CN109116819A - A kind of Internet of Things big data acquisition processing system - Google Patents

A kind of Internet of Things big data acquisition processing system Download PDF

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Publication number
CN109116819A
CN109116819A CN201810900709.5A CN201810900709A CN109116819A CN 109116819 A CN109116819 A CN 109116819A CN 201810900709 A CN201810900709 A CN 201810900709A CN 109116819 A CN109116819 A CN 109116819A
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flow
sensor
internet
group
data acquisition
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段宏亮
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Anhui Family Union Technology Co Ltd
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Anhui Family Union Technology Co Ltd
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • G05B19/418Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/4183Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01FMEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F25/00Testing or calibration of apparatus for measuring volume, volume flow or liquid level or for metering by volume
    • G01F25/10Testing or calibration of apparatus for measuring volume, volume flow or liquid level or for metering by volume of flowmeters
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems
    • G05B2219/31From computer integrated manufacturing till monitoring
    • G05B2219/31282Data acquisition, BDE MDE
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02PCLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

The present invention discloses a kind of Internet of Things big data acquisition processing system, the accuracy of flow sensor (i.e. production scene or laboratory in) is judged on platform by a large amount of data rule, it makes and timely judging, and be automatically repaired the accuracy of measurement data.It solves the influence of the laboratory environment of product and the variation flow sensor of live use environment, realizes the real-time amendment of accuracy variation in use.The present invention receives the real-time instantaneous flow of flow sensor for two identical bores being mounted on same pipeline, and compares the instantaneous flow of two flow sensors of identical bore;When deviation occurs in the instantaneous flow for judging two flow sensors, instantaneous flow of the instantaneous flow of two flow sensors respectively with respective primitive accumulation compares;If the instantaneous flow of some flow sensor and the instantaneous flow deviation of its primitive accumulation are more than a preset value, it is abnormal that Internet of Things big data acquisition processing system judges that this flow sensor occurs.

Description

A kind of Internet of Things big data acquisition processing system
Technical field
The present invention relates to one of big data application field Internet of Things big data acquisition processing systems.
Background technique
In practical applications, product quality goes wrong the principle taken and controlled afterwards existing flow sensor, maintenance Or replacement.However can not guarantee that product before going wrong, is analyzed by data, to judge the failure of instrument, accomplish in advance Prevention.
Flow sensor is divided into two parts, a part be it is indoor in production scene or experiment, be defined herein as demarcating The measurement error of flow sensor, this kind of flow sensor is close to zero, and all kinds of coefficients are very stable.One flow sensor There are many flow points, the repeatability of flow point refers to one group that a flow sensor is repeatedly measured in the same flow point The repeatability of data.Flow sensor is to have certain error in certain range of flow, is examined in practical application with method of comparison Survey is to generate cumulative errors.The accuracy error of flow sensor is 3 times of repeatability error under normal circumstances.Based on same The precision and the difference in different range abilities of the data of one flow point repeatability, the even same application traffic sensing Device can also generate different measured values for the measurement of same target in different moments;It is answered for same target using different Different measured values can also be generated by being measured with flow sensor.Therefore, these how to be calibrated and is in different use sites The accuracy of measurement of flow sensor seems particularly critical in practical applications.
Summary of the invention
The purpose of the present invention is to provide a kind of Internet of Things big data acquisition processing system, it can guarantee that product is going wrong Before, it is analyzed by data, to judge the failure of instrument, accomplishes to prevent in advance, also with repeatability error come detection flows sensing The repeatability of different points within the scope of the certain flow of device, thus to determine the accuracy in range of flow.
The present invention is implemented with the following technical solutions: a kind of Internet of Things big data acquisition processing system, for each of user Kind of appliance device carries out data acquisition, then does various data processings to the data of acquisition back, then will treated data It is shown, or is uploaded to network, or collected to have access to server for client and check,
The flow that the Internet of Things big data acquisition processing system receives two identical bores being mounted on same pipeline passes The real-time instantaneous flow of sensor, and compare the instantaneous flow of two flow sensors of identical bore;
When deviation occurs in the instantaneous flow that the Internet of Things big data acquisition processing system analyzes two flow sensors, will also Instantaneous flow of the instantaneous flow of two flow sensors respectively with respective primitive accumulation compares, the wink of the primitive accumulation Shi Liuliang is the instantaneous flow average value in a period of time;
If the Internet of Things big data acquisition processing system judges the instantaneous flow and its original product of some flow sensor Tired instantaneous flow deviation is more than a preset value, then gives a warning abnormal to prompt this flow sensor to occur;
The Internet of Things big data acquisition processing system receives the average repeatability of each flow point of calibrational capacity sensor Data, thus to obtain data group W1Are as follows: x1, x2... ..., xn, wherein n indicates the flow point quantity of flow sensor, xnIndicate mark Average repeated data of the constant current quantity sensor in n-th of flow point, the multiple repeated data acquisition of each flow point progress Afterwards, the average repeated data that corresponding discharge point can be obtained are averaging;
Later, the Internet of Things big data receiving processing system acquires each application stream of different use sites in the same time The repeated data of each flow point of quantity sensor, thus to obtain data group W2Are as follows: { (y11, y12... ..., y1n), (y21, y22... ..., y2n) ... ..., (ym1, ym2... ..., ymn), wherein m indicates the quantity of application traffic sensor, ymnIndicate m Repeated data of a application traffic sensor in n-th of flow point;
Then, the Internet of Things big data acquisition processing system puts repetition in different times and receives the every of different use sites The repeated data of each flow point of a application traffic sensor, thus to obtain multi-group data group W2
Then, the Internet of Things big data acquisition processing system is directed to the same flow point of same application flow sensor, will The repeated data and data group W of corresponding discharge point1The average repeated data of correspondence compare and take difference, obtain multi-group data Group W3, every group of data group W3Are as follows: { (Δ11=y11-x1, Δ12=y12-x2... ..., Δ1n=y1n-xn), (Δ21=y11-x1, Δ22 =y12-x2... ..., Δ2n=y1n-xn) ... ..., (Δn1=y11-x1, Δn2=y12-x2... ..., Δnn=y1n-xn)};
Followed by the Internet of Things big data acquisition processing system is averaged all differences, obtains average value Δ, then judge Every group of data group W3In difference whether be equal to average value Δ, if every group of data group W3In difference be equal to average value Δ, then tie Beam, if every group of data group W3In difference be not equal to average value Δ, then adjust respective application flow sensor coefficient until Corresponding difference is equal to difference average value Δ.
As a further improvement of the foregoing solution, if the Internet of Things big data acquisition processing system judges that some flow passes The instantaneous flow of sensor and the instantaneous flow deviation of its primitive accumulation are zero, then it is assumed that the measurement of this flow sensor is accurate.
As a further improvement of the foregoing solution, the coefficient of application traffic sensor uses hardware adjustments.
Further, directly the coefficient magnitude of respective application flow sensor is adjusted.
As a further improvement of the foregoing solution, the coefficient of application traffic sensor uses software compensation.
Further, it the case where difference average value Δ being less than to corresponding difference, is just compensated, it is poor to be greater than to corresponding difference The case where being worth average value Δ, carry out negative compensation.
As a further improvement of the foregoing solution, the Internet of Things big data acquisition processing system is also received in production scene Or the instantaneous flow k of the indoor calibrational capacity sensor of experiment0;Keeping the original factory coefficient of application traffic sensor not again Under conditions of change, the instantaneous flow of the application traffic sensor of different use sites is received, data group w is obtained5Are as follows: k11, k12... ..., k1m
The Internet of Things big data acquisition processing system then calibrates the coefficient of the application traffic sensor of different use sites, And after the calibration of the coefficient of application traffic sensor, in different times, different process ranges, the Internet of Things big data is adopted Collection processing system also receives the instantaneous flow of the application traffic sensor of different use sites, obtains multi-group data group W6, every group Data group W6Are as follows: k21, k22... ..., k2m;Again by every group of data group W6With data group W5It compares, obtains multi-group data group W7, often Group data group W7Are as follows: k21-k11, k22-k12... ..., k2m-k1m;Later, to more data group W7It is averaged Δ ';
When detecting instantaneous flow, the Internet of Things big data acquisition processing system connects the application traffic sensor of use site Receive the instantaneous flow k of the instantaneous flow k and calibrational capacity sensor that detect0It compares, and judges whether comparison result is equal to Average value Δ ', if comparison result be not equal to average value Δ ', the Internet of Things big data acquisition processing system to instantaneous flow k into Row software compensation is until comparison result is equal to average value Δ '.
Further, the coefficient calibration of application traffic sensor is higher using the precision of ratio of precision application traffic sensor Instrument goes to measure and calibrate.
Further, the Internet of Things big data acquisition processing system is software compensation to the compensation of instantaneous flow k.
Internet of Things big data acquisition processing system of the invention can be judged in time using normal sensing data, to not Normal sensor on-call maintenance or replacement reduce the risk that equipment fault causes production to be stopped work, to the standard of the data of sensor True property accomplishes to monitor constantly.Internet of Things big data acquisition processing system of the invention it is also assumed that calibrational capacity sensor repeated number According to being exactly accurately, by the repeated data using calibrational capacity sensor as reference standard, then to be answered by big data With, use the average value for the repeated data for widely applying flow sensor for reference standard, it is covert to calibrate calibrational capacity sensing The error of the physical presence of device, while the application traffic sensor of deviation average is adjusted, to improve application traffic sensor Accuracy.
Detailed description of the invention
Fig. 1 is the flow diagram of the accuracy of measurement method of the raising flow sensor of embodiment 1.
Fig. 2 is the modular structure schematic diagram of the accuracy of measurement device of the raising flow sensor of embodiment 2.
Fig. 3 is the flow diagram of the accuracy support method of the flow sensor of embodiment 7.
Fig. 4 is the flow diagram of the accuracy support method of another flow sensor of embodiment 8.
Fig. 5 is the modular structure schematic diagram of the accuracy ensuring equipment of the flow sensor of embodiment 9.
Fig. 6 is the flow diagram of the product quality support method of the flow sensor of embodiment 16.
Fig. 7 is the modular structure schematic diagram of the product quality ensuring equipment of the flow sensor of embodiment 17.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not For limiting the present invention.
Embodiment 1
The present embodiment discloses a kind of accurate come the measurement for improving flow sensor based on flow sensor with repeated data Degree method.One flow sensor has many flow points, and the repeatability of flow point refers to a flow sensor in the same stream Amount point repeatedly measures the repeatability of one group of obtained data.Flow sensor is to have certain mistake in certain range of flow Difference with method of comparison detection is that can generate cumulative errors in practical application.The accuracy error of flow sensor is not under normal circumstances Obtain 3 times more than repeatability error.Precision based on the data in same flow point repeatability and in different range abilities The accuracy of measurement method of difference, the raising flow sensor of the present embodiment is with repeatability error come detection flows sensor The repeatability of different points within the scope of certain flow, thus to determine the accuracy in range of flow.
The flow sensor to be mentioned of the present embodiment is divided into two parts, and a part is in production scene or laboratory , it is defined herein as calibrational capacity sensor, the measurement error of this kind of flow sensor is close to zero, and all kinds of coefficients are very steady It is fixed, therefore in the accuracy of measurement method of the raising flow sensor subsequently to be introduced, in production scene or laboratory Interior flow sensor only needs to acquire when acquiring data once substantially.Another part is the flow in different use sites Sensor, each use site install at least one flow sensor, are defined herein as application traffic sensor.Use site Environment is different from production scene or laboratory, and condition much deviates ideal conditions.Therefore, the measurement of application traffic sensor is deposited In probabilistic error, the even same application traffic sensor also can in different moments for the measurement of same target Generate different measured values;Different surveys can also be generated by being measured for same target using different application traffic sensors Magnitude.Therefore, the accuracy of measurement for how calibrating the flow sensor that these are in different use sites is shown in practical applications It obtains particularly critical.
Referring to Fig. 1, the accuracy of measurement method of the raising flow sensor of the present embodiment includes the following steps.
Step S11, defines production scene or the indoor flow sensor of experiment is calibrational capacity sensor, acquisition calibration stream The average repeated data of each flow point of quantity sensor, thus to obtain data group W1Are as follows: x1, x2... ..., xn, wherein n table Show the flow point quantity of flow sensor, xnIndicate calibrational capacity sensor in the average repeated data of n-th of flow point.
Wherein, after each flow point carries out repeated data acquisition at least three times, corresponding discharge is can be obtained in averaging The average repeated data of point.
Step S12, define each use site flow sensor be application traffic sensor, calibrational capacity sensor and Application traffic sensor has same model;The every of each application traffic sensor of different use sites is acquired in the same time The repeated data of a flow point, thus to obtain data group W2Are as follows: { (y11, y12... ..., y1n), (y21, y22... ..., y2n) ... ..., (ym1, ym2... ..., ymn), wherein m indicates the quantity of application traffic sensor, ymnIndicate m-th of application stream Repeated data of the quantity sensor in n-th of flow point.
Step S13, point repeats step S12 in different times, thus to obtain multi-group data group W2
Step S14, for the same flow point of same application flow sensor, by every group of data group W2Middle corresponding discharge point Repeated data and data group W1The average repeated data of correspondence compare and take difference, obtain multi-group data group W3, every group of number According to a group W3Are as follows: { (Δ11=y11-x1, Δ12=y12-x2... ..., Δ1n=y1n-xn), (Δ21=y11-x1, Δ22=y12- x2... ..., Δ2n=y1n-xn) ... ..., (Δn1=y11-x1, Δn2=y12-x2... ..., Δnn=y1n-xn)}。
Step S15 is averaged (i.e. to multi-group data group W all differences3It is averaged), obtain average value Δ.
Step S16 judges every group of data group W3In difference whether be equal to average value Δ.
If every group of data group W3In difference be equal to average value Δ, then terminate.Otherwise (i.e. if every group of data group W3In Difference be not equal to average value Δ), then carry out step S17: adjusting the coefficient of respective application flow sensor until corresponding difference Equal to difference average value Δ.
Wherein, the coefficient for adjusting respective application flow sensor can use hardware adjustments, such as directly to respective application stream The coefficient magnitude of quantity sensor is adjusted.Software adjustment can also be used, to the measured value of respective application flow sensor into Row software compensation, such as to corresponding difference be less than difference average value Δ the case where, just compensated, to corresponding difference greater than difference put down The case where mean value Δ, carries out negative compensation.In the repeated DATA REASONING of subsequent application traffic sensor, so that it may continuous It adopts and goes to correct in this way.
The accuracy of measurement method of the raising flow sensor of the present embodiment, it is first assumed that calibrational capacity sensor is averaged Accurately, by the average repeated data using calibrational capacity sensor as reference standard, then repeated data are exactly By big data application, use the average value for the repeated data for widely applying flow sensor for reference standard, it is covert to calibrate The error of the physical presence of calibrational capacity sensor, while the application traffic sensor of deviation average is adjusted, it is answered to improve With the accuracy of flow sensor.
Embodiment 2
Present embodiments provide a kind of accuracy of measurement device for improving flow sensor, the accuracy of measurement of the present embodiment Device uses the accuracy of measurement method of the raising flow sensor of embodiment 1.
Referring to Fig. 2, accuracy of measurement device includes repeated data acquisition unit 1, repeated data acquisition unit 22, difference comparing unit 3, average value acquiring unit 4, dif ference judgment unit 5, adjusting unit 6.
Defining production scene or the indoor flow sensor of experiment is calibrational capacity sensor, repeated data acquisition unit The average repeated data of one 1 each flow point for acquiring calibrational capacity sensor, thus to obtain data group W1Are as follows: x1, x2... ..., xn.Wherein, n indicates the flow point quantity of flow sensor, xnIndicate calibrational capacity sensor in n-th of flow point Average repeated data.Wherein, after each flow point carries out repeated data acquisition at least three times, averaging be can be obtained The average repeated data of corresponding discharge point.
The flow sensor for defining each use site is application traffic sensor, calibrational capacity sensor and application traffic Sensor has same model.Repeated data acquisition unit 22 is used to acquire each of different use sites in the same time The repeated data of each flow point of application traffic sensor, thus to obtain data group W2Are as follows: { (y11, y12... ..., y1n), (y21, y22... ..., y2n) ... ..., (ym1, ym2... ..., ymn)}.Wherein, m indicates the quantity of application traffic sensor, ymnIt indicates Repeated data of m-th of application traffic sensor in n-th of flow point.
Each application traffic of 22 multi collect difference use site of repeated data acquisition unit is put in different times The repeated data of each flow point of sensor, thus to obtain multi-group data group W2
Difference comparing unit 3 is used for the same flow point for same application flow sensor, by the weight of corresponding discharge point Renaturation data and data group W1The average repeated data of correspondence compare and take difference, obtain multi-group data group W3, every group of data group W3Are as follows: { (Δ11=y11-x1, Δ12=y12-x2... ..., Δ1n=y1n-xn), (Δ21=y11-x1, Δ22=y12-x2... ..., Δ2n=y1n-xn) ... ..., (Δn1=y11-x1, Δn2=y12-x2... ..., Δnn=y1n-xn)}。
Average value acquiring unit 4 obtains average value Δ for being averaged to all differences.
Dif ference judgment unit 5 is for judging every group of data group W3In difference whether be equal to average value Δ.If every group of number According to a group W3In difference be equal to average value Δ, then terminate.If adjusting unit 6 is used for every group of data group W3In difference be not equal to Average value Δ then adjusts the coefficient of respective application flow sensor until corresponding difference is equal to difference average value Δ.
Wherein, unit 6 is adjusted when adjusting the coefficient of respective application flow sensor, can use hardware adjustments, it is such as straight It connects and the coefficient magnitude of respective application flow sensor is adjusted;Software adjustment can also be used, respective application flow is passed The case where measured value of sensor carries out software compensation, is such as less than difference average value Δ to corresponding difference, is just compensated, to corresponding Difference is greater than the case where difference average value Δ, carries out negative compensation.
The accuracy of measurement device of embodiment has the phase of the accuracy of measurement method of the raising flow sensor of embodiment 1 Same beneficial effect.
Embodiment 3
The present embodiment provides a kind of terminals comprising memory, processor and is stored on the memory And the computer program that can be run on the processor.The processor realizes the raising of embodiment 1 when executing described program The step of accuracy of measurement method of flow sensor.
The accuracy of measurement method of the raising flow sensor of embodiment 1 in use, can be answered in the form of software With being such as designed to independently operated program, on computer terminals, terminal can be computer, smart phone etc. for installation. It can also be designed to the program of embedded operation, installation on computer terminals, is such as mounted on single-chip microcontroller.
Embodiment 4
The present embodiment provides a kind of computer readable storage mediums, are stored thereon with computer program.Described program is located Manage device execute when, realize embodiment 1 raising flow sensor accuracy of measurement method the step of.
The accuracy of measurement method of the raising flow sensor of embodiment 1 in use, can be answered in the form of software With, be such as designed to computer readable storage medium can independently operated program, computer readable storage medium can be USB flash disk, if U-shield is counted into, is designed to start the program of entire method by external triggering by USB flash disk.
Embodiment 5
The present embodiment provides a kind of flow sensor, the flow sensor uses the raising flow sensor of embodiment 1 Accuracy of measurement method adjust or correct, to improve the accuracy of measurement.
Embodiment 6
The present embodiment provides a kind of Internet of Things big data acquisition processing system, the Internet of Things big data acquisition processing system is directed to The various appliance devices of user carry out data acquisition, then do the various processing needed to the data of acquisition back, then will place Data after reason are shown, or are uploaded to network, or are collected to have access to server for client and be checked.
Defining production scene or the indoor flow sensor of experiment is calibrational capacity sensor, defines each use site Flow sensor is application traffic sensor, and calibrational capacity sensor and application traffic sensor have same model.
The Internet of Things big data acquisition processing system first receives the average repetition of each flow point of calibrational capacity sensor Property data, thus to obtain data group W1Are as follows: x1, x2... ..., xn, wherein n indicates the flow point quantity of flow sensor, xnIt indicates Average repeated data of the calibrational capacity sensor in n-th of flow point.Wherein, each flow point carries out repetition at least three times Property data acquisition after, be averaging the average repeated data that corresponding discharge point can be obtained.
Later, the Internet of Things big data receiving processing system acquires each application stream of different use sites in the same time The repeated data of each flow point of quantity sensor, thus to obtain data group W2Are as follows: { (y11, y12... ..., y1n), (y21, y22... ..., y2n) ... ..., (ym1, ym2... ..., ymn), wherein m indicates the quantity of application traffic sensor, ymnIndicate m Repeated data of a application traffic sensor in n-th of flow point.
Then, the Internet of Things big data acquisition processing system puts repetition in different times and receives the every of different use sites The repeated data of each flow point of a application traffic sensor, thus to obtain multi-group data group W2
Then, the Internet of Things big data acquisition processing system is directed to the same flow point of same application flow sensor, will The repeated data and data group W of corresponding discharge point1The average repeated data of correspondence compare and take difference, obtain multi-group data Group W3, every group of data group W3Are as follows: { (Δ11=y11-x1, Δ12=y12-x2... ..., Δ1n=y1n-xn), (Δ21=y11-x1, Δ22 =y12-x2... ..., Δ2n=y1n-xn) ... ..., (Δn1=y11-x1, Δn2=y12-x2... ..., Δnn=y1n-xn)}。
Followed by the Internet of Things big data acquisition processing system is averaged all differences, obtains average value Δ, then judge Every group of data group W3In difference whether be equal to average value Δ, if every group of data group W3In difference be equal to average value Δ, then tie Beam, if every group of data group W3In difference be not equal to average value Δ, then adjust respective application flow sensor coefficient until Corresponding difference is equal to difference average value Δ.
Wherein, the coefficient for adjusting respective application flow sensor can use hardware adjustments, such as directly to respective application stream The coefficient magnitude of quantity sensor is adjusted.Software adjustment can also be used by the Internet of Things big data acquisition processing system, The case where is carried out by software compensation, is such as less than difference average value Δ to corresponding difference for the measured value of respective application flow sensor, The case where just being compensated, being greater than difference average value Δ to corresponding difference, carries out negative compensation.
Embodiment 7
The detection data of the application traffic sensor of use site, precision can by environment variation and change.It is general this Kind variation is ignored, and only can consider that environmental change or even precision from laboratory (namely production environment) to use site are wanted Not high flowmeter is sought, all without considering the environmental change in laboratory to use site.
For the environmental change in laboratory to use site, and for the constant flow sensor of instantaneous flow, this Embodiment provides a kind of accuracy support method of flow sensor.Referring to Fig. 3, the flow sensor of the present embodiment is accurate Degree support method includes the following steps.
Step S21 calibrates the coefficient of the application traffic sensor of different use sites.The coefficient school of application traffic sensor Quasi- generally to be gone to measure and be calibrated using the higher instrument of precision of ratio of precision application traffic sensor, regulation uses precision in national standard The instrument more three times higher than sensor to be measured.
Step S22 detects instantaneous flow of each application traffic sensor in different process ranges, obtains multiple groups Data group W4, every group of data group W4Are as follows: Z21, Z22... ..., Z2u, wherein u represents the quantity of process range.
Step S23, the application traffic sensor of use site is when detecting instantaneous flow, the instantaneous flow k that will test With the Z within the scope of corresponding process2uIt compares.
If the Z within the scope of instantaneous flow k and corresponding process2uIt is identical, then terminate, otherwise, carries out step S24: to wink Shi Liuliang k, which carries out software compensation, makes the Z within the scope of instantaneous flow k and corresponding process2uIt is identical.
The present embodiment pass through calibrate in advance different use sites application traffic sensor coefficient, then with calibration after Instantaneous flow of the application traffic sensor in different range abilities brings the reference as subsequent detection as standard value, because This can be modified subsequent if generating error and the comparison with standard value.
Embodiment 8
The present embodiment provides a kind of accuracy support methods of flow sensor.The variation of laboratory environment is to calibrational capacity Sensor affects, and the variation of live use environment can similarly be affected to application traffic sensor, the ring in laboratory It is relatively low that border changes probability, that is to say, that and environment is relatively stable, but does not represent this laboratory and environmental change is not present, if Ignore the variation of laboratory environment, then the accuracy of application traffic sensor will decline.However in practical application, often neglect The slightly variation of laboratory environment, on the other hand, application traffic sensor itself occurs from production scene to use site in environment When variation, measurement error is also generated.So needing in the use process with flow sensor, flow sensor is used in amendment in real time Accuracy variable quantity.The difference of the present embodiment and embodiment 7 is, the environment of the use site of the present embodiment and it is non-constant not Become, goes the accuracy for ensureing flow sensor in such a situa-tion.
Referring to Fig. 4, the accuracy support method of the flow sensor of the present embodiment includes the following steps.
Step S31, instantaneous flow k of the detection in production scene or the indoor calibrational capacity sensor of experiment0
Step S32 is detected different using existing under conditions of the original factory coefficient of holding application traffic sensor is constant The instantaneous flow of the application traffic sensor of field, obtains data group w5Are as follows: k11, k12... ..., k1m
Step S33 calibrates the coefficient of the application traffic sensor of different use sites.The coefficient school of application traffic sensor Quasi- generally to be gone to measure and be calibrated using the higher instrument of precision of ratio of precision application traffic sensor, regulation uses precision in national standard The instrument more three times higher than sensor to be measured.Step S34 is detected different using existing in different times, different process ranges The instantaneous flow of the application traffic sensor of field, obtains multi-group data group W6, every group of data group W6Are as follows: k21, k22... ..., k2m
Step S35, by every group of data group W6With data group W5It compares, obtains multi-group data group W7, every group of data group W7 Are as follows: k21-k11, k22-k12... ..., k2m-k1m
Step S36, to more data group W6It is averaged Δ '.
Step S37, the application traffic sensor of use site is when detecting instantaneous flow, the instantaneous flow k that will test With the instantaneous flow k of calibrational capacity sensor0It compares, and judges whether comparison result is equal to average value Δ '.
If comparison result is equal to average value Δ ', terminate, otherwise (i.e. if comparison result is not equal to average value Δ '), It carries out step S38: software compensation being carried out to instantaneous flow k until comparison result is equal to average value Δ '.
The present embodiment judges on platform that the flow (i.e. in production scene or laboratory) is passed by a large amount of data rule The accuracy of sensor, makes and timely judging, and is automatically repaired the accuracy of measurement data.Solves the laboratory environment of product With the influence of the variation flow sensor of live use environment, repairing in real time for accuracy variation in use is realized Just.
Embodiment 9
Present embodiments provide a kind of accuracy ensuring equipment of flow sensor, the standard of the flow sensor of the present embodiment Exactness ensuring equipment uses the accuracy support method of the flow sensor of embodiment 7.
Referring to Fig. 5, accuracy ensuring equipment includes detection unit 1, detection unit 2 12, calibration unit 13, detection Unit 3 14, instantaneous flow comparing unit 15, unit 17 of averaging, average value judging unit 18, compensating unit 19.
Defining production scene or the indoor flow sensor of experiment is calibrational capacity sensor, defines each use site Flow sensor is application traffic sensor, and calibrational capacity sensor and application traffic sensor have same model.
Detection unit 1 is used to detect the instantaneous flow in production scene or the indoor calibrational capacity sensor of experiment k0.Detection unit 2 12 is used under conditions of the original factory coefficient of holding application traffic sensor is constant, and detection difference makes With the instantaneous flow of the application traffic sensor at scene, data group w is obtained5Are as follows: k11, k12... ..., k1m.Calibration unit 13 is used for Calibrate the coefficient of the application traffic sensor of different use sites.
Detection unit 3 14 is used to detect the application of different use sites in different times, different process ranges The instantaneous flow of flow sensor obtains multi-group data group W6, every group of data group W6Are as follows: k21, k22... ..., k2m
Instantaneous flow comparing unit 15 is used for every group of data group W6With data group W5It compares, obtains multi-group data group W7, Every group of data group W7Are as follows: k21-k11, k22-k12... ..., k2m-k1m.Unit 17 of averaging is used for more data group W7It is averaged Δ'.Average value judging unit 18 is used for the application traffic sensor of use site when detecting instantaneous flow, the wink that will test The instantaneous flow k of Shi Liuliang k and calibrational capacity sensor0It compares, and judges whether comparison result is equal to average value Δ '.Such as Fruit comparison result is equal to average value Δ ', then terminate, otherwise (i.e. if comparison result is not equal to average value Δ '), compensating unit 19 For carrying out software compensation to instantaneous flow k until comparison result is equal to average value Δ '.
The accuracy of measurement device of embodiment has the phase of the accuracy of measurement method of the raising flow sensor of embodiment 1 Same beneficial effect.
Embodiment 10
The present embodiment provides a kind of terminals comprising memory, processor and is stored on the memory And the computer program that can be run on the processor.The processor is realized embodiment 7 or is implemented when executing described program The step of accuracy support method of the flow sensor of example 8.
The accuracy support method of the flow sensor of embodiment 7 or embodiment 8 in use, can in the form of software into Row application is such as designed to independently operated program, and on computer terminals, terminal can be computer, intelligent hand for installation Machine etc..It can also be designed to the program of embedded operation, installation on computer terminals, is such as mounted on single-chip microcontroller.
Embodiment 11
The present embodiment provides a kind of computer readable storage mediums, are stored thereon with computer program.Described program is located When managing device and executing, the step of realizing the accuracy support method of the flow sensor of embodiment 7 or embodiment 8.
The accuracy support method of the flow sensor of embodiment 7 or embodiment 8 in use, can in the form of software into Row application, be such as designed to computer readable storage medium can independently operated program, computer readable storage medium can be U Disk is designed to U-shield, is designed to start the program of entire method by external triggering by USB flash disk.
Embodiment 12
The present embodiment provides a kind of flow sensor, the flow sensor is passed using the flow of embodiment 7 or embodiment 8 The accuracy support method of sensor come correct application traffic sensor detection instantaneous flow, to ensure the standard of flow sensor Exactness.
Embodiment 13
The present embodiment provides a kind of flow sensor, the flow sensor is not only with the stream of embodiment 7 or embodiment 8 The accuracy support method of quantity sensor come correct application traffic sensor detection instantaneous flow, to ensure flow sensor Accuracy, also adjust or correct using the accuracy of measurement method of the raising flow sensor of embodiment 1, to improve survey The accuracy of amount.
Embodiment 14
The present embodiment provides a kind of Internet of Things big data acquisition processing system, the Internet of Things big data acquisition processing system is directed to The various appliance devices of user carry out data acquisition, then do the various processing needed to the data of acquisition back, then will place Data after reason are shown, or are uploaded to network, or are collected to have access to server for client and be checked.
Defining production scene or the indoor flow sensor of experiment is calibrational capacity sensor, defines each use site Flow sensor is application traffic sensor, and calibrational capacity sensor and application traffic sensor have same model.
The Internet of Things big data acquisition processing system is first received in production scene or the indoor calibrational capacity sensing of experiment The instantaneous flow k of device0;Again under conditions of the original factory coefficient of holding application traffic sensor is constant, different uses are received The instantaneous flow of the application traffic sensor at scene, obtains data group w5Are as follows: k11, k12... ..., k1m
The Internet of Things big data acquisition processing system then calibrates the coefficient of the application traffic sensor of different use sites, And after the calibration of the coefficient of application traffic sensor, in different times, different process ranges, the Internet of Things big data is adopted Collection processing system also receives the instantaneous flow of the application traffic sensor of different use sites, obtains multi-group data group W6, every group Data group W6Are as follows: k21, k22... ..., k2m;Again by every group of data group W6With data group W5It compares, obtains multi-group data group W7, often Group data group W7Are as follows: k21-k11, k22-k12... ..., k2m-k1m;Later, to more data group W7It is averaged Δ '.
When detecting instantaneous flow, the Internet of Things big data acquisition processing system connects the application traffic sensor of use site Receive the instantaneous flow k of the instantaneous flow k and calibrational capacity sensor that detect0It compares, and judges whether comparison result is equal to Average value Δ '.If comparison result is equal to average value Δ ', terminate, otherwise (i.e. if comparison result is not equal to average value Δ '), the Internet of Things big data acquisition processing system carries out software compensation to instantaneous flow k until comparison result is equal to average value Δ’。
Internet of Things big data acquisition processing system judges on platform (i.e. production scene or experiment by a large amount of data rule It is indoor) flow sensor accuracy, make and timely judging, and be automatically repaired the accuracy of measurement data.Solves production The influence of the variation flow sensor of the laboratory environment of product and live use environment, realizes accuracy in use The real-time amendment of variation.
Embodiment 15
For the present embodiment on the basis of embodiment 14, Internet of Things big data acquisition processing system also receives calibrational capacity sensor Each flow point average repeated data, thus to obtain data group W1Are as follows: x1, x2... ..., xn, wherein n indicates that flow passes The flow point quantity of sensor, xnIndicate calibrational capacity sensor in the average repeated data of n-th of flow point.Wherein, each After flow point carries out repeated data acquisition at least three times, it is averaging the average repeated number that corresponding discharge point can be obtained According to.
Later, the Internet of Things big data receiving processing system acquires each application stream of different use sites in the same time The repeated data of each flow point of quantity sensor, thus to obtain data group W2Are as follows: { (y11, y12... ..., y1n), (y21, y22... ..., y2n) ... ..., (ym1, ym2... ..., ymn), wherein m indicates the quantity of application traffic sensor, ymnIndicate m Repeated data of a application traffic sensor in n-th of flow point.
Then, the Internet of Things big data acquisition processing system puts repetition in different times and receives the every of different use sites The repeated data of each flow point of a application traffic sensor, thus to obtain multi-group data group W2
Then, the Internet of Things big data acquisition processing system is directed to the same flow point of same application flow sensor, will The repeated data and data group W of corresponding discharge point1The average repeated data of correspondence compare and take difference, obtain multi-group data Group W3, every group of data group W3Are as follows: { (Δ11=y11-x1, Δ12=y12-x2... ..., Δ1n=y1n-xn), (Δ21=y11-x1, Δ22 =y12-x2... ..., Δ2n=y1n-xn) ... ..., (Δn1=y11-x1, Δn2=y12-x2... ..., Δnn=y1n-xn)}。
Followed by the Internet of Things big data acquisition processing system is averaged all differences, obtains average value Δ, then judge Every group of data group W3In difference whether be equal to average value Δ, if every group of data group W3In difference be equal to average value Δ, then tie Beam, if every group of data group W3In difference be not equal to average value Δ, then adjust respective application flow sensor coefficient until Corresponding difference is equal to difference average value Δ.
Wherein, the coefficient for adjusting respective application flow sensor can use hardware adjustments, such as directly to respective application stream The coefficient magnitude of quantity sensor is adjusted.Software adjustment can also be used by the Internet of Things big data acquisition processing system, The case where is carried out by software compensation, is such as less than difference average value Δ to corresponding difference for the measured value of respective application flow sensor, The case where just being compensated, being greater than difference average value Δ to corresponding difference, carries out negative compensation.
Embodiment 16
The present embodiment provides a kind of product quality support methods of flow sensor.Existing flow sensor is actually being answered In, product quality goes wrong the principle taken and controlled afterwards, maintenance or replacement.The present embodiment can guarantee that product is being asked It before topic, is analyzed by data, to judge the failure of instrument, accomplishes to prevent in advance.
Referring to Fig. 6, the product quality support method of the flow sensor of the present embodiment includes the following steps.
Step S41, same pipeline install two flow sensors of identical bore.
Step S42, real-time synchronization acquire the instantaneous flow of two flow sensors, and mutually compare.
Step S43, when there is deviation in the instantaneous flow of two flow sensors, the instantaneous flow of two flow sensors The instantaneous flow with respective primitive accumulation compares respectively, and the instantaneous flow of the primitive accumulation is instantaneous in a period of time Flow average value.
Step S44, if the instantaneous flow of some flow sensor and the instantaneous flow deviation of its primitive accumulation are more than one It is abnormal then to judge that this flow sensor occurs for a preset value.Therefore, this flow sensor needs on-call maintenance and replacement. Abnormal sensor on-call maintenance or replacement can reduce the risk that equipment fault causes production to be stopped work.
Step S45, if the instantaneous flow of some flow sensor and the instantaneous flow deviation of its primitive accumulation are zero, Judge that the measurement of this flow sensor is accurate.
The product quality support method of the flow sensor of the present embodiment, done at runtime by two flow sensors and When compare, when occur data deviate in time and respective primitive accumulation comparing, cause to give birth to so as to reduce equipment fault The risk stopped work is produced, and the accuracy of the data of flow sensor accomplishes to monitor constantly.
Embodiment 17
A kind of product quality ensuring equipment of flow sensor is present embodiments provided, the flow sensor of the present embodiment Product quality ensuring equipment uses the product quality support method of the flow sensor of embodiment 15.
Referring to Fig. 7, the product quality ensuring equipment of flow sensor include comparing unit 1, comparing unit 2 33, Two instantaneous flow acquisition units 31, conclusion judging unit 34.
Two instantaneous flow acquisition units 31 are used for respectively to the flow for two identical bores being mounted on same pipeline Sensor real-time synchronization acquires instantaneous flow.
Comparing unit 1 is used to compare to the instantaneous flow of two flow sensors of identical bore.
Comparing unit 2 33 is used for when deviation occurs in the instantaneous flow of two flow sensors, two flow sensors Instantaneous flow of the instantaneous flow respectively with respective primitive accumulation compares, and the instantaneous flow of the primitive accumulation is a period of time Interior instantaneous flow average value.
If conclusion judging unit 34 is for the instantaneous flow of some flow sensor and the instantaneous flow of its primitive accumulation Deviation is more than a preset value, then it is abnormal to judge that this flow sensor occurs;If the instantaneous flow of some flow sensor Instantaneous flow deviation with its primitive accumulation is zero, then judges that the measurement of this flow sensor is accurate.When judge this flow pass When sensor occurs abnormal, this flow sensor needs on-call maintenance and replacement.Therefore, abnormal sensor on-call maintenance or Replacement can reduce the risk that equipment fault causes production to be stopped work.
Embodiment 18
The present embodiment provides a kind of terminals comprising memory, processor and is stored on the memory And the computer program that can be run on the processor.The processor realizes the flow of embodiment 16 when executing described program The step of product quality support method of sensor.
The product quality support method of the flow sensor of embodiment 16 in use, can be answered in the form of software With being such as designed to independently operated program, on computer terminals, terminal can be computer, smart phone etc. for installation. It can also be designed to the program of embedded operation, installation on computer terminals, is such as mounted on single-chip microcontroller.
Embodiment 19
The present embodiment provides a kind of computer readable storage mediums, are stored thereon with computer program.Described program is located Manage device execute when, realize embodiment 16 flow sensor product quality support method the step of.
The product quality support method of the flow sensor of embodiment 16 in use, can be answered in the form of software With, be such as designed to computer readable storage medium can independently operated program, computer readable storage medium can be USB flash disk, if U-shield is counted into, is designed to start the program of entire method by external triggering by USB flash disk.
Embodiment 20
The present embodiment provides a kind of flow sensor, the flow sensor uses the production of the flow sensor of embodiment 16 Quality support method ensures the product quality of flow sensor in use process, causes production to stop to reduce equipment fault The risk of work.
Embodiment 21
The present embodiment provides a kind of flow sensor, the flow sensor is not only with the flow sensor of embodiment 16 Product quality support method ensure the product quality of flow sensor in use process, so that reducing equipment fault causes to give birth to The risk stopped work is produced, application traffic is also corrected using the accuracy support method of the flow sensor of embodiment 7 or embodiment 8 The instantaneous flow of sensor detection, to ensure the accuracy of flow sensor.
Embodiment 22
The present embodiment provides a kind of flow sensor, the flow sensor first aspect is passed using the flow of embodiment 16 The product quality support method of sensor ensures the product quality of flow sensor in use process, leads to reduce equipment fault The risk for causing production to stop work;Second aspect also uses the accuracy support method of the flow sensor of embodiment 7 or embodiment 8 The instantaneous flow for correcting the detection of application traffic sensor, to ensure the accuracy of flow sensor;The third aspect also uses reality The accuracy of measurement method of the raising flow sensor of example 1 is applied to adjust or correct, to improve the accuracy of measurement.
Embodiment 23
The present embodiment provides a kind of Internet of Things big data acquisition processing system, the Internet of Things big data acquisition processing system is directed to The various appliance devices of user carry out data acquisition, then do the various processing needed to the data of acquisition back, then will place Data after reason are shown, or are uploaded to network, or are collected to have access to server for client and be checked.
Defining production scene or the indoor flow sensor of experiment is calibrational capacity sensor, defines each use site Flow sensor is application traffic sensor, and calibrational capacity sensor and application traffic sensor have same model.
The Internet of Things big data acquisition processing system receives the stream for two identical bores being mounted on same pipeline respectively The real-time instantaneous flow of quantity sensor, and compare the instantaneous flow of two flow sensors of identical bore.The big number of Internet of Things (the instantaneous stream of i.e. two flow sensors when there is deviation according to the instantaneous flow that acquisition processing system analyzes two flow sensors When measuring not identical), the Internet of Things big data acquisition processing system also by the instantaneous flow of two flow sensors respectively and respectively The instantaneous flow of primitive accumulation compare, the instantaneous flow of the primitive accumulation is that the instantaneous flow in a period of time is average Value.
If the Internet of Things big data acquisition processing system judges the instantaneous flow and its original product of some flow sensor Tired instantaneous flow deviation is more than a preset value, then gives a warning abnormal to prompt this flow sensor to occur;The object If the instantaneous flow of some flow sensor of the United Nations General Assembly's data acquisition processing system judgement and the instantaneous flow of its primitive accumulation are inclined Difference is zero, then it is assumed that the measurement of this flow sensor is accurate.When thinking that the measurement of this flow sensor is accurate, it can not issue and appoint What is alerted.When judging that this flow sensor occurs abnormal, this flow sensor needs on-call maintenance and replacement.Therefore, Abnormal sensor on-call maintenance or replacement can reduce the risk that equipment fault causes production to be stopped work.
Embodiment 24
On the basis of embodiment 23, Internet of Things big data acquisition processing system also first receives and is in production scene the present embodiment Or the instantaneous flow k of the indoor calibrational capacity sensor of experiment0;Keeping the original factory coefficient of application traffic sensor not again Under conditions of change, the instantaneous flow of the application traffic sensor of different use sites is received, data group w is obtained5Are as follows: k11, k12... ..., k1m;Then the coefficient of the application traffic sensor of different use sites is calibrated.
The Internet of Things big data acquisition processing system is after the coefficient calibration of application traffic sensor, in different times, no When with process range, the Internet of Things big data acquisition processing system also receives the application traffic sensor of different use sites Instantaneous flow, obtain multi-group data group W6, every group of data group W6Are as follows: k21, k22... ..., k2m;Again by every group of data group W6Sum number According to a group W5It compares, obtains multi-group data group W7, every group of data group W7Are as follows: k21-k11, k22-k12... ..., k2m-k1m;Later, right More data group W7It is averaged Δ '.
When detecting instantaneous flow, the Internet of Things big data acquisition processing system connects the application traffic sensor of use site Receive the instantaneous flow k of the instantaneous flow k and calibrational capacity sensor that detect0It compares, and judges whether comparison result is equal to Average value Δ '.If comparison result is equal to average value Δ ', terminate, otherwise (i.e. if comparison result is not equal to average value Δ '), the Internet of Things big data acquisition processing system carries out software compensation to instantaneous flow k until comparison result is equal to average value Δ’。
Internet of Things big data acquisition processing system judges on platform (i.e. production scene or experiment by a large amount of data rule It is indoor) flow sensor accuracy, make and timely judging, and be automatically repaired the accuracy of measurement data.Solves production The influence of the variation flow sensor of the laboratory environment of product and live use environment, realizes accuracy in use The real-time amendment of variation.
Embodiment 25
For the present embodiment on the basis of embodiment 24, Internet of Things big data acquisition processing system also receives calibrational capacity sensor Each flow point average repeated data, thus to obtain data group W1Are as follows: x1, x2... ..., xn, wherein n indicates that flow passes The flow point quantity of sensor, xnIndicate calibrational capacity sensor in the average repeated data of n-th of flow point.Wherein, each After flow point carries out repeated data acquisition at least three times, it is averaging the average repeated number that corresponding discharge point can be obtained According to.
Later, the Internet of Things big data receiving processing system acquires each application stream of different use sites in the same time The repeated data of each flow point of quantity sensor, thus to obtain data group W2Are as follows: { (y11, y12... ..., y1n), (y21, y22... ..., y2n) ... ..., (ym1, ym2... ..., ymn), wherein m indicates the quantity of application traffic sensor, ymnIndicate m Repeated data of a application traffic sensor in n-th of flow point.
Then, the Internet of Things big data acquisition processing system puts repetition in different times and receives the every of different use sites The repeated data of each flow point of a application traffic sensor, thus to obtain multi-group data group W2
Then, the Internet of Things big data acquisition processing system is directed to the same flow point of same application flow sensor, will The repeated data and data group W of corresponding discharge point1The average repeated data of correspondence compare and take difference, obtain multi-group data Group W3, every group of data group W3Are as follows: { (Δ11=y11-x1, Δ12=y12-x2... ..., Δ1n=y1n-xn), (Δ21=y11-x1, Δ22 =y12-x2... ..., Δ2n=y1n-xn) ... ..., (Δn1=y11-x1, Δn2=y12-x2... ..., Δnn=y1n-xn)}。
Followed by the Internet of Things big data acquisition processing system is averaged all differences, obtains average value Δ, then judge Every group of data group W3In difference whether be equal to average value Δ, if every group of data group W3In difference be equal to average value Δ, then tie Beam, if every group of data group W3In difference be not equal to average value Δ, then adjust respective application flow sensor coefficient until Corresponding difference is equal to difference average value Δ.
Wherein, the coefficient for adjusting respective application flow sensor can use hardware adjustments, such as directly to respective application stream The coefficient magnitude of quantity sensor is adjusted.Software adjustment can also be used by the Internet of Things big data acquisition processing system, The case where is carried out by software compensation, is such as less than difference average value Δ to corresponding difference for the measured value of respective application flow sensor, The case where just being compensated, being greater than difference average value Δ to corresponding difference, carries out negative compensation.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.

Claims (10)

1. a kind of Internet of Things big data acquisition processing system carries out data acquisition for the various appliance devices of user, then right The data of acquisition back do various data processings, and then by treated, data are shown, or are uploaded to network, or collect extremely Server is had access to for client and is checked, which is characterized in that
The Internet of Things big data acquisition processing system receives the flow sensor for two identical bores being mounted on same pipeline Real-time instantaneous flow, and compare the instantaneous flow of two flow sensors of identical bore;
When deviation occurs in the instantaneous flow that the Internet of Things big data acquisition processing system analyzes two flow sensors, also by two Instantaneous flow of the instantaneous flow of flow sensor respectively with respective primitive accumulation compares, the instantaneous stream of the primitive accumulation Amount is the instantaneous flow average value in a period of time;
If the Internet of Things big data acquisition processing system judges instantaneous flow and its primitive accumulation of some flow sensor Instantaneous flow deviation is more than a preset value, then gives a warning abnormal to prompt this flow sensor to occur;
The Internet of Things big data acquisition processing system receives the average repeated data of each flow point of calibrational capacity sensor, Thus to obtain data group W1Are as follows: x1, x2... ..., xn, wherein n indicates the flow point quantity of flow sensor, xnIndicate calibration stream Average repeated data of the quantity sensor in n-th of flow point;
Later, the Internet of Things big data receiving processing system is passed in each application traffic that the same time acquires different use sites The repeated data of each flow point of sensor, thus to obtain data group W2Are as follows: { (y11, y12... ..., y1n), (y21, y22... ..., y2n) ... ..., (ym1, ym2... ..., ymn), wherein m indicates the quantity of application traffic sensor, ymnIndicate m Repeated data of a application traffic sensor in n-th of flow point, the multiple repeated data acquisition of each flow point progress Afterwards, the average repeated data that corresponding discharge point can be obtained are averaging;
Then, the Internet of Things big data acquisition processing system, which puts repetition in different times and receives each of different use sites, answers With the repeated data of each flow point of flow sensor, thus to obtain multi-group data group W2
Then, the Internet of Things big data acquisition processing system is directed to the same flow point of same application flow sensor, will be corresponding The repeated data and data group W of flow point1The average repeated data of correspondence compare and take difference, obtain multi-group data group W3, Every group of data group W3Are as follows: { (Δ11=y11-x1, Δ12=y12-x2... ..., Δ1n=y1n-xn), (Δ21=y11-x1, Δ22= y12-x2... ..., Δ2n=y1n-xn) ... ..., (Δn1=y11-x1, Δn2=y12-x2... ..., Δnn=y1n-xn)};
Followed by the Internet of Things big data acquisition processing system is averaged all differences, obtains average value Δ, then judge every group Data group W3In difference whether be equal to average value Δ, if every group of data group W3In difference be equal to average value Δ, then terminate, If every group of data group W3In difference be not equal to average value Δ, then adjust the coefficient of respective application flow sensor until corresponding Difference is equal to difference average value Δ.
2. Internet of Things big data acquisition processing system as described in claim 1, which is characterized in that each flow point carries out at least three After secondary repeated data acquisition, it is averaging the average repeated data that corresponding discharge point can be obtained.
3. Internet of Things big data acquisition processing system as described in claim 1, which is characterized in that at the Internet of Things big data acquisition If reason system judges that the instantaneous flow of some flow sensor and the instantaneous flow deviation of its primitive accumulation are zero, then it is assumed that this A flow sensor measurement is accurate.
4. Internet of Things big data acquisition processing system as described in claim 1, it is characterised in that: the coefficient of application traffic sensor It adjusts and uses hardware adjustments.
5. Internet of Things big data acquisition processing system as claimed in claim 4, it is characterised in that: directly passed to respective application flow The coefficient magnitude of sensor is adjusted.
6. Internet of Things big data acquisition processing system as described in claim 1, it is characterised in that: the coefficient of application traffic sensor It adjusts and uses software compensation.
7. Internet of Things big data acquisition processing system as claimed in claim 6, it is characterised in that: flat less than difference to corresponding difference The case where the case where mean value Δ, just being compensated, being greater than difference average value Δ to corresponding difference, carries out negative compensation.
8. Internet of Things big data acquisition processing system as described in claim 1, it is characterised in that: at the Internet of Things big data acquisition Reason system also receives the instantaneous flow k in production scene or the indoor calibrational capacity sensor of experiment0;Keeping application stream again Under conditions of the original factory coefficient of quantity sensor is constant, the instantaneous stream of the application traffic sensor of different use sites is received Amount, obtains data group w5Are as follows: k11, k12... ..., k1m
The Internet of Things big data acquisition processing system then calibrates the coefficient of the application traffic sensor of different use sites, and After the coefficient calibration of application traffic sensor, in different times, different process ranges, at the Internet of Things big data acquisition Reason system also receives the instantaneous flow of the application traffic sensor of different use sites, obtains multi-group data group W6, every group of data Group W6Are as follows: k21, k22... ..., k2m;Again by every group of data group W6With data group W5It compares, obtains multi-group data group W7, every group of number According to a group W7Are as follows: k21-k11, k22-k12... ..., k2m-k1m;Later, to more data group W7It is averaged Δ ';
For the application traffic sensor of use site when detecting instantaneous flow, the Internet of Things big data acquisition processing system receives inspection The instantaneous flow k of the instantaneous flow k and calibrational capacity sensor that measure0It compares, and it is average to judge whether comparison result is equal to It is worth Δ ', if comparison result is not equal to average value Δ ', the Internet of Things big data acquisition processing system carries out instantaneous flow k soft Part compensation is until comparison result is equal to average value Δ '.
9. Internet of Things big data acquisition processing system as claimed in claim 8, it is characterised in that: the coefficient of application traffic sensor Calibration is gone to measure and be calibrated using the higher instrument of precision of ratio of precision application traffic sensor.
10. Internet of Things big data acquisition processing system as claimed in claim 8, it is characterised in that: the Internet of Things big data acquisition Processing system is software compensation to the compensation of instantaneous flow k.
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