Acquisition equipment for flow meter and data acquisition method and system

Abstract

The embodiment of the invention discloses acquisition equipment for a flowmeter and a data acquisition method and system.The data acquisition method comprises the steps that S1, data acquisition is conducted on a plurality of preset sampling points in a current rule recognition period, so that a sample sequence including sample data corresponding to each preset sampling point is obtained; s2, on the basis of sample data corresponding to each preset sampling point in the sample sequence, obtaining data change features corresponding to each data pair comprising two adjacent sample data; s3, adjusting each preset sampling point based on the corresponding data change feature of each data pair; s4, determining a gas consumption rule based on each adjusted preset sampling point, taking a next rule identification period as a current rule identification period, taking each adjusted preset sampling point as a new preset sampling point, and returning to S1; and S5, data acquisition is carried out on the flowmeter based on the gas utilization rule. And the data acquisition energy efficiency and the feasibility of battery power supply are improved.

Description

Technical Field

[0001] Several embodiments of this specification relate to the field of flow meter data acquisition technology, and more specifically to performance optimization of flow meter data acquisition devices. Background Technology

[0002] In the field of natural gas metering management, gas flow meters are commonly used for accurate metering by industrial and commercial users with large gas consumption, rather than gas meters suitable for low-flow scenarios. These flow meters are usually designed as independent metering instruments and do not have data networking capabilities. However, gas companies need to monitor users' gas consumption in real time to achieve supply and demand balance, anomaly monitoring, and billing management. Therefore, they need to rely on external data acquisition equipment to collect and remotely transmit flow meter data.

[0003] In current mainstream solutions, data acquisition devices connect to the flow meter via an RS485 wired communication interface to periodically or continuously read its metering data. The acquired data needs to be uploaded to a cloud management system via a wired network (such as fiber optic) or a wireless network (such as 4G / 5G). Because data acquisition devices generally consume a lot of power and rely heavily on AC220V mains power, this method has the following drawbacks: 1. Limited applicability: it cannot be used in remote sites without mains power (such as voltage regulating stations or temporary construction sites); 2. Complex and costly deployment: mains power requires on-site wiring, and additional cables need to be laid in areas without power. Furthermore, gas usage environments are hazardous locations, requiring the installation of explosion-proof enclosures and electrical isolation modules to meet safety regulations, further increasing hardware costs and installation complexity. While a battery-powered solution simplifies installation, the real-time online transmission mode causes the device to operate at high power consumption for extended periods, even when the flow meter is not running, accelerating battery lifespan. Summary of the Invention

[0004] This specification provides a data acquisition device and data acquisition method / system for flow meters. The system performs sparse sample acquisition of flow meters according to set rules and identifies the gas consumption patterns of the corresponding gas-consuming devices. This allows for dynamic adjustment of the data acquisition strategy of the acquisition device, minimizing meaningless data acquisition during periods of non-gas consumption. This improves energy efficiency, significantly extends the operating time when using battery power, and greatly enhances the feasibility of using a battery-powered acquisition device.

[0005] The technical solution is as follows: Firstly, embodiments of this specification provide a data acquisition method for a data acquisition device used in a flow meter, comprising: S1: Collect data from multiple preset sampling points within the current pattern recognition period to obtain a sample sequence including the sample data corresponding to each preset sampling point; S2: Based on the sample data corresponding to each preset sampling point in the sample sequence, obtain the data change characteristics of each data pair including two adjacent sample data. S3: Adjust each preset sampling point based on the corresponding data change characteristics of each data point; S4: Determine the gas consumption pattern based on each adjusted preset sampling point, and return to S1 with the next pattern identification cycle as the current pattern identification cycle and each adjusted preset sampling point as the new preset sampling point. S5: Data acquisition from the flow meter based on gas consumption patterns.

[0006] As a preferred embodiment, the data change characteristics include the amount of data change; The adjustment of each preset sampling point based on the corresponding data change characteristics for each data point includes: If the sum of the changes in the corresponding data for any two adjacent data pairs is less than a first preset value, delete the common preset sampling point of the two data pairs. When the change in any data pair exceeds the second preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

[0007] As a preferred embodiment, the data change characteristics include the amount of data change; The adjustment of each preset sampling point based on the corresponding data change characteristics for each data point includes: If the sum of the changes in the corresponding data for any two adjacent data pairs is less than a first preset value, delete the common preset sampling point of the two data pairs. When the change in any data pair exceeds the second preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

[0008] As a preferred embodiment, the step of adding a preset sampling point between the two preset sampling points corresponding to any data pair when the data change of any data pair is greater than a second preset value includes: When the change in any data pair is greater than the second preset value, and the interval between the two preset sampling points corresponding to the data pair is greater than the third preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

[0009] As a preferred embodiment, the step of adding a preset sampling point between the two preset sampling points corresponding to any data pair when the data change of any data pair is greater than a second preset value includes: When the change in any data pair is greater than the second preset value, and the total number of preset sampling points within the current pattern recognition period is less than the fourth preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

[0010] As a preferred embodiment, the data change characteristics also include the data change rate; The step of adding a preset sampling point between the two preset sampling points corresponding to any data pair when the data change of any data pair is greater than the second preset value includes: When the data change of any data pair is greater than the second preset value, the insertion density is obtained based on the data change rate of the data pair. Based on the insertion density, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

[0011] As a preferred embodiment, the step of adjusting each preset sampling point based on the corresponding data change characteristics for each data point further includes: Based on the changes in the corresponding preset sampling points before and after each adjustment, the number of gas consumption pattern identification cycles is obtained. When the number of gas usage pattern recognition cycles exceeds the fifth preset value, reset the length of the current pattern recognition cycle.

[0012] As a preferred embodiment, the step of obtaining the number of gas consumption pattern identification cycles based on the changes in the corresponding preset sampling points before and after each adjustment includes: When the preset sampling points are not completely the same before and after any adjustment of the preset sampling points, the cumulative number of gas consumption pattern identification cycles is counted. If the preset sampling points are exactly the same before and after any adjustment, reset the number of gas consumption pattern recognition cycles.

[0013] As a preferred embodiment, the initial position of each preset sampling point is determined based on a preset sampling interval within a regularity recognition period; The preset sampling interval is set based on the shortest gas consumption cycle of the gas-consuming device corresponding to the flow meter.

[0014] Secondly, embodiments of this specification provide a data acquisition system for a flow meter's data acquisition device, including a sample acquisition unit, a sample processing unit, a sampling optimization unit, a pattern recognition unit, and a data acquisition unit: The sample acquisition unit collects data from multiple preset sampling points within the current pattern recognition period to obtain a sample sequence including the sample data corresponding to each preset sampling point. The sample processing unit acquires the data change characteristics of each data pair, which includes two adjacent sample data, based on the sample data corresponding to each preset sampling point in the sample sequence. The sampling optimization unit adjusts each preset sampling point based on the data change characteristics corresponding to each data point. The pattern recognition unit determines the gas consumption pattern based on each adjusted preset sampling point; The sample acquisition unit also uses the next pattern recognition period as the current pattern recognition period and uses each adjusted preset sampling point as a new preset sampling point to re-acquire data. The data acquisition unit collects data from the flow meter based on the gas consumption pattern.

[0015] Thirdly, the embodiments of this specification provide a data acquisition device for a flow meter, including a gas consumption data acquisition module that acquires data from the flow meter based on the gas consumption pattern obtained by the steps described in the first aspect of the above embodiments.

[0016] Fourthly, embodiments of this specification provide an electronic device, including a processor and a memory; the processor is connected to the memory; the memory is used to store executable program code; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to perform the steps described in the first aspect of the above embodiments.

[0017] Fifthly, embodiments of this specification provide a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the steps described in the first aspect of the above embodiments.

[0018] The beneficial effects of the technical solutions provided in some embodiments of this specification include at least the following: By intelligently identifying the actual gas flow patterns of the flow meter, the gas consumption patterns of the corresponding gas-consuming equipment are determined, thereby dynamically adjusting the data acquisition strategy. A dynamic sampling frequency replaces a fixed sampling frequency, solving the problem of ineffective power consumption in traditional data acquisition schemes under battery-powered scenarios. This effectively reduces the number of meaningless data acquisitions during equipment idle periods, improving the effectiveness and value of the data. It also reduces the power consumption of the acquisition equipment, significantly extends battery life, and greatly improves the feasibility of using a battery-powered solution for the acquisition equipment.

[0019] It can gradually adjust the data collection plan within several pattern recognition cycles to adapt to new gas consumption patterns based on changes in the actual operating mode of gas-consuming equipment (such as the addition of new operating time periods).

[0020] Based on the data change of the collected sample data, the sampling density is increased (interpolation) during periods of high data change (frequent equipment operation) and decreased (deletion) during periods of low data change (long-term equipment idleness) by using preset thresholds and rules. Ultimately, the distribution of sampling points is adaptively focused on the actual working period of the equipment.

[0021] The key parameters are configurable, which allows the solution to be flexibly applied to equipment monitoring scenarios with different industries and operating characteristics. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic flowchart illustrating a data acquisition method for a flow meter acquisition device provided in the embodiments of this specification.

[0024] Figures 2 to 8 This is a schematic diagram showing the sampling times of each preset sampling point corresponding to different adjustment cycles in the examples in this manual.

[0025] Figure 9 This is a schematic diagram of the structure of a data acquisition system for a flow meter acquisition device provided in the embodiments of this specification.

[0026] Figure 10 This is a schematic diagram of the structure of an electronic device provided in the embodiments of this specification. Detailed Implementation

[0027] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings.

[0028] The terms "first," "second," "third," etc., in the description, claims, and accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0029] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this specification. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0030] Gas companies need to monitor users' gas consumption in real time to achieve supply and demand balance, anomaly monitoring, and billing management. Since flow meters usually do not have data networking capabilities, they need to rely on external data acquisition equipment to collect and remotely transmit flow meter data.

[0031] In existing technologies, data acquisition equipment relies on AC220V mains power. Mains power supply requires on-site wiring construction, and additional cables need to be laid in areas without power. Moreover, the gas usage environment is a hazardous location, and explosion-proof enclosures and electrical isolation modules are required to meet safety regulations. The deployment is complex and costly. Furthermore, it is not applicable to remote sites without mains power (such as pressure regulating stations and temporary construction sites), which limits the applicable scenarios.

[0032] While battery power simplifies installation, the real-time online transmission mode keeps the equipment in a high-power state for extended periods, consuming power even when the flow meter is not running. This leads to rapid battery depletion and frequent battery replacements, making maintenance too difficult for gas companies and hindering large-scale application. Therefore, this application is submitted. Example 1

[0033] A data acquisition method for a data acquisition device used in flow meters.

[0034] Reference Figure 1 As shown, Figure 1 This is a schematic flowchart illustrating a data acquisition method for a flow meter acquisition device provided in the embodiments of this specification.

[0035] Data acquisition methods may include at least the following steps: S1: Collect data from multiple preset sampling points within the current pattern recognition period to obtain a sample sequence including the sample data corresponding to each preset sampling point; S2: Based on the sample data corresponding to each preset sampling point in the sample sequence, obtain the data change characteristics of each data pair including two adjacent sample data. S3: Adjust each preset sampling point based on the corresponding data change characteristics of each data point; S4: Determine the gas consumption pattern based on each adjusted preset sampling point, and return to S1 with the next pattern identification cycle as the current pattern identification cycle and each adjusted preset sampling point as the new preset sampling point. S5: Data acquisition from the flow meter based on gas consumption patterns.

[0036] Explained, the data acquisition device is battery-powered, easy to install, and does not require explosion-proof isolation equipment, resulting in relatively low cost. However, even if the flow meter is not used for a long time, the data acquisition device will still collect gas consumption data periodically according to the set cycle, thus wasting battery power and shortening battery life. This embodiment identifies the user's gas consumption patterns through an adaptive adjustment strategy based on data change characteristics, and collects data in a targeted manner according to the gas consumption patterns, thereby avoiding data collection during periods when the user is not using natural gas, ensuring that only meaningful data of change is collected. Furthermore, it improves energy efficiency and significantly extends the operating time when using battery power, avoiding the inconvenience of mains power supply.

[0037] For illustrative purposes, the pattern recognition period is the duration of a single analysis selected based on the application scenario. Preset sampling points are the sampling times within the pattern recognition period; each preset sampling point corresponds to one sample data point, and all sample data within the pattern recognition period are sorted by time to form a sample sequence. Data change characteristics reflect the gas consumption behavior patterns of the gas-using equipment corresponding to the flow meter between two adjacent sampling points, such as the gas consumption or the rate of change of gas consumption between two adjacent sampling points. After adjusting each preset sampling point, the start-up and shutdown times of the gas-using equipment are estimated based on the distribution of the preset sampling points within the pattern recognition period. This is used as the gas consumption pattern of the gas-using equipment, and the data acquisition device collects gas consumption data during the estimated operating period of the gas-using equipment. After adjusting each preset sampling point, the gas consumption pattern is re-identified and updated using the same identification process.

[0038] Understandably, for a gas usage pattern to align with actual gas usage patterns, adjustments to the preset sampling points are typically required across multiple pattern recognition cycles. During these adjustments, data acquisition from the flow meter based on the gas usage pattern still captures complete gas usage data, although some invalid data acquisition remains. Once the determined gas usage pattern aligns with actual gas usage, the preset sampling points for each pattern recognition cycle will remain unchanged, and the determined gas usage pattern will also remain unchanged. However, the pattern recognition process continues in a loop to detect changes in the actual gas usage pattern. When the user's actual gas usage pattern changes (e.g., a factory adds a night shift), the new gas usage pattern will be gradually detected in subsequent pattern recognition cycles, eventually adapting to the new mode. The number of samples and computational loads in the pattern recognition process are minimal, and the increased power consumption from continuous pattern recognition is negligible.

[0039] In one embodiment of this specification, the initial position of each preset sampling point is determined based on a preset sampling interval within a pattern recognition period; The preset sampling interval is set based on the shortest gas consumption cycle of the gas-consuming equipment corresponding to the flow meter.

[0040] To illustrate, for efficient estimation of the start-up and shutdown times of gas-using equipment, the initial preset sampling points are evenly distributed within the pattern recognition period at preset sampling intervals t. The preset sampling interval t determines the initial detection density; an excessively high t will miss key change points, affecting the algorithm's accuracy in determining the start-up and shutdown points of gas usage patterns and increasing the number of periods required to determine the pattern. Typically, t can be set based on the minimum possible operating time of the gas-using equipment to ensure that the initial detection covers potential changes. There is a coupling relationship between the preset sampling interval t and the pattern recognition period T. When T is fixed at 24 hours, if t is set to 10 minutes, the initial sampling points can reach as high as 144.

[0041] Specifically, the larger t is, the sparser the initial detection is, the more likely it is to miss important state transition points, affecting the accuracy of the algorithm in judging the boundary of gas consumption patterns, and the algorithm needs more pattern recognition cycles T to accurately identify the pattern; the smaller t is, the more data points are collected, increasing the computational load and storage pressure when the acquisition equipment adjusts the sampling points.

[0042] In one embodiment of this specification, the data change characteristics include the amount of data change; Step S3: Adjust each preset sampling point based on the corresponding data change characteristics for each data point, including: If the sum of the changes in the corresponding data for any two adjacent data pairs is less than a first preset value, delete the common preset sampling point of the two data pairs. When the change in any data pair exceeds the second preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

[0043] Explanatoryly, some preset sampling points are deleted to determine the start-up and shutdown times between the operating and idle periods of the gas-consuming equipment. Some preset sampling points are added to verify in a later pattern recognition cycle whether a short idle period is included between two sampling points, thus refining the estimation of start-up and shutdown times. By adding and deleting preset sampling points, the operating periods of the gas-consuming equipment are gradually determined.

[0044] For illustrative purposes, when adding or deleting preset sampling points, the number and method of addition or deletion are not specifically limited here. The design goal is to balance the accuracy of identifying the start and stop times of gas-using equipment with the number of regular identification cycles required to determine the actual gas usage patterns.

[0045] In one embodiment of this specification, when the data change of any data pair is greater than a second preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair, including: When the change in any data pair is greater than the second preset value, and the interval between the two preset sampling points corresponding to the data pair is greater than the third preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

[0046] To prevent meaningless data insertion during excessively short sampling intervals, which would increase computational load, a minimum sampling interval, i.e., a third preset value, is set. If, after inserting a sampling point, the sampling interval formed by that point and either of the two preset sampling points corresponding to the data pair is less than the minimum sampling interval, it is assumed that there is no idle period for the gas-using equipment in the two preset sampling points corresponding to that data pair. Therefore, no further sampling points are inserted to prevent over-dense sampling.

[0047] In one embodiment of this specification, when the data change of any data pair is greater than a second preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair, including: When the change in any data pair is greater than the second preset value, and the total number of preset sampling points within the current pattern recognition period is less than the fourth preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

[0048] For explanatory purposes, to control the number of samples used to identify gas consumption patterns and prevent excessive power consumption from excessive sampling, a maximum number of samplings, i.e., a fourth preset value, is set. If the total number of preset sampling points exceeds the fourth preset value during the adjustment of preset sampling points within a certain pattern identification period, no more sampling points will be inserted.

[0049] It should be noted that the initial settings for the pattern recognition period T and the preset acquisition interval t must be considered in conjunction with the minimum acquisition interval and the maximum number of acquisitions. Ensure that the ratio of the pattern recognition period T to the preset acquisition interval t is much smaller than the maximum number of acquisitions to allow sufficient room for dynamic adjustment, and ensure that the preset acquisition interval t is significantly larger than the minimum acquisition interval to allow for sufficient interpolation cycles for dynamic adjustment.

[0050] For example, the pattern recognition period T is set to 24 hours, the preset collection interval t is 1 hour, the minimum collection interval is 30 minutes, and the operating hours of the gas-consuming equipment corresponding to the flow meter are 6:00-8:00, 11:30-13:30, and 17:00-20:00 daily. The gas consumption every 30 minutes is less than the second preset value, and the gas consumption every 10 minutes is less than the first preset value. (See attached...) Figure 2 To be continued Figure 8 The process of identifying actual gas consumption patterns is explained. Figures 2 to 8 This is a schematic diagram showing the sampling times of each preset sampling point corresponding to different adjustment cycles in the examples in this manual.

[0051] The preset sampling points corresponding to the first pattern recognition period T1 are as follows: Figure 2 As shown; The preset sampling points corresponding to the second pattern recognition period T2 are as follows: Figure 3As shown, new data collection points have been added for equipment operating time periods: 6:30, 7:30, 11:30, 12:30, 13:30, 17:30, 18:30, and 19:30 (interpolated at the midpoint between the two preset sampling points corresponding to the data pair). Data collection points for non-operating time periods have been deleted: 0:00, 1:00, 2:00, 3:00, 4:00, 5:00, 9:00, 10:00, 15:00, 16:00, 21:00, 22:00, and 23:00. The preset sampling points corresponding to the third pattern recognition period T3 are as follows: Figure 4 As shown, the sampling points during non-operational periods of the equipment were deleted: 11:00 and 14:00. At this time, the gas consumption patterns were determined based on the distribution characteristics of the preset sampling points: 6:00~8:00, 11:30~13:30, and 17:00~20:00. That is, the actual gas consumption patterns were identified, and the same preset sampling points will be used to identify the patterns in subsequent cycles. If the gas-using equipment has added an operating period of 15:00~15:30; The preset sampling points corresponding to the first pattern recognition period T4 after the addition are as follows: Figure 5 As shown, a new data collection point for the device's operating time period has been added: 15:30 (the midpoint between 13:30 and 17:30). The preset sampling points corresponding to the second pattern recognition period T5 after the addition are as follows: Figure 6 As shown, a new data collection point for the device's operating time period has been added: 14:30 (the midpoint between 13:30 and 15:30). The preset sampling points corresponding to the third pattern recognition period T6 after the addition are as follows: Figure 7 As shown, a new data collection point for the device's operating time period has been added: 15:00 (the midpoint between 14:30 and 15:30). The preset sampling points corresponding to the newly added fourth pattern recognition period T7 are as follows: Figure 8 As shown, the sampling point during the non-operational period of the equipment, 14:30, was deleted. At this time, the gas consumption patterns were determined based on the distribution characteristics of the preset sampling points: 6:00~8:00, 11:30~13:30, 15:00~15:30, and 17:00~20:00. In other words, the actual gas consumption patterns were identified, and the same preset sampling points will be used to adjust the subsequent pattern identification cycles.

[0052] In one embodiment of this specification, the data change feature further includes the data change rate; When the change in any data pair exceeds a second preset value, a new preset sampling point is added between the two preset sampling points corresponding to that data pair, including: When the data change of any data pair is greater than the second preset value, the insertion density is obtained based on the data change rate of the data pair. Based on the insertion density, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

[0053] Interpretive, when the data change of the corresponding two preset sampling points meets the interpolation condition, the insertion density is determined by combining the data change rate between the two preset sampling points.

[0054] To illustrate, when the data change rate is small, it indicates that the actual running segment within that interval is relatively small. Therefore, inserting a higher density of sampling points and deleting irrelevant sampling points in the next pattern recognition cycle can quickly determine the boundary range of the running segment. When the data change rate is large, it indicates that the actual running segment within that interval is relatively large. If a higher density of sampling points is still inserted, most of the inserted sampling points will fall within the running segment. Although the boundary range of the running segment can still be determined, there will be fewer irrelevant sampling points, and many less useful sampling points will occupy the maximum number of collections. Therefore, only a lower density of sampling points can be inserted to roughly determine the boundary range of the running segment. Subsequent insertions can then gradually improve the accuracy of identifying the start and stop times of gas-using equipment, thus making room for the insertion of other data pairs.

[0055] Understandably, when the preset sampling points are much smaller than the maximum number of samplings, the minimum sampling interval can be used directly without considering the rate of data change, in order to reduce the number of cycles required to identify the actual gas consumption patterns.

[0056] In one embodiment of this specification, S3: Based on the data change characteristics corresponding to each data point, the preset sampling points are adjusted, and then the method further includes: Based on the changes in the corresponding preset sampling points before and after each adjustment, the number of gas consumption pattern identification cycles is obtained. When the number of gas usage pattern recognition cycles exceeds the fifth preset value, reset the length of the current pattern recognition cycle.

[0057] For explanatory purposes, if a stable gas consumption pattern is not obtained after multiple pattern recognition cycles, the pattern recognition cycle needs to be automatically or manually reset based on the periodic patterns of the collected historical gas consumption data.

[0058] To illustrate, if the initial pattern recognition period T does not match the actual operating patterns of the gas-consuming equipment, the method may fall into a state of "ineffective adjustment" or even "continuous oscillation," failing to execute the optimal data acquisition plan and diminishing the effect of extending battery life. Therefore, the initial pattern recognition period T must be set based on an understanding of the dominant operating patterns of the gas-consuming equipment to improve the robustness of high-efficiency data acquisition. For most scenarios with daily operating patterns, setting T to a default 24-hour period is the optimal starting point.

[0059] Specifically, if the time interval (T) is set too short, while the actual operating cycle of the equipment is 8 hours per day, dense sampling points will be inserted if a certain pattern recognition cycle corresponds to a certain operating period. Conversely, a large number of sampling points will be deleted if the same pattern recognition cycle corresponds to a certain operating period. The result is that useless work is done within each T, making it impossible to identify the actual gas usage pattern. If T is much longer than the actual operating cycle, with multiple operating cycles contained within one T, optimization requires completing a full T before adjustments can be made. This results in slow response times, inaccurate insertion and deletion decisions, and an overly "coarse" data acquisition strategy.

[0060] In one embodiment of this specification, the number of gas consumption pattern identification cycles is obtained based on the changes in the corresponding preset sampling points before and after each adjustment, including: When the preset sampling points are not completely the same before and after any adjustment of the preset sampling points, the cumulative number of gas consumption pattern identification cycles is counted. If the preset sampling points are exactly the same before and after any adjustment, reset the number of gas consumption pattern recognition cycles.

[0061] It is important to note that: 1. This invention is applicable not only to battery-powered solutions but also to AC power-powered solutions, and can also improve energy efficiency.

[0062] 2. This invention offers significant advantages in scenarios where the actual operating time of equipment is far less than its idle time, minimizing the waste of electricity collected during idle periods. The effect is particularly pronounced for equipment with relatively fixed operating times.

[0063] 3. This invention can be applied not only to gas flow metering scenarios, but also to other scenarios such as smart water management and industrial IoT, in order to reduce waste energy consumption and improve energy efficiency.

[0064] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous. Example 2

[0065] A data acquisition system for a flow meter acquisition device.

[0066] Please see Figure 9 , Figure 9 This is a schematic diagram of the structure of a data acquisition system for a flow meter acquisition device provided in the embodiments of this specification.

[0067] The data acquisition system 200 includes a sample acquisition unit 201, a sample processing unit 202, a sampling optimization unit 203, a pattern recognition unit 204, and a data acquisition unit 205. The sample acquisition unit 201 acquires data from multiple preset sampling points within the current pattern recognition period to obtain a sample sequence including the sample data corresponding to each preset sampling point. The sample processing unit 202 acquires the data change characteristics of each data pair, which includes two adjacent sample data, based on the sample data corresponding to each preset sampling point in the sample sequence. The sampling optimization unit 203 adjusts each preset sampling point based on the data change characteristics corresponding to each data point. The pattern recognition unit 204 determines the gas consumption pattern based on each adjusted preset sampling point; The sample acquisition unit 201 also uses the next pattern recognition period as the current pattern recognition period and each adjusted preset sampling point as a new preset sampling point to re-acquire data. The data acquisition unit 205 acquires data from the flow meter based on the gas consumption pattern.

[0068] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the data acquisition system embodiments are basically similar to the data acquisition method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the data acquisition method embodiments. Example 3

[0069] A data acquisition device for flow meters.

[0070] The data acquisition device includes a gas consumption data acquisition module that obtains the gas consumption pattern using a data acquisition method for a flow meter as described in Example 1, and then acquires data from the flow meter based on the gas consumption pattern.

[0071] To illustrate, the data acquisition device periodically collects gas consumption data from the flow meter and stores it in its internal memory. It only powers on and connects to the network after collecting a certain amount of data, and then uploads the data to the management system via a wireless network (such as 4G / 5G) to further reduce the power consumption of the data acquisition device. Example 4

[0072] An electronic device.

[0073] Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of an electronic device provided in the embodiments of this specification.

[0074] Electronic device 300 may include at least one processor 301, at least one network interface 304, user interface 303, memory 305, and at least one communication bus 302.

[0075] The communication bus 302 can be used to realize the connection and communication of the above components.

[0076] The user interface 303 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0077] The network interface 304 may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0078] The processor 301 may include one or more processing cores. The processor 301 connects to various parts within the electronic device 300 using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form selected from DSP, FPGA, and PLC. The processor 301 may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip, without being integrated into the processor 301.

[0079] The memory 305 may include RAM or ROM. Optionally, the memory 305 may include a non-transitory computer-readable medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. As a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface module, and a data acquisition application program. The processor 301 may be used to call the data acquisition application program stored in the memory 305 and execute the steps of the data acquisition method mentioned in the foregoing embodiments. Example 5

[0080] A computer-readable storage medium.

[0081] This specification also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps in the above-described data acquisition method embodiments. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0082] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this specification is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0083] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and its implementation can be combined arbitrarily.

[0084] The above embodiments are merely preferred embodiments described in this specification and are not intended to limit the scope of this specification. Any modifications and improvements made by those skilled in the art to the technical solutions of this specification without departing from the spirit of this specification should fall within the protection scope defined by the claims of this specification.

Claims

1. A data acquisition method for a data acquisition device used in a flow meter, characterized in that, Includes the following steps: S1: Collect data from multiple preset sampling points within the current pattern recognition period to obtain a sample sequence including the sample data corresponding to each preset sampling point; S2: Based on the sample data corresponding to each preset sampling point in the sample sequence, obtain the data change characteristics of each data pair including two adjacent sample data. S3: Adjust each preset sampling point based on the corresponding data change characteristics of each data point; S4: Determine the gas consumption pattern based on each adjusted preset sampling point, and return to S1 with the next pattern identification cycle as the current pattern identification cycle and each adjusted preset sampling point as the new preset sampling point. S5: Data acquisition from the flow meter based on gas consumption patterns.

2. The data acquisition method for a flow meter acquisition device according to claim 1, characterized in that, The data change characteristics include the amount of data change; The adjustment of each preset sampling point based on the corresponding data change characteristics for each data point includes: If the sum of the changes in the corresponding data for any two adjacent data pairs is less than a first preset value, delete the common preset sampling point of the two data pairs. When the change in any data pair exceeds the second preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

3. The data acquisition method for a data acquisition device for a flow meter according to claim 2, characterized in that, The step of adding a preset sampling point between the two preset sampling points corresponding to any data pair when the data change of any data pair is greater than the second preset value includes: When the change in any data pair is greater than the second preset value, and the interval between the two preset sampling points corresponding to the data pair is greater than the third preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

4. The data acquisition method for a flow meter acquisition device according to claim 2, characterized in that, The step of adding a preset sampling point between the two preset sampling points corresponding to any data pair when the data change of any data pair is greater than the second preset value includes: When the change in any data pair is greater than the second preset value, and the total number of preset sampling points within the current pattern recognition period is less than the fourth preset value, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

5. A data acquisition method for a flow meter acquisition device according to claim 2, characterized in that, The data change characteristics also include the data change rate; The step of adding a preset sampling point between the two preset sampling points corresponding to any data pair when the data change of any data pair is greater than the second preset value includes: When the data change of any data pair is greater than the second preset value, the insertion density is obtained based on the data change rate of the data pair. Based on the insertion density, a new preset sampling point is added between the two preset sampling points corresponding to the data pair.

6. The data acquisition method for a flow meter acquisition device according to claim 1, characterized in that, The process of adjusting each preset sampling point based on the corresponding data change characteristics for each data point further includes: Based on the changes in the corresponding preset sampling points before and after each adjustment, the number of gas consumption pattern identification cycles is obtained. When the number of gas usage pattern recognition cycles exceeds the fifth preset value, reset the length of the current pattern recognition cycle.

7. A data acquisition method for a flow meter acquisition device according to claim 6, characterized in that, The method of obtaining the number of gas consumption pattern identification cycles based on the changes in the corresponding preset sampling points before and after each adjustment includes: When the preset sampling points are not completely the same before and after any adjustment of the preset sampling points, the cumulative number of gas consumption pattern identification cycles is counted. If the preset sampling points are exactly the same before and after any adjustment, reset the number of gas consumption pattern recognition cycles.

8. The data acquisition method for a flow meter acquisition device according to claim 1, characterized in that, The initial position of each of the preset sampling points is determined based on a preset sampling interval within a regularity recognition period; The preset sampling interval is set based on the shortest gas consumption cycle of the gas-consuming device corresponding to the flow meter.

9. A data acquisition system for a flow meter acquisition device, characterized in that, It includes a sample acquisition unit, a sample processing unit, a sampling optimization unit, a pattern recognition unit, and a data acquisition unit: The sample acquisition unit collects data from multiple preset sampling points within the current pattern recognition period to obtain a sample sequence including the sample data corresponding to each preset sampling point. The sample processing unit acquires the data change characteristics of each data pair, which includes two adjacent sample data, based on the sample data corresponding to each preset sampling point in the sample sequence. The sampling optimization unit adjusts each preset sampling point based on the data change characteristics corresponding to each data point. The pattern recognition unit determines the gas consumption pattern based on each adjusted preset sampling point; The sample acquisition unit also uses the next pattern recognition period as the current pattern recognition period and uses each adjusted preset sampling point as a new preset sampling point to re-acquire data. The data acquisition unit collects data from the flow meter based on the gas consumption pattern.

10. A data acquisition device for a flow meter, characterized in that: The invention includes a gas consumption data acquisition module that uses a data acquisition device for a flow meter as described in any one of claims 1 to 8 to obtain the gas consumption pattern and then acquires data from the flow meter based on the gas consumption pattern.

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