A smart gateway communication method, system, and device for edge computing.

Abstract

This invention relates to the field of data transmission technology, specifically to a smart gateway communication method, system, and device for edge computing. The method includes: acquiring several flow velocity detection mode data segments based on the turbulent fluctuations in flow velocity data from a radar-ultrasonic multi-functional flow meter in response to sudden changes in water flow velocity; determining the degree of constraint on the true flow velocity information by comparing the actual fluctuations with the ideal simulated fluctuations within each flow velocity detection mode data segment based on the smoothness of the flow velocity data changes within that segment; and further acquiring trend interference when the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch between each other, thereby enabling smart gateway communication with the radar-ultrasonic multi-functional flow meter. This invention improves the accuracy of data prediction, thereby increasing communication efficiency.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and more specifically to a smart gateway communication method, system, and device for edge computing. Background Technology

[0002] In enclosed, humid, corrosive, and flood-prone environments such as drainage pipe networks, drainage outlets, sluice gate pumping stations, and waterways, radar ultrasonic multi-functional flow meters are widely used due to their advantages of high precision, low power consumption, corrosion resistance, wide applicability, and ease of installation. In these environments, the data transmission requirements for these radar ultrasonic multi-functional flow meters are high response, low latency, and the ability to continue data caching and operation even during network outages without manual monitoring or adjustment. Edge computing, on the other hand, is a distributed computing architecture that shifts data transmission capabilities from centralized cloud data centers to closer locations than the data source. Its core idea is to "process data near the data source," rather than transmitting all data to a distant cloud for processing, which better meets the data transmission needs of intelligent sensing devices. Therefore, edge computing technology is widely used in intelligent sensing devices to achieve intelligent gateway communication.

[0003] When the radar ultrasonic multi-functional flow meter loses network access, it will pause the actual detection of the corresponding data. At this time, the corresponding edge computing architecture will usually use the Holt-Winters algorithm (triple exponential smoothing) to predict future data and cache the predicted data on the edge server storage layer in the edge computing architecture. Then, the predicted data will be sent to the radar ultrasonic multi-functional flow meter, so that the radar ultrasonic multi-functional flow meter can continue to perform data simulation detection on the surface. When the radar ultrasonic multi-functional flow meter restores network communication, it can automatically re-transmit the data to the cloud to ensure data integrity.

[0004] The radar ultrasonic multi-functional flow meter has two detection modes for detecting water flow velocity: ultrasonic detection and radar detection. These two modes automatically switch depending on the environment in which the flow meter is located. For example, when detecting water flow in a drain pipe, the flow meter uses radar detection mode when the pipe is not full and ultrasonic detection mode when the pipe is full. In real-world scenarios, when the flow meter automatically switches between these two detection modes, the differences in data acquisition between them inevitably lead to variations in the data. This causes interference with the existing Holt-Winters algorithm (triple exponential smoothing) when predicting data based on data collected from both detection modes before network outages. This reduces the accuracy of the predictions and consequently reduces the integrity of the data automatically transmitted to the cloud after the flow meter regains network communication. Summary of the Invention

[0005] This invention provides a smart gateway communication method, system, and device for edge computing to solve the existing problem: When a radar ultrasonic multi-function flow meter automatically switches detection modes to detect water flow velocity, the incompatibility between the detection modes causes the flow velocity data collected by the radar ultrasonic multi-function flow meter to contain some incompatible factors between the detection modes. This causes the existing Holt-Winters algorithm (triple exponential smoothing method) to interfere with the analysis of trend changes when making data predictions based on data collected from two detection modes before the network outage, thus reducing the accuracy of data prediction.

[0006] The present invention provides a smart gateway communication method for edge computing, which adopts the following technical solution:

[0007] Includes the following steps:

[0008] Obtain the real environmental flow velocity sequence of the radar ultrasonic multi-functional flow meter before network disconnection; the real environmental flow velocity sequence contains several flow velocity data.

[0009] When the radar ultrasonic multi-function flow meter operates in different flow velocity detection modes, the fluctuation and disorder of the flow velocity data when responding to sudden changes in water flow velocity is used as the basis for sequence segmentation. The real environmental flow velocity sequence is divided into several radar flow velocity detection mode data segments and ultrasonic flow velocity detection mode data segments; each of the radar flow velocity detection mode data segments and the ultrasonic flow velocity detection mode data segments is a flow velocity detection mode data segment.

[0010] This study analyzes the smoothness of flow velocity data changes within each flow velocity detection mode data segment of a radar-ultrasonic multi-functional flowmeter when operating different flow velocity detection modes. The degree of difference between the actual fluctuation of flow velocity data and the ideal simulated fluctuation within each flow velocity detection mode data segment is used as the degree of limitation on the true flow velocity information of each flow velocity detection mode data segment. When the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch between each other, based on the degree of limitation on the true flow velocity information, the degree of interference on the true trend when the flow velocity is compatible between different flow velocity detection modes is used as the trend interference when the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch between each other.

[0011] Based on the trend of interference, intelligent gateway communication is implemented for radar ultrasonic multi-functional flow meters.

[0012] Preferably, the method for acquiring the flow velocity detection mode data segment is as follows:

[0013] In a real-world flow velocity sequence, the data segment where the flow velocity continuously changes in the same direction is taken as the unidirectional flow velocity fluctuation segment; any unidirectional flow velocity fluctuation segment is taken as the target unidirectional flow velocity fluctuation segment, and each unidirectional flow velocity fluctuation segment before the target unidirectional flow velocity fluctuation segment is taken as the historical unidirectional flow velocity fluctuation segment of the target unidirectional flow velocity fluctuation segment; by comprehensively comparing the rate of change of flow velocity between adjacent historical unidirectional flow velocity fluctuation segments, the historical flow velocity fluctuation of the target unidirectional flow velocity fluctuation segment is obtained.

[0014] Based on historical flow velocity fluctuations, the real-world flow velocity sequence is divided into several radar flow velocity detection mode data segments and ultrasonic flow velocity detection mode data segments; and each radar flow velocity detection mode data segment and ultrasonic flow velocity detection mode data segment are collectively referred to as a flow velocity detection mode data segment.

[0015] Preferably, the method for obtaining the historical flow velocity fluctuation is as follows:

[0016] Obtain the fluctuation rate of flow velocity data within each historical unidirectional fluctuation segment; accumulate the product of the difference in fluctuation rate between adjacent historical unidirectional fluctuation segments and the historical unidirectional fluctuation segment, and use this as the historical flow velocity volatility of the target flow velocity unidirectional fluctuation segment.

[0017] Preferably, the method for obtaining the degree of limitation of the actual flow velocity information is as follows:

[0018] Take any ultrasonic flow velocity detection mode data segment as the target ultrasonic detection mode data segment; obtain the delay hazard data segment and the sudden change response data segment of the target ultrasonic detection mode data segment; compare the flow velocity data in the delay hazard data segment and the sudden change response data segment with the flow velocity data under the ideal simulation state to obtain the degree of limitation of the true flow velocity information of the target ultrasonic detection mode.

[0019] Take any radar flow velocity detection mode data segment as the target radar detection mode data segment; analyze the difference between the actual fluctuation and the ideal simulated fluctuation of the flow velocity data in the target radar detection mode data segment to obtain the degree of limitation of the true flow velocity information of the target radar detection mode.

[0020] Preferably, the method for obtaining the delay risk data segment and the sudden change response data segment is as follows:

[0021] In the target ultrasonic detection mode data segment, the data segment with continuous and obvious flow velocity changes is designated as the ultrasonic flow velocity sudden change data segment; the first flow velocity data in the ultrasonic flow velocity sudden change data segment is designated as the dividing point, and the data segments composed of a preset number of flow velocity data on the left and right sides of the dividing point are designated as the delay hidden danger data segment and the sudden change response data segment, respectively.

[0022] Preferably, the method for obtaining the trend interference is as follows:

[0023] When switching between radar flow velocity detection mode data segment and ultrasonic flow velocity detection mode data segment, based on the limitation of the actual flow velocity information, the degree of interference of the incompatibility of the radar and ultrasonic multi-functional flow meter when automatically switching flow velocity detection modes on the overall flow velocity is analyzed, and the overall trend interference degree when switching between radar flow velocity detection mode data segment and ultrasonic flow velocity detection mode data segment is obtained.

[0024] By comparing the interference differences of different flow velocity detection modes on their respective flow velocity information when the radar and ultrasonic multi-functional flow meters automatically switch flow velocity detection modes, the incompatibility of the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment when switching between them is obtained.

[0025] By combining the overall trend interference with docking incompatibility, the trend interference when the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch between each other is obtained.

[0026] Preferably, the method for obtaining the overall trend interference degree is as follows:

[0027] The cumulative mapping of the degree of limitation on the true flow velocity information between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment is used as the overall trend interference degree when switching between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment.

[0028] Preferably, the method for obtaining the docking incompatibility is as follows:

[0029] The difference in the degree of limitation of the actual flow velocity information between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment is used as the interoperability incompatibility when switching between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment.

[0030] A smart gateway communication system for edge computing includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0031] A smart gateway communication device for edge computing, the device comprising:

[0032] The system comprises a real-environment flow velocity sequence acquisition module, a trend interference acquisition module, and a smart gateway communication module. The real-environment flow velocity sequence acquisition module acquires the real-environment flow velocity sequence of the radar ultrasonic multi-function flow meter before network disconnection. The trend interference acquisition module obtains the trend interference by calling a computer program to implement the steps of a smart gateway communication method for edge computing. The smart gateway communication module then performs smart gateway communication with the radar ultrasonic multi-function flow meter based on the trend interference.

[0033] The beneficial effects of the technical solution of this invention are as follows: This invention uses the fluctuation and disorder of flow velocity data in response to sudden changes in water flow velocity by a radar ultrasonic multi-functional flow meter as the basis for sequence segmentation, dividing several flow velocity detection mode data segments from the real environmental flow velocity sequence; it solves the problem of ambiguous classification of the collected flow velocity data caused by the automatic switching of flow velocity detection modes by the radar ultrasonic multi-functional flow meter; then it analyzes the degree of closeness between the actual change and fluctuation of flow velocity data within different flow velocity detection mode data segments and the ideal simulated change and fluctuation, and obtains the degree of limitation of the true flow velocity information of the corresponding flow velocity detection mode data segment; it clarifies the interference of the radar ultrasonic multi-functional flow meter on the true information of the trend of the collected flow velocity data when operating different flow velocity detection modes; then, based on the degree of limitation of the true flow velocity information, it analyzes the degree of interference on the true trend when the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch between each other, and obtains the trend interference. This invention distinguishes the flow velocity data segments corresponding to different flow velocity detection modes of a radar ultrasonic multi-functional flow meter. It further analyzes the incompatible interference caused by mode switching when the flow meter automatically switches between different flow velocity detection modes, obtaining trend interference information. This allows for intelligent gateway communication with the radar ultrasonic multi-functional flow meter, resulting in more accurate trend representation of the flow velocity data collected by the flow meter, improved data prediction accuracy, and ultimately, increased communication efficiency. Attached Figure Description

[0034] 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.

[0035] Figure 1 This is a flowchart illustrating the steps of a smart gateway communication method for edge computing according to the present invention.

[0036] Figure 2 This is a schematic diagram of the radar ultrasonic multifunctional flow meter of the present invention;

[0037] Figure 3 This is a simplified flowchart illustrating the application of edge computing technology in the radar ultrasonic multifunctional flow meter of the present invention. Detailed Implementation

[0038] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a smart gateway communication method, system, and device for edge computing proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0040] The following description, in conjunction with the accompanying drawings, details a specific solution for an intelligent gateway communication method, system, and device for edge computing provided by the present invention.

[0041] Please see Figure 1 The diagram illustrates a flowchart of a smart gateway communication method for edge computing according to an embodiment of the present invention, the method comprising the following steps:

[0042] Step S001: Obtain the real environmental flow velocity sequence of the radar ultrasonic multi-functional flow meter before the network is disconnected; the real environmental flow velocity sequence contains several flow velocity data.

[0043] It should be noted that the radar ultrasonic multi-functional flow meter has two detection modes for detecting water flow velocity: ultrasonic detection and radar detection. These two modes automatically switch depending on the environment in which the flow meter is located. For example, when detecting water flow in a drain pipe, the flow meter uses radar detection mode when the pipe is not full and ultrasonic detection mode when the pipe is full. In real-world scenarios, when the flow meter automatically switches between detection modes for water flow velocity detection, the differences in data acquisition between the two modes will inevitably lead to variations in the data collected under each mode. This causes interference in the trend analysis of the existing Holt-Winters algorithm (triple exponential smoothing method) when predicting data based on data collected from both detection modes before the network outage. This reduces the accuracy of data prediction and consequently reduces the integrity of the data automatically transmitted to the cloud after the flow meter regains network communication.

[0044] In one specific implementation of this invention, the method for obtaining the real-world flow velocity sequence is as follows: a radar ultrasonic multi-functional flow meter is installed inside a drainage pipe, and a preset detection period of several days is established. Data recording frequency Wireless network connectivity detection Heaven, with The frequency of recording liquid level data and flow rate data is used; the sequence of the acquired flow rate data arranged in order of acquisition time from early to late is used as the real environmental flow rate sequence of the radar ultrasonic multifunctional flow meter before the network is disconnected.

[0045] It should be noted that, in this embodiment, the following is used: , This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0046] It should also be noted that the radar-ultrasonic multifunctional flow meter in this embodiment combines radar and ultrasonic technologies, highly integrating radar flow meter, radar water level gauge, and ultrasonic flow meter modules to achieve flow measurement in all scenarios, including full-pipe and non-full-pipe systems. Its working principle can be briefly described as follows: the radar-ultrasonic multifunctional flow meter measures flow velocity and water level by emitting high-frequency radar waves or ultrasonic pulses and receiving signals emitted from the fluid surface. Then, combined with cross-sectional information of culverts, box culverts, or pipelines and a pipe network algorithm model, it calculates the fluid's flow velocity, flow rate, and water level. Please refer to [link to relevant documentation]. Figure 2 The diagram shows a physical schematic of a radar ultrasonic multi-functional flow meter.

[0047] Thus, the actual environmental flow velocity sequence of the radar ultrasonic multi-functional flow meter before the network outage was obtained through the above method.

[0048] Step S002: When the radar ultrasonic multi-function flow meter operates in different flow velocity detection modes, the fluctuation and disorder of the flow velocity data when responding to sudden changes in water flow velocity is used as the basis for sequence segmentation. The real environmental flow velocity sequence is divided into several radar flow velocity detection mode data segments and ultrasonic flow velocity detection mode data segments; each of the radar flow velocity detection mode data segments and the ultrasonic flow velocity detection mode data segments is a flow velocity detection mode data segment.

[0049] It should be noted that, under normal circumstances, when the radar ultrasonic multi-functional flow meter loses network access, the flow meter, as a terminal device, uploads access logs to the edge gateway deployed near the terminal device. The edge gateway uses the various detection data collected during the network outage as a basis for prediction, employing the Holt-Winters algorithm (triple exponential smoothing) to predict the data after the network outage. At this point, the predicted data is virtual data; that is, the predicted data is not actually collected through environmental detection but is merely a simulation. Then, the decision-maker sends a caching instruction to the edge server storage layer, temporarily caching the predicted data there and returning the cached content to the terminal device to ensure the integrity of the data recorded by the terminal device. Once the terminal device regains network communication, it intelligently selects key data to re-upload to the cloud for storage. Please refer to [link to relevant documentation]. Figure 3 It shows a simplified flowchart of the application of edge computing technology in radar ultrasonic multi-function flow meters.

[0050] It should be further explained that in the above process, the radar ultrasonic multi-functional flow meter uses two detection modes when detecting water flow velocity: ultrasonic detection and radar detection. Due to differences in the sensors used to collect data, their sensitivity, and the methods by which the built-in chip calculates the corresponding flow velocity, the flow velocity data acquired by these two modes retains certain characteristics of these devices, and these characteristics differ somewhat. Furthermore, the radar ultrasonic multi-functional flow meter automatically switches detection modes according to the actual environment, causing the flow velocity data acquired before the network outage to contain varying lengths of device characteristics. The existing Holt-Winters algorithm (triple exponential smoothing method), when analyzing the overall flow velocity data acquired before the network outage, primarily focuses on three aspects: horizontality, trend changes, and seasonal factors. In real-world scenarios, the network outage of radar ultrasonic multi-functional flow meters is usually short-term, so it has a relatively weak impact on the algorithm's seasonal factor analysis. However, since the distribution of equipment factor characteristics in the overall flow velocity data acquired before the network outage mainly changes the expression of the trend of change within local data segments, the automatic switching of detection modes by radar ultrasonic multi-functional flow meters according to the actual environment will mainly cause significant interference to the algorithm when performing trend change analysis.

[0051] Furthermore, it needs to be clarified that the flow velocity data collected by the radar ultrasonic multi-functional flow meter before network disconnection is obtained through automatic switching of detection modes. The edge gateway receives the entire flow velocity data before the network disconnection. Since the device controlling the switching of detection modes is a processing flow independent of the data acquisition process, the switching of detection modes does not specifically identify the flow velocity data. Therefore, it is necessary to identify the detection mode of the radar ultrasonic multi-functional flow meter based on the actual environmental flow velocity sequence before network disconnection. In a real environment, when the pipe is not full, the radar ultrasonic multi-functional flow meter will activate the radar detection mode, making the radar flow velocity probe active while the ultrasonic flow velocity probe stops. When the pipe is full, the radar ultrasonic multi-functional flow meter will activate the ultrasonic detection mode, making the ultrasonic flow velocity probe active while the radar flow velocity probe stops. The flow velocity data detected by radar is essentially the surface point velocity, while the flow velocity data detected by ultrasonic detection is essentially the cross-sectional average velocity. This makes radar detection susceptible to environmental changes such as wind, raindrops, and floating objects, resulting in high fluctuations in the collected flow velocity data. Ultrasonic detection, on the other hand, is less susceptible to environmental changes and thus less prone to high fluctuations in the collected flow velocity data because it occurs within the water body. Therefore, when the radar ultrasonic multi-function flow meter operates in different flow velocity detection modes, the fluctuation and disorder of the flow velocity data when responding to sudden changes in water flow velocity can be used as the basis for dividing the sequence into segments. The real environmental flow velocity sequence can be divided into several radar flow velocity detection mode data segments and ultrasonic flow velocity detection mode data segments.

[0052] Preferably, in some implementations of the present invention, the method for obtaining the flow velocity detection mode data segment is as follows: In the real environment flow velocity sequence, the data segment where the flow velocity continuously changes in the same direction is taken as the flow velocity unidirectional fluctuation segment; any one flow velocity unidirectional fluctuation segment is taken as the target flow velocity unidirectional fluctuation segment, and each flow velocity unidirectional fluctuation segment before the target flow velocity unidirectional fluctuation segment is taken as the historical flow velocity unidirectional fluctuation segment of the target flow velocity unidirectional fluctuation segment; by comprehensively comparing the rate of change of flow velocity between adjacent historical flow velocity unidirectional fluctuation segments, the historical flow velocity fluctuation of the target flow velocity unidirectional fluctuation segment is obtained; based on the historical flow velocity fluctuation, the real environment flow velocity sequence is divided into several radar flow velocity detection mode data segments and ultrasonic flow velocity detection mode data segments; and each radar flow velocity detection mode data segment and ultrasonic flow velocity detection mode data segment are collectively referred to as one flow velocity detection mode data segment. The specific process is as follows:

[0053] In a real-world flow velocity sequence, the data segment where the flow velocity data continuously changes in the same direction is defined as a unidirectional flow velocity fluctuation segment. Any given unidirectional flow velocity fluctuation segment is designated as the target unidirectional flow velocity fluctuation segment, and each preceding unidirectional flow velocity fluctuation segment is considered a historical unidirectional flow velocity fluctuation segment of the target segment. Within each unidirectional flow velocity fluctuation segment, the flow velocity data changes in only one direction: either increasing in the positive direction or decreasing in the negative direction.

[0054] Preferably, in some implementations of the present invention, the method for obtaining historical flow velocity volatility is as follows: obtaining the fluctuation rate of flow velocity data within each historical unidirectional flow velocity fluctuation segment; accumulating the product of the difference in fluctuation rate between adjacent historical unidirectional flow velocity fluctuation segments and the historical unidirectional flow velocity fluctuation segment, as the historical flow velocity volatility of the target flow velocity unidirectional flow velocity fluctuation segment. The specific process is as follows:

[0055] Taking any two adjacent velocity data points within a single unidirectional velocity fluctuation segment as an example, the difference between the second and first velocity data points is taken as the velocity change of the second velocity data point; the average velocity change of all velocity data points within the unidirectional velocity fluctuation segment is taken as the average velocity change of that segment; and the ratio of the average velocity change to the duration of the unidirectional velocity fluctuation segment is taken as the fluctuation rate of the velocity data within that segment. The fluctuation rate of the velocity data within each unidirectional velocity fluctuation segment is then obtained.

[0056] It should be noted that in this embodiment, the flow rate change of the first flow rate data in each unidirectional flow rate fluctuation segment is set to 0 by default.

[0057] Furthermore, as an example, historical flow velocity volatility can be calculated using the following formula:

[0058]

[0059] In the formula, This indicates the historical velocity variability of the target velocity unidirectional fluctuation segment; This indicates the number of all historical unidirectional flow velocity fluctuation segments in the target flow velocity fluctuation segment. Indicates the first The fluctuation rate of flow velocity data within a historical unidirectional fluctuation segment. Indicates the first The fluctuation rate of flow velocity data within a historical unidirectional fluctuation segment. Indicates taking the absolute value; This represents the normalization function, used to normalize the historical velocity fluctuations of all unidirectional velocity fluctuation segments.

[0060] It should be noted that the greater the historical flow velocity fluctuation, the more frequent the flow velocity data collected before that unidirectional flow velocity fluctuation segment was, reflecting that the flow velocity data collected before the target flow velocity unidirectional fluctuation segment was more likely to have been collected by the radar ultrasonic multi-function flow meter in radar detection mode.

[0061] Furthermore, the historical velocity volatility of each unidirectional velocity fluctuation segment is obtained. A preset historical velocity volatility threshold is used. In real-world flow velocity sequences, the historical flow velocity fluctuations are continuously greater than [a certain value]. The data segment consisting of the entire unidirectional fluctuation segment of the flow velocity is used as the radar flow velocity detection mode data segment; the historical flow velocity fluctuation is continuously less than or equal to The data segment consisting of the unidirectional velocity fluctuation segments is designated as the ultrasonic flow velocity detection mode data segment. Each radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment are collectively referred to as a flow velocity detection mode data segment. Each flow velocity detection mode data segment contains multiple unidirectional velocity fluctuation segments.

[0062] It should be noted that in this embodiment, the historical velocity fluctuation of the first unidirectional velocity fluctuation segment is preset to 0 by default. This embodiment uses... This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0063] It should be noted that in real-world flow velocity sequences, radar flow velocity detection mode data segments and ultrasonic flow velocity detection mode data segments will appear alternately, and the number of flow velocity data contained in the two segments is not necessarily the same.

[0064] Thus, several flow velocity detection mode data segments were obtained through the above method.

[0065] Step S003: Analyze the smoothness of flow velocity data changes within each flow velocity detection mode data segment when the radar ultrasonic multi-functional flow meter is running in different flow velocity detection modes. The degree of difference between the actual fluctuation of flow velocity data and the ideal simulated fluctuation within each flow velocity detection mode data segment is used as the degree of limitation of the true flow velocity information for each flow velocity detection mode data segment. When the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch between each other, based on the degree of limitation of the true flow velocity information, the degree of interference to the true trend when the flow velocity is compatible between different flow velocity detection modes is used as the trend interference when the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch between each other.

[0066] It should be noted that the ultrasonic detection mode and radar detection mode of the radar ultrasonic multi-functional flow meter have different device characteristics in terms of flow velocity retention, resulting in different levels of interference with the true flow velocity information. Furthermore, since data trends are primarily represented by the dynamic states between data points, the automatic switching between ultrasonic and radar detection modes, and the continuous acquisition of flow velocity, will inevitably lead to a certain degree of incompatibility during the interoperability of the two modes. This will cause fluctuations in flow velocity within corresponding stages, thus interfering with the Holt-Winters algorithm's accurate trend assessment.

[0067] It should be further noted that the output of flow velocity data in radar detection mode is basically an instantaneous value; while the output of flow velocity data in ultrasonic detection mode is basically a value calculated by the built-in chip. Compared with the instantaneous value mentioned above, ultrasonic detection mode is relatively slower in terms of data response. Therefore, when the water flow velocity suddenly increases in a real environment, radar detection mode can almost instantly capture the sudden increase in flow velocity, while ultrasonic detection mode will take several seconds to capture the sudden increase in flow velocity, resulting in a relatively smoother change in flow velocity data in ultrasonic detection mode.

[0068] Furthermore, it should be noted that the flow velocity data in radar detection mode is essentially the surface point velocity (local point at the gas-liquid interface); while the flow velocity data in ultrasonic detection mode is essentially the cross-sectional average velocity (the sound path penetrates the entire fluid). Comparing the detection paths of these two detection modes in detecting water flow velocity, it can be seen that radar detection mode is more susceptible to data interference. Therefore, in real-world environments, when the water surface is affected by environmental factors such as wind and raindrops, radar detection mode will frequently exhibit irregular flow velocity fluctuations, while ultrasonic detection mode will generally not have such issues. In summary, whether switching from radar detection mode to ultrasonic detection mode or vice versa, the flow velocity data collected by the radar-ultrasonic multi-function flowmeter will approximate the flow velocity data under simulated smooth conditions. Therefore, we can analyze the smoothness of flow velocity data changes within each flow velocity detection mode data segment when the radar-ultrasonic multi-function flowmeter operates in different flow velocity detection modes. The degree of difference between the actual fluctuation of flow velocity data within each flow velocity detection mode data segment and the ideal simulated fluctuation can be used as the degree of limitation on the true flow velocity information of each flow velocity detection mode data segment. When the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch to each other, based on the degree of limitation on the true flow velocity information, the degree of interference on the true trend when the flow velocity is compatible between different flow velocity detection modes can be used as the trend interference when the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch to each other.

[0069] Preferably, in some implementations of the present invention, the method for obtaining the degree of limitation of the true flow velocity information is as follows: Any ultrasonic flow velocity detection mode data segment is taken as the target ultrasonic detection mode data segment; the delay vulnerability data segment and the sudden change response data segment of the target ultrasonic detection mode data segment are obtained; the difference between the flow velocity data in the delay vulnerability data segment and the sudden change response data segment and the flow velocity data under ideal simulation is compared to obtain the degree of limitation of the true flow velocity information of the target ultrasonic detection mode; any radar flow velocity detection mode data segment is taken as the target radar detection mode data segment; the difference between the actual fluctuation and the ideal simulation fluctuation of the flow velocity data in the target radar detection mode data segment is analyzed to obtain the degree of limitation of the true flow velocity information of the target radar detection mode. The specific process is as follows:

[0070] Preferably, in some implementations of the present invention, the method for obtaining the delay hazard data segment and the sudden change response data segment is as follows: In the target ultrasonic detection mode data segment, the data segment with continuous and obvious flow velocity changes is taken as the ultrasonic flow velocity sudden change data segment; the first flow velocity data in the ultrasonic flow velocity sudden change data segment is taken as the dividing point, and the data segments composed of a preset number of flow velocity data on both sides of the dividing point are respectively taken as the delay hazard data segment and the sudden change response data segment. The specific process is as follows:

[0071] Taking any two adjacent flow velocity data points in the target ultrasonic detection mode data segment as an example, the absolute value of the difference between the second flow velocity data point and the first flow velocity data point is taken as the flow velocity change of the second flow velocity data point; the flow velocity change of all flow velocity data points is then obtained. A preset flow velocity change threshold is established. The change in flow velocity is continuously greater than The data segments consisting of flow velocity data are used as ultrasonic flow velocity abrupt change data segments; all ultrasonic flow velocity abrupt change data segments in the target ultrasonic detection mode data segment are acquired. Each ultrasonic detection mode data segment may contain multiple ultrasonic flow velocity abrupt change data segments.

[0072] It should be noted that the velocity change of the first velocity data in the target ultrasonic detection mode data segment is preset to 1 by default in this embodiment. Additionally, this embodiment uses... This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0073] Furthermore, taking any one of the ultrasonic velocity change data segments in the target ultrasonic detection mode data segment as an example, a preset threshold for the number of velocity data is set. Centered on the first velocity data in the ultrasonic velocity change data segment, the left side... The data segment consisting of flow velocity data is used as the data segment with potential delay risks, and the right side is used as the data segment. The data segment consisting of flow velocity data serves as the sudden change response data segment. Each ultrasonic detection mode data segment corresponds to a delay potential data segment and a sudden change response data segment.

[0074] It should be noted that if the actual number of velocity data points on both sides of the first velocity data point in the ultrasonic velocity change data segment does not meet the preset limit... If the actual number of flow velocity data on both sides of the first flow velocity data in the ultrasonic flow velocity sudden change data segment is used as the standard, the delay potential data segment and the sudden change response data segment are obtained.

[0075] Furthermore, the curve obtained by curve fitting the delayed hazard data segment is taken as the delayed hazard fitting curve; the absolute value of the difference between each flow velocity data in the delayed hazard data segment and the corresponding fitted data on the delayed hazard fitting curve is taken as the smooth approximation of each flow velocity data in the delayed hazard data segment; the mean of the smooth approximation of all flow velocity data in the delayed hazard data segment is taken as the smooth approximation mean of the delayed hazard data segment. Similarly, referring to the method for obtaining the smooth approximation mean, the delayed hazard data segment is replaced with the sudden change response data segment, and the smooth approximation mean of the sudden change response data segment is obtained.

[0076] Furthermore, as an example, the degree of limitation on the true flow velocity information of the target ultrasonic detection mode can be calculated using the following formula:

[0077]

[0078] In the formula, This indicates the degree to which the actual flow velocity information of the target ultrasonic detection mode is limited; This represents the smoothed approximation of the mean of the data segment containing the potential delay. This represents the smoothed approximation of the mean of the sudden change response data segment; This indicates the last flow rate data within the data segment containing the potential delay. This represents the first velocity data within the sudden change response data segment; This indicates the preset denominator hyperparameter; in this embodiment, it is preset. This is used to prevent the denominator from being 0; This indicates taking the absolute value.

[0079] It should be noted that the greater the limitation on the true flow velocity information in the target ultrasonic detection mode, the more obvious the contrast between the flow velocity interference state between the delayed potential data segment and the sudden change response data segment in the target ultrasonic detection mode. The smoother the data transition between the delayed potential data segment and the sudden change response data segment, the greater the delay impact on the flow velocity data collected in the target ultrasonic detection mode, and the greater the limitation on the true flow velocity information represented by the ultrasonic detection mode of the radar ultrasonic multi-functional flowmeter.

[0080] Furthermore, any radar flow velocity detection mode data segment is used as the target radar detection mode data segment; the curve obtained by curve fitting the target radar detection mode data segment is used as the radar fitting curve. As an example, the degree of limitation of the true flow velocity information of the target radar detection mode data segment can be calculated using the following formula:

[0081]

[0082] In the formula, This indicates the degree to which the actual flow velocity information of the target radar detection mode is limited; This indicates the number of all flow velocity data within the target radar detection mode. Indicates the first target detection mode within the radar detection mode. Flow rate data; Indicates the first Fitting data of flow velocity data on radar fitting curves; This indicates taking the absolute value.

[0083] It should be noted that the greater the limitation on the true flow velocity information in the target radar detection mode data segment, the smoother the fluctuation of the flow velocity data collected within the target radar detection mode data segment tends to be. This reflects that the environmental influence on the target radar detection mode data segment is less obvious, and the true flow velocity information it represents is more limited by the radar detection mode of the radar ultrasonic multi-function flow meter.

[0084] Preferably, in some implementations of the present invention, the method for obtaining trend interference is as follows: when switching between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment, based on the limitation degree of the actual flow velocity information, the degree of interference of the incompatibility of the radar ultrasonic multi-functional flow meter when automatically switching flow velocity detection modes on the overall flow velocity is analyzed to obtain the overall trend interference degree when switching between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment; the interference difference of different flow velocity detection modes on their respective corresponding flow velocity information when the radar ultrasonic multi-functional flow meter automatically switches flow velocity detection modes is compared to obtain the docking incompatibility when switching between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment; the overall trend interference degree and the docking incompatibility are combined to obtain the trend interference when the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch between each other. The specific process is as follows:

[0085] Preferably, in some implementations of the present invention, the method for obtaining the overall trend interference degree is as follows: The cumulative mapping of the degree of limitation of the true flow velocity information between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment is used as the overall trend interference degree when switching between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment. The specific process is as follows:

[0086] Taking any two adjacent radar flow velocity detection mode data segments and ultrasonic flow velocity detection mode data segments as examples, the product of the degree of restriction of the true flow velocity information of the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment is used as the overall trend interference degree when the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment switch between each other.

[0087] It should be noted that the greater the overall trend interference, the greater the overall interference from different flow velocity detection modes of the radar ultrasonic multi-function flowmeter when switching between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment. This results in stronger interference with the overall trend of the flow velocity data collected within the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment.

[0088] Preferably, in some implementations of the present invention, the method for obtaining the incompatibility is as follows: the difference in the degree of restriction of the actual flow velocity information between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment is used as the incompatibility when switching between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment. The specific process is as follows:

[0089] The absolute value of the difference in the degree of limitation of the actual flow velocity information between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment is used as the interfacing incompatibility when switching between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment.

[0090] It should be noted that the greater the incompatibility, the stronger the interference of different flow velocity detection modes on the true trend of flow velocity information during the transition when switching between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment, and the more incompatible the true flow velocity information is during the mode transition.

[0091] Furthermore, the normalized value of the product of the overall trend interference degree and the docking incompatibility is used as the trend interference degree when the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch between each other.

[0092] It should be noted that the greater the trend interference, the greater the interference of different flow velocity detection modes on the expression of the true trend of water flow velocity when the radar ultrasonic multi-function flow meter switches between the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment.

[0093] It should be noted that the normalization process in this embodiment defaults to using... The normalization function is used for processing, and the normalization function can be determined according to the actual situation.

[0094] Furthermore, the trend interference during the switching of all adjacent flow velocity detection mode data segments is obtained.

[0095] Thus, the trend interference when the radar flow velocity detection mode data segment and the ultrasonic flow velocity detection mode data segment automatically switch between each other is obtained through the above method.

[0096] Step S004: Based on the trend interference, establish intelligent gateway communication for the radar ultrasonic multi-functional flow meter.

[0097] In one specific implementation of this invention, taking any two adjacent flow velocity detection mode data segments as an example, a trend smoothing factor is preset. The trend interference and trend smoothing factor when automatically switching between the two flow velocity detection mode data segments. The product of these factors is used as the trend smoothing coefficient for the second flow velocity detection mode data segment. Based on the trend smoothing coefficients of all flow velocity detection mode data segments, data prediction is performed, and the predicted simulated flow velocity data is acquired in real time. The acquired simulated flow velocity data is cached in the edge server storage layer, and then the simulated flow velocity data is returned to the radar ultrasonic multi-functional flow meter. After communication is restored, data prediction stops, and the simulated flow velocity data is automatically transmitted to cloud storage, completing the smart gateway communication.

[0098] It should be noted that the process of predicting data based on the trend smoothing coefficient is a well-known part of the Holt-Winters algorithm (triple exponential smoothing method), and will not be described again in this embodiment.

[0099] In this embodiment, This example is used for illustration; no specific limitations are set in this embodiment. This can be determined based on the specific implementation. Additionally, this embodiment does not consider the trend smoothing coefficient of the first flow velocity detection mode data segment in the real-world flow velocity sequence.

[0100] This concludes the embodiment.

[0101] Another embodiment of the present invention provides an intelligent gateway communication system for edge computing, the system including a memory and a processor, wherein when the processor executes a computer program stored in the memory, it performs the above method steps S001 to S004.

[0102] Another embodiment of the present invention provides a smart gateway communication device for edge computing, the device including a real environment flow rate sequence acquisition module, a trend interference acquisition module, and a smart gateway communication module; when the device calls a computer program, it executes the above method steps S001 to S004.

[0103] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A smart gateway communication method for edge computing, characterized in that, The method comprises the following steps: Obtaining a real environment flow rate sequence of the radar ultrasonic multifunctional flowmeter before network interruption; the real environment flow rate sequence comprises a plurality of flow rate data; When the radar ultrasonic multifunctional flowmeter operates in different flow rate detection modes, taking the fluctuation and disorder of flow rate data of the radar ultrasonic multifunctional flowmeter in response to sudden change of water flow rate as the basis for dividing the sequence into segments, the real environment flow rate sequence is divided into a plurality of radar flow rate detection mode data segments and ultrasonic flow rate detection mode data segments; the radar flow rate detection mode data segment and the ultrasonic flow rate detection mode data segment are each a flow rate detection mode data segment; Analyzing the smooth state of flow rate data change in each flow rate detection mode data segment when the radar ultrasonic multifunctional flowmeter operates in different flow rate detection modes, taking the difference between the actual fluctuation of flow rate data in each flow rate detection mode data segment and the ideal simulated fluctuation as the flow rate real information limitation degree of each flow rate detection mode data segment; when the radar flow rate detection mode data segment and the ultrasonic flow rate detection mode data segment automatically switch with each other, according to the flow rate real information limitation degree, taking the interference degree of real trend when the flow rates of different flow rate detection modes are compatible as the trend interference when the radar flow rate detection mode data segment and the ultrasonic flow rate detection mode data segment automatically switch with each other; According to the trend interference, performing intelligent gateway communication on the radar ultrasonic multifunctional flowmeter; The method for obtaining the flow rate real information limitation degree comprises the following steps: Taking any ultrasonic flow rate detection mode data segment as a target ultrasonic detection mode data segment, obtaining a delay hidden danger data segment and a sudden change response data segment of the target ultrasonic detection mode data segment; comparing the difference between the flow rate data in the delay hidden danger data segment and the sudden change response data segment and the flow rate data in the ideal simulation state to obtain the flow rate real information limitation degree of the target ultrasonic detection mode; Taking any radar flow rate detection mode data segment as a target radar detection mode data segment, analyzing the difference between the actual fluctuation of flow rate data in the target radar detection mode data segment and the ideal simulated fluctuation to obtain the flow rate real information limitation degree of the target radar detection mode. 2.The intelligent gateway communication method for edge computing of claim 1, wherein, The method for obtaining the flow rate detection mode data segment comprises the following steps: In the real environment flow rate sequence, taking a data segment in which the flow rate continuously changes in the same direction as a unidirectional fluctuation segment; taking any unidirectional fluctuation segment as a target unidirectional fluctuation segment, and taking each unidirectional fluctuation segment before the target unidirectional fluctuation segment as a historical unidirectional fluctuation segment of the target unidirectional fluctuation segment; comprehensively comparing the change rates of flow rates between adjacent historical unidirectional fluctuation segments to obtain the historical flow rate fluctuation of the target unidirectional fluctuation segment; According to the historical flow rate fluctuation, dividing the real environment flow rate sequence into a plurality of radar flow rate detection mode data segments and ultrasonic flow rate detection mode data segments; and collectively referring to each radar flow rate detection mode data segment and ultrasonic flow rate detection mode data segment as a flow rate detection mode data segment. 3.The intelligent gateway communication method for edge computing of claim 2, wherein, The method for obtaining the historical flow rate fluctuation comprises the following steps: Obtain fluctuation rate of flow rate data in each historical flow rate unidirectional fluctuation section; accumulate product relation of difference value of fluctuation rate between adjacent historical flow rate unidirectional fluctuation sections and historical flow rate unidirectional fluctuation section as historical flow rate fluctuation of target flow rate unidirectional fluctuation section. 4.The intelligent gateway communication method for edge computing of claim 1, wherein, The method for obtaining the delay hidden danger data section and the sudden change response data section is: In the target ultrasonic detection mode data section, the data section with continuous and obvious flow rate change is taken as the ultrasonic flow rate sudden change data section; the first flow rate data in the ultrasonic flow rate sudden change data section is taken as a demarcation point, and the data sections composed of each preset number of flow rate data on the left and right sides of the demarcation point are taken as the delay hidden danger data section and the sudden change response data section respectively.

5. The intelligent gateway communication method for edge computing of claim 1, wherein, The method for obtaining the trend interference is: When the radar flow rate detection mode data section and the ultrasonic flow rate detection mode data section are switched to each other, according to the flow rate true information limitation degree, the interference degree of the incompatible situation existing in the radar ultrasonic multifunctional flowmeter when automatically switching the flow rate detection mode to the overall flow rate is analyzed to obtain the overall trend interference degree when the radar flow rate detection mode data section and the ultrasonic flow rate detection mode data section are switched to each other; The interference difference of different flow rate detection modes to the corresponding flow rate information when the radar ultrasonic multifunctional flowmeter automatically switches the flow rate detection mode is compared to obtain the interfacing incompatibility when the radar flow rate detection mode data section and the ultrasonic flow rate detection mode data section are switched to each other; The overall trend interference degree and the interfacing incompatibility are combined to obtain the trend interference when the radar flow rate detection mode data section and the ultrasonic flow rate detection mode data section are automatically switched to each other. 6.The intelligent gateway communication method for edge computing of claim 5, wherein, The method for obtaining the overall trend interference degree is: The accumulation mapping of the flow rate true information limitation degree between the radar flow rate detection mode data section and the ultrasonic flow rate detection mode data section is taken as the overall trend interference degree when the radar flow rate detection mode data section and the ultrasonic flow rate detection mode data section are switched to each other. 7.The intelligent gateway communication method for edge computing of claim 5, wherein, The method for obtaining the interfacing incompatibility is: The difference amount of the flow rate true information limitation degree between the radar flow rate detection mode data section and the ultrasonic flow rate detection mode data section is taken as the interfacing incompatibility when the radar flow rate detection mode data section and the ultrasonic flow rate detection mode data section are switched to each other.

8. An intelligent gateway communication system for edge computing, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to realize the steps of the intelligent gateway communication method for edge computing according to any one of claims 1-7.

9. An intelligent gateway communication device for edge computing, characterized by, The device comprises: a real environment flow rate sequence obtaining module, a trend interference obtaining module and an intelligent gateway communication module; wherein the real environment flow rate sequence module is used to obtain the real environment flow rate sequence of the radar ultrasonic multifunctional flowmeter before the network is disconnected, the trend interference obtaining module realizes the steps of the intelligent gateway communication method for edge computing according to any one of claims 1-7 by calling the computer program to obtain the trend interference, and the intelligent gateway communication module performs intelligent gateway communication on the radar ultrasonic multifunctional flowmeter according to the trend interference.

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