Equipment start-stop strategy optimization method and device for pipe network metering equipment

Abstract

The invention discloses an equipment start-stop strategy optimization method and device for pipe network metering equipment. The method is characterized by comprising the following steps: acquiring operation state data of at least one metering device in a to-be-processed pipe network area; for each metering device, determining at least one start-stop event based on the operation state data of the metering device; determining a typical start-stop combination mode between the metering devices based on all start-stop events of the metering devices; and establishing a metering equipment start-stop strategy of the to-be-processed pipe network area based on each typical start-stop combination mode. The abnormal start-stop frequency of the equipment is reduced through the start-stop strategy of the metering equipment, the service life of the equipment is prolonged, the operation safety and economy are improved, and the cooperative efficiency of operation of multiple equipment in the station and the stability and economy of overall operation of the system are improved through scientific optimization of the start-stop strategy of the equipment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of pipe network metering equipment control management, and in particular to a pipe network metering equipment device start-stop strategy optimization method and device. BACKGROUND

[0002] With the rapid development of industrial automation, intelligent manufacturing and Internet of Things technology, metering equipment, as an important part of production and monitoring systems, is widely used in energy management, manufacturing process control, environmental monitoring and other fields. The running state of metering equipment is directly related to the overall performance and operation safety of the system, especially in large-scale device clusters, the start-stop events between different devices affect each other, and reasonable management of start-stop state is the key to ensure the efficient and stable operation of the system. However, due to the variety of metering equipment, complex operating environment, and the characteristics of diversified and dynamic changes of device start-stop events, it is difficult to fully grasp the overall operation of the device by relying solely on manual experience or local data.

[0003] Traditional device start-stop management methods mainly rely on the running state monitoring of individual devices, often only focusing on the start-stop time, duration or fault records of a single device, lacking systematic analysis of the correlation between multiple device start-stop events. Especially in the prior art, there is a lack of scientific identification and quantitative evaluation means for the start-stop combination mode of multiple metering devices, which cannot effectively reflect the synchronization, combination characteristics of device start-stop and its influence on device performance and system stability. This limitation leads to a lack of precise basis for start-stop strategy adjustment, and the overall start-stop control cannot be optimized by fully utilizing the running data of the device group, thereby affecting the service life and maintenance cost of the device.

[0004] Therefore, how to efficiently collect and process the running state data of large-scale metering equipment, accurately identify device start-stop events, and construct device start-stop combination mode based on time series has become a technical problem that needs to be solved urgently. SUMMARY

[0005] The present application provides a pipe network metering equipment device start-stop strategy optimization method and device to solve the problem that the prior art cannot systematically analyze and evaluate the correlation between multiple metering equipment start-stop events and cannot effectively identify and utilize start-stop combination mode to optimize device start-stop strategy.

[0006] According to an aspect of the present application, a pipe network metering equipment device start-stop strategy optimization method is provided, comprising:

[0007] Obtaining the running state data of at least one metering device in a to-be-processed pipe network area;

[0008] For each metering device, determining at least one start-stop event based on the running state data of the metering device;

[0009] determine typical start-stop combination patterns between each of the metering devices based on all start-stop events of the metering devices;

[0010] establish a metering device start-stop strategy for the to-be-processed pipe network region based on each of the typical start-stop combination patterns.

[0011] According to another aspect of the present application, there is provided a device start-stop strategy optimization apparatus for a pipe network metering device, comprising:

[0012] a device data acquisition module configured to acquire running state data of at least one metering device in a to-be-processed pipe network region;

[0013] a device data analysis module configured to, for each of the metering devices, determine at least one start-stop event based on the running state data of the metering device;

[0014] a device start-stop combination module configured to determine typical start-stop combination patterns between each of the metering devices based on all start-stop events of the metering devices;

[0015] a device start strategy module configured to establish a metering device start-stop strategy for the to-be-processed pipe network region based on each of the typical start-stop combination patterns.

[0016] According to another aspect of the present application, there is provided an electronic device, comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein

[0019] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the device start-stop strategy optimization method for a pipe network metering device according to any one of the embodiments of the present application.

[0020] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for enabling a processor to perform the device start-stop strategy optimization method for a pipe network metering device according to any one of the embodiments of the present application when executed by the processor.

[0021] The technical scheme of the embodiment of the present application obtains the running state data of at least one metering device in the to-be-processed pipe network region, comprehensively collects the running state data of the metering device, can identify all metering devices, and improves the accuracy and timeliness of strategy optimization; for each metering device, at least one start-stop event is determined based on the running state data of the metering device, the start-stop event of each metering device is accurately identified, isolated judgment on a single device state is avoided, and the accuracy and timeliness of start-stop event identification are improved; a typical start-stop combination mode between each metering device is determined based on all start-stop events of the metering device, a start-stop combination mode under a unified time axis is constructed, a representative and stable start-stop combination mode is extracted, and stronger data support is provided for strategy making; a metering device start-stop strategy of the to-be-processed pipe network region is established based on each typical start-stop combination mode, the metering device start-stop strategy is used to reduce the frequency of abnormal start-stop of the device, prolong the service life of the device, and improve the operation safety and economy. In the prior art, the correlation between multiple metering device start-stop events cannot be systematically analyzed and evaluated, and the start-stop combination mode cannot be effectively identified and used to optimize the device start-stop strategy. The scientific optimization of the device start-stop strategy is achieved, and the collaborative efficiency of multiple devices in the station and the stability and economy of the overall operation of the system are improved.

[0022] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0024] Figure 1 A flowchart of a device start-stop strategy optimization method for a pipe network metering device is provided for the embodiments of the present application;

[0025] Figure 2 A flowchart of a device start-stop strategy optimization method for a pipe network metering device is provided for the embodiments of the present application;

[0026] Figure 3 A flowchart of a device start-stop strategy optimization method for a pipe network metering device is provided for the embodiments of the present application;

[0027] Figure 4 A structural schematic diagram of a device start-stop strategy optimization device for a pipe network metering device is provided for the embodiments of the present application;

[0028] Figure 5 A structural diagram of an electronic device 10 that can be used to implement an embodiment of the present application is shown. DETAILED DESCRIPTION

[0029] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely below in combination with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, not all. Based on the embodiment in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0031] Figure 1 A flowchart of a device start-stop strategy optimization method of a pipe network metering device is provided for the embodiment of the present application. The embodiment can be applicable to the optimization of the start-stop strategy of the metering device of the pipe network battlefield. The method can be executed by a pipe network metering device device start-stop strategy optimization device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device. As shown in the figure, the method comprises: Figure 1

[0032] S110, obtaining running state data of at least one metering device in a to-be-processed pipe network area.

[0033] It should be noted that the to-be-processed pipe network area is a specific range selected for strategy optimization of the metering device. It should be noted that the to-be-processed pipe network area is a specific range, and in the to-be-processed pipe network area, the metering device serves the functions of the pipe network battlefield. Exemplarily, the to-be-processed pipe network area can be any one of a district pipe network or a pipe network branch line.

[0034] ​Optionally, the metering devices are devices for measuring parameters of the pipe network in the pipe network region to be processed, and the number of the metering devices is at least one. Exemplarily, the metering devices can be flow metering devices, pressure metering devices, temperature metering devices, etc.

[0035] Optionally, the running state data can be state data of the metering devices running in the pipe network region to be processed. It should be noted that the running state data includes start-stop time, running duration, running frequency, and fault log of the metering devices; the start-stop time of the metering devices can be the start time of the metering devices from the stop state to the running state, and the stop time from the running state to the stop state; the running duration can be the stop time of the metering devices from one continuous start to stop minus the start time; the running frequency can be the number of complete cycles of start, running, and stop of the device within a certain time range; and the fault log can be understood as a log file of all abnormal events existing in the running process of the metering devices.

[0036] Specifically, for the pipe network region to be processed which needs to be optimized in the start-stop strategy, the running state data of each metering device in the pipe network region to be processed is obtained.

[0037] S120, for each of the metering devices, at least one start-stop event is determined based on the running state data of the metering device.

[0038] Optionally, the start-stop event can be understood as a normal start-stop state change of the metering device in the running process. It should be noted that in the running process of the metering device, the running state of the metering device can be frequently fluctuated due to at least one factor of signal jitter, short false alarm, or data acquisition anomaly, resulting in abnormal start-stop state change of the metering device. By identifying the normal start-stop state change of the metering device in the running process, it is recorded as a start-stop event of the metering device.

[0039] Optionally, after obtaining the running state data of each metering device, the data of each metering device is processed respectively to identify the normal start-stop state change of the metering device in the running process, and the start-stop event of each metering device is obtained.

[0040] Specifically, for each of the metering devices, at least one start-stop event is determined based on the running state data of the metering device.

[0041] S130, based on all start-stop events of the metering devices, a typical start-stop combination mode between each of the metering devices is determined.

[0042] Optionally, the typical start-stop combination mode can be understood as the associated combination of the start and stop of each metering device in the to-be-processed pipe network region. It should be noted that the typical start-stop combination mode is the cooperative start and stop of multiple devices, and the typical start-stop combination mode is the fixed association of multiple metering devices in multiple start-stop events. The selection of the metering device association cooperative start and stop multiple times from all device start-stop events is the typical start-stop combination mode.

[0043] Optionally, the typical start-stop combination mode is the association between each metering device, and then needs to be selected from all start-stop events. The indexes corresponding to the start-stop overlap rate, start-stop frequency coefficient, start-stop synchronicity index, and abnormal start-stop frequency are collected and calculated to determine the typical start-stop combination mode between different metering devices. The number of typical start-stop combination modes is at least one.

[0044] Specifically, the typical start-stop combination mode between each metering device is determined based on all start-stop events of the metering device.

[0045] S140, establish the metering device start-stop strategy of the to-be-processed pipe network region based on each typical start-stop combination mode.

[0046] Optionally, the metering device start-stop strategy can be a cooperative working rule for controlling each metering device in the to-be-processed pipe network region. It should be noted that by combining each typical start-stop combination mode of the to-be-processed pipe network region, the time scale is decomposed into executable parameters and linkage logic to form the metering device start-stop strategy.

[0047] Specifically, the metering device start-stop strategy of the to-be-processed pipe network region is established based on each typical start-stop combination mode.

[0048] The technical solution of this invention acquires the operating status data of at least one metering device within the pipeline network area to be treated. By comprehensively collecting the operating status data of the metering devices, all metering devices can be identified, improving the accuracy and timeliness of strategy optimization. For each metering device, at least one start-stop event is determined based on the operating status data of the metering device, accurately identifying the start-stop events of each metering device and avoiding isolated judgments of the status of a single device, thus improving the accuracy and timeliness of start-stop event identification. Based on all start-stop events of the metering devices, typical start-stop combination patterns among the metering devices are determined, constructing start-stop combination patterns under a unified time axis, and extracting representative and stable start-stop combination patterns, providing stronger data support for strategy formulation. Based on each typical start-stop combination pattern, a start-stop strategy for the metering devices in the pipeline network area to be treated is established. This strategy reduces the frequency of abnormal start-stop operations, extends equipment lifespan, and improves operational safety and economy. Existing technologies cannot systematically analyze and evaluate the correlation between start-stop events of multiple metering devices and cannot effectively identify and utilize start-stop combination patterns to optimize equipment start-stop strategies. Scientific optimization of equipment start-up and shutdown strategies has been achieved, improving the collaborative efficiency of multiple devices operating within the station and the overall stability and economy of the system.

[0049] Figure 2 This is a flowchart illustrating a method for optimizing the start-up and shutdown strategy of pipeline metering equipment according to an embodiment of the present invention. The relationship between this embodiment and the previous embodiments is that this specifically describes the process of identifying start-up and shutdown events of each metering device. For example... Figure 2 As shown, the method includes:

[0050] S210. Obtain the operating status data of at least one metering device within the pipeline network area to be processed.

[0051] S220. For each of the metering devices, the operating status data of the metering device is differentially processed to determine the operating differential data.

[0052] Optionally, the operational differential data can be the difference between the operational status data of metering equipment at adjacent times on a time scale. It should be noted that differential processing of the operational status data involves processing the start-up and stop times, operating durations, operating frequencies, and fault logs of the metering equipment separately on a time scale, and then combining the processed data results into operational differential data. For example, the difference in start-up and stop times can be the difference between two adjacent start-up and stop times, using the time interval between the two adjacent start-up and stop times; the difference in operating duration can be the difference between the current operating duration and the duration of the previous operating cycle to obtain the duration change; the difference in operating frequency can be the difference between the operating frequencies within two identical time ranges as the frequency fluctuation value; and the differential processing of fault logs can be performed by differentiating the time nodes of fault events to obtain the temporal fluctuation of the fault events.

[0053] Specifically, for each metering device, the operating status data of the metering device is differentially processed to determine the operating differential data.

[0054] S230. Identify at least one state change node of the metering device based on the operational differential data.

[0055] Optionally, the state change nodes can be the time points when the metering equipment undergoes a state change during operation. It should be noted that the critical moments when the metering equipment changes from "running" to "stopping" or from "stopping" to "running" are statistically analyzed in the operational differential data as the various state change nodes of the metering equipment.

[0056] Specifically, for each metering device, at least one state change node of the metering device is identified based on operational differential data.

[0057] S240. Identify the corresponding change time intervals between each of the state change nodes, and determine a start / stop event based on the change time intervals.

[0058] Optionally, the change time interval can be the time period between two adjacent state change nodes. It should be noted that the change time interval can be obtained by calculating the time difference between adjacent state change nodes.

[0059] Optionally, after obtaining the change time interval between two adjacent state change nodes, a state change node is identified as a normal start-stop event based on the change time interval. If the identification is successful, the state change node is considered to be a start-stop event; if the identification fails, the state change node is considered not to be a start-stop event.

[0060] Specifically, for each metering device, the corresponding time interval between each state change node is identified, and a start / stop event is determined based on the time interval.

[0061] Optionally, in another optional embodiment of the present invention, determining a start / stop event based on the changing time interval includes: determining a start / stop event based on the changing time interval includes:

[0062] If the change time interval is not greater than a preset interval threshold, the state change node corresponding to the change time interval is regarded as an invalid state change node; if the change time interval is greater than the preset interval threshold, the state change node corresponding to the change time interval is regarded as a valid state change node; at least one valid state change node corresponding to the metering device is regarded as a start / stop event.

[0063] The preset interval threshold can be a pre-set time threshold used to identify whether a state change node is an abnormal state change. It should be noted that metering equipment will experience frequent fluctuations during operation. If these fluctuations are not identified and directly treated as start-up or shutdown events, it can easily lead to distorted subsequent analysis results. The preset interval threshold mechanism filters out state change nodes with insufficient duration, thereby significantly improving the accuracy and robustness of start-up and shutdown event identification.

[0064] Optionally, if the change time interval is not greater than a preset interval threshold, the state change node corresponding to the change time interval is regarded as an invalid state change node; if the change time interval is greater than the preset interval threshold, the state change node corresponding to the change time interval is regarded as a valid state change node.

[0065] Optionally, upon identifying all valid state change nodes of a metering device, each valid state change node of the metering device can be treated as a start / stop event of the metering device.

[0066] Specifically, if the change time interval is not greater than the preset interval threshold, the state change node corresponding to the change time interval is regarded as an invalid state change node; if the change time interval is greater than the preset interval threshold, the state change node corresponding to the change time interval is regarded as a valid state change node; at least one valid state change node corresponding to the metering device is regarded as a start / stop event.

[0067] S250. Determine typical start-stop combination patterns among the various metering devices based on all start-stop events of the metering devices.

[0068] S260. Establish the start-stop strategy for the metering equipment in the pipeline network area to be processed based on each of the typical start-stop combination modes.

[0069] This invention, through differential processing of operational status data, enables the system to quickly identify nodes experiencing status changes. By calculating the time difference between adjacent nodes experiencing status changes and comparing it with a preset interval threshold, nodes with insufficient duration are filtered out. This allows for automatic cleaning and structured labeling of operational status data, providing high-quality foundational data for constructing start-stop combination patterns and identifying collaborative behaviors. This process combines real-time performance with accuracy, making it particularly suitable for multi-equipment collaborative operation scenarios such as oil and gas stations. It provides a solid data foundation and technical support for system-level operational optimization. This invention improves the logical accuracy of the start-stop identification process, laying an accurate foundation for subsequent data mining and combined analysis. Combined with the set preset interval threshold, it eliminates invalid events such as short-term fluctuations and false triggers, enhancing the robustness of the algorithm in complex field data environments.

[0070] Figure 3 This is a flowchart illustrating a method for optimizing the start-up and shutdown strategies of pipeline metering equipment according to an embodiment of the present invention. The relationship between this embodiment and the previous embodiments is that this specifically describes a method for extracting typical start-up and shutdown combination patterns among metering devices. Figure 3 As shown, the method includes:

[0071] S310. Obtain the operating status data of at least one metering device within the pipeline network area to be processed.

[0072] S320. For each of the metering devices, at least one start / stop event is determined based on the operating status data of the metering device.

[0073] S330. Align the start-stop events associated with each metering device to a unified time axis, and collect the start-stop states corresponding to each start-stop event through a preset time window to determine the window start-stop vector.

[0074] Optionally, the preset time window can be a fixed time window that is set in advance. The preset time window is used to integrate the scattered start and stop events of different metering devices according to a fixed time granularity to obtain the start and stop event combination corresponding to each preset time window.

[0075] Optionally, the time axis can be understood as a fixed time dimension. It should be noted that in the pipeline network area to be processed, because the time dimensions of the metering data from different metering devices are inconsistent, when performing correlation analysis on the start and stop events of all metering devices, the different time dimensions may cause analysis interruptions, anomalies, or errors. Therefore, it is necessary to align the start and stop events of all metering devices to a unified time axis to prevent time misalignment of states from affecting the accuracy of the analysis.

[0076] Optionally, aggregation processing can be used to convert the start-stop events of each start-stop device within a preset time window into quantitative data indicators.

[0077] Optionally, the window start / stop vector can be a window start / stop vector converted from the running status data corresponding to the start / stop events of each start / stop device within a preset time window. It should be noted that each preset time window corresponds to one window start / stop vector.

[0078] Specifically, the start and stop events associated with each metering device are aligned to a unified time axis, and the start and stop states corresponding to each start and stop event are aggregated through a preset time window to determine the window start and stop vector.

[0079] S340. Arrange the window start / stop vectors corresponding to each time window in chronological order to construct a time window and start / stop state matrix for each metering device.

[0080] Optionally, the start-stop status matrix can be a feature matrix constructed based on the time window dimension and the start-stop status dimension of the metering equipment. It should be noted that in the start-stop status matrix, each row corresponds to a combination of start-stop statuses of the metering equipment within a time window, while the columns of the start-stop status matrix correspond to different metering equipment.

[0081] Optionally, a start / stop state matrix of time windows and each metering device can be constructed by arranging the start / stop vectors corresponding to each time window in each column according to time order.

[0082] Specifically, the start / stop vectors corresponding to each time window are arranged in chronological order to construct a matrix of start / stop states of the time window and each metering device.

[0083] S350. Calculate the vector similarity of each window start / stop vector in each row of the start / stop state matrix to determine the vector similarity between each window start / stop vector and the corresponding window start / stop vector.

[0084] Optionally, vector similarity can describe the degree of similarity between different device start-stop states between different window start-stop vectors, thereby reflecting the similarity and differences between different window start-stop vectors.

[0085] Optionally, when calculating the similarity between any two vectors, any vector similarity calculation method can be used to calculate the vector similarity between each pair of window start and stop vectors. The vector similarity calculation method can be algorithms such as Hamming distance, cosine similarity, Euclidean distance, and Manhattan distance.

[0086] S360. Cluster each of the window start-stop vectors based on the vector similarity to determine the start-stop combination category, and determine the typical start-stop combination mode based on each start-stop combination category.

[0087] Optionally, the start-stop combination category can be understood as a set of time windows for a group of metering devices with similar start-stop events and similar operating characteristics. It should be noted that after calculating the vector similarity between the start-stop vectors of each window and the start-stop vectors of other windows, the similarity of the start-stop vectors of each window is used as a metric, and the vectors with high similarity are grouped into the same start-stop combination category by using a clustering algorithm, thus forming multiple start-stop combination categories.

[0088] Optionally, a typical start-stop combination mode can be selected based on each start-stop combination category.

[0089] Specifically, the start-stop vectors of each window are clustered based on vector similarity to determine the start-stop combination category, and typical start-stop combination patterns are determined based on each start-stop combination category.

[0090] Optionally, in another optional embodiment of the present invention, determining the typical start-stop combination mode based on each of the start-stop combination categories includes:

[0091] For each start-stop combination category, the frequency of occurrence of each window start-stop vector in all time windows is identified; the state consistency score and volatility between each window start-stop vector are evaluated based on the frequency of occurrence; the Hamming distance between each pair of window start-stop vectors in the start-stop combination category is identified; the cluster center vector of the start-stop combination category is identified, and the average distance between each window start-stop vector and the cluster center vector is determined; the stability score is determined based on the average distance using a preset stability calculation formula; the distribution concentration of the start-stop combination category is identified; the representative score corresponding to the start-stop combination category is determined based on the distribution concentration, the stability score, and the frequency of occurrence; the median score of the representative score corresponding to each start-stop combination category is identified, and the start-stop combination category is selected as the typical start-stop combination pattern based on the median score.

[0092] Optionally, the frequency of occurrence can be understood as the number of times a window start-stop vector appears in all start-stop combination categories. It should be noted that for a window start-stop vector, if the frequency of occurrence is high, it means that the device start-stop combination mode corresponding to the window start-stop vector is common in actual operation; if the frequency of occurrence is low, it means that the device start-stop combination mode is a special device start-stop combination mode in actual operation.

[0093] Optionally, the state consistency score can be understood as the stability score of the start-stop vector within the start-stop combination category; the volatility can be understood as revealing the volatility range and variability of the start-stop vector within the start-stop combination category.

[0094] Optionally, the cluster center vector can be the window start-stop vector that appears most frequently in the start-stop combination category, and this window start-stop vector is used as the cluster center vector. Within the start-stop combination category, the Hamming distances between each window start-stop vector and the cluster center vector are averaged to obtain the average distance. The average distance is used to reflect the distribution concentration within the start-stop combination category, reflecting the density and typicality of the category.

[0095] Optionally, the preset stability calculation formula can be a pre-set formula for calculating the stability score of the start-stop combination category. For example, the stability calculation formula may be as follows:

[0096]

[0097] Where S is the stability score of the start-stop combination category, m is the number of window start-stop vectors in the start-stop combination category, n is the vector dimension, and x i Let x be the start / stop vector of the i-th window within the cluster category. j Let j be the start / stop vector of the j-th window within the cluster category. Let be the cluster center vector, HD represent the Hamming distance, λ1 and λ2 are weight coefficients, and the sum of λ1 and λ2 is 1.

[0098] Optionally, the distribution concentration can be understood as a quantitative description of the density of the window start-stop vector space distribution within the start-stop combination category. It should be noted that the distribution concentration of a start-stop combination category is obtained by performing discreteness statistics on the Hamming distance of each window start-stop vector within a start-stop combination category.

[0099] Optionally, the representative score can be understood as a comprehensive evaluation of start-stop combination patterns within the start-stop combination category. It should be noted that the representative score is calculated by weighting and summing the distribution concentration of the start-stop combination category, the stability score of the start-stop combination category, and the frequency of occurrence of start-stop vectors in each window.

[0100] Optionally, the median score refers to the representative score that is in the middle after all start-stop combination categories are sorted from smallest to largest.

[0101] Optionally, after obtaining the representative scores of each start-stop combination category, the median score of the representative scores of each start-stop combination category is used as the evaluation index. Then, the representative scores of the start-stop combination categories are compared with the median score, and the start-stop combination categories with scores greater than the median score are selected. The start-stop state patterns of these start-stop combination categories are taken as typical start-stop combination patterns.

[0102] Specifically, for each start-stop combination category, the frequency of occurrence of each window start-stop vector across all time windows is identified; based on the frequency of occurrence, the state consistency score and volatility among the start-stop vectors of each window are evaluated; the Hamming distance between each pair of start-stop vectors in all windows within the start-stop combination category is identified; the cluster center vector of the start-stop combination category is identified, and the average Hamming distance between each window start-stop vector and the cluster center vector is determined; a stability score is determined based on the average distance using a preset stability calculation formula; the distribution concentration of the start-stop combination category is identified; a representative score corresponding to the start-stop combination category is determined based on the distribution concentration, stability score, and frequency of occurrence; the median score of the representative score corresponding to each start-stop combination category is identified, and the start-stop combination category is selected as a typical start-stop combination pattern based on the median score. Through the above methods, key combination patterns can be extracted from complex multi-device start-stop data, providing a scientific basis for formulating reasonable start-stop strategies and helping to improve the operating efficiency of equipment groups and the overall stability of the system.

[0103] S370. Establish the start-stop strategy for the metering equipment in the pipeline network area to be processed based on each of the typical start-stop combination modes.

[0104] Optionally, in another optional embodiment of the present invention, establishing the metering equipment start-up and shutdown strategy for the pipeline network area to be processed based on each of the typical start-up and shutdown combination modes includes:

[0105] Based on a pre-set calculation method, the combination score of each typical start-stop combination mode under each time stamp is calculated sequentially; for each time stamp, the typical start-stop combination mode with the highest combination score is selected as the preferred start-stop combination mode; the preferred start-stop combination mode corresponding to each time stamp is constructed into a start-stop combination mode time series; based on the start-stop combination mode time series, the start-stop rules and linkage relationships of each metering device are determined, and the device start-stop strategy matrix is ​​constructed; based on the device start-stop strategy matrix, the start-stop strategy of the metering devices in the pipeline network area to be processed is established.

[0106] Optionally, a timestamp can be understood as each time sampling point within a continuous time series.

[0107] Optionally, the combined score can be understood as a weighted score of the evaluation indicators for the start-stop state modes in a typical start-stop combination mode. It should be noted that the pre-set calculation method can be a weighted evaluation method based on evaluation indicators. These indicators can evaluate each start-stop event in the start-stop state mode; for example, evaluation indicators could include start-stop overlap rate, start-stop frequency coefficient, start-stop synchronicity index, or abnormal start-stop frequency. The pre-set calculation formula is as follows:

[0108]

[0109] Where t is the number of evaluation indicators, wg is the weight of the g-th evaluation indicator, and Lg is the score of the g-th indicator.

[0110] Specifically, based on a pre-set calculation method, the combined score of each typical start-stop combination mode at each time stamp is calculated sequentially.

[0111] Optionally, the preferred start-stop combination mode can be the typical start-stop combination mode with the highest combination score at each time stamp. For each time stamp, the combination scores of each typical start-stop combination mode are sorted in reverse order, and the combination score ranked first is determined as the preferred start-stop combination mode for that time stamp.

[0112] Optionally, the start-stop combination mode time series can be a sequence obtained by combining the preferred start-stop combination modes corresponding to each timestamp based on the time order of the timestamps. When the pipeline area to be processed reaches each timestamp in the start-stop combination mode time series, the preferred start-stop combination mode is selected to control the start-stop of the metering equipment.

[0113] Optionally, the equipment start-stop strategy matrix can be an executable strategy matrix describing the scheduling of metering equipment in the pipeline network area to be processed. It should be noted that the start-stop combination mode time series reflects the complex start-stop patterns and inter-interaction relationships among various metering devices on a time scale. The equipment start-stop strategy matrix is ​​constructed based on the collaborative working status of multiple devices on a time scale.

[0114] Optionally, after obtaining the equipment start-up and shutdown strategy matrix, the equipment start-up and shutdown strategy matrix is ​​converted into executable scheduling instructions for each metering device within a time range, and combined into a metering device start-up and shutdown strategy for the pipeline area to be processed.

[0115] Specifically, based on a pre-set calculation method, the combination score of each typical start-stop combination mode at each time stamp is calculated sequentially; for each time stamp, the typical start-stop combination mode with the highest combination score is selected as the preferred start-stop combination mode; the preferred start-stop combination modes corresponding to each time stamp are constructed into a start-stop combination mode time series; based on the start-stop combination mode time series, the start-stop rules and linkage relationships of each metering device are determined, and a device start-stop strategy matrix is ​​constructed; based on the device start-stop strategy matrix, the start-stop strategy of the metering devices in the pipeline network area to be processed is established.

[0116] Optionally, in another optional embodiment of the present invention, establishing the metering equipment start-up and stop strategy for the pipeline network area to be processed based on the equipment start-up and stop strategy matrix includes:

[0117] Acquire historical operating status data and equipment fault logs of the equipment; determine the operating data and fault frequency of the metering equipment within the time range associated with the equipment start-stop strategy matrix based on the historical operating status data and the equipment fault logs; perform fault correlation analysis based on the metering equipment operating data and the fault frequency to determine the fault start-stop mode; score the equipment start-stop strategy matrix based on the fault start-stop mode using a preset performance scoring method to determine the performance score; optimize the start-stop strategy matrix based on the performance score and preset start-stop strategy adjustment rules to determine the target strategy matrix; establish the metering equipment start-stop strategy for the pipeline network area to be processed based on the target strategy matrix.

[0118] Optionally, the historical operating status data of the equipment can be the operating status data of each metering device within a historical time range; the equipment fault log can be the log data that records the equipment faults that occurred in each metering device within a historical time range.

[0119] Optionally, to ensure the real-time accuracy of metering equipment scheduling in the pipeline network area to be processed, the equipment start-stop strategy matrix typically serves a valid time range. By searching for relevant data from the equipment's historical operating status data and equipment fault logs within the time range associated with the current start-stop strategy matrix, the operating data of at least one metering device during the historical period and the fault frequency of the metering device within that time range can be obtained. The fault frequency can be understood as the number of faults occurring during the historical period within the time range served by the equipment start-stop strategy matrix; the metering equipment operating data can be the start-stop events of each metering device when a fault occurs.

[0120] Optionally, after obtaining the operating data and failure frequency of the metering equipment, fault correlation analysis is used to identify equipment failures or performance degradation caused by start-up and shutdown events of each metering device based on the operating data and failure frequency. This process uncovers the intrinsic relationship between failures and start-up / shutdown events, yielding the fault start-up / shutdown patterns that cause failures in the metering equipment. The fault start-up / shutdown patterns can be start-up / shutdown events that cause failures in the metering equipment itself or other metering devices. Fault correlation analysis can employ at least one method selected from time-series association rule mining, correlation analysis, survival analysis, and machine learning classification.

[0121] Optionally, the preset performance scoring method can be a pre-set method for quantitatively evaluating the equipment start-up and shutdown strategy matrix based on multiple scoring indicators; the scoring indicators can be operating efficiency, failure frequency, energy consumption level, and lifespan degradation; the performance score can be understood as the score corresponding to the performance and lifespan of the metering equipment for the start-up and shutdown strategies of the metering equipment in the equipment start-up and shutdown strategy matrix. For example, the preset performance scoring method can be as follows:

[0122]

[0123] Where P is the equipment start-up and shutdown strategy matrix to be evaluated, f(P) represents the failure frequency of the metering equipment under the equipment start-up and shutdown strategy matrix P, MTTF(P) represents the mean time between failures (MTTF) of the metering equipment under the equipment start-up and shutdown strategy matrix P, e(P) represents the start-up and shutdown frequency of the metering equipment under the equipment start-up and shutdown strategy matrix P, and L(P) represents the predicted remaining life of the metering equipment under the equipment start-up and shutdown strategy matrix. max To preset the failure frequency, MTTF max To preset the time without failure, e max To preset the start / stop frequency, L max The preset lifespan prediction value is defined by w1, w2, w3, and w4, which are weighting coefficients, and the sum of w1, w2, w3, and w4 is 1.

[0124] Optionally, the target strategy matrix can be an optimized device start-up and shutdown strategy matrix. It should be noted that the target strategy matrix is ​​based on performance scores and optimized using preset start-up and shutdown strategy adjustment rules. These preset start-up and shutdown strategy adjustment rules can be pre-set rules for adjusting start-up and shutdown strategies. For example, these rules can consist of fault risk avoidance rules, stability improvement rules, and performance degradation correction rules. Fault risk avoidance rules reduce start-up and shutdown modes that are prone to failure, preventing damage to the metering equipment. Stability improvement rules make start-up and shutdown modes more concentrated and start-up and shutdown parameters more stable. Performance degradation correction rules can be used to restore the original performance level of the metering equipment by adjusting the start-up and shutdown strategy.

[0125] Specifically, the process involves acquiring historical operating status data and equipment fault logs; determining the metering equipment operating data and fault frequency within the time range associated with the equipment start-up and shutdown strategy matrix based on the historical operating status data and equipment fault logs; performing fault correlation analysis based on the metering equipment operating data and fault frequency to determine the fault start-up and shutdown mode; scoring the equipment start-up and shutdown strategy matrix based on the fault start-up and shutdown mode using a preset performance scoring method to determine the performance score; optimizing the start-up and shutdown strategy matrix based on the performance score and preset start-up and shutdown strategy adjustment rules to determine the target strategy matrix; and establishing the metering equipment start-up and shutdown strategy for the pipeline network area to be processed based on the target strategy matrix.

[0126] This invention enables the scientific evaluation of start-up and shutdown strategies, ensuring that adjusted strategies improve equipment performance, extend equipment lifespan, and meet actual operational needs. Continuous monitoring and iterative optimization algorithms dynamically update the start-up and shutdown strategy matrix, ensuring real-time responses to changes in the equipment's operating environment. This intelligent and refined management approach promotes a shift in equipment management from passive response to proactive optimization, enhancing the system's overall adaptability and operational efficiency. Continuous optimization of start-up and shutdown strategies not only reduces equipment failure risks but also improves system stability and reliability, achieving modernization and intelligence in the operation and management of oil and gas metering equipment.

[0127] Figure 4 This is a schematic diagram of a device for optimizing the start-up and shutdown strategy of a pipeline metering equipment, provided as an embodiment of the present invention. Figure 4 As shown, the device includes: a device data acquisition module 410, a device data analysis module 420, a device start / stop combination module 430, and a device start-up strategy module 440; wherein,

[0128] The equipment data acquisition module 410 is used to acquire the operating status data of at least one metering device in the pipeline network area to be processed.

[0129] Equipment data analysis module 420 is used to determine at least one start / stop event for each metering device based on the operating status data of the metering device;

[0130] The equipment start-stop combination module 430 is used to determine a typical start-stop combination mode among the various metering devices based on all start-stop events of the metering devices.

[0131] The equipment startup strategy module 440 is used to establish a startup and shutdown strategy for the metering equipment in the pipeline network area to be processed based on each of the typical startup and shutdown combination modes.

[0132] The technical solution of this invention acquires the operating status data of at least one metering device within the pipeline network area to be treated. By comprehensively collecting the operating status data of the metering devices, all metering devices can be identified, improving the accuracy and timeliness of strategy optimization. For each metering device, at least one start-stop event is determined based on the operating status data of the metering device, accurately identifying the start-stop events of each metering device and avoiding isolated judgments of the status of a single device, thus improving the accuracy and timeliness of start-stop event identification. Based on all start-stop events of the metering devices, typical start-stop combination patterns among the metering devices are determined, constructing start-stop combination patterns under a unified time axis, and extracting representative and stable start-stop combination patterns, providing stronger data support for strategy formulation. Based on each typical start-stop combination pattern, a start-stop strategy for the metering devices in the pipeline network area to be treated is established. This strategy reduces the frequency of abnormal start-stop operations, extends equipment lifespan, and improves operational safety and economy. Existing technologies cannot systematically analyze and evaluate the correlation between start-stop events of multiple metering devices and cannot effectively identify and utilize start-stop combination patterns to optimize equipment start-stop strategies. Scientific optimization of equipment start-up and shutdown strategies has been achieved, improving the collaborative efficiency of multiple devices operating within the station and the overall stability and economy of the system.

[0133] Optionally, the device data analysis module 420 is specifically used for:

[0134] The operating status data of the metering equipment is differentially processed to determine the operating differential data;

[0135] Based on the operational differential data, at least one state change node of the metering equipment is identified;

[0136] Identify the corresponding change time intervals between each of the state change nodes, and determine a start / stop event based on the change time intervals.

[0137] Optionally, the device data analysis module 420 is also specifically used for:

[0138] If the change time interval is not greater than a preset interval threshold, then the state change node corresponding to the change time interval is regarded as an invalid state change node.

[0139] If the change time interval is greater than the preset interval threshold, then the state change node corresponding to the change time interval is regarded as a valid state change node.

[0140] At least one valid state change node corresponding to the metering device is taken as a start / stop event.

[0141] Optionally, the device start / stop combination module 430 is specifically used for:

[0142] Align the start and stop events associated with each metering device to a unified time axis, and collect the start and stop states corresponding to each start and stop event through a preset time window to determine the window start and stop vector.

[0143] Arrange the start / stop vectors corresponding to each time window in chronological order to construct a matrix of start / stop states of the time window and each metering device.

[0144] For each row of the window start / stop vector in the start / stop state matrix, calculate the vector similarity to determine the vector similarity between each window start / stop vector and the corresponding window start / stop vector;

[0145] Clustering is performed on each of the window start-stop vectors based on the vector similarity to determine the start-stop combination category, and the typical start-stop combination pattern is determined based on each start-stop combination category.

[0146] Optionally, the equipment start / stop combination module 430 is also specifically used for:

[0147] For each of the start / stop combination categories, identify the frequency of occurrence of each window start / stop vector across all the time windows;

[0148] The state consistency score and fluctuation degree among the start and stop vectors of each window are evaluated based on the frequency of occurrence.

[0149] Identify the Hamming distances between all pairwise window start / stop vectors in the start / stop combination category;

[0150] Identify the cluster center vectors of the start-stop combination categories, and determine the average Hamming distance between each window start-stop vector and the cluster center vector;

[0151] A stability score is determined based on the average distance using a preset stability calculation formula.

[0152] Identify the distribution concentration of the start-stop combination categories;

[0153] The representative score corresponding to the start-stop combination category is determined based on the distribution concentration, the stability score, and the occurrence frequency.

[0154] Identify the median score of the representative scores corresponding to each start-stop combination category, and select the start-stop combination category as the typical start-stop combination mode based on the median score.

[0155] Optionally, the device startup strategy module 440 is specifically used for:

[0156] Based on a pre-set calculation method, the combined score of each typical start-stop combination mode under each timestamp is calculated sequentially.

[0157] For each timestamp, the typical start-stop combination mode with the highest combined score is selected as the preferred start-stop combination mode;

[0158] Construct a start-stop combination pattern time series for each timestamp corresponding to the preferred start-stop combination pattern.

[0159] Based on the time series of the start-stop combination mode, the start-stop rules and linkage relationships of each metering device are determined, and the device start-stop strategy matrix is ​​constructed.

[0160] The start-up and shutdown strategy for metering equipment in the pipeline network area to be processed is established based on the equipment start-up and shutdown strategy matrix.

[0161] Optionally, the device startup strategy module 440 is also specifically used for:

[0162] Acquire historical operating status data and equipment fault logs of the equipment;

[0163] Based on the historical operating status data of the equipment and the equipment fault log, determine the metering equipment operating data and fault occurrence frequency within the time range associated with the equipment start-stop strategy matrix;

[0164] Based on the operating data of the metering equipment and the frequency of fault occurrence, a fault correlation analysis is performed to determine the fault start-up and shutdown mode.

[0165] The performance score is determined by scoring the device start-stop strategy matrix based on the fault start-stop mode using a preset performance scoring method.

[0166] The start-stop strategy matrix is ​​optimized based on the performance score and the preset start-stop strategy adjustment rules to determine the target strategy matrix;

[0167] The start-up and shutdown strategy for metering equipment in the pipeline network area to be processed is established based on the target strategy matrix.

[0168] The device for optimizing the start-up and shutdown strategy of pipeline metering equipment provided in this embodiment of the invention can execute the device start-up and shutdown strategy optimization method of pipeline metering equipment provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0169] Figure 5A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their patterns are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0170] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0171] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer grids such as the Internet and / or various telecommunications grids.

[0172] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the equipment start-up and shutdown strategy optimization method for pipeline metering equipment.

[0173] In some embodiments, the device start-up and shutdown strategy optimization method for pipeline metering equipment can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the device start-up and shutdown strategy optimization method for pipeline metering equipment described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the device start-up and shutdown strategy optimization method for pipeline metering equipment by any other suitable means (e.g., by means of firmware).

[0174] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0175] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the patterns / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0176] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0177] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0178] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or grid browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication grid). Examples of communication grids include local area networks (LANs), wide area networks (WANs), blockchain grids, and the Internet.

[0179] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS servers, such as high management difficulty and weak business scalability.

[0180] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0181] This embodiment provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the program implements the steps of the equipment start-up and shutdown strategy optimization method for pipeline metering equipment as provided in any embodiment of the present invention. The method includes:

[0182] Acquire the operating status data of at least one metering device within the pipeline network area to be processed;

[0183] For each of the metering devices, at least one start / stop event is determined based on the operating status data of the metering device;

[0184] Based on all start-stop events of the metering equipment, determine the typical start-stop combination patterns among the various metering equipment;

[0185] A start-stop strategy for metering equipment in the pipeline network area to be processed is established based on each of the typical start-stop combination modes.

[0186] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0187] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0188] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0189] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of mesh, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0190] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a grid of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0191] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0192] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for optimizing the start-up and shutdown strategy of pipeline metering equipment, characterized in that, include: Acquire the operating status data of at least one metering device within the pipeline network area to be processed; For each of the metering devices, at least one start / stop event is determined based on the operating status data of the metering device; Based on all start-stop events of the metering equipment, determine the typical start-stop combination patterns among the various metering equipment; A start-stop strategy for metering equipment in the pipeline network area to be processed is established based on each of the typical start-stop combination modes.

2. The method according to claim 1, characterized in that, The determination of at least one start / stop event based on the operating status data of the metering device includes: The operating status data of the metering equipment is differentially processed to determine the operating differential data; Based on the operational differential data, at least one state change node of the metering equipment is identified; Identify the corresponding change time intervals between each of the state change nodes, and determine a start / stop event based on the change time intervals.

3. The method according to claim 2, characterized in that, Determining a start / stop event based on the changing time interval includes: If the change time interval is not greater than a preset interval threshold, then the state change node corresponding to the change time interval is regarded as an invalid state change node. If the change time interval is greater than the preset interval threshold, then the state change node corresponding to the change time interval is regarded as a valid state change node. At least one valid state change node corresponding to the metering device is taken as a start / stop event.

4. The method according to claim 1, characterized in that, The determination of typical start-stop combination patterns among the various metering devices based on all start-stop events of the metering devices includes: Align the start and stop events associated with each metering device to a unified time axis, and collect the start and stop states corresponding to each start and stop event through a preset time window to determine the window start and stop vector. Arrange the start / stop vectors corresponding to each time window in chronological order to construct a matrix of start / stop states of the time window and each metering device. For each row of the window start / stop vector in the start / stop state matrix, calculate the vector similarity to determine the vector similarity between each window start / stop vector and the corresponding window start / stop vector; Clustering is performed on each of the window start-stop vectors based on the vector similarity to determine the start-stop combination category, and the typical start-stop combination pattern is determined based on each start-stop combination category.

5. The method according to claim 4, characterized in that, The step of determining the typical start-stop combination mode based on each start-stop combination category includes: For each of the start / stop combination categories, identify the frequency of occurrence of each window start / stop vector across all the time windows; The state consistency score and fluctuation degree among the start and stop vectors of each window are evaluated based on the frequency of occurrence. Identify the Hamming distances between all pairwise window start / stop vectors in the start / stop combination category; Identify the cluster center vectors of the start-stop combination categories, and determine the average Hamming distance between each window start-stop vector and the cluster center vector; A stability score is determined based on the average distance using a preset stability calculation formula. Identify the distribution concentration of the start-stop combination categories; The representative score corresponding to the start-stop combination category is determined based on the distribution concentration, the stability score, and the occurrence frequency. Identify the median score of the representative scores corresponding to each start-stop combination category, and select the start-stop combination category as the typical start-stop combination mode based on the median score.

6. The method according to claim 1, characterized in that, The establishment of a start / stop strategy for metering equipment in the pipeline network area to be processed based on each of the typical start / stop combination modes includes: Based on a pre-set calculation method, the combined score of each typical start-stop combination mode under each timestamp is calculated sequentially. For each timestamp, the typical start-stop combination mode with the highest combination score is selected as the preferred start-stop combination mode; Construct a start-stop combination pattern time series for each timestamp corresponding to the preferred start-stop combination pattern. Based on the time series of the start-stop combination mode, the start-stop rules and linkage relationships of each metering device are determined, and the device start-stop strategy matrix is ​​constructed. The start-up and shutdown strategy for metering equipment in the pipeline network area to be processed is established based on the equipment start-up and shutdown strategy matrix.

7. The method according to claim 6, characterized in that, The step of establishing the metering equipment start-up and shutdown strategy for the pipeline network area to be processed based on the equipment start-up and shutdown strategy matrix includes: Acquire historical operating status data and equipment fault logs of the equipment; Based on the historical operating status data of the equipment and the equipment fault log, determine the metering equipment operating data and fault occurrence frequency within the time range associated with the equipment start-stop strategy matrix; Based on the operating data of the metering equipment and the frequency of fault occurrence, a fault correlation analysis is performed to determine the fault start-up and shutdown mode. The performance score is determined by scoring the device start-stop strategy matrix based on the fault start-stop mode using a preset performance scoring method. The start-stop strategy matrix is ​​optimized based on the performance score and the preset start-stop strategy adjustment rules to determine the target strategy matrix; The start-up and shutdown strategy for metering equipment in the pipeline network area to be processed is established based on the target strategy matrix.

8. A device for optimizing the start-up and shutdown strategy of pipeline metering equipment, characterized in that, include: The equipment data acquisition module is used to acquire the operating status data of at least one metering device in the pipeline network area to be processed; The equipment data analysis module is used to determine at least one start / stop event for each metering device based on the operating status data of the metering device. The equipment start-stop combination module is used to determine the typical start-stop combination mode among the various metering devices based on all start-stop events of the metering devices; The equipment startup strategy module is used to establish a startup and shutdown strategy for the metering equipment in the pipeline network area to be processed based on each of the typical startup and shutdown combination modes.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the equipment start-up and shutdown strategy optimization method for pipeline metering equipment according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the equipment start-up and shutdown strategy optimization method for the pipeline metering equipment as described in any one of claims 1-7.

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