CN107462299B - Data processing method for calibrating oil tank volume meter by using integral - Google Patents
Data processing method for calibrating oil tank volume meter by using integral Download PDFInfo
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- CN107462299B CN107462299B CN201710703334.9A CN201710703334A CN107462299B CN 107462299 B CN107462299 B CN 107462299B CN 201710703334 A CN201710703334 A CN 201710703334A CN 107462299 B CN107462299 B CN 107462299B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F17/00—Methods or apparatus for determining the capacity of containers or cavities, or the volume of solid bodies
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F25/00—Testing or calibration of apparatus for measuring volume, volume flow or liquid level or for metering by volume
- G01F25/0084—Testing or calibration of apparatus for measuring volume, volume flow or liquid level or for metering by volume for measuring volume
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Abstract
The invention discloses a data processing method for calibrating an oil tank capacity meter by using integral, which comprises the following steps of collecting oil gun data of an oil tank and storing the oil gun data in a database; step two, screening the acquired data, and simultaneously acquiring a tank gun relation table of each oil tank of the gas station; thirdly, performing data preprocessing on the screened data according to a tank gun relation table to obtain effective data pairs; step four, performing curve fitting on the effective data pair, integrating the fitted curve formula to obtain a mathematical expression of the height and the volume of the oil tank, and generating a volume table; and step five, carrying out error analysis on the generated volume table. By implementing the invention, the normal operation of the gas station is ensured and the number quality management of the gas station is improved under the condition of not stopping the industry and cleaning the tank.
Description
Technical Field
The invention relates to a data processing method, in particular to a data processing method for calibrating an oil tank volume table by using integral.
Background
In the whole operation management process of the gas station, the quality management of the oil products plays a very important role. In the numerical quality management work, the calibration work of the oil tank volume table can reduce the oil product loss to a certain extent, optimize the oil product distribution and strengthen the environmental protection and safety. The oil tank volume table refers to the corresponding relation between the height and the volume of the buried oil tank. The traditional volumetric meter calibration method mainly comprises a geometric measurement method and a volumetric method. The geometric measurement method comprises an external geometric measurement method and an internal geometric measurement method, and the height of each circle of plate of the oil tank and the corresponding outer diameter or inner diameter are measured, so that the oil tank volume table is obtained through geometric calculation. The volumetric method is to clean the oil tank to be calibrated, then inject the determined amount of the liquid medium in the standard container into the oil tank, measure the height of the oil tank at the same time, repeat the process from low to high and then obtain the volumetric chart of the oil tank through difference. Both methods need the stop of a gas station, tank cleaning and manual measurement, and have certain data errors and safety problems.
Disclosure of Invention
The invention provides a data processing method and a data processing system for oil tank volumetric table calibration, aiming at the defect of large data error in the prior art.
In order to solve the technical problem, the invention is solved by the following technical scheme:
a data processing method for calibrating an oil tank volume table by using integral comprises the following steps:
collecting data: acquiring related data in the oil tank and related data of the oil gun at fixed time intervals, respectively generating a related data snapshot data table in the oil tank and a related data snapshot data table of the oil gun from the related data in the oil tank and the related data of the oil gun, and storing the data snapshot data tables in a database;
screening the collected related data: selecting a time period meeting conditions, screening all snapshot records of a certain gas station in the time period from a related data snapshot data table in the oil tank and a related data snapshot data table of the oil gun, and acquiring a tank gun relation table of all oil tanks from the gas station;
preprocessing the screened data to obtain effective data pairs: constructing a relation model of the height of the oil tank and the volume of the oil tank according to the screened snapshot records, obtaining the height difference, the volume difference and the height midpoint between two adjacent gun lifting records of the oil tank according to the snapshot records and a tank gun relation table, and representing the height midpoint as hiThe derivative at the midpoint of the height is expressed as yiThen the valid data pair is recorded as (h)i，yi)；
Fitting and integrating the effective data pairs to generate a volume table: fitting the data of the derivative at the point and the height midpoint to obtain a derivative function curve of the corresponding relation between the height and the volume, and integrating the derivative function to establish the corresponding relation between the height and the volume and generate a volume table;
and (3) carrying out error analysis on the generated volume table: performing error analysis through the oil discharge data, calculating the corresponding oil volume according to the height difference before and after oil discharge, calculating the relative error between the corresponding oil volume and the real oil discharge volume, and if all the errors are within the error allowable range, generating a volume table as a qualified volume table; and if all errors are not within the error allowable range, re-screening the data, and performing preprocessing, fitting and integrating until a qualified volume table is generated.
As an implementation manner, the step of preprocessing the screened data to obtain valid data pairs includes:
deriving a snapshot record of any oil tank in any time range at the gun lifting moment from the database, recording the snapshot record as m pieces, and reading an oil gun connected with the tank from a tank gun relation table as n pieces;
at any moment, each group of snapshot records comprises 1 piece of oil tank information and n pieces of oil gun pump code information, one oil gun state or a plurality of oil gun states in each snapshot record is a gun lifting state, the obtained effective data is m/(n +1) groups, the first oil gun state in each group of snapshot records is a data record related to the oil tank, the difference value between the height of the oil tank in the j +1 group record and the height of the oil tank in the j group record is taken and recorded as delta h ═ (h ═ h)j+1‐hj) The average of the tank height in the j +1 th group record and the tank height in the j group record is taken and recorded as h ═ h (h)j+hj+1)/2；
And acquiring the oil volume difference value between two adjacent groups of gun lifting records according to the sum of the j +1 th group of pump code values and the sum of the j group of pump code values: recording the sum of n oil gun pump code values recorded in the j +1 th group as s1The sum of n oil gun pump code values recorded in the jth group is recorded as s2If Δ v is equal to s1‐s2And Deltav represents the difference of oil volume values between two adjacent groups of gun lifting records, wherein j is within the range of 1-m/(n + 1);
the m/(n +1) groups of data are processed pairwise according to the time sequence to finally obtainAnd recording and storing the height midpoint value h and the derivative y of the group of oil tanks as delta v/delta h, and recording the height and the derivative of the oil tank in the ith group of records as (h)i，yi) That is, a valid data pair is obtained, wherein
As an implementable manner, the valid data pair is processed: will be provided withD, i is more than or equal to 1 and less than or equal to d, and the effective data pair (h)i，yi) Calculating the sum of squares of deviations R2：
Wherein R is2Is a constant;
fitting the expression: in sequence to a0,a1……akAnd (5) solving the partial derivatives, and further processing to obtain the following matrix:
wherein h isiThe tank heights in the ith group of records are indicated;
the matrix is further simplified, resulting in the following simplified matrix:
obtaining an expression of a height and derivative function through the simplified matrix: obtaining a coefficient matrix A by the simplified matrix, wherein A is ═ a0,a1,a2…ak]The fitting expression obtained by the coefficient matrix a is y (h) ═ a0+a1h+a2h2+…+akhkWherein a is0,a1,a2…akRepresenting the coefficients, k representing the order of the polynomial fit;
the volumetric table is obtained by fitting the expression: the functional expression of the volume v of the oil tank and the height h of the oil tank is expressed as v ═ f (h), y ═ df (h)/dh, and y (h) is subjected to indefinite integration, so that the relational expression of the volume v of the oil tank and the height is obtained: and v ═ f (h), and a volume table is generated.
Due to the adoption of the technical scheme, the invention has the remarkable technical effects that:
the invention fully utilizes data resources to carry out data fitting, can reduce volume errors caused by manual measurement, and improves the phenomenon of inaccurate volume table of the gas station to a certain extent. Firstly, screening data through effectively acquired gun lifting and hanging data of the oil station to obtain snapshot records before data processing, wherein the snapshot records comprise oil level height information and real-time oil gun pump code information. Secondly, preprocessing the screened data according to a tank gun relation table to obtain effective data pairs. And then, performing curve fitting on the effective data pair, integrating the mathematical expression obtained by fitting to obtain the mathematical expression of the height and the volume of the oil tank, and completing the calibration of the oil tank volume table. And finally, carrying out error analysis on the generated volume table. By implementing the invention, the normal operation of the gas station is ensured and the number quality management of the gas station is improved under the condition of not stopping the industry and cleaning the tank.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a graph of an overall data flow during calibration of a volumetric meter;
FIG. 3 is a flow chart of data preprocessing;
FIG. 4 is a diagram of the particular application of the Lagrangian median theorem in the present invention;
fig. 5 is a flowchart of calculating matrix H and matrix Y.
Detailed Description
The present invention will be described in further detail with reference to examples, which are illustrative of the present invention and are not to be construed as being limited thereto.
Example 1:
a data processing method for calibration of an oil tank volume table is shown in FIG. 1, and comprises the following steps:
s1, collecting data: acquiring related data in the oil tank and related data of the oil gun at fixed time intervals, respectively generating a related data snapshot data table in the oil tank and a related data snapshot data table of the oil gun from the related data in the oil tank and the related data of the oil gun, and storing the data snapshot data tables in a database;
s2, screening the collected related data: selecting a time period meeting conditions, screening all snapshot records of a certain gas station in the time period from a related data snapshot data table in the oil tank and a related data snapshot data table of the oil gun, and acquiring a tank gun relation table of all oil tanks from the gas station;
s3, preprocessing the screened data to obtain effective data pairs: constructing a relation model of the height of the oil tank and the volume of the oil tank according to the screened snapshot records, obtaining the height difference, the volume difference and the height midpoint between two adjacent gun lifting records of the oil tank according to the snapshot records and a tank gun relation table, and representing the height midpoint as hiThe derivative at the midpoint of the height is expressed as yiThen the valid data pair is recorded as (h)i，yi)；
S4, fitting and integrating the effective data pairs to generate a volume table: fitting the data of the derivative at the point and the height midpoint to obtain a derivative function curve of the corresponding relation between the height and the volume, and integrating the derivative function to establish the corresponding relation between the height and the volume and generate a volume table;
s5, carrying out error analysis on the generated volume table: performing error analysis through the oil discharge data, calculating the corresponding oil volume according to the height difference before and after oil discharge, calculating the relative error between the corresponding oil volume and the real oil discharge volume, and if all the errors are within the error allowable range, generating a volume table as a qualified volume table; and if all errors are not within the error allowable range, re-screening the data, and performing preprocessing, fitting and integrating until a qualified volume table is generated.
The design format of the snapshot data table is as follows, and the snapshot data table mainly comprises two equipment types of an oil tank and an oil gun:
the design format of the tank gun relation table is as follows, wherein the oil tank numbers are displayed in rows in a descending order. The oil tank and the connected oil guns are in the same row and are separated by using equal numbers as separators, and the oil gun numbers of the connected oil tank are separated by using blank spaces as separators and are written into a file;
| oil tank number … … |
| Oil gun number … … |
| …＝………… |
The screening process of the collected data comprises the following steps:
deriving a fuel tank data table of a certain fuel station from the database, and observing the trend of the height along with the change of time; and selecting a time range with larger change of the height of the oil tank, then exporting all snapshot records in the period of time and storing the snapshot records in a snapshot file, and simultaneously obtaining a tank gun relationship file oilguinfo of the gas station. The specific steps of step S3 are summarized as follows:
deriving a snapshot record of any oil tank in any time range at the gun lifting moment from the database, recording the snapshot record as m pieces, and reading an oil gun connected with the tank from a tank gun relation table as n pieces;
at any moment, each group of snapshot records comprises 1 piece of oil tank information and n pieces of oil gun pump code information, one oil gun state or a plurality of oil gun states in each snapshot record is a gun lifting state, the obtained effective data is m/(n +1) groups, the first oil gun state in each group of snapshot records is a data record related to the oil tank, the difference value between the height of the oil tank in the j +1 group record and the height of the oil tank in the j group record is taken and recorded as delta h ═ (h ═ h)j+1‐hj) The average of the tank height in the j +1 th group record and the tank height in the j group record is taken and recorded as h ═ h (h)j+hj+1)/2；
And acquiring the oil volume difference value between two adjacent groups of gun lifting records according to the sum of the j +1 th group of pump code values and the sum of the j group of pump code values: recording the sum of n oil gun pump code values recorded in the j +1 th group as s1The sum of n oil gun pump code values recorded in the jth group is recorded as s2If Δ v is equal to s1‐s2And Deltav represents the difference of oil volume values between two adjacent groups of gun lifting records, wherein j is within the range of 1-m/(n + 1).
The m/(n +1) groups of data are processed pairwise according to the time sequence to finally obtainAnd recording and storing the height midpoint value h and the derivative y of the group of oil tanks as delta v/delta h, and recording the height and the derivative of the oil tank in the ith group of records as (h)i，yi) That is, a valid data pair is obtained, wherein
The data fitting, integrating and generating the volumetric table are combined with the accompanying figure 5, and the steps comprise:
drawing a two-dimensional scatter diagram according to data generated by data preprocessing, defining a function expression as a polynomial, and setting k to be 3; calculate the matrix H with the input [ H1,h2…hd]And the method is realized by four layers of circulation:
a first layer of loops, constructed cyclically (starting from 0,0) in q columns according to the position of the elements in the matrix, i.e. p rows,
second layer cycle, cumulative sumObtaining matrix elements;
a third layer of circulation, wherein a row matrix is obtained according to the second layer of circulation;
a fourth layer of circulation, wherein an H matrix is obtained according to the third layer of circulation;
calculate matrix Y, input as (h)1,y1),(h2,y2)…(hd,yd) And the method is realized by utilizing three layers of circulation:
first layer of loops, constructed according to the position of the elements in the matrix, i.e. p rows (starting from 0) of loops, hp*y；
Second layer cycle, cumulative sumObtaining row elements;
a third layer of circulation, wherein a matrix is obtained according to the second layer of circulation;
designing an algorithm according to A ═ H '. H-1. H'. Y, taking the matrix H and the matrix Y as input, and outputting to obtain a coefficient matrix A;
and obtaining an A matrix according to the algorithm to obtain a derivative function curve expression of the height and the volume, performing indefinite integration on a mathematical expression of the derivative function curve, and obtaining a mathematical relation between the height and the volume with an initial value of (0,0), thereby generating an oil tank volume table.
In this embodiment, the snapshot data table in step S1 includes record number, gas station number, tank number, oil level, oil temperature, gun number, gun pump number, and status (including gun lifting and hanging status). And when a refueling record is generated, the oil tank information and snapshot data acquisition steps are as follows:
starting the oiling machine and the oil gun, initializing a program, and establishing a gun lifting pump code value according to gun lifting information before receiving a legal oiling record;
the oil gun is lifted to be filled with oil, and an oil filling record is generated. If the refueling record is legal, establishing a lance-hanging pump code value according to lance-hanging information, and if the record is illegal, not operating;
the illegal transaction record is that the end time of the last oiling record on the oil gun appears after the oiling record begins;
the legal deal record is that the end time of the last oiling record on the oil gun is before the oiling record of the oil gun;
and continuously updating the refueling transaction record, updating the gun lifting information pump code value and the gun hanging information pump code value of the oil gun in real time, and storing the values into a snapshot table of a database.
In S3, all the gun raising pump code values are used as data samples to obtain data with high effectiveness. The reason is that the hanging and robbing moment is soon after the oiling is finished, the fluctuation of the oil level in the tank is large, and errors may exist in the acquired data. The gun lifting moment is the starting moment before the refueling, the time interval from the last refueling ending moment is large, the oil level fluctuation is small, and the acquired data are stable.
In S4, the valid data pair (h) can be usedi，yi) The reason for this fit is, according to the Lagrangian median theorem, in (h) as shown in FIG. 4i，hi+1) There is a point such that the derivative of the tank height at this point with respect to the tank volume is equal to the ratio av/ah of the volume difference to the height difference recorded twice; because the oil tank has larger volume and the height difference between every two refueling records is smaller, the derivative at the midpoint of the height recorded by two adjacent lifting guns can be approximately considered as the ratio delta v/delta h of the volume difference and the height difference between the two records in the invention;
at S4, performing fitting and integration processing on the valid data pairs to generate a volume table: fitting the data of the height midpoint and the derivative at the point to obtain a derivative function curve of the corresponding relation between the height and the volume, integrating the derivative function to establish the corresponding relation between the height and the volume, and generating the volume table by the following specific steps:
curve fitting is carried out on the obtained midpoint value h and the derivative y ═ delta v/delta h of the height of the oil tank, a fitting expression is recorded as y (h), a function expression of the height of the oil tank and the volume of the oil tank is expressed as v ═ f (h), and y (h) ═ df (h)/dh is obtained;
for convenience of description, it is recorded asWherein i is more than or equal to 1 and less than or equal to d, the data fitting is carried out on the height midpoint and the derivative thereof by adopting a least square method, polynomial fitting is adopted, and the expected fitting expression is given as y (h) a0+a1h+a2h2+…+akhkWherein A ═ a0,a1,a2…ak]Coefficient matrix representing polynomial, k representing order of polynomial fit, for valid data pair (h)i，yi) Calculating the sum of squares of deviations R2(R2As a constant):
to obtain the coefficient matrix A, on both sides of the equation, a is respectively paired0,a1……akThe partial derivatives are calculated to obtain the following (k +1) formulas,
the above equations are collated and the options for h are placed to the left and the options for y are placed to the right, and,
the conversion into a matrix form results in the following formula one,
this vandermonde matrix is simplified to obtain,
the above expression is expressed as matrix H + a ═ Y, and from the matrix operation, a ═ H '+ H-1+ H' + Y is obtained, and coefficient matrix a is obtained, and a fitting curve, i.e. the relation Y (H) of height and derivative function is obtained.
Since y (h) ═ df (h)/dh, the derivative curve is integrated indefinitely according to the expression y (h) and the initial value (0,0) of the derivative curve, and the relational expression of height and volume can be obtained, and v ═ f (h). The physical meaning of the initial value (0,0) is that the oil tank volume is zero when the oil tank height is zero.
In addition, it should be noted that the specific embodiments described in the present specification may differ in the shape of the components, the names of the components, and the like. All equivalent or simple changes of the structure, the characteristics and the principle of the invention which are described in the patent conception of the invention are included in the protection scope of the patent of the invention. Various modifications, additions and substitutions for the specific embodiments described may be made by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
Claims (2)
1. A data processing method for calibrating an oil tank volume table by using integral is characterized by comprising the following steps:
collecting data: acquiring related data in the oil tank and related data of the oil gun at fixed time intervals, respectively generating a related data snapshot data table in the oil tank and a related data snapshot data table of the oil gun from the related data in the oil tank and the related data of the oil gun, and storing the data snapshot data tables in a database;
screening the collected related data: selecting a time period meeting conditions, screening all snapshot records of a certain gas station in the time period from a related data snapshot data table in the oil tank and a related data snapshot data table of the oil gun, and acquiring a tank gun relation table of all oil tanks from the gas station;
preprocessing the screened data to obtain effective data pairs: constructing a relation model of the height of the oil tank and the volume of the oil tank according to the screened snapshot records, obtaining the height difference, the volume difference and the height midpoint between two adjacent gun lifting records of the oil tank according to the snapshot records and a tank gun relation table, and representing the height midpoint as hiThe derivative at the midpoint of the height is expressed as yiThen the valid data pair is recorded as (h)i，yi)；
Fitting and integrating the effective data pairs to generate a volume table: fitting the data of the height midpoint and the derivative at the point to obtain a derivative function curve of the corresponding relation between the height and the volume, integrating the derivative function to establish the corresponding relation between the height and the volume, generating a volume table, and processing the effective data pair: will be provided withD, i is more than or equal to 1 and less than or equal to d, and the effective data pair (h)i，yi) Calculating the sum of squares of deviations R2：
Wherein R is2As a constant, deriving a snapshot record of any oil tank in any time range at the gun lifting moment from the database, recording the snapshot record as m pieces, and reading oil guns connected with the oil tank from a tank gun relation table as n pieces;
fitting the above formula: in sequence to a0,a1……akAnd (5) solving the partial derivatives, and further processing to obtain the following matrix:
wherein h isiThe tank heights in the ith group of records are indicated;
the matrix is further simplified, resulting in the following simplified matrix:
obtaining an expression of a height and derivative function through the simplified matrix: obtaining a coefficient matrix A by the simplified matrix, wherein A is ═ a0,a1,a2…ak]The fitting expression obtained by the coefficient matrix a is y (h) ═ a0+a1h+a2h2+…+akhkWherein a is0,a1,a2…akRepresenting the coefficients, k representing the order of the polynomial fit;
the volumetric table is obtained by fitting the expression: the functional expression of the volume v of the oil tank and the height h of the oil tank is expressed as v ═ f (h), y ═ df (h)/dh, and y (h) is subjected to indefinite integration, so that the relational expression of the volume v of the oil tank and the height is obtained: f (h), and generating a volume table;
and (3) carrying out error analysis on the generated volume table: performing error analysis through the oil discharge data, calculating the corresponding oil volume according to the height difference before and after oil discharge, calculating the relative error between the corresponding oil volume and the real oil discharge volume, and if all the errors are within the error allowable range, generating a volume table as a qualified volume table; and if all errors are not within the error allowable range, re-screening the data, and performing preprocessing, fitting and integrating until a qualified volume table is generated.
2. The method of claim 1, wherein the step of preprocessing the screened data to obtain valid data pairs comprises:
at any moment, each group of snapshot records comprises 1 piece of oil tank information and n pieces of oil gun pump code information, one oil gun state or a plurality of oil gun states in each snapshot record is a gun lifting state, the obtained effective data is m/(n +1) groups, the first oil gun state in each group of snapshot records is a data record related to the oil tank, the difference value between the height of the oil tank in the j +1 group record and the height of the oil tank in the j group record is taken and recorded as delta h ═ (h ═ h)j+1-hj) The average of the tank height in the j +1 th group record and the tank height in the j group record is taken and recorded as h ═ h (h)j+hj+1)/2；
And acquiring the oil volume difference value between two adjacent groups of gun lifting records according to the sum of the j +1 th group of pump code values and the sum of the j group of pump code values: recording the sum of n oil gun pump code values recorded in the j +1 th group as s1The sum of n oil gun pump code values recorded in the jth group is recorded as s2If Δ v is equal to s1-s2And Deltav represents the difference of oil volume values between two adjacent groups of gun lifting records, wherein j is within the range of 1-m/(n + 1);
the m/(n +1) groups of data are processed pairwise according to the time sequence to finally obtainAnd recording and storing the height midpoint value h and the derivative y of the group of oil tanks as delta v/delta h, and recording the height and the derivative of the oil tank in the ith group of records as (h)i，yi) That is, a valid data pair is obtained, wherein
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| CN112461336B (en) * | 2020-11-13 | 2024-04-26 | 王刚 | Method for calibrating filling station oil tank volume meter |
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