An ultrasonic gas meter internet of things intelligent monitoring control system
By introducing abrupt change density and distribution entropy into the ultrasonic gas meter, a flow sequence is constructed and the correlation is calculated, enabling precise control of the gas flow state. This solves the problem of distinguishing between gas leaks and environmental noise, and improves the accuracy and reliability of monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANXI HUATENG ENERGY TECH CO LTD
- Filing Date
- 2026-05-21
- Publication Date
- 2026-06-30
AI Technical Summary
Existing ultrasonic gas meters cannot effectively distinguish between gas leaks and environmental noise in flow monitoring, resulting in a high false alarm rate or a high false alarm rate, and cannot improve detection sensitivity without sacrificing safety.
By introducing mutation density and mutation distribution entropy, a baseline flow sequence and an evolved flow sequence are constructed. Global correlation and weighted fluctuation amplitude are calculated. Combined with a dynamic weighting mechanism, the stability and anomalies of gas flow state are identified, enabling precise control of gas flow.
Without sacrificing safety, it significantly reduces the false alarm rate, ensures that minute and continuous leak signals are not filtered out, avoids the system from accidentally closing valves, and improves the accuracy of gas meter monitoring and control.
Smart Images
Figure CN122306180A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of gas control, specifically to an ultrasonic gas meter Internet of Things (IoT) intelligent monitoring and control system. Background Technology
[0002] As a core device in smart gas metering, ultrasonic gas meters undertake the dual tasks of flow measurement and safety monitoring in IoT applications. Existing ultrasonic gas meter anomaly detection technologies mainly rely on absolute flow threshold determination or simple differential calculations between adjacent sampling points.
[0003] However, in actual operation, both gas leaks and environmental noise interference exhibit fluctuations in flow data, but their spatiotemporal distribution characteristics are quite different: gas leaks, reflected in the data stream, show highly concentrated and orderly distribution of abrupt changes on the time axis (for example, an abrupt change occurs every fixed sampling period); while environmental noise (such as electromagnetic interference and pipeline mechanical vibration) usually exhibits random, discrete, and irregular jumps, and reflected in the data stream, the distribution of abrupt changes on the time axis is chaotic and scattered.
[0004] Existing differential or threshold methods can only detect "whether fluctuations exist" and "the magnitude of fluctuations," but lack the ability to quantify the distribution characteristics of mutation events over time (such as mutation density and temporal distribution entropy). This deficiency leads to a dilemma for existing technologies: if sensitivity is increased to detect minute leaks, a large amount of random noise will be misjudged as leaks, causing valves to close frequently and seriously interfering with normal gas usage; if sensitivity is reduced or filtering delay is increased to avoid false alarms, it is easy to filter out real leak signals that are small in amplitude but have a continuous regularity, thus creating potential safety hazards. Summary of the Invention
[0005] To address the shortcomings of existing technologies, this invention provides an intelligent monitoring and control system for ultrasonic gas meters via the Internet of Things (IoT), which solves the technical problems mentioned in the background by introducing mutation density and mutation distribution entropy.
[0006] To achieve the above objectives, the present invention provides the following technical solution: An ultrasonic gas meter IoT intelligent monitoring and control system, the system comprising: The original sequence construction module is used to construct the original flow sequence of the ultrasonic gas meter at N sampling times; The dual-sequence separation module is used to separate and reassemble the baseline flow sequence and the evolved flow sequence from the original flow sequence; The correlation calculation module is used to calculate the global correlation between the baseline flow sequence and the evolved flow sequence; The relevance comparison module is used to obtain a preset relevance threshold and compare the global relevance with the relevance threshold; The state determination module is used to determine whether the gas flow state is stable if the global correlation is greater than or equal to the correlation threshold, and to generate a valve opening command; otherwise, it determines that the gas flow state is abnormal and generates a state instability command. The state response module is used to calculate the M weighted fluctuation amplitudes between the baseline flow sequence and the evolved flow sequence in response to the state instability command. The mutation acquisition module is used to obtain the mutation density and mutation distribution entropy based on M weighted fluctuation amplitudes. The gas control module is used to control the flow rate of the gas meter based on the mutation density and mutation distribution entropy.
[0007] In some specific embodiments, the original sequence construction module is specifically used for: S1-1, Control the probe A located at the first end of the pipe axis and the probe B located at the second end of the pipe axis to alternately emit ultrasonic waves; S1-2, the time of downstream transmission and downstream reception of the anchoring ultrasonic wave from probe A to probe B, and the time of upstream transmission and downstream reception of the anchoring ultrasonic wave from probe B to probe A; S1-3. Calculate the time difference between the downstream reception time and the downstream transmission time, as well as between the upstream reception time and the upstream transmission time, to obtain the downstream propagation time and the upstream propagation time. S1-4. Calculate the difference and product of the propagation time with the current and the propagation time against the current to generate a time difference parameter that characterizes the propagation time difference and a time product parameter that characterizes the static sound speed reference. S1-5. Calculate the axial velocity of the gas at the sampling time by performing a ratio calculation between the time difference parameter and the time product parameter, and multiplying the product with the ultrasonic propagation path length. S1-6. Read the preset effective cross-sectional area of the pipe; S1-7. Calculate the product of the effective cross-sectional area of the pipeline and the axial velocity of the gas to obtain the gas flow rate at the sampling time; S1-8. Collect gas flow rate continuously at N sampling times with a fixed sampling period; S1-9. Arrange the N gas flow rates in the order of their sampling times to form the original flow sequence; where the continuous time period covered by the N sampling times is defined as the current monitoring window, and N is a preset even number.
[0008] In some specific embodiments, the double sequence separation module is specifically used for: S2-1. Assign monotonically continuous time sequence numbers to the N gas flow rates of the original flow sequence; wherein the initial time sequence number is an odd number 1, and the subsequent time sequence numbers are incremented by 1 in turn; S2-2. From N time sequence numbers, select all odd and even numbers; S2-3. Mark the gas flow rate corresponding to odd-numbered numbers as the baseline flow rate, and the gas flow rate corresponding to even-numbered numbers as the evolution flow rate; S2-4. Based on the ascending order of the time sequence number, rearrange all the baseline traffic and evolved traffic to generate the baseline traffic sequence and evolved traffic sequence respectively.
[0009] In some specific embodiments, the relevance calculation module is specifically used for: S3-1. Reassign unified pairing numbers to the baseline flow sequence and the evolved flow sequence; wherein the pairing number ranges from 1 to M, and M=N / 2; S3-2. Based on the baseline flow and evolved flow corresponding to the pairing sequence number, calculate the global correlation between the baseline flow sequence and the evolved flow sequence.
[0010] In some specific embodiments, the state determination module is specifically used for: S5-1. Based on the relevance threshold and global relevance, construct a global dynamic weight that represents the unified adjustment of traffic change trends; S5-2. In the reference flow sequence and the evolved flow sequence, select the reference flow and the evolved flow with the same pairing number, and pair them as flow observation pairs; S5-3. Perform normalized difference operation on the flow observation pair to generate the relative fluctuation amplitude of the flow observation pair; S5-4. Multiply the relative fluctuation amplitude with the global dynamic weight to generate a weighted fluctuation amplitude; S5-5. Traverse the baseline flow sequence and evolved flow sequence corresponding to the M paired sequence numbers until M weighted fluctuation amplitudes are generated.
[0011] In some specific embodiments, a global dynamic weight is constructed based on a relevance threshold and global relevance to characterize the unified adjustment of traffic change trends, including: S5-1-1. Calculate the difference between the relevance threshold and the global relevance to obtain the relevance offset; S5-1-2. Calculate the ratio of the correlation offset to the global correlation to obtain the instability ratio; S5-1-3. Obtain the preset baseline weights and calculate their product with the instability ratio to obtain the dynamic compensation weights. S5-1-4. Sum the dynamic compensation weights and the baseline weights to generate the global dynamic weights.
[0012] In some specific embodiments, the status response module is specifically used for: S6-1. Identify J abrupt change amplitudes among M weighted fluctuation amplitudes; S6-2. Calculate the ratio of J mutation amplitudes to M weighted fluctuation amplitudes to generate the mutation density p of the current monitoring window; S6-3, Anchor the J mutation timestamps corresponding to the J mutation amplitudes; S6-4. Based on J mutation timestamps, calculate the mutation distribution entropy of the mutation magnitude within the current monitoring window.
[0013] In some specific embodiments, J abrupt changes are identified from the M weighted fluctuation amplitudes, including: S6-1-1, Obtain the set benchmark fluctuation threshold; S6-1-2. Compare the M weighted fluctuation amplitudes with the benchmark fluctuation threshold respectively; S6-1-3. If the weighted volatility amplitude is greater than the benchmark volatility threshold, the corresponding weighted volatility amplitude will be marked as the sudden change amplitude. S6-1-4. Traverse the M weighted fluctuation amplitudes and identify the J labeled mutation amplitudes.
[0014] This invention provides an IoT-based intelligent monitoring and control system for ultrasonic gas meters, which has the following advantages: Based on the high-frequency, continuous, and orderly distribution characteristics of abrupt changes in gas leaks, this invention identifies the high abrupt change density and low abrupt change distribution entropy state to ensure that minute, continuous leak signals are not filtered out, thus completely eliminating the risk of missed detection. Furthermore, based on the random, discrete, and disordered abrupt change characteristics of electromagnetic interference or pipeline vibration, its high abrupt change distribution entropy state is identified as noise interference, thus avoiding frequent false valve closures in the system. Furthermore, based on the identification of the temporal distribution of abrupt change points, the false alarm rate is significantly reduced without sacrificing safety. Simultaneously, combined with a dynamic weighting mechanism to adaptively adjust detection sensitivity, the system achieves a fundamental distinction between real leak signals and environmental noise under complex operating conditions, improving the accuracy of ultrasonic gas meter monitoring and control. Attached Figure Description
[0015] Figure 1 This is a structural block diagram of an ultrasonic gas meter IoT intelligent monitoring and control system according to the present invention; Figure 2 This is a schematic diagram of the processing flow of an ultrasonic gas meter IoT intelligent monitoring and control system according to the present invention; Figure 3 This is a schematic diagram illustrating the generation process of the baseline flow sequence and the evolved flow sequence described in this invention; Figure 4 This is a schematic diagram of the global dynamic weight generation process described in this invention; Figure 5 This is a schematic diagram illustrating the calculation process of mutation density and mutation distribution entropy as described in this invention. Detailed Implementation
[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0017] Example 1: Please refer to Figures 1 to 2 This invention provides an IoT-based intelligent monitoring and control system for ultrasonic gas meters, the system comprising the following modules: An ultrasonic gas meter IoT smart monitoring and control system includes: The original sequence construction module is used to construct the original flow sequence of the ultrasonic gas meter at N sampling times; The dual-sequence separation module is used to separate and reassemble the baseline flow sequence and the evolved flow sequence from the original flow sequence; The correlation calculation module is used to calculate the global correlation between the baseline flow sequence and the evolved flow sequence; The relevance comparison module is used to obtain a preset relevance threshold and compare the global relevance with the relevance threshold; The state determination module is used to determine whether the gas flow state is stable if the global correlation is greater than or equal to the correlation threshold, and to generate a valve opening command; otherwise, it determines that the gas flow state is abnormal and generates a state instability command. The state response module is used to calculate the M weighted fluctuation amplitudes between the baseline flow sequence and the evolved flow sequence in response to the state instability command. The mutation acquisition module is used to obtain the mutation density and mutation distribution entropy based on M weighted fluctuation amplitudes. The gas control module is used to control the flow rate of the gas meter based on the mutation density and mutation distribution entropy. Specifically, the flow control performs the following logical control based on predefined rules: If the mutation density is greater than the density threshold and the mutation distribution entropy is less than the entropy threshold, it is determined to be continuous high-frequency turbulence (gas leakage), triggering the valve shut-off command; If the mutation density is greater than the density threshold and the mutation distribution entropy is greater than or equal to the entropy threshold, it is determined to be random noise interference, and the valve remains open. If the mutation density is less than the density threshold, it is determined to be normal gas usage or minor fluctuation, and the valve remains open.
[0018] Example 2: See Figures 3 to 5 The technical solution of this embodiment 2 differs from that of embodiment 1 in that it discloses the specific application steps of each module in embodiment 1.
[0019] Specifically, in this embodiment, the original sequence construction module is used for: S1-1, Control the probe A located at the first end of the pipe axis and the probe B located at the second end of the pipe axis to alternately emit ultrasonic waves; Specifically, probe A and probe B represent transducers installed on both sides of the pipe, with their axes opposite each other, and used to transmit and receive ultrasonic signals. S1-2, the time of downstream transmission and downstream reception of the anchoring ultrasonic wave from probe A to probe B, and the time of upstream transmission and downstream reception of the anchoring ultrasonic wave from probe B to probe A; S1-3. Calculate the time difference between the downstream reception time and the downstream transmission time, as well as between the upstream reception time and the upstream transmission time, to obtain the downstream propagation time and the upstream propagation time. S1-4. Calculate the difference and product of the propagation time with the current and the propagation time against the current to generate a time difference parameter that characterizes the propagation time difference and a time product parameter that characterizes the static sound speed reference. It should be noted that, in the downstream direction, the speed of sound is accelerated by the gas flow, and its downstream propagation time is... for: ; in: L represents the path length of the ultrasonic wave, c represents the speed of sound of the ultrasonic wave in the gas, and v represents the axial velocity of the gas; in the counter-current flow, the speed of sound is slowed down by the airflow, and its counter-current propagation time is... for: ; Therefore, time difference parameter The derivation process is as follows: ; The derivation process of the time-product parameter is as follows: ; Because the axial velocity v of gas in gas pipelines is typically <30 m / s, which is much smaller than the ultrasonic velocity c in natural gas (represented as 400-500 m / s); that is, the axial velocity of gas... Therefore, the square of the axial velocity of the gas Compared to the square of the speed of sound of ultrasound It can be ignored; that is: ; Therefore, the time-product parameter can be approximated as ; In other words, the time-product parameter reflects the static propagation path length and velocity of the sound wave, and is almost unaffected by the axial velocity of the gas flow, making it a stable reference quantity.
[0020] S1-5. Calculate the axial velocity of the gas at the sampling time by performing a ratio calculation between the time difference parameter and the time product parameter, and multiplying the product with the ultrasonic propagation path length. Specifically, by proportionally calculating the time difference parameter to the time product parameter, we can see that: ; Therefore, the axial velocity v of the gas can be calculated by inverse solution: ; Furthermore, the sampling time refers to the time node when the current forward and reverse flow gas flow rate is collected and the flow rate calculation result is output.
[0021] S1-6. Read the preset effective cross-sectional area of the pipe; S1-7. Calculate the product of the effective cross-sectional area of the pipeline and the axial velocity of the gas to obtain the gas flow rate at the sampling time; S1-8. Collect gas flow rate continuously at N sampling times with a fixed sampling period; S1-9. Arrange the N gas flow rates in the order of their sampling times to form the original flow sequence; where the continuous time period covered by the N sampling times is defined as the current monitoring window, and N is a preset even number.
[0022] In this embodiment, based on the characteristic that the gas flow velocity is much smaller than the speed of sound, the product of the propagation time in the forward and reverse directions is constructed as a static reference quantity that is not affected by the flow velocity. Then, the propagation time difference is compared with this static reference quantity to directly calculate the axial flow velocity of the gas that eliminates the interference of sound velocity fluctuations. Furthermore, based on this anti-interference flow velocity and the pipe cross-sectional area, N flow data are continuously collected and arranged in order to ensure that each flow in the original flow sequence has eliminated measurement errors.
[0023] Specifically, in this embodiment, the double sequence separation module is used for: S2-1. Assign monotonically continuous time sequence numbers to the N gas flow rates of the original flow sequence; wherein the initial time sequence number is an odd number 1, and the subsequent time sequence numbers are incremented by 1 in turn; S2-2. From N time sequence numbers, select all odd and even numbers; S2-3. Mark the gas flow rate corresponding to odd-numbered numbers as the baseline flow rate, and the gas flow rate corresponding to even-numbered numbers as the evolution flow rate; Specifically, the baseline flow rate represents the flow rate before the change, which is used to provide a benchmark value for comparison; the evolving flow rate represents the flow rate immediately following the baseline flow rate, which is used to characterize the instantaneous trend of flow rate change.
[0024] S2-4. Based on the ascending order of the time sequence number, rearrange all the baseline traffic and evolved traffic to generate the baseline traffic sequence and evolved traffic sequence respectively; Specifically, the k-th baseline flow and the k-th evolved flow are temporally adjacent in the original sequence, forming a set of sequential correspondences.
[0025] In this embodiment, by sequentially splitting the original flow sequence into odd-numbered baseline flow and even-numbered evolved flow and recombining them, two temporally adjacent flows are transformed into two spatially corresponding sets of sequences. This allows each data point in the baseline flow sequence to form a strict pairing with the data point at the same position in the evolved flow sequence, thereby transforming the dynamic change of flow over time into a static difference relationship between the two sets of sequences.
[0026] Specifically, in this embodiment, the relevance calculation module is used for: S3-1. Reassign unified pairing numbers to the baseline flow sequence and the evolved flow sequence; wherein the pairing number ranges from 1 to M, and M=N / 2; S3-2. Based on the baseline flow and evolved flow corresponding to the pairing sequence number, calculate the global correlation between the baseline flow sequence and the evolved flow sequence; In this embodiment, the global relevance can be selected using the Pearson coefficient, which is calculated as follows: ; Where M = N / 2, representing the number of pairs; k represents the pairing number, ranging from 1 to M; 2K-1 and 2k represent the original time series numbers; when the pairing number is k, the corresponding reference flow is at position 2K-1 in the original sequence (denoted as...). The corresponding evolutionary flux is located at the position of the first element in the original sequence. digit (denoted as) ); This represents the mean of the baseline flow series. R represents the mean of the evolving flow sequence; R represents the global correlation, which characterizes the degree of synchronization of flow change trends at adjacent times. The closer R is to 1, the more stable the fluid state.
[0027] In this embodiment, the time-adjacent baseline flow rate and evolved flow rate are bound into paired data using pairing numbers. By calculating the global correlation between the two sets of sequences, the stability of the fluid state is quantified into a normalized index that is not affected by the absolute flow rate, which can directly reflect the degree of synchronization of the flow rate change trend at adjacent times.
[0028] Specifically, in this embodiment, the state determination module is used for: S5-1. Based on the relevance threshold and global relevance, construct a global dynamic weight that represents the unified adjustment of traffic change trends; S5-2. In the reference flow sequence and the evolved flow sequence, select the reference flow and the evolved flow with the same pairing number, and pair them as flow observation pairs; S5-3. Perform normalized difference operation on the flow observation pair to generate the relative fluctuation amplitude of the flow observation pair; The expression for the relative fluctuation amplitude is: ; in, It represents the absolute change in flow rate. A total flow rate benchmark used to eliminate the influence of the absolute value of the gas flow rate; This represents a small constant to prevent the denominator from being zero; in this embodiment, a value of 10 can be selected. -6 , This represents the relative magnitude of the sudden change in gas flow rate over a very short period of time under the k-th pairing number; S5-4. Multiply the relative fluctuation amplitude with the global dynamic weight to generate a weighted fluctuation amplitude; S5-5. Traverse the baseline flow sequence and evolved flow sequence corresponding to the M paired sequence numbers until M weighted fluctuation amplitudes are generated.
[0029] In this embodiment, by normalizing the flow observation pairs, the interference of the absolute flow value is eliminated, and the relative change amplitude in a very short time is extracted. Then, this amplitude is multiplied by the global dynamic weight that represents the degree of synchronization of the overall trend, so that the generated weighted fluctuation amplitude has the dual attributes of reflecting local instantaneous changes and adapting to the overall state of the gas fluid.
[0030] Furthermore, step S5-1 also includes: S5-1-1. Calculate the difference between the relevance threshold and the global relevance to obtain the relevance offset; Specifically, since the global correlation R is calculated using the Pearson coefficient, its numerical range has been naturally normalized to the [-1,1] interval. Therefore, the correlation offset represents the degree to which the current flow state deviates from the stability threshold.
[0031] S5-1-2. Calculate the ratio of the correlation offset to the global correlation to obtain the instability ratio; Specifically, the instability ratio is used to characterize the degree of deviation of the current flow state from the stable state, and the smaller the global correlation R, the larger the instability ratio.
[0032] S5-1-3. Obtain the preset baseline weights and calculate their product with the instability ratio to obtain the dynamic compensation weights. The benchmark weight value represents the basic adjustment factor when the gas flow state is at critical stability. S5-1-4. Sum the dynamic compensation weights and the baseline weights to generate the global dynamic weights.
[0033] Specifically: When the gas flow state is stable (global correlation R ≈ correlation threshold) With the instability ratio approaching 0, the global dynamic weight is approximately equal to the baseline weight, thus maintaining the basic detection sensitivity. When the gas flow state is abnormal (global correlation R < correlation threshold) The decrease in global correlation as a denominator term leads to a significant increase in the instability ratio. The global dynamic weight is automatically superimposed and amplified on the basis of the benchmark weight, thereby improving the detection sensitivity of small mutation signals. When the gas flow state is highly stable (global correlation R > correlation threshold) The instability ratio is negative, and the global dynamic weight is less than the benchmark weight, thereby moderately suppressing the detection sensitivity and avoiding false triggering due to environmental disturbances.
[0034] Specifically, in this embodiment, the status response module is used for: S6-1. Identify J abrupt change amplitudes among M weighted fluctuation amplitudes; S6-2. Calculate the ratio of J mutation amplitudes to M weighted fluctuation amplitudes to generate the mutation density p of the current monitoring window; S6-3, Anchor the J mutation timestamps corresponding to the J mutation amplitudes; Since each flow observation pair contains two time points (baseline flow time and evolved flow time), a unified time base must be used to accurately calculate the time interval between abrupt events. In this embodiment, the actual sampling time of the evolved flow is always taken as the timestamp of the abrupt event; that is, the 2kth sampling time in the original sequence is taken as the timestamp of the abrupt event. The timestamps of k mutation events are used because the evolution flow represents the latest moment after the state change is completed. Using it as a benchmark can better reflect the time point when the mutation occurred, ensuring that all J timestamps have a unified time dimension.
[0035] S6-4. Based on J mutation timestamps, calculate the mutation distribution entropy of the mutation magnitude within the current monitoring window; The formula for calculating the mutation distribution entropy is: ; Where D is the set of time intervals between adjacent mutation timestamps; Let d be the probability of time interval d occurring in set D; H represents the logarithm operation to the base 2; H represents the mutation distribution entropy on the time axis: the smaller the H value, the more concentrated the mutations (showing persistence), and the larger the H value, the more dispersed the mutations (showing randomness). The calculation process for the mutation distribution entropy is as follows: ① Arrange the J mutation timestamps in chronological order, and then anchor J-1 adjacent mutation timestamps in turn; ② For each adjacent mutation timestamp, calculate the corresponding time interval, and obtain a total of J-1 time intervals, thus forming a set D of time intervals; ③ Calculate the probability of occurrence of each different time interval in set D; ④ Perform a logarithmic operation with the probability of occurrence to the base 2 to obtain the logarithmic probability value; ⑤ Multiply the logarithm of the probability by its probability of occurrence to obtain the distribution characteristic value of the time interval type; ⑥ Sum the distribution characteristic values of all time interval types and take the negative number to obtain the final mutation distribution entropy; Specifically, in the low-entropy case (small H): if a gas leak occurs, the abrupt changes are often continuous and regular (e.g., an abrupt change occurs every sampling period), and the time intervals are mostly equal (e.g., all equal to the sampling period ΔT). This means that there are only one or a very few interval values in the set D, and the probability distribution is highly concentrated (a certain p≈1, the rest≈0), and the calculated entropy threshold H approaches 0, representing "order and continuity".
[0036] High entropy case (large H): If the interference is random noise, the timing of mutations is chaotic, with varying time intervals and a highly dispersed distribution. In this case, the probability distribution tends to be uniform, and the calculated entropy threshold H is large. This represents "disorder and randomness".
[0037] Furthermore, step S6-1 also includes: S6-1-1, Obtain the set benchmark fluctuation threshold; S6-1-2. Compare the M weighted fluctuation amplitudes with the benchmark fluctuation threshold respectively; S6-1-3. If the weighted volatility amplitude is greater than the benchmark volatility threshold, the corresponding weighted volatility amplitude will be marked as the sudden change amplitude. S6-1-4. Traverse the M weighted fluctuation amplitudes and identify the J labeled mutation amplitudes.
[0038] In this embodiment, based on the benchmark fluctuation threshold as a "sieve", the M weighted fluctuation amplitudes after global dynamic weight correction are screened one by one; only the weighted fluctuation amplitudes exceeding the threshold are marked as abrupt change amplitudes, thereby locating J flow abrupt change amplitudes with significant abnormal characteristics.
[0039] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are 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 a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, 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., infrared, wireless, microwave, etc.) means.
[0040] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0041] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0042] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.
Claims
1. An intelligent monitoring and control system for ultrasonic gas meters via the Internet of Things, characterized in that, include: The original sequence construction module is used to construct the original flow sequence of the ultrasonic gas meter at N sampling times; The dual-sequence separation module is used to separate and reassemble the baseline flow sequence and the evolved flow sequence from the original flow sequence; The correlation calculation module is used to calculate the global correlation between the baseline flow sequence and the evolved flow sequence; The relevance comparison module is used to obtain a preset relevance threshold and compare the global relevance with the relevance threshold; Status determination module; If the global correlation is greater than or equal to the correlation threshold, the gas flow state is determined to be stable, and a valve opening command is generated; otherwise, the gas flow state is determined to be abnormal, and a state instability command is generated. The state response module is used to calculate the M weighted fluctuation amplitudes between the baseline flow sequence and the evolved flow sequence in response to the state instability command. The mutation acquisition module is used to obtain the mutation density and mutation distribution entropy based on M weighted fluctuation amplitudes. The gas control module is used to control the flow rate of the gas meter based on the mutation density and mutation distribution entropy.
2. The ultrasonic gas meter IoT intelligent monitoring and control system according to claim 1, characterized in that, The original sequence construction module is specifically used for: S1-1, Control the probe A located at the first end of the pipe axis and the probe B located at the second end of the pipe axis to alternately emit ultrasonic waves; S1-2, the time of downstream transmission and downstream reception of the anchoring ultrasonic wave from probe A to probe B, and the time of upstream transmission and downstream reception of the anchoring ultrasonic wave from probe B to probe A; S1-3. Calculate the time difference between the downstream reception time and the downstream transmission time, as well as between the upstream reception time and the upstream transmission time, to obtain the downstream propagation time and the upstream propagation time. S1-4. Calculate the difference and product of the propagation time with the current and the propagation time against the current to generate a time difference parameter that characterizes the propagation time difference and a time product parameter that characterizes the static sound speed reference. S1-5. Calculate the axial velocity of the gas at the sampling time by performing a ratio calculation between the time difference parameter and the time product parameter, and multiplying the product with the ultrasonic propagation path length. S1-6. Read the preset effective cross-sectional area of the pipe; S1-7. Calculate the product of the effective cross-sectional area of the pipeline and the axial velocity of the gas to obtain the gas flow rate at the sampling time; S1-8. Collect gas flow rate continuously at N sampling times with a fixed sampling period; S1-9. Arrange the N gas flow rates in the order of their sampling times to form the original flow sequence; where the continuous time period covered by the N sampling times is defined as the current monitoring window, and N is a preset even number.
3. The ultrasonic gas meter IoT intelligent monitoring and control system according to claim 1, characterized in that, The dual-sequence separation module is specifically used for: S2-1. Assign monotonically continuous time sequence numbers to the N gas flow rates of the original flow sequence; wherein the initial time sequence number is an odd number 1, and the subsequent time sequence numbers are incremented by 1 in turn; S2-2. From N time sequence numbers, select all odd and even numbers; S2-3. Mark the gas flow rate corresponding to odd-numbered numbers as the baseline flow rate, and the gas flow rate corresponding to even-numbered numbers as the evolution flow rate; S2-4. Based on the ascending order of the time sequence number, rearrange all the baseline traffic and evolved traffic to generate the baseline traffic sequence and evolved traffic sequence respectively.
4. The ultrasonic gas meter IoT intelligent monitoring and control system according to claim 1, characterized in that, The correlation calculation module is specifically used for: S3-1. Reassign unified pairing numbers to the baseline flow sequence and the evolved flow sequence; wherein the pairing number ranges from 1 to M, and M=N / 2; S3-2. Based on the baseline flow and evolved flow corresponding to the pairing sequence number, calculate the global correlation between the baseline flow sequence and the evolved flow sequence.
5. The ultrasonic gas meter IoT intelligent monitoring and control system according to claim 1, characterized in that, The state determination module is specifically used for: S5-1. Based on the relevance threshold and global relevance, construct a global dynamic weight that represents the unified adjustment of traffic change trends; S5-2. In the reference flow sequence and the evolved flow sequence, select the reference flow and the evolved flow with the same pairing number, and pair them as flow observation pairs; S5-3. Perform normalized difference operation on the flow observation pair to generate the relative fluctuation amplitude of the flow observation pair; S5-4. Multiply the relative fluctuation amplitude with the global dynamic weight to generate a weighted fluctuation amplitude; S5-5. Traverse the baseline flow sequence and evolved flow sequence corresponding to the M paired sequence numbers until M weighted fluctuation amplitudes are generated.
6. The ultrasonic gas meter IoT intelligent monitoring and control system according to claim 5, characterized in that, Based on relevance thresholds and global relevance, a global dynamic weight is constructed to characterize the unified adjustment of traffic change trends, including: S5-1-1. Calculate the difference between the relevance threshold and the global relevance to obtain the relevance offset; S5-1-2. Calculate the ratio of the correlation offset to the global correlation to obtain the instability ratio; S5-1-3. Obtain the preset baseline weights and calculate their product with the instability ratio to obtain the dynamic compensation weights. S5-1-4. Sum the dynamic compensation weights and the baseline weights to generate the global dynamic weights.
7. The ultrasonic gas meter IoT intelligent monitoring and control system according to claim 1, characterized in that, The status response module is specifically used for: S6-1. Identify J abrupt change amplitudes among M weighted fluctuation amplitudes; S6-2. Calculate the ratio of J mutation amplitudes to M weighted fluctuation amplitudes to generate the mutation density p of the current monitoring window; S6-3, Anchor the J mutation timestamps corresponding to the J mutation amplitudes; S6-4. Based on J mutation timestamps, calculate the mutation distribution entropy of the mutation magnitude within the current monitoring window.
8. The ultrasonic gas meter IoT intelligent monitoring and control system according to claim 7, characterized in that, Among the M weighted fluctuation amplitudes, J abrupt change amplitudes were identified, including: S6-1-1, Obtain the set benchmark fluctuation threshold; S6-1-2. Compare the M weighted fluctuation amplitudes with the benchmark fluctuation threshold respectively; S6-1-3. If the weighted volatility amplitude is greater than the benchmark volatility threshold, the corresponding weighted volatility amplitude will be marked as the sudden change amplitude. S6-1-4. Traverse the M weighted fluctuation amplitudes and identify the J labeled mutation amplitudes.