CN117478261B - Online synchronous calibration method and system for flow data - Google Patents
Online synchronous calibration method and system for flow data Download PDFInfo
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Abstract
The invention provides an online synchronous calibration method and system for flow data.A control terminal sets a synchronous calibration instruction which is sent to a cloud service end and contains calibration object information and future synchronous time information; the cloud service end sends the future synchronous time information to a plurality of specified flow acquisition modules based on the calibration object information; the flow acquisition module selects one or more flow data with a time tag nearest to the future synchronous time information from a data pre-storing queue based on the future synchronous time information, and uploads the one or more flow data to the cloud server; and after receiving the plurality of flow data, the cloud server sends the flow data to the control terminal. An integral system is formed by the control terminal, the cloud service end and the flow acquisition module, a flow online synchronous calibration system with excellent synchronism and small error is established, and efficient and controllable flow online synchronous calibration is realized.
Description
Technical Field
The invention belongs to the technical field of flowmeter metering, and particularly relates to an online synchronous calibration method and system for flow data.
Background
In many situations, pipelines are used for conveying fluid, such as water, oil, gas and the like, and the pipelines are generally closed pipelines, in order to calibrate or meter the flow rate of the fluid conveyed in the pipelines on line, in the prior art, the flow rate of multiple pipelines is calibrated by adding a flowmeter on the pipelines, determining the accumulated amount by pinching a stopwatch, or determining the instantaneous amount by directly reading through eyes, and reading data by using flowmeters on different pipelines.
However, the method has the advantages of large error, poor synchronism, low automation degree, wide area where the pipeline is located, large manpower and material resources are needed to be input for carrying out flow on-line calibration, and the error rate is large.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an online synchronous calibration method and system for flow data, which are mainly used for solving the defects that the online synchronous calibration of flow cannot be realized for multiple pipelines in the prior art.
In order to solve the problems, the technical scheme adopted by the invention is as follows:
In a first aspect, the present invention provides an online synchronous calibration method for flow data, including:
The control terminal sets a synchronous calibration instruction, and sends the synchronous calibration instruction to the cloud server, wherein the synchronous calibration instruction comprises calibration object information and future synchronous time information;
the cloud service end sends the future synchronous time information to a plurality of specified flow acquisition modules based on the calibration object information;
the flow acquisition module selects one or more flow data with the time label nearest to the future synchronous time information from a data pre-storing queue based on the future synchronous time information, and uploads the one or more flow data to the cloud server;
and after receiving a plurality of flow data, the cloud server sends the flow data to the control terminal.
In some embodiments, the flow acquisition module includes a flow meter for measuring a flow of a medium within the conduit and a camera for capturing measurement data of the flow meter according to a set frequency to present an interface.
In some embodiments, the flow collection module is provided with a preview mode, the camera shoots the flowmeter according to a set frequency, and stores preview pictures in a preset time period to form a data pre-storing queue, and each preview picture is provided with a time tag.
In some embodiments, after receiving a plurality of preview pictures from different flow collection modules, the cloud service end extracts a time tag of each preview picture, selects a maximum time value and a minimum time value, calculates a time difference, and if the time difference is less than or equal to two frames of interval time under a set frequency, identifies that the data is valid; if the time difference is greater than the interval time of two frames under the set frequency, the data is determined to be invalid, and two corresponding flow acquisition modules are identified for re-uploading the data.
In some embodiments, the flow collection module is an intelligent flow meter, and the intelligent flow meter is used for measuring the medium flow in the pipeline and forming electronic flow data, configuring a time tag for each electronic flow data, and forming a data pre-storing queue according to the time sequence;
The intelligent flowmeter is configured to construct a data pre-storing queue in a preset time period set based on the future synchronous time information according to the synchronous calibration instruction, select one electronic flow data with a time tag nearest to the future synchronous time information, and upload the electronic flow data to the cloud server.
In some embodiments, after receiving a plurality of electronic flow data from different intelligent flowmeters, the cloud service end extracts a time tag of each electronic flow data, selects a maximum time value and a minimum time value, calculates a time difference, and if the time difference is less than or equal to a set interval time, identifies that the data is valid; if the time difference is larger than the set interval time, the data is determined to be invalid, and two corresponding flow acquisition modules are identified for re-uploading the data.
In some embodiments, the method further comprises the step of time calibrating:
The cloud server sends a time calibration instruction to the flow acquisition module, wherein the time calibration instruction comprises current time information;
And the flow acquisition module calibrates the built-in clock based on the current time information, and after unifying the time of the clock, the flow acquisition module is allowed to receive the synchronous calibration instruction only in a first time threshold value when the time calibration action is completed.
In some embodiments, a standard flow module is set, wherein the standard flow module is an external clamp type ultrasonic flowmeter for detecting the flow in a closed pipeline based on a time difference method principle;
The control terminal issues performance verification instructions to the flow acquisition module and the standard flow module through the cloud service end, wherein the performance verification instructions comprise detection time interval information;
The flow acquisition module and the standard flow module respectively upload verification flow data in verification time intervals;
The cloud service end calculates a maximum error value and a repeatability parameter based on the verification flow data;
When the maximum error value is +/-2.5%, and the repeatability parameter is not more than 1/2 of the absolute value of the maximum error value, judging that the performance verification is finished; otherwise, the assay is re-calibrated.
In some embodiments, after receiving a plurality of flow data, the control terminal extracts a flow value, and forms a metering calibration report according to a preset metering format.
In a second aspect, the present invention provides an online synchronous calibration system for flow data, where the online synchronous calibration method for flow data includes:
the control terminal is used for setting a synchronous calibration instruction and sending the synchronous calibration instruction to the cloud server, wherein the synchronous calibration instruction comprises calibration object information and future synchronous time information;
The cloud server is used for transmitting the future synchronous time information to a plurality of specified flow acquisition modules based on the calibration object information, and transmitting the flow data to the control terminal after receiving the flow data;
And the flow acquisition module is used for selecting one or more flow data with the time label nearest to the future synchronous time information from the data pre-storing queue based on the future synchronous time information, and uploading the flow data to the cloud server.
Compared with the prior art, the invention at least comprises the following beneficial effects:
(1) The control terminal sends a synchronous calibration instruction, the synchronous calibration instruction can be sent to a plurality of designated flow acquisition modules through the cloud server, the flow acquisition modules upload one or more pieces of flow data closest to future synchronous time information to the cloud server, and an integral system is formed by the control terminal, the cloud server and the flow acquisition modules, so that an online synchronous flow calibration system with excellent synchronism and small error is established, and efficient and controllable online synchronous flow calibration is realized;
(2) In order to improve the synchronism of the data uploaded by the flow acquisition modules and eliminate the network delay time error, analysis processing is carried out on the time label of each preview picture, and the data is considered to be valid only when the time difference between the maximum time value and the minimum time value is less than or equal to the two-frame interval time under the set frequency, so that the influence of the network delay on the flow acquisition modules is avoided and the data with errors is uploaded.
The invention is described in further detail below with reference to the drawings and the detailed description.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
Fig. 1 is a flowchart of an online synchronous calibration method for flow data according to the present embodiment.
Fig. 2 is a schematic diagram of a framework of an online synchronous calibration system for flow data according to the present embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, when it is described that a specific device is located between a first device and a second device, an intervening device may or may not be present between the specific device and the first device or the second device. When it is described that a particular device is connected to other devices, the particular device may be directly connected to the other devices without intervening devices, or may be directly connected to the other devices without intervening devices.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
The inventors found that:
The pipeline layout is complex, pipelines needing to be subjected to flow online calibration can be distributed in each area, network environments where the pipelines are located are different, if a stop watch is manually pinched and accumulated or instantaneous values are read in a visual direct reading mode, the problem exists that the data at which time point is used as the standard, and metering action is started when the data is used, and the problem that the current technical means has poor synchronism when the flow online calibration is performed leads to large calibration errors, large subsequent data processing workload and easy error.
In view of this, in order to solve the above existing problems, in a first aspect, referring to fig. 1, an embodiment of the present invention provides an online synchronous calibration method for flow data, including:
The control terminal sets a synchronous calibration instruction, and sends the synchronous calibration instruction to the cloud server, wherein the synchronous calibration instruction comprises calibration object information and future synchronous time information;
the cloud service end sends the future synchronous time information to a plurality of specified flow acquisition modules based on the calibration object information;
The flow acquisition module selects one or more flow data with a time tag nearest to the future synchronous time information from a data pre-storing queue based on the future synchronous time information, and uploads the one or more flow data to the cloud server;
and after receiving the plurality of flow data, the cloud server sends the flow data to the control terminal.
It should be noted that, each flow acquisition module has a communication identity tag, the mapping relation between all the communication identity tags and the flow acquisition modules is organized into a data table, and the data table is stored at the cloud server, the control terminal can select a designated plurality of target flow acquisition modules, designate a future synchronization time information, and send the two data to the cloud server after forming a synchronization calibration instruction;
After the cloud service end recognizes the synchronous calibration instruction, firstly reading calibration object information in the synchronous calibration instruction, finding out corresponding communication identity tags from a data table, at the moment, the cloud service end obtains a plurality of communication identity tags, installing a set communication path according to future synchronous time information in the synchronous calibration instruction and the communication identity tags, and sending the communication path to a corresponding flow acquisition module, wherein each flow acquisition module can receive the future synchronous time information; it should be noted that, because the cloud server has strong data processing capability and high efficiency, the processing of the mapping relationship and the reading and analysis of the instruction can be performed by the cloud server, and only the information of the future synchronization time is sent to the flow acquisition module, so that the data complexity between the cloud server and the flow acquisition module is reduced;
Further, each flow acquisition module responds to the future synchronous time information, a corresponding data pre-storage queue is automatically established, the data pre-storage queue comprises a plurality of flow data which are arranged according to time sequence, each flow data is provided with a time tag and used for reflecting the real time for acquiring the flow data, then one flow data with the time tag closest to the future synchronous time information is selected from the data pre-storage queue, and the flow data is sent to the cloud server; in order to reduce the data transmission pressure of the flow acquisition module, only one selected flow data is uploaded;
And the cloud service end sends the flow data to the control terminal after receiving the flow data, and the control terminal performs summarization display to complete online calibration of the flow.
Therefore, in order to reduce calibration errors, the defects of poor synchronism, low efficiency and the like caused by distributed field visual calibration can be avoided, for example, the current synchronization time is 12 points, the control terminal can set the first, third and fifth flow acquisition modules, calibrate the flow data at the 12 points by 10 minutes and form a synchronous calibration instruction, the cloud service end calls out the communication identity labels of the first, third and fifth flow acquisition modules according to the synchronous calibration instruction, sends the communication identity labels of the first, third and fifth flow acquisition modules for the future synchronization time information of the 12 points by 10 minutes, the first, third and fifth flow acquisition modules establish corresponding data pre-stored queues after receiving the future synchronization time information, for example, establish a data pre-stored queue in a period of time from the 12 points 09 to the 12 points by 11 minutes, transfer the error of the current synchronization to the future synchronization time, obtain the flow data closest to the future synchronization time information by a mode of presetting in advance and delaying selection, effectively improve the synchronization by setting a delay problem caused by network transmission in a mode of isolating the future time, and reduce the error.
As an embodiment, the future synchronization time information is information of a time period, for example, the time period from 12:10 to 12:11 minutes is taken as calibration time, the flow collection module establishes a data pre-storage queue in the time period from 12:09 to 12:12, and selects the flow data closest to the future synchronization time information from 12:10 to 12:11, wherein the flow data comprises a plurality of flow data, and the time period formed by the flow data is1 minute, so as to form a flow data group which can represent a certain time period, and then upload the flow data; when the flow data set is built, the two time endpoints of 12 points 10 and 12 points 11 are identified, two flow data closest to the two time endpoints are selected respectively, and then all flow data between the two flow data are selected to form the flow data set.
As an implementation manner, because the positions of the pipelines are different, the network environments of the pipelines are different, so that the network communication speed between the cloud service end and each flow acquisition module can be divided into two, in order to solve the problem of network delay, the cloud service end is used for detecting the network communication speed of each flow acquisition module and recording the network delay time of each flow acquisition module, the step can occur between the current moment and the future synchronous time, for example, the current moment is 12 points, the flow data of 10 minutes at 12 points need to be calibrated, the data pre-storing queue is established in the time period from 12 points 09 minutes to 12 points 11 minutes, and the action of detecting the network communication speed can occur between 12 points 09 minutes to 12 points 09 minutes;
optionally, the cloud service end only detects the network communication speed of the specified flow acquisition modules corresponding to the calibration object information;
After the cloud service end obtains the network delay time of each flow acquisition module, time compensation can be carried out on the future synchronous time information sent to the flow acquisition module, and the future synchronous time information received by the flow acquisition module is guaranteed to have high synchronism.
Optionally, the cloud server inputs the network delay time of each flow acquisition module into the coding network to extract the perception characteristics; performing cooperative communication based on the sensing characteristics, and aligning and fusing the network delay time sensing characteristics of each flow acquisition module to obtain asynchronous characteristics and edge characteristic fusion attention weights when each flow acquisition module receives the asynchronous characteristics and the edge characteristics; acquiring a history feature based on an asynchronous feature, fusing the history feature with an edge feature, and obtaining a modulation result through feature weight symbiotic prediction and time modulation through a delay compensation network; the decoder is utilized to decode the modulation result into final perception and output the time compensation result aiming at each flow acquisition module, and the targeted time compensation can be carried out when the subsequent future synchronous time information is issued and the flow data is uploaded.
Further, the delay compensation network comprises two parts, one part is based on the feature weight symbiotic prediction of the pyramid-long-short-term memory estimation network, and the other part is time modulation. Pyramid-long-short term memory estimation network representation: the matrix multiplication in the long-short-term memory network is replaced by a network obtained by a multidimensional convolution structure, and the network is used for modeling a series of historical cooperation information and estimating the current state so as to capture the cooperation characteristics related to time. The delay compensation network is based on a double-branch pyramid long-short-term memory system structure, so that synchronous evaluation of two types of key cooperative information (including characteristics and cooperative attention weights) is promoted, and mutual enhancement is realized; the network promotes robust multi-agent perception by reducing the influence of unavoidable communication delays, enhancing the performance and security of a collaborative awareness system in a real communication scenario.
Example 1:
in this embodiment 1, the flow collection module includes a flowmeter and a camera, the flowmeter is used for measuring the medium flow in the pipeline, and the camera is used for shooting the measurement data of the flowmeter according to the set frequency to present an interface;
optionally, the flowmeter is one of an ultrasonic flowmeter, an electromagnetic flowmeter and a water meter.
The camera shoots the measurement data presentation interface of the flowmeter, optionally, the camera is a camera combined on the intelligent communication equipment, the intelligent communication equipment is in wireless connection with the cloud service end, under the control of the cloud service end, the intelligent communication equipment controls the camera to shoot, the shooting can be the shooting or the shooting of video, the data obtained in the two modes are all commonly referred to as flow data, the intelligent communication equipment sends the flow data to the cloud service end again, and the uploading of the data is completed.
As an implementation mode, the flow acquisition module is provided with a preview mode, the camera shoots the flowmeter according to a set frequency and stores preview pictures in a preset time period to form a data pre-storing queue, and each preview picture is provided with a time tag. The preset time period is set according to the future synchronous time information, and if the future synchronous time information is a specific moment, the preset time period is set to be a time interval formed by one minute before and after the specific moment; if the future synchronization time information is a specific time interval, the preset time period is set to be a larger time interval formed by one minute before and after the specific time interval; after receiving the information of the future synchronous time, the flow acquisition module enters a preview mode, a preview picture can be formed within a preset time period, the preview picture is stored in the intelligent communication equipment, a data pre-storage queue is formed by utilizing the preview picture, an operator can call the data pre-storage queue at any future time to review or correct, and as the camera is utilized to shoot, each obtained frame of preview picture is provided with a time tag.
As an implementation manner, after receiving a plurality of preview frames from different flow collection modules, the cloud service end extracts a time tag of each preview frame, and one flow collection module can upload one preview frame or upload a plurality of preview frames, which is determined according to the nature of the future synchronization time information.
If a flow acquisition module uploads a preview picture, selecting a maximum time value and a minimum time value from all the preview pictures, calculating a time difference, and if the time difference is less than or equal to two frames of interval time under a set frequency, determining that the data is valid; if the time difference is greater than the interval time of two frames under the set frequency, the data is determined to be invalid, and two corresponding flow acquisition modules are identified for re-uploading the data.
Optionally, the preview frames are collected according to the frequency of 30 frames/second, that is, the time for generating a single frame of preview frame is 33.333ms, and in the data pre-storing queue corresponding to the same flow collecting module, the maximum interval between two frames of preview frames is 66.666ms. By the method, the absolute time error of uploading flow data of cameras under the control of different intelligent communication equipment can be controlled below 100ms, and the synchronism is greatly improved.
If one flow acquisition module uploads a plurality of preview pictures, selecting flow data from the flow data corresponding to each flow acquisition module, namely, the flow data at the head end and the tail end, then selecting a maximum time value and a minimum time value from the flow data at the head end uploaded by all the flow acquisition modules, and calculating the time difference; selecting a maximum time value and a minimum time value from the tail end flow data uploaded by all flow acquisition modules, and calculating the time difference of the maximum time value and the minimum time value; and judging whether the time difference is valid according to the judgment standard.
Example 2:
in this embodiment 2, the flow collection module is an intelligent flow meter, and the intelligent flow meter is used for measuring the medium flow in the pipeline and forming electronic flow data, configuring a time tag for each electronic flow data, and forming a data pre-storing queue according to the time sequence;
The intelligent flowmeter is configured to construct a data pre-storing queue in a preset time period set based on the future synchronous time information according to the synchronous calibration instruction, select one electronic flow data with a time tag nearest to the future synchronous time information, and upload the electronic flow data to the cloud server.
It should be noted that, the difference between the embodiment 2 and the embodiment 1 is that the form of the flow collection module is different, the embodiment 1 is that the specific image is shot by the camera to form the flow data, and the embodiment 2 uses the electronic information to form the flow data for uploading, except that the form of the flow data is different, and the other transmission modes and the time selection mode are the same.
As an implementation manner, after receiving a plurality of electronic flow data from different intelligent flowmeters, the cloud service end extracts a time tag of each electronic flow data, and a flow acquisition module can upload one electronic flow data or upload a plurality of electronic flow data, which is determined according to the nature of future synchronous time information.
If one flow collection module uploads one electronic flow data, a maximum time value and a minimum time value are selected from all the electronic flow data, the time difference is calculated, and if the time difference is smaller than or equal to the set interval time, the data is considered to be valid; if the time difference is larger than the set interval time, the data is determined to be invalid, and two corresponding flow acquisition modules are identified for re-uploading the data.
If one flow acquisition module uploads a plurality of electronic flow data, selecting flow data from the flow data corresponding to each flow acquisition module, namely, the flow data at the head end and the tail end, then selecting a maximum time value and a minimum time value from the flow data at the head end uploaded by all flow acquisition modules, and calculating the time difference; selecting a maximum time value and a minimum time value from the tail end flow data uploaded by all flow acquisition modules, and calculating the time difference of the maximum time value and the minimum time value; and judging whether the time difference is valid according to the judgment standard.
Preferably, the set interval time is 100ms.
Example 3:
in this embodiment 3, a time calibration step is further included:
The cloud server sends a time calibration instruction to the flow acquisition module, wherein the time calibration instruction comprises current time information;
The flow acquisition module calibrates a built-in clock based on the current time information, and after unifying the time of the clock, the flow acquisition module is allowed to receive a synchronous calibration instruction only in a first time threshold value when the time calibration action is completed.
It should be noted that, since each flow acquisition module has a corresponding built-in clock, the built-in clock may have time deviation, so that after the time of the unified clock, the time synchronism needs to be ensured within a limited first time threshold, for example, within 30 minutes after the time calibration is completed, the synchronous calibration instruction is allowed to be received, that is, the future synchronous time information is received, and the data can be uploaded.
Furthermore, the time calibration action is performed after the network communication speed is detected and the time compensation result is obtained, so that when the time calibration instruction is sent, the cloud server already performs time compensation, and the current time information obtained by each flow acquisition module is guaranteed to have high synchronism.
As one implementation mode, a standard flow module is set, and the standard flow module is an external clamp type ultrasonic flowmeter based on a time difference method principle and used for detecting the flow in a closed pipeline;
The control terminal issues performance verification instructions to the flow acquisition module and the standard flow module through the cloud service end, wherein the performance verification instructions comprise detection time interval information;
the flow acquisition module and the standard flow module respectively upload verification flow data in verification time intervals;
the cloud service end calculates a maximum error value and a repeatability parameter based on the verification flow data;
When the maximum error value is +/-2.5%, and the repeatability parameter is not more than 1/2 of the absolute value of the maximum error value, judging that the performance verification is finished; otherwise, the assay is re-calibrated.
By setting a standard flow module, calibrating other flow acquisition modules by using the standard flow module, verifying the flow data in a specified time period, and ensuring the standardization of each flow acquisition module by limiting the maximum error value and the repeatability parameter.
As an implementation manner, after receiving a plurality of flow data, the control terminal extracts a flow value, and forms a metering calibration report according to a preset metering format.
And a plurality of report templates are prestored in the control terminal, and after a plurality of flow data are received, the required target flow values are automatically extracted from the report templates, the repeatability of the flow metering error meter is automatically calculated, and metering calibration reports are automatically issued according to the metering format of the report templates, and the flow metering error meter is output by one key, so that the flow meter is convenient and quick.
Referring to fig. 2, in a second aspect, an embodiment of the present invention provides an online synchronous calibration system for flow data, where an online synchronous calibration method for flow data as described above is applied, including:
The control terminal is used for setting a synchronous calibration instruction and sending the synchronous calibration instruction to the cloud server, wherein the synchronous calibration instruction comprises calibration object information and future synchronous time information;
The cloud server is used for transmitting the future synchronous time information to a plurality of specified flow acquisition modules based on the calibration object information, and transmitting the flow data to the control terminal after receiving the flow data;
and the flow acquisition module is used for selecting one flow data with the time label nearest to the future synchronous time information from the data pre-storing queue based on the future synchronous time information, and uploading the flow data to the cloud server.
Optionally, the control terminal is a mobile phone, and the mobile phone is used for sending related instructions to the cloud server, and then the cloud server sends the instructions to each flow acquisition module.
In summary, compared with the prior art, the embodiment provides an online synchronous calibration method and system for flow data, wherein a synchronous calibration instruction is sent through a control terminal, the synchronous calibration instruction can be sent to a plurality of designated flow acquisition modules through a cloud server, the flow acquisition modules upload flow data closest to future synchronous time information to the cloud server, and an integral system is formed by the control terminal, the cloud server and the flow acquisition modules, so that an online synchronous calibration system for flow with excellent synchronism and small error is established, and efficient and controllable online synchronous calibration for flow is realized;
In order to improve the synchronism of the data uploaded by the flow acquisition modules and eliminate the network delay time error, analysis processing is carried out on the time label of each preview picture, and the data is considered to be valid only when the time difference between the maximum time value and the minimum time value is less than or equal to the two-frame interval time under the set frequency, so that the influence of the network delay on the flow acquisition modules is avoided and the data with errors is uploaded.
The above embodiments are only preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but any insubstantial changes and substitutions made by those skilled in the art on the basis of the present invention are intended to be within the scope of the present invention as claimed.
Claims (7)
1. An online synchronous calibration method for flow data, which is characterized by comprising the following steps:
The control terminal sets a synchronous calibration instruction, and sends the synchronous calibration instruction to the cloud server, wherein the synchronous calibration instruction comprises calibration object information and future synchronous time information;
the cloud service end sends the future synchronous time information to a plurality of specified flow acquisition modules based on the calibration object information;
the flow acquisition module selects one or more flow data with the time label nearest to the future synchronous time information from a data pre-storing queue based on the future synchronous time information, and uploads the one or more flow data to the cloud server;
the cloud server receives a plurality of flow data and then sends the flow data to the control terminal;
the flow acquisition module comprises a flowmeter and a camera, wherein the flowmeter is used for measuring the medium flow in a pipeline, and the camera is used for shooting the measurement data of the flowmeter according to a set frequency to present an interface;
the flow acquisition module is provided with a preview mode, the camera shoots the flowmeter according to a set frequency and stores preview pictures in a preset time period to form a data pre-storing queue, and each preview picture is provided with a time tag;
The cloud service end extracts time labels of each preview picture after receiving a plurality of preview pictures from different flow acquisition modules, selects a maximum time value and a minimum time value, calculates a time difference, and if the time difference is less than or equal to two frames of time interval under a set frequency, considers that the data is valid; if the time difference is greater than the interval time of two frames under the set frequency, the data is determined to be invalid, and two corresponding flow acquisition modules are identified for re-uploading the data.
2. An online synchronous calibration method for traffic data as recited in claim 1, wherein,
The flow acquisition module is an intelligent flow meter and is used for measuring the medium flow in the pipeline, forming electronic flow data, configuring a time tag for each electronic flow data, and forming a data pre-storing queue according to the time sequence;
The intelligent flowmeter is configured to construct a data pre-storing queue in a preset time period set based on the future synchronous time information according to the synchronous calibration instruction, select one electronic flow data with a time tag nearest to the future synchronous time information, and upload the electronic flow data to the cloud server.
3. An online synchronous calibration method for traffic data according to claim 2, wherein,
The cloud service end extracts time labels of each electronic flow data after receiving a plurality of electronic flow data from different intelligent flowmeters, selects a maximum time value and a minimum time value, calculates a time difference, and determines that the data are valid if the time difference is smaller than or equal to a set interval time; if the time difference is larger than the set interval time, the data is determined to be invalid, and two corresponding flow acquisition modules are identified for re-uploading the data.
4. A method of on-line synchronous calibration of flow data as claimed in any one of claims 1 to 3, further comprising the step of time calibration:
The cloud server sends a time calibration instruction to the flow acquisition module, wherein the time calibration instruction comprises current time information;
And the flow acquisition module calibrates the built-in clock based on the current time information, and after unifying the time of the clock, the flow acquisition module is allowed to receive the synchronous calibration instruction only in a first time threshold value when the time calibration action is completed.
5. An online synchronous calibration method for traffic data as recited in claim 4, wherein,
Setting a standard flow module, wherein the standard flow module is an external clamp type ultrasonic flowmeter for detecting the flow in a closed pipeline based on a time difference method principle;
The control terminal issues performance verification instructions to the flow acquisition module and the standard flow module through the cloud service end, wherein the performance verification instructions comprise detection time interval information;
The flow acquisition module and the standard flow module respectively upload verification flow data in verification time intervals;
The cloud service end calculates a maximum error value and a repeatability parameter based on the verification flow data;
When the maximum error value is +/-2.5%, and the repeatability parameter is not more than 1/2 of the absolute value of the maximum error value, judging that the performance verification is finished; otherwise, the assay is re-calibrated.
6. An online synchronous calibration method for traffic data as recited in claim 5, wherein,
And after receiving a plurality of flow data, the control terminal extracts the flow value and forms a metering calibration report according to a preset metering format.
7. An online synchronous calibration system for flow data, applied to an online synchronous calibration method for flow data according to any one of claims 1 to 6, comprising:
the control terminal is used for setting a synchronous calibration instruction and sending the synchronous calibration instruction to the cloud server, wherein the synchronous calibration instruction comprises calibration object information and future synchronous time information;
The cloud server is used for transmitting the future synchronous time information to a plurality of specified flow acquisition modules based on the calibration object information, and transmitting the flow data to the control terminal after receiving the flow data;
And the flow acquisition module is used for selecting one or more flow data with the time label nearest to the future synchronous time information from the data pre-storing queue based on the future synchronous time information, and uploading the flow data to the cloud server.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104216376A (en) * | 2014-09-04 | 2014-12-17 | 南京富士通南大软件技术有限公司 | Remote monitoring system and remote monitoring method for water utilization equipment |
| CN206878993U (en) * | 2017-06-20 | 2018-01-12 | 青岛清万水技术有限公司 | Ultrasonic flowmeter data acquisition and inquiry system |
| CN112597441A (en) * | 2020-12-25 | 2021-04-02 | 成都轨道交通产业技术研究院有限公司 | Flow detection system with data analysis and internet data sharing functions |
| WO2022116071A1 (en) * | 2020-12-01 | 2022-06-09 | 中国计量科学研究院 | Time trusted calibration system used for traffic monitoring network and method for operation thereof |
| CN114739445A (en) * | 2022-01-27 | 2022-07-12 | 厦门万宾科技有限公司 | Enhanced scanning method and system for urban drainage pipe network |
| CN115268317A (en) * | 2022-07-19 | 2022-11-01 | 江西五十铃汽车有限公司 | Automobile big data acquisition and uploading method System, storage medium, and vehicle |
-
2023
- 2023-10-30 CN CN202311422185.0A patent/CN117478261B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104216376A (en) * | 2014-09-04 | 2014-12-17 | 南京富士通南大软件技术有限公司 | Remote monitoring system and remote monitoring method for water utilization equipment |
| CN206878993U (en) * | 2017-06-20 | 2018-01-12 | 青岛清万水技术有限公司 | Ultrasonic flowmeter data acquisition and inquiry system |
| WO2022116071A1 (en) * | 2020-12-01 | 2022-06-09 | 中国计量科学研究院 | Time trusted calibration system used for traffic monitoring network and method for operation thereof |
| CN112597441A (en) * | 2020-12-25 | 2021-04-02 | 成都轨道交通产业技术研究院有限公司 | Flow detection system with data analysis and internet data sharing functions |
| CN114739445A (en) * | 2022-01-27 | 2022-07-12 | 厦门万宾科技有限公司 | Enhanced scanning method and system for urban drainage pipe network |
| CN115268317A (en) * | 2022-07-19 | 2022-11-01 | 江西五十铃汽车有限公司 | Automobile big data acquisition and uploading method System, storage medium, and vehicle |
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