each sampling point within the period is calculated as a state criterion with action meaning; second, adjacent and similar sampling points are merged into continuous state segments; and finally, these state segments are connected in chronological order to obtain the state sequence corresponding to that period. This set of state sequences is the output of step two. .

[0026] The specific calculations begin with sampling points within the cycle. Considering the different total strokes and load amplitudes of different valves, step two first performs intra-cycle normalization on the stroke and load records within the same cycle. This process originates from the interval normalization method in mathematics, whose original form is "current value minus the minimum value of the interval, then divided by the interval range," used to map data of different magnitudes to a uniform scale. For the first... Within the first cycle Each sampling point defines the location ratio. and load ratio for ; in, Indicates the first The first cycle The relative travel position of each sampling point within this period; This indicates the relative load level of the same sampling point within that period; For the first step of this cycle The travel records for each sampling point are from the position sensor; and These are the minimum and maximum values ​​among all trip records for this period, obtained from the trip records for this period. For the first step of this cycle The load record of each sampling point comes from the current acquisition module or the pressure feedback module; and These are the minimum and maximum values, respectively, among all load records for that period, obtained from statistics of the load records for that period. Position Proportion Used to characterize the position the valve has advanced to during this opening and closing cycle, and the load ratio. This is used to characterize the relative load that the actuator has borne at that position. When all load records are the same within a given start-stop cycle, i.e. At that time, all within that period The value is uniformly set to 0, and this period is interpreted as a load stabilization period. Taking a start-stop cycle as an example, if the travel record of a certain sampling point in this cycle is 55, and the minimum value of all travel records in this cycle is 10 and the maximum value is 90, then the position ratio of that point is... If the load record at the same sampling point is 2.8, and the minimum load record for all load records in that period is 2.0 and the maximum load record is 6.5, then the load ratio at that point is... This result indicates that the sampling point had advanced to a slightly later position in the middle of the entire opening and closing process, while the relative load remained at a low level.

[0027] In obtaining and Then, step two continues to construct single-point state criteria. This criterion originates from the displacement-force coupling relationship during valve opening and closing: when the valve is in the starting phase, the position ratio is small, and the load ratio is also low; during the main motion phase, the position ratio continuously increases while the load ratio remains low; when entering the final clamping phase, the position ratio approaches the end of the cycle, while the load ratio increases significantly. To express this mechanical characteristic as a calculable quantity, a single-point state criterion is constructed by superimposing three parts: positive enhancement of position ratio, negative suppression of load ratio, and local marking of maintenance events. ; in, Indicates the first The first cycle Single-point state criterion for each sampling point; The preset weighting coefficient is determined by factory debugging data or historical operating data and is used to adjust the suppression strength of the load ratio on the state criterion. Write the first step In the nth periodic data unit The event location identifier for each sampling point is set to 1 when the time window corresponding to that sampling point covers the maintenance event time, and 0 otherwise. The formation process of this formula can be understood as: using... Provides basic criteria for determining "where the progress has been", using After removing the influence of "whether the force has increased significantly", then use The maintenance event is written into the entire lifecycle as an explicit boundary hint when subsequent phases are segmented. Using the example above, if we take... And this cycle does not cover maintenance events, i.e. The single-point state criterion for this sampling point is: If the travel record of adjacent subsequent sampling points increases to 84 and the load record increases to 4.4, then the corresponding position ratio is: The load ratio is Substituting the values, we obtain the single-point state criterion as follows: When the sampling is advanced to the final sampling point, the travel distance is recorded as 90 and the load as 6.5. At this point, the position ratio reaches 1 and the load ratio also reaches 1. The single-point state criterion is then... The calculation results show that the criterion value of the middle sampling point is higher, corresponding to the main motion stage, while the criterion value of the end sampling point, although the position ratio is the highest, drops due to the synchronous increase in the load ratio, corresponding to the clamping stage. This is consistent with the actual mechanical action of the valve.

[0028] Complete each sampling point After calculation, step two begins to transform the "points" into a "sequence". The system follows the sequence... All time-direction scans of each cycle And form a state segment according to the continuous segment merging rule: when a group of adjacent sampling points It remains at a low level and the corresponding When still in the early part of the cycle, this set of sampling points is merged into the starting state segment; when a set of adjacent sampling points When the level continues to rise or remains at a high level, it is merged into the main motion state segment; when a set of adjacent sampling points It is nearing the end of the cycle and When the price falls from a high level, it is merged into a final compressed state segment. Here, "merging" means grouping temporally adjacent sampling points with the same state category into a continuous state segment, and recording the start and end sampling times, state category, and corresponding event identifier of that segment. Therefore, for the first... After several cycles, the system eventually obtains a sequence of state segments arranged in chronological order; summing up the state segment sequences corresponding to all cycles yields the output of step two. It is output from step one. Through "intra-period normalization calculation" Single-point state criterion calculation The system generates a state segment by merging adjacent sampling points of the same type into a state segment in three consecutive steps. Assuming that the single-point state criteria for six sampling points in a certain period are calculated to be 0.12, 0.28, 0.49, 0.63, 0.58, and 0.37 respectively, the system will merge the first two sampling points into a starting state segment, merge the middle three sampling points into a main motion state segment, and merge the last sampling point and its adjacent end sampling points into an end-compression state segment. These three state segments arranged in chronological order together constitute the state sequence corresponding to that period.

[0029] S3: Based on the maintenance event identifier, reconstruct the continuous valve opening and closing cycle state sequence, calculate the arithmetic mean of each state segment plus the beginning and end trend correction to obtain the segment-level state value, and aggregate adjacent state segments into an operation stage with the state segment containing the maintenance event identifier as the boundary. Calculate the segmented state value of the state segment within the operation stage to form the valve segmented state result.

[0030] Specifically, step three directly processes the valve opening and closing cycle state sequence output from step two. The state segments arranged in chronological order are further reconstructed into valve segment state results. In this plan, Each start-stop cycle consists of several consecutive state segments, and each state segment contains a set of single-point state criteria. ,in Indicates the start / stop cycle number. This indicates the state segment number within that cycle. This indicates the sampling point number within the state segment. Meanwhile, since maintenance event information has already been written into the corresponding period in step two, the state segment itself carries a clear time sequence and maintenance boundary information in step three. The processing logic in step three is not to re-analyze the original travel records and load records, but rather to further elevate the "action semantic expression" obtained in step two to an "operational phase expression." Specifically, first, a stable segment-level state value is obtained for each state segment; then, multiple adjacent state segments are reorganized into the same operational phase using maintenance events as boundaries; finally, the state segments within this operational phase are aggregated to form a segmented state result that can be directly used for generating subsequent monitoring conclusions. Thus, step two provides information on "what changes occur within a single start-up and shut-down cycle," while step three provides information on "what operational phases are formed after multiple start-up and shut-down cycles unfold over time, and how these operational phases are distinguished before and after maintenance." A clear connection is established between the two steps.

[0031] When evaluating a single state segment, a combination of "arithmetic mean plus initial and final trend correction" is used. This is derived partly from the arithmetic mean in statistics, used to characterize the overall level of a state segment over the entire duration; and partly from the initial and final differences of a discrete sequence, used to characterize whether the state segment rises, falls, or remains stable during its duration. The valve opening and closing process exhibits distinct engineering characteristics: the initiation segment typically corresponds to a lower state criterion and gradually rises; the main motion segment typically corresponds to a higher and more stable state criterion; and the final clamping segment typically shows the state criterion falling from a high level and converging. Therefore, introducing initial and final trend correction when evaluating segments can more accurately reflect this mechanical process. For the first... The first of the start-stop cycles Each state segment defines a segment-level state value. for ; in, Indicates the first The first start-stop cycle The segment-level state values ​​of each state segment; This indicates the number of sampling points contained within the state segment, which comes directly from the state segment division result in step two; Indicates the first state segment within this state segment. The single-point state criterion for each sampling point is derived from the state criterion sequence that has been calculated in step two. This represents the single-point state criterion for the last sampling point in this state segment; This represents the single-point state criterion for the first sampling point in this state segment; This is the trend correction coefficient, whose value is derived from the valve's factory commissioning curve, on-site commissioning records, or historical operating samples. The derivation of this formula can be expanded along two fundamental quantities: first using... Extract the overall level of this state segment, and then use... Extract whether the state segment rises or falls during its duration, and finally use the coefficients... This trend's impact is then superimposed onto the overall level. Because... The value originates from the scaled state criterion in step two. Since the average value within a segment and the difference between the beginning and end are on the same scale, they can be directly linearly combined. Taking a main motion state segment as an example, if the single-point state criterion within this segment is 0.24, 0.31, 0.39, and 0.46 respectively, and taking... The average value within the segment is The difference between the first and last values ​​is After substituting, we get If the single-point state criteria within the other end-compression state segment are 0.67, 0.61, and 0.54 respectively, then the value is still taken as... The average value within the segment is The difference between the first and last values ​​is After substituting, we get These two sets of calculation results reflect the differences between different stages of the action: the former has an overall value in the middle to high range with a clear upward trend, while the latter, although the overall value is still relatively high, has already shown a decline and is closer to the final compression and convergence state.

[0032] Obtain the segment-level state value for each state segment. Next, step three begins to form the actual segmented state results. This uses a "boundary-driven continuous grouping" approach: reading the state segments of all start-stop cycles sequentially along time, and checking whether each state segment contains the event location identifiers written in step one and passed down from the sampling points in step two. The sampling points; when the event location identifier first appears within a certain state segment, the starting position of that state segment is used as the operation phase switching boundary. State segments within the continuous time range before this boundary are grouped into the same operation phase, and the state segment containing this boundary and the state segments within the continuous time range after it are grouped into the next operation phase. The resulting operation phases are not divided by periodic numbering, but rather organized based on the actual landing point of the maintenance event as the anchor point and the continuity of state segments. For the first... Each operational phase, assuming it includes... Each state segment corresponds to a segment-level state value. Then define the segmented state values ​​for this running phase. for ; in, Indicates the first Segmented state values ​​for each operational phase; This indicates the number of state segments in this operational phase, which is derived from the maintenance boundary location and continuous grouping results; Indicates the first Within the first operational phase The segment-level state value of each state segment is derived from the calculation result of the previous formula; This represents the segment-level state value of the last state segment in this operational phase; This represents the segment-level state value of the first state segment in this operational phase; This is a segmented trend correction coefficient, whose value is derived from long-term valve operation samples or stage evolution statistics during the installation and commissioning phase. For operation phases starting after a maintenance event, the boundary markers are... The value is 1, and the value is 0 in other running stages, and is related to... Parallel writing This formula also originates from an extended form of "mean aggregation plus first and last trend correction": the first term uses the average of all state segments to represent the overall state level of the operational phase; the second term uses the difference between the first and last segment-level state values ​​to represent whether the operational phase is generally improving, deteriorating, or remaining stable. To illustrate with specific values, if there are three state segments in an operational phase before maintenance, with segment-level state values ​​of 0.62, 0.57, and 0.51 respectively, then... Then there is an average value. The difference between the first and last values ​​is Substituting into If there are three state segments within a certain post-maintenance operation phase, and their segment-level state values ​​are 0.69, 0.72, and 0.71 respectively, then take the same... The average value is The difference between the first and last values ​​is Substituting into This set of results directly shows that the overall operational status improved after maintenance, and the internal state of the phase remained relatively stable.

[0033] When all stages of operation After the calculation is completed, the system records the start time, end time, and segmented state values ​​for each running stage. and boundary markings Write them together into the result set to form the valve segmentation status result. .therefore, The path is clear and continuous: Step two first transforms each start-stop cycle into a state sequence with action semantics; Step three then uses segment-level state values... The calculation compresses the state sequence into comparable state segments, then aggregates multiple state segments into operational phases using maintenance events as boundaries, and finally segments state values. The operational phase is expressed as a stable, traceable, and quantitative result. The "action changes during a single start-up and shutdown process" has been elevated to "state changes during operation before and after maintenance," so subsequent steps can directly revolve around this. It generates remote monitoring conclusions without having to recalculate at the original sampling point or single-cycle state sequence level.

[0034] S4: Based on the valve segmentation status results, combined with the difference between the segmentation status value of the current operating stage and the segmentation status value of the previous operating stage, and the maintenance event boundary correction, calculate the monitoring evaluation value, and output the valve remote monitoring conclusion containing the current stage status, change direction, and maintenance priority according to the monitoring evaluation value.

[0035] Specifically, step four follows the valve segmentation state results formed in step three. The process continues, and the various operational stages, already arranged chronologically, are further transformed into conclusions for remote valve monitoring. In this plan, Each segmented state unit already contains the segmented state values ​​for that running phase. The start and end times of this operational phase and the boundary markers of this operational phase. Therefore, the input for step four is no longer the raw sensor data, nor the state segment within a single start-up / shutdown cycle, but rather a phased result with "clear maintenance boundaries and clear stage evolution." Step four organizes calculations around three engineering questions: First, what state level is the current operating stage itself at? Second, relative to the previous operating stage, is it tending towards recovery, stabilization, or degradation? Third, does this change occur immediately after a maintenance event? Step four addresses these three questions within the remote monitoring system... Each operational stage is quantitatively evaluated sequentially, and the evaluation results are then mapped to monitoring conclusions that can be directly displayed and scheduled. This process forms... Instead of remaining at the numerical status level, it directly provides three types of results: "current stage status", "direction of change" and "maintenance priority", so that the remote monitoring conclusions correspond one-to-one with the valve maintenance actions.

[0036] For the When evaluating the operational phase, a combined calculation of "current level plus phase difference plus maintenance boundary correction" is used. This calculation originates from two parts: the first part is the first-order backward difference commonly used in control engineering and time series analysis, which uses the current phase value minus the previous phase value to represent the direction of phase change; the second part is the linear weighting concept in the evaluation model, where the current state level and the direction of state change jointly determine the final evaluation result. This scheme introduces a maintenance boundary correction term on this basis, enabling the operational phases after maintenance to be clearly distinguished when generating conclusions. Therefore, for the [missing information - likely a specific operational phase], [the following is a possible interpretation of the calculation method], the [missing information - likely a specific operational phase] can be evaluated. The first step in each operational phase is to determine the benchmark. :when hour, Take the segmented state values ​​from the previous running phase. ;when hour, Take the initial stable phase segmented state values ​​of the same model valve after installation and commissioning. Based on this, define the monitoring evaluation value. for ; in, Indicates the first Monitoring and evaluation values ​​for each operational phase; This indicates the result of step three. Segmented state values ​​for each operational phase; Indicates the first The benchmark for comparison at each operational stage, when Time equals ,when The time is equal to the piecewise state value of the initial stable phase. ; Indicates the first The boundary marker for each operational phase is set to 1 if the operational phase begins after a maintenance event, and 0 otherwise. This value comes from the output of step three. ; The stage change weighting coefficient is derived from the operating samples or historical monitoring samples accumulated during the valve installation and commissioning stage, and is used to adjust the influence of the stage change direction on the monitoring evaluation value. To maintain the boundary correction coefficient, its value is derived from debugging records or historical samples, and is used to explicitly write the impact of maintenance events at the conclusion level into the evaluation value; This represents the segmented state values ​​of the initial stable phase recorded after the installation and commissioning of valves of the same model. These values ​​are derived from the segmented state results of the valve's initial commissioning phase. The derivation of this formula can be understood in three steps: first, use... This indicates the current state level of the current running phase; then use Indicates the direction and magnitude of change relative to the reference standard; finally, uses By incorporating the information of "whether this phase is adjacent to a maintenance event" into the evaluation results, the recovery phase after maintenance can have a clear boundary marker in remote monitoring.

[0037] To illustrate how this evaluation value is formed, a calculation process can be used as an example. Assume that the segmented state value of a valve during its operation before maintenance is... The current operational phase after maintenance corresponds to the segment status value of the segment. And this phase begins after the maintenance event, therefore If the settings are obtained in the commissioning record of this valve model... , Then there is This result indicates that the current stage not only has a relatively high state level, but also shows a significant increase compared to the previous stage. Furthermore, this increase occurs immediately after a maintenance event, therefore it can be directly identified as a post-maintenance recovery to a stable state. Let's consider a continuously degrading scenario where the segmented state value of the previous stage is... The current stage segmentation state value is And the current stage is not at the maintenance post-boundary, that is still take , Then there is This result indicates that the current stage's state level has declined, and there has been a significant decrease compared to the previous stage. Therefore, it should be marked as a key focus stage in the remote monitoring interface. Through this quantification process, the remote monitoring system can directly... The numerical range outputs different monitoring conclusions: when When it is in the high range, it corresponds to "recovery to stability" or "stable operation"; when If the value is in the middle range and the change is small, it corresponds to "normal monitoring"; when When the performance drops to a low range or continues to decline for multiple consecutive stages, it corresponds to "performance degradation, maintenance recommended." The range thresholds here are derived from the equipment's factory commissioning records and historical operating sample statistics. For example, they can be based on typical operating conditions of the same model of valve, such as normal opening and closing, recovery after maintenance, and continuous degradation. The distribution range is set and written into the remote monitoring system parameter table, which can be directly invoked after deployment to the site.

[0038] After obtaining the monitoring and evaluation values ​​for each operational stage. The system then encapsulates the evaluation value along with the start and end times of the corresponding operational phase and boundary markers to form a remote valve monitoring conclusion. Here Specifically, it manifests as a set of conclusion units arranged chronologically, each conclusion unit corresponding to an operational phase, and containing at least four items: the time range of the operational phase, the monitoring and evaluation values ​​of the operational phase, etc. The status category of this operational phase and the corresponding maintenance and handling recommendations for this phase. The status category is determined by the remote monitoring system. The relevant interval is automatically written, such as "Restored to Stability," "Operating Normally," "Key Focus," or "Maintenance Recommendation"; maintenance and handling suggestions are directly generated based on the status category, such as "Maintain Periodic Inspections," "Shorten Monitoring Intervals," or "Schedule Maintenance Window." Thus, step four forms... The phased state results output in step three are further transformed into remote monitoring conclusions that can be directly used in industrial site monitoring screens, mobile monitoring interfaces, and maintenance work order triggering logic. Step one organizes the field data into opening and closing cycle inputs; step two organizes the opening and closing cycle inputs into state sequences; step three organizes the state sequences into segmented state results; and step four organizes the segmented state results into remote monitoring conclusions. These four steps converge layer by layer along the same link, ultimately enabling the valve's state changes before and after maintenance and during long-term operation to be continuously and accurately presented in the remote monitoring system.

[0039] In one or more embodiments, such as Figure 2 As shown, a valve remote monitoring system based on the Internet of Things is disclosed, the system comprising: The data acquisition and organization module is used to collect valve opening and closing stroke data, actuator load data, and maintenance event identifier data. Based on the range normalization result of the valve opening and closing stroke data, it identifies the effective opening and closing cycle. Based on the effective opening and closing cycle, it synchronously extracts the actuator load data to obtain the valve monitoring input. The valve monitoring input includes time-synchronized valve opening and closing stroke data and actuator load data within the effective opening and closing cycle, as well as maintenance event identifiers aligned with the sampling points. The valve opening and closing cycle state sequence generation module is used to perform intra-cycle normalization processing on the valve opening and closing stroke data and actuator load data in the valve monitoring input. Based on the positive enhancement of position ratio, the negative suppression of load ratio, and the superposition of local markers of maintenance events, a single-point state criterion is constructed. Based on the continuity of the single-point state criterion, adjacent sampling points of the same type are merged into a state segment to generate a valve opening and closing cycle state sequence including the start-up state segment, the main motion state segment, and the end-pressing state segment. The segmented state result reconstruction module is used to reconstruct multiple consecutive valve opening and closing cycle state sequences based on the maintenance event identifier, calculate the arithmetic mean of each state segment plus the beginning and end trend correction for the segment-level state value, aggregate adjacent state segments into an operating stage with the state segment containing the maintenance event identifier as the boundary, calculate the segmented state value of the state segment within the operating stage, and form the valve segmented state result. The monitoring conclusion output module is used to calculate the monitoring evaluation value based on the valve segment status results, combined with the difference between the segment status value of the current operating stage and the segment status value of the previous operating stage, and the maintenance event boundary correction, and output the valve remote monitoring conclusion containing the current stage status, change direction and maintenance priority according to the monitoring evaluation value.

[0040] It is worth noting that the specific workflow of the IoT-based valve remote monitoring system provided in this embodiment is the same as that of the IoT-based valve remote monitoring method described in the above embodiments, and will not be repeated here.

[0041] This invention also provides an IoT-based remote valve monitoring device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the above-described IoT-based remote valve monitoring method embodiments, for example... Figure 1 The steps S1 to S4 described above; or, when the processor executes the computer program, it implements the functions of each module in the above system embodiments.

[0042] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the IoT-based valve remote monitoring device.

[0043] The IoT-based remote valve monitoring device can be a desktop computer, laptop, handheld computer, or cloud server, among other computing devices. This device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the IoT-based remote valve monitoring device may also include input / output devices, network access devices, and buses.

[0044] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the IoT-based valve remote monitoring device, connecting all parts of the device via various interfaces and lines.

[0045] The memory can be used to store the computer program and / or modules. The processor implements various functions of the IoT-based valve remote monitoring device by running or executing the computer program and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created according to the operation of the controller, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0046] The module integrated into the IoT-based valve remote monitoring device, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0047] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0048] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A valve remote monitoring method based on the Internet of Things, characterized in that, The method includes: Valve opening and closing stroke data, actuator load data, and maintenance event identifier data are collected. Based on the range normalization result of the valve opening and closing stroke data, the effective opening and closing cycle is identified. Based on the effective opening and closing cycle, the actuator load data is synchronously extracted to obtain the valve monitoring input. The valve monitoring input includes time-synchronized valve opening and closing stroke data and actuator load data within the effective opening and closing cycle, as well as maintenance event identifiers aligned with the sampling points. The valve opening and closing stroke data and actuator load data in the valve monitoring input are normalized within the cycle. A single-point state criterion is constructed based on the positive enhancement of position ratio, the negative suppression of load ratio, and the superposition of local markers of maintenance events. Based on the continuity of the single-point state criterion, adjacent sampling points of the same type are merged into a state segment to generate a valve opening and closing cycle state sequence including the start-up state segment, the main motion state segment, and the end-pressing state segment. Based on the maintenance event identifier, the continuous valve opening and closing cycle state sequence is reconstructed. The arithmetic mean of each state segment is calculated and the segment-level state value is corrected for the beginning and end trends. Adjacent state segments are aggregated into an operation stage with the state segment containing the maintenance event identifier as the boundary. The segmented state values ​​of the state segments within the operation stage are calculated to form the valve segmented state result. Based on the valve segmentation status results, combined with the difference between the segmentation status value of the current operating stage and the segmentation status value of the previous operating stage, and the maintenance event boundary correction, a monitoring evaluation value is calculated. Based on the monitoring evaluation value, a remote valve monitoring conclusion including the current stage status, change direction, and maintenance priority is output.

2. The valve remote monitoring method based on the Internet of Things according to claim 1, characterized in that, The step of collecting valve opening and closing stroke data, actuator load data, and maintenance event identifier data, and identifying valid opening and closing cycles based on the range normalization result of the valve opening and closing stroke data, includes: Read the valve opening and closing stroke data output by the position sensor, and calculate the difference between the maximum and minimum stroke values ​​within the candidate opening and closing segment as the actual stroke change range; Divide the actual stroke variation by the total designed stroke range of the valve to obtain the stroke variation ratio; When the stroke change ratio is greater than a preset ratio threshold, the candidate opening / closing segment is confirmed as a valid opening / closing cycle.

3. The valve remote monitoring method based on the Internet of Things according to claim 1, characterized in that, The step of merging adjacent similar sampling points into a state segment based on the continuity of the single-point state criterion includes: When the single-point state criteria of a group of adjacent sampling points are all less than the first state threshold, and the normalized position ratio of the valve opening and closing stroke data of the sampling points is all less than the first position threshold, the group of sampling points is merged into a starting state segment. When the single-point state criterion of a group of adjacent sampling points continues to rise, or when the single-point state criterion is greater than the second state threshold, the group of sampling points is merged into the main motion state segment. When the normalized position ratio of a group of adjacent sampling points is greater than the second position threshold, and the single-point state criterion falls from a high position, the group of sampling points is merged into an end-compression state segment.

4. The valve remote monitoring method based on the Internet of Things according to claim 1, characterized in that, The step of calculating the segment-level state value by adding the arithmetic mean of each state segment to the beginning and end trend correction includes: Calculate the arithmetic mean of all single-point state criteria within the state segment; The difference between the single-point state criterion of the last sampling point and the single-point state criterion of the first sampling point in this state segment is used as the beginning and end trend. The weighted value of the arithmetic mean and the first and last trends is used as the segment-level state value of this state segment.

5. The valve remote monitoring method based on the Internet of Things according to claim 1, characterized in that, The step of aggregating adjacent state segments into an operational phase using the state segment containing the maintenance event identifier as the boundary includes: Read the status segments of all start-up and stop cycles in chronological order and check whether each status segment contains a maintenance event identifier. When a maintenance event identifier first appears within a certain state segment, the starting position of that state segment is taken as the boundary for the operation phase switching. The state segments within the continuous time range preceding the boundary are classified into the same operating phase, and the state segments within the continuous time range following the boundary are classified into the next operating phase.

6. The valve remote monitoring method based on the Internet of Things according to claim 1, characterized in that, The step of calculating the segmented state values ​​of the state segments within the operation phase includes: Calculate the average value of the segment-level state values ​​for all state segments within this operational phase; Calculate the difference between the segment-level state value of the last state segment and the segment-level state value of the first state segment in this operation phase; The weighted sum of the average value and the difference is used as the segmented state value for this operating phase.

7. The valve remote monitoring method based on the Internet of Things according to claim 1, characterized in that, The step of calculating the monitoring evaluation value based on the valve segmentation status results, combined with the difference between the segmentation status value of the current operating stage and the segmentation status value of the previous operating stage, and the maintenance event boundary correction, includes: Obtain the segmented state value and comparison benchmark of the current running stage, wherein the comparison benchmark is the segmented state value of the previous running stage or the segmented state value of the initial stable stage. Calculate the difference between the segmented state value of the current operating stage and the comparison benchmark to obtain the stage change. If the current operational phase begins after a maintenance event, a maintenance boundary correction factor is introduced; The monitoring evaluation value is obtained by weighting the segmented state value of the current operating stage, the stage change amount, and the maintenance boundary correction coefficient.

8. The valve remote monitoring method based on the Internet of Things according to claim 7, characterized in that, The step of outputting a valve remote monitoring conclusion based on the monitoring evaluation value, including the current stage status, direction of change, and maintenance priority, includes: The monitoring evaluation value is compared with a preset interval threshold, which includes a high interval, a middle interval, and a low interval. If the monitoring evaluation value is in the high range, a state category of recovery to stability or stable operation is generated, along with maintenance and operation recommendations for maintaining periodic inspections. If the monitoring evaluation value is in the middle range and the change is less than the preset value, then a normal monitoring status category is generated; If the monitoring evaluation value is in a low range or the monitoring evaluation value continues to decline for multiple consecutive operating phases, a status category of performance degradation or maintenance recommendation will be generated, along with maintenance handling suggestions for scheduling maintenance windows.

9. The valve remote monitoring method based on the Internet of Things according to claim 1, characterized in that, The steps for collecting valve opening and closing stroke data, actuator load data, and maintenance event identification data include: For electric actuators, the actuator load data is read through a current sampling module; for pneumatic actuators, the actuator load data is read through an air source pressure feedback module or a thrust feedback module. Maintenance event identifier data, including valve equipment identifier, event time, and event type, is written through the work order system, on-site handheld terminal, or maintenance confirmation interface. The event type includes valve seat replacement completed and maintenance completed.

10. A valve remote monitoring system based on the Internet of Things, characterized in that, The system includes: The data acquisition and organization module is used to collect valve opening and closing stroke data, actuator load data, and maintenance event identifier data. Based on the range normalization result of the valve opening and closing stroke data, it identifies the effective opening and closing cycle. Based on the effective opening and closing cycle, it synchronously extracts the actuator load data to obtain the valve monitoring input. The valve monitoring input includes time-synchronized valve opening and closing stroke data and actuator load data within the effective opening and closing cycle, as well as maintenance event identifiers aligned with the sampling points. The valve opening and closing cycle state sequence generation module is used to perform intra-cycle normalization processing on the valve opening and closing stroke data and actuator load data in the valve monitoring input. Based on the positive enhancement of position ratio, the negative suppression of load ratio, and the superposition of local markers of maintenance events, a single-point state criterion is constructed. Based on the continuity of the single-point state criterion, adjacent sampling points of the same type are merged into a state segment to generate a valve opening and closing cycle state sequence including the start-up state segment, the main motion state segment, and the end-pressing state segment. The segmented state result reconstruction module is used to reconstruct multiple consecutive valve opening and closing cycle state sequences based on the maintenance event identifier, calculate the arithmetic mean of each state segment plus the beginning and end trend correction for the segment-level state value, aggregate adjacent state segments into an operating stage with the state segment containing the maintenance event identifier as the boundary, calculate the segmented state value of the state segment within the operating stage, and form the valve segmented state result. The monitoring conclusion output module is used to calculate the monitoring evaluation value based on the valve segment status results, combined with the difference between the segment status value of the current operating stage and the segment status value of the previous operating stage, and the maintenance event boundary correction, and output the valve remote monitoring conclusion containing the current stage status, change direction and maintenance priority according to the monitoring evaluation value.

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