CN114526782A - Water meter filtering method, system, computer and medium based on Kalman filtering - Google Patents

Water meter filtering method, system, computer and medium based on Kalman filtering Download PDF

Info

Publication number
CN114526782A
CN114526782A CN202111607352.XA CN202111607352A CN114526782A CN 114526782 A CN114526782 A CN 114526782A CN 202111607352 A CN202111607352 A CN 202111607352A CN 114526782 A CN114526782 A CN 114526782A
Authority
CN
China
Prior art keywords
value
preset
kalman filtering
optimized
water meter
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111607352.XA
Other languages
Chinese (zh)
Inventor
毛祖宾
张民
袁振宇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Ake Technology Co ltd
Original Assignee
Guangdong Ake Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Ake Technology Co ltd filed Critical Guangdong Ake Technology Co ltd
Priority to CN202111607352.XA priority Critical patent/CN114526782A/en
Publication of CN114526782A publication Critical patent/CN114526782A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F15/00—Details of, or accessories for, apparatus of groups G01F1/00 - G01F13/00 insofar as such details or appliances are not adapted to particular types of such apparatus
    • G01F15/06—Indicating or recording devices
    • G01F15/061—Indicating or recording devices for remote indication
    • G01F15/063—Indicating or recording devices for remote indication using electrical means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F15/00—Details of, or accessories for, apparatus of groups G01F1/00 - G01F13/00 insofar as such details or appliances are not adapted to particular types of such apparatus
    • G01F15/07—Integration to give total flow, e.g. using mechanically-operated integrating mechanism
    • G01F15/075—Integration to give total flow, e.g. using mechanically-operated integrating mechanism using electrically-operated integrating means
    • G01F15/0755—Integration to give total flow, e.g. using mechanically-operated integrating mechanism using electrically-operated integrating means involving digital counting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/15—Correlation function computation including computation of convolution operations
    • Y—GENERAL 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Fluid Mechanics (AREA)
  • Mathematical Physics (AREA)
  • Computational Mathematics (AREA)
  • Algebra (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Measuring Volume Flow (AREA)

Abstract

The invention discloses a water meter filtering method based on Kalman filtering, which comprises the following steps: constructing an initial Kalman filtering model; optimizing the Q value in the initial Kalman filtering model according to a preset Q value calculation formula to obtain a first optimized Kalman filtering model; optimizing the R value in the second optimized Kalman filtering model according to a preset R value calculation formula to obtain a second optimized Kalman filtering model; and filtering the acquired water meter data according to the second optimized Kalman filtering model. The invention also discloses a water meter filtering system based on Kalman filtering, computer equipment and a readable storage medium. By adopting the method and the device, the problems of the existing Kalman filtering-based water meter filtering technology can be solved, and the accuracy of water meter measurement is further improved.

Description

Water meter filtering method, system, computer and medium based on Kalman filtering
Technical Field
The invention relates to the field of water meters, in particular to a water meter filtering method, a water meter filtering system, a water meter filtering computer and a water meter filtering medium based on Kalman filtering.
Background
In data processing, a kalman filtering algorithm is often used, parameters Q and R of a conventional kalman filter are calculated by a fixed formula, and then data is continuously processed by iteration. The Kalman filtering effect is still significant before data amplification, but if the data is amplified, the Kalman filtered curve has small jitter due to the fixed Q value and R value. Therefore, the existing water meter filtering technology based on the Kalman filtering cannot adapt to the requirements of different flow data, and the accuracy of water meter measurement is further influenced.
Disclosure of Invention
The invention aims to provide a water meter filtering method, a water meter filtering system, a water meter filtering computer and a water meter filtering medium based on Kalman filtering, which can solve the problems of the existing water meter filtering technology based on Kalman filtering and further improve the accuracy of water meter measurement.
In order to solve the technical problem, the invention provides a water meter filtering method based on Kalman filtering, which comprises the following steps: constructing an initial Kalman filtering model; optimizing the Q value in the initial Kalman filtering model according to a preset Q value calculation formula to obtain a first optimized Kalman filtering model; optimizing the R value in the second optimized Kalman filtering model according to a preset R value calculation formula to obtain a second optimized Kalman filtering model; and filtering the collected water meter data according to the second optimized Kalman filtering model.
Preferably, the preset Q value calculation formula is: q ═ O1-O2L wherein O1As output value of the last Kalman filter, O2Is the output value of the current Kalman filter.
Preferably, the preset R value calculation formula is: r ═ O1K, wherein O1And K is a proportional parameter preset according to the flow, and is the output value of the last Kalman filter.
Preferably, the preset R value calculation formula is: r ═ O1K + M, wherein O1The output value of the last Kalman filter, K is a proportional parameter preset according to the flow, and M is preAnd setting a compensation value.
Preferably, the step of optimizing the R value in the second optimized kalman filtering model according to a preset R value calculation formula to obtain a second optimized kalman filtering model includes: acquiring the flow of the water meter; judging whether the flow of the water meter is larger than a preset flow or not; if so, optimizing the R value in the second optimized Kalman filtering model according to a first preset R value calculation formula to obtain a second optimized Kalman filtering model; if not, optimizing the R value in the second optimized Kalman filtering model according to a second preset R value calculation formula to obtain a second optimized Kalman filtering model; wherein the first preset R value is calculated by the formula of R ═ O1*K1And the second preset R value is calculated by the formula of R ═ O1*K2，O1As the last Kalman filter output value, K1And K2Are all proportional parameters preset according to the flow, K1＞K2。
Preferably, the step of optimizing the R value in the second optimized kalman filtering model according to a preset R value calculation formula to obtain a second optimized kalman filtering model includes: acquiring the flow of the water meter; judging whether the flow of the water meter is larger than a preset flow or not; if so, optimizing the R value in the second optimized Kalman filtering model according to a first preset R value calculation formula to obtain a second optimized Kalman filtering model; if not, optimizing the R value in the second optimized Kalman filtering model according to a second preset R value calculation formula to obtain a second optimized Kalman filtering model; wherein the third preset R value is calculated by the formula of R ═ O1*K1+ M, and the fourth preset R value is calculated by the formula of R ═ O1*K2+M，O1As the last Kalman filter output value, K1And K2Are proportional parameters preset according to the flow, M is a preset compensation value, K1＞K2。
The invention also provides a water meter filtering system based on Kalman filtering, which is used for realizing any one of the water meter filtering methods based on Kalman filtering, and comprises the following steps: the model construction module is used for constructing an initial Kalman filtering model; the first optimization module is used for optimizing the Q value in the initial Kalman filtering model according to a preset Q value calculation formula to obtain a first optimized Kalman filtering model; the second optimization module is used for optimizing the R value in the second optimized Kalman filtering model according to a preset R value calculation formula to obtain a second optimized Kalman filtering model; and the filtering module is used for filtering the acquired water meter data according to the second optimized Kalman filtering model.
Preferably, the second optimization module further comprises: the flow acquiring unit is used for acquiring the flow of the water meter; the judging unit is used for judging whether the flow of the water meter is larger than the preset flow; the first optimization unit is used for optimizing the R value in the second optimized Kalman filtering model according to a first preset R value calculation formula to obtain a second optimized Kalman filtering model when the judgment unit judges that the R value is positive; and the second optimization unit is used for optimizing the R value in the second optimized Kalman filtering model according to a second preset R value calculation formula to obtain a second optimized Kalman filtering model when the judgment unit judges that the R value is not the first preset R value calculation formula.
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the above methods when executing the computer program.
The invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of any of the above-described methods.
The beneficial effects of the implementation of the invention are as follows:
the invention provides a water meter filtering method, a water meter filtering system, a computer device and a readable storage medium based on Kalman filtering, which are implemented by constructing an initial Kalman filtering model; optimizing the Q value in the initial Kalman filtering model according to a preset Q value calculation formula to obtain a first optimized Kalman filtering model; optimizing the R value in the second optimized Kalman filtering model according to a preset R value calculation formula to obtain a second optimized Kalman filtering model; and finally, filtering the collected water meter data according to the second optimized Kalman filtering model. By adopting the method and the device, the problems of the existing Kalman filtering-based water meter filtering technology can be solved, and the accuracy of water meter measurement is further improved.
Drawings
FIG. 1 is a flow chart of a water meter filtering method based on Kalman filtering provided by the invention;
FIG. 2 is a flow chart of a first embodiment of an optimization method provided by the present invention;
FIG. 3 is a flow chart of a second embodiment of an optimization method provided by the present invention;
FIG. 4 is a schematic diagram of a Kalman filtering based water meter filtering system provided by the present invention;
FIG. 5 is a schematic diagram of a second optimization module provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings. It is only noted that the invention is intended to be limited to the specific forms set forth herein, including any reference to the drawings, as well as any other specific forms of embodiments of the invention.
As shown in fig. 1, the present invention provides a water meter filtering method based on kalman filtering, including:
s101, constructing an initial Kalman filtering model;
s102, optimizing a Q value in the initial Kalman filtering model according to a preset Q value calculation formula to obtain a first optimized Kalman filtering model;
s103, optimizing the R value in the second optimized Kalman filtering model according to a preset R value calculation formula to obtain a second optimized Kalman filtering model;
and S104, filtering the acquired water meter data according to the second optimized Kalman filtering model.
It should be noted that, in data processing, a kalman filter algorithm is often used, and parameters Q and R of a conventional kalman filter are calculated by a fixed formula, and then data is continuously processed by iteration. The Kalman filtering effect is still significant before data amplification, but if the data is amplified, the Kalman filtered curve has small jitter due to the fixed Q value and R value. Therefore, the existing water meter filtering technology based on the Kalman filtering cannot adapt to the requirements of different flow data, and the accuracy of water meter measurement is further influenced.
The method comprises the steps of constructing an initial Kalman filtering model; optimizing the Q value in the initial Kalman filtering model according to a preset Q value calculation formula to obtain a first optimized Kalman filtering model; optimizing the R value in the second optimized Kalman filtering model according to a preset R value calculation formula to obtain a second optimized Kalman filtering model; and finally, filtering the acquired water meter data according to the second optimized Kalman filtering model. By adopting the method and the device, the problems of the existing Kalman filtering-based water meter filtering technology can be solved, and the accuracy of water meter measurement is further improved.
Preferably, the preset Q value calculation formula is: q ═ O1-O2L in which O1As the last Kalman filter output value, O2Is the output value of the current Kalman filter. Further, the preset R value calculation formula is: r ═ O1K, wherein O1And K is a proportional parameter preset according to the flow, and is the output value of the last Kalman filter.
It should be noted that, in the embodiment, in the precision of the secondary meter in the water meter industry, the precision requirement below Q2 is 5%, and above Q2 is 2%, so that the R value is processed in segments, and values are assigned according to different flow intervals, so as to achieve the purpose that when large-flow data is processed, a fast response is required but a filtering requirement is low, and when small-flow data is processed, a filtering requirement is high but a fast response is not required.
More preferably, the preset R value is calculated by the following formula: r ═ O1K + M, wherein O1And K is a proportional parameter preset according to the flow, and M is a preset compensation value.
In this embodiment, in order to prevent the R value from approaching to 0 due to too low flow rate, the present embodiment adds a compensation value M.
As shown in fig. 2, the step of optimizing the R value in the second optimized kalman filtering model according to a preset R value calculation formula to obtain a second optimized kalman filtering model includes:
s201, acquiring the flow of the water meter;
s202, judging whether the flow of the water meter is larger than a preset flow or not;
s203, if the first optimized Kalman filtering model is judged to be the first optimized Kalman filtering model, optimizing the R value in the second optimized Kalman filtering model according to a first preset R value calculation formula to obtain the second optimized Kalman filtering model;
s204, when the judgment result is no, optimizing the R value in the second optimized Kalman filtering model according to a second preset R value calculation formula to obtain a second optimized Kalman filtering model;
wherein the first preset R value is calculated by the formula of R ═ O1*K1And the second preset R value is calculated by the formula of R ═ O1*K2，O1As the last Kalman filter output value, K1And K2Are all proportional parameters preset according to the flow, K1＞K2。
It should be noted that, in this embodiment, by obtaining the flow rate of the water meter and determining whether the flow rate of the water meter is greater than a preset flow rate, if yes, the R value in the second kalman filter model is optimized according to a first preset R value calculation formula to obtain a second kalman filter model, and if no, the R value in the second kalman filter model is optimized according to a second preset R value calculation formula to obtain the second kalman filter model. By adopting the embodiment, assignment can be carried out according to different flow intervals, so that the purposes that quick response is needed but the filtering requirement is low when large-flow data is processed, and the filtering requirement is high but quick response is not needed when small-flow data is processed are achieved.
As shown in fig. 3, the step of optimizing the R value in the second optimized kalman filtering model according to a preset R value calculation formula to obtain a second optimized kalman filtering model includes:
s301, acquiring the flow of the water meter;
s302, judging whether the flow of the water meter is larger than a preset flow or not;
s303, if yes, optimizing the R value in the second optimized Kalman filtering model according to a third preset R value calculation formula to obtain a second optimized Kalman filtering model;
s304, when the judgment result is no, optimizing the R value in the second optimized Kalman filtering model according to a fourth preset R value calculation formula to obtain a second optimized Kalman filtering model;
wherein the third predetermined R value is calculated by the formula of R ═ O1*K1+ M, and the fourth preset R value is calculated by the formula of R ═ O1*K2+M，O1As the last Kalman filter output value, K1And K2Are proportional parameters preset according to the flow, M is a preset compensation value, K1＞K2。
It should be noted that, in this embodiment, by obtaining the flow rate of the water meter and determining whether the flow rate of the water meter is greater than a preset flow rate, if yes, the R value in the second kalman filter model is optimized according to a third preset R value calculation formula to obtain a second kalman filter model, and if no, the R value in the second kalman filter model is optimized according to a fourth preset R value calculation formula to obtain the second kalman filter model. By adopting the embodiment, assignment can be carried out according to different flow intervals, so that the purposes that the fast response is required but the filtering requirement is low when large-flow data is processed, and the filtering requirement is high but the fast response is not required when small-flow data is processed are achieved; and meanwhile, the condition that the R value approaches to 0 due to too low flow is prevented.
As shown in fig. 4, the present invention further provides a kalman filtering based water meter filtering system 100, which is configured to implement any one of the above kalman filtering based water meter filtering methods, and includes: the model building module 1 is used for building an initial Kalman filtering model; the first optimization module 2 is used for optimizing the Q value in the initial Kalman filtering model according to a preset Q value calculation formula to obtain a first optimized Kalman filtering model; the second optimization module 3 is configured to optimize the R value in the second optimized kalman filtering model according to a preset R value calculation formula to obtain a second optimized kalman filtering model; and the filtering module 4 is used for filtering the acquired water meter data according to the second optimized Kalman filtering model.
It should be noted that, in data processing, a kalman filter algorithm is often used, and parameters Q and R of a conventional kalman filter are calculated by a fixed formula, and then data is continuously processed by iteration. The Kalman filtering effect is still significant before data amplification, but if the data is amplified, the Kalman filtered curve has small jitter due to the fixed Q value and R value. Therefore, the existing water meter filtering technology based on the Kalman filtering cannot adapt to the requirements of different flow data, and the accuracy of water meter measurement is further influenced.
According to the invention, an initial Kalman filtering model is constructed through the model construction module 1, a Q value in the initial Kalman filtering model is optimized according to a preset Q value calculation formula through the first optimization module 2 to obtain a first optimized Kalman filtering model, an R value in the second optimized Kalman filtering model is optimized according to a preset R value calculation formula through the second optimization module 3 to obtain a second optimized Kalman filtering model, and collected water meter data is filtered through the filtering module 4 according to the second optimized Kalman filtering model. By adopting the method and the device, the problems of the existing Kalman filtering-based water meter filtering technology can be solved, and the accuracy of water meter measurement is further improved.
As shown in fig. 5, the second optimization module 3 further includes: a flow rate obtaining unit 31, configured to obtain a flow rate of the water meter; the judging unit 32 is used for judging whether the flow of the water meter is larger than the preset flow; the first optimization unit 33 is configured to optimize the R value in the second optimized kalman filter model according to a first preset R value calculation formula to obtain a second optimized kalman filter model when the determination unit 32 determines that the R value is the first preset R value; and the second optimizing unit 34 is configured to optimize the R value in the second optimized kalman filtering model according to a second preset R value calculation formula to obtain the second optimized kalman filtering model when the determining unit 32 determines that the R value is negative.
In this embodiment, the flow rate of the water meter is obtained by the flow rate obtaining unit 31; judging whether the flow of the water meter is larger than a preset flow through the judging unit 32; by the first optimizing unit 33, when the determining unit 32 determines that the R value in the second optimized kalman filter model is the first preset R value calculation formula, optimizing the R value in the second optimized kalman filter model to obtain a second optimized kalman filter model; by the second optimizing unit 34, when the determining unit 32 determines that the R value in the second optimized kalman filter model is negative, the R value in the second optimized kalman filter model is optimized according to a second preset R value calculation formula to obtain a second optimized kalman filter model.
Specifically, formula optimization is performed in two cases:
the first method comprises the steps of obtaining the flow of the water meter, judging whether the flow of the water meter is larger than a preset flow, if so, optimizing the R value in the second optimized Kalman filtering model according to a first preset R value calculation formula to obtain a second optimized Kalman filtering model, and if not, optimizing the R value in the second optimized Kalman filtering model according to a second preset R value calculation formula to obtain the second optimized Kalman filtering model. The present embodiment was adoptedAnd the assignment can be carried out according to different flow intervals, so that the purposes that the filtering requirement is low when large-flow data is processed, and the filtering requirement is high but the quick response is not needed when small-flow data is processed are achieved. Wherein the first preset R value is calculated by the formula of R ═ O1*K1And the second preset R value is calculated by the formula of R ═ O1*K2，O1As the last Kalman filter output value, K1And K2Are all proportional parameters preset according to the flow, K1＞K2。
And secondly, by acquiring the flow of the water meter and judging whether the flow of the water meter is greater than the preset flow, if so, optimizing the R value in the second optimized Kalman filtering model according to a third preset R value calculation formula to obtain a second optimized Kalman filtering model, and if not, optimizing the R value in the second optimized Kalman filtering model according to a fourth preset R value calculation formula to obtain the second optimized Kalman filtering model. By adopting the embodiment, assignment can be carried out according to different flow intervals, so that the purposes that quick response is needed but the filtering requirement is low when large-flow data is processed, and the filtering requirement is high but quick response is not needed when small-flow data is processed are achieved; and meanwhile, the condition that the R value approaches to 0 due to too low flow is prevented. Wherein the third preset R value is calculated by the formula of R ═ O1*K1+ M, and the fourth preset R value is calculated by the formula of R ═ O1*K2+M，O1The last output value of the Kalman filter, K1And K2Are proportional parameters preset according to the flow, M is a preset compensation value, K1＞K2。
The invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the above methods when executing the computer program.
The invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of any of the above-described methods.
In summary, in the field of water meters, a kalman filter algorithm may be used for data processing. The parameters Q and R of the traditional Kalman filter are calculated by a fixed formula, then the iteration is continued to process data continuously, but the parameters do not accord with the characteristic of water meter data processing, therefore, under the condition that the original Kalman filter is not changed, the difference value between the output value O1 of the last Kalman filter and the measured value O2 is used as Q, and R is divided into two conditions, because the required precision of the Q2 is 5% below the precision requirement in the secondary table precision of the water meter industry, and the required precision is 2% above, the R value is processed in a segmented mode, in the low-speed flow measurement interval, the R value is the percentage K1 of the last output value O1, in the high-speed flow measurement interval, the R value is the percentage K2 of the last output value, and K1 is more than K2; in order to prevent the R value from approaching 0 due to too low flow, a compensation value M needs to be added. The summary is that: q ═ O1-O2 |; the high flow rate interval R is Q1 × K2+ M, and the low flow rate interval R is Q1 × K1+ M.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (10)

1. A water meter filtering method based on Kalman filtering is characterized by comprising the following steps:
constructing an initial Kalman filtering model;
optimizing the Q value in the initial Kalman filtering model according to a preset Q value calculation formula to obtain a first optimized Kalman filtering model;
optimizing the R value in the second optimized Kalman filtering model according to a preset R value calculation formula to obtain a second optimized Kalman filtering model;
and filtering the collected water meter data according to the second optimized Kalman filtering model.
2. The kalman filtering based water meter filtering method according to claim 1, wherein the predetermined Q value calculation formula is:
Q＝|O1-O2l wherein O1As output value of the last Kalman filter, O2Is the output value of the current Kalman filter.
3. The kalman filtering based water meter filtering method according to claim 1, wherein the predetermined R value is calculated by the following formula:
R＝O1k, wherein O1And K is a proportional parameter preset according to the flow, and is the output value of the last Kalman filter.
4. The kalman filtering based water meter filtering method according to claim 1, wherein the predetermined R value is calculated by the following formula:
R＝O1k + M, wherein O1And K is a proportional parameter preset according to the flow, and M is a preset compensation value.
5. The kalman filter-based water meter filtering method according to claim 3, wherein the step of optimizing the R value in the second optimized kalman filter model according to a predetermined R value calculation formula to obtain a second optimized kalman filter model comprises:
acquiring the flow of the water meter;
judging whether the flow of the water meter is larger than a preset flow or not;
if yes, optimizing the R value in the second optimized Kalman filtering model according to a first preset R value calculation formula to obtain a second optimized Kalman filtering model;
if not, optimizing the R value in the second optimized Kalman filtering model according to a second preset R value calculation formula to obtain a second optimized Kalman filtering model;
wherein the first preset R value is calculated by the formula of R ═ O1*K1And the second preset R value is calculated by the formula of R ═ O1*K2，O1As the last Kalman filter output value, K1And K2Are all proportional parameters preset according to the flow, K1＞K2。
6. The kalman filter-based water meter filtering method according to claim 4, wherein the step of optimizing the R value in the second optimized kalman filter model according to a predetermined R value calculation formula to obtain a second optimized kalman filter model comprises:
acquiring the flow of the water meter;
judging whether the flow of the water meter is larger than a preset flow or not;
if so, optimizing the R value in the second optimized Kalman filtering model according to a first preset R value calculation formula to obtain a second optimized Kalman filtering model;
if not, optimizing the R value in the second optimized Kalman filtering model according to a second preset R value calculation formula to obtain a second optimized Kalman filtering model;
wherein the third preset R value is calculated by the formula of R ═ O1*K1+ M, and the fourth preset R value is calculated by the formula of R ═ O1*K2+M，O1As the last Kalman filter output value, K1And K2Are proportional parameters preset according to the flow, M is a preset compensation value, K1＞K2。
7. A water meter filtering system based on Kalman filtering is characterized in that the water meter filtering method based on Kalman filtering according to any one of claims 1 to 6 is implemented by the following steps:
the model construction module is used for constructing an initial Kalman filtering model;
the first optimization module is used for optimizing the Q value in the initial Kalman filtering model according to a preset Q value calculation formula to obtain a first optimized Kalman filtering model;
the second optimization module is used for optimizing the R value in the second optimized Kalman filtering model according to a preset R value calculation formula to obtain a second optimized Kalman filtering model;
and the filtering module is used for filtering the acquired water meter data according to the second optimized Kalman filtering model.
8. The kalman filter-based water meter filtering system of claim 7, wherein the second optimization module further includes:
the flow acquiring unit is used for acquiring the flow of the water meter;
the judging unit is used for judging whether the flow of the water meter is larger than the preset flow;
the first optimization unit is used for optimizing the R value in the second optimized Kalman filtering model according to a first preset R value calculation formula to obtain a second optimized Kalman filtering model when the judgment unit judges that the R value is positive;
and the second optimization unit is used for optimizing the R value in the second optimized Kalman filtering model according to a second preset R value calculation formula to obtain a second optimized Kalman filtering model when the judgment unit judges that the R value is not the first preset R value calculation formula.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 6 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
CN202111607352.XA 2021-12-27 2021-12-27 Water meter filtering method, system, computer and medium based on Kalman filtering Pending CN114526782A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111607352.XA CN114526782A (en) 2021-12-27 2021-12-27 Water meter filtering method, system, computer and medium based on Kalman filtering

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111607352.XA CN114526782A (en) 2021-12-27 2021-12-27 Water meter filtering method, system, computer and medium based on Kalman filtering

Publications (1)

Publication Number Publication Date
CN114526782A true CN114526782A (en) 2022-05-24

Family

ID=81619889

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111607352.XA Pending CN114526782A (en) 2021-12-27 2021-12-27 Water meter filtering method, system, computer and medium based on Kalman filtering

Country Status (1)

Country Link
CN (1) CN114526782A (en)

Similar Documents

Publication Publication Date Title
CN109115257B (en) Method, device, equipment and storage medium for correcting sensor characteristic curve
US20130251195A1 (en) Electronic device and method for measuring point cloud of object
CN114065114B (en) Method and system for predicting metering error of capacitive voltage transformer
CN114112459B (en) Air conditioner test operation control method and air conditioner
CN114553192A (en) Clock chip frequency compensation method and device, electronic equipment and storage medium
CN106997391A (en) A kind of method of steady state condition data in quick screening large scale process data
CN110749532A (en) Hydraulic engineering seepage intelligent monitoring system and method
CN108180935B (en) Fault detection method and device of sensor
CN114526782A (en) Water meter filtering method, system, computer and medium based on Kalman filtering
CN116499609A (en) Automatic identification method, system, computer equipment and storage medium for fluctuation transient of nuclear power plant
US20140035559A1 (en) Transient recovery voltage measuring device, transient recovery voltage measuring method, and transient recovery voltage measuring program
CN111580456B (en) Method for realizing cutter radius compensation on small line segment continuous track
CN110988787B (en) Method for realizing optimal direction finding angle measurement based on cluster analysis in wireless signal direction finding monitoring
CN108154292B (en) Method and system for establishing prediction model of road surface performance
US20160179282A1 (en) Background signal processing system and background signal processing method
TW202004331A (en) Etching time detection method and system thereof
CN113298256B (en) Adaptive curve learning method and device, computer equipment and storage medium
CN115164343B (en) PM value compensation method and device and air purification equipment
CN116823958B (en) Yaw angle estimation method, device, equipment, vehicle and medium of vehicle-mounted camera
CN115062273B (en) Photoelectric sensor precision control method and system for industrial internet
CN117278425B (en) Information technology operation and maintenance management method and system
CN117991845B (en) Battery piece drying furnace parameter control method and system for improving silicon wafer performance
CN117806166A (en) Machine control method, device, equipment and medium based on manual push rod
CN117471211A (en) Method, device, equipment and storage medium for determining reference neutral point
CN117936134A (en) Gain coefficient correction method, device, equipment and medium for power detector

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination