CN115357070B - Branch pipe heating real-time temperature monitoring feedback method, system and storage medium - Google Patents
Branch pipe heating real-time temperature monitoring feedback method, system and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a system for monitoring and feeding back the heating real-time temperature of a branch pipe and a storage medium, which relate to the technical field of branch pipe forming monitoring and comprise the following steps: according to the branch pipe forming process of the current processing, a branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model is established in advance; solving the error value of the pipe heating environment in the current processing environment; calculating to obtain a medium-frequency heating frequency correction value according to the pipe heating environment error value; calculating the intermediate frequency heating power in the current processing environment state to obtain an actual value of the intermediate frequency heating power; and (4) heating the pipe according to the actual value of the medium-frequency heating power, detecting the temperature of a pipe heating area in real time, and judging whether the pipe heating area meets the standard heating temperature of the branch pipe. The invention has the advantages that: a regression model of parameters of branch pipe temperature-intermediate frequency heating power and pipe wall thickness is established, and the stability of pipe heating temperature in the actual branch pipe processing process is effectively guaranteed.
Description
Technical Field
The invention relates to the technical field of branch pipe forming monitoring, in particular to a branch pipe heating real-time temperature monitoring feedback method, a branch pipe heating real-time temperature monitoring feedback system and a storage medium.
Background
The nickel-based alloy is a high-temperature alloy material which takes nickel as a matrix and has higher strength and good oxidation resistance and corrosion resistance in the temperature range of 650 to 1000 ℃, and is widely applied to a plurality of fields such as oceans, environmental protection, energy, petrochemical industry and the like. At present, more and more projects are demanding higher performance requirements for stainless steels, and the demand for nickel-based alloys is also increasing. However, there are still many places to be improved for the production and treatment process of nickel base, during the hot extrusion forming process of nickel base alloy, when the extrusion temperature is too low, the nickel base alloy will precipitate sigma-phase brittle tissue, and the continuous extrusion of the nickel base alloy will cause cracking of the inner and outer surfaces of the extrusion branch pipe, so in the actual forming process of the nickel base alloy branch pipe, it is very important to accurately control the extrusion temperature.
Based on the method, the real-time temperature monitoring feedback method capable of realizing high speed and high responsiveness of the forming and processing of the nickel-based alloy branch pipe is very important.
Disclosure of Invention
In order to solve the technical problems, the technical scheme provides a method, a system and a storage medium for monitoring and feeding back the heating real-time temperature of the branch pipe, and aims to provide a method for monitoring and feeding back the real-time temperature of the nickel-based alloy branch pipe with high speed and high responsiveness, so that the extrusion temperature can be accurately controlled in the nickel-based alloy branch pipe forming process.
In order to achieve the purpose, the invention adopts the technical scheme that:
a real-time temperature monitoring feedback method for branch pipe forming comprises the following steps:
according to the current branch pipe forming process, a branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model is established in advance, and the branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model is stored in a database;
obtaining branch pipe forming parameters, and determining the standard heating temperature of the branch pipe according to the branch pipe forming parameters;
calculating the intermediate frequency heating power under the current processing technology according to the branch pipe heating temperature information, the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter regression model to obtain an intermediate frequency heating power theoretical value;
heating the pipe according to the theoretical value of the medium-frequency heating power, detecting the temperature of a pipe heating area in real time to obtain the real-time pipe heating temperature, and calculating the error value of the pipe heating environment in the current processing environment according to the difference between the real-time pipe heating temperature and the standard branch pipe heating temperature;
calculating to obtain a medium-frequency heating frequency correction value according to the pipe heating environment error value;
calculating the medium-frequency heating power in the current processing environment state according to the medium-frequency heating frequency corrected value to obtain a medium-frequency heating power actual value;
heating the pipe according to the actual value of the medium-frequency heating power, detecting the temperature of a pipe heating area in real time, judging whether the temperature meets the standard heating temperature of the branch pipe, if so, feeding back a qualified temperature signal, and if not, feeding back an unqualified temperature signal;
the method specifically comprises the following steps of establishing a regression model of branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameters:
respectively obtaining pipes with different wall thicknesses according to a preset gradient;
respectively heating the pipes with different wall thicknesses according to a set intermediate frequency heating power gradient, detecting the stable temperature of the pipes, and acquiring temperature sample data of a plurality of branch pipes;
establishing a mapping relation between the temperature sample data of each branch pipe and the intermediate-frequency heating power and the wall thickness of the pipe to obtain a plurality of groups of branch pipe temperature sample mapping data;
performing regression calculation on coefficients of a regression model of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter according to the branch pipe temperature sample mapping data to obtain a regression model of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter;
the regression calculation of the regression model coefficients of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameters comprises the following steps:
first, the following model is established:
equation 1
In the formula (I), wherein
，
Wherein the content of the first and second substances,
heating temperature for the branch pipe;
is provided with
Is estimated as
And then:
then the multiple regression equation of equation 1 is:
equation 2
Wherein C satisfies:
equation 1
Solving equation 1 to obtain regression equation coefficients
Is estimated by least squares.
Preferably, the method includes the following steps of heating the pipe according to the theoretical value of the medium-frequency heating power, detecting the temperature of a pipe heating area in real time to obtain a real-time pipe heating temperature, and calculating a pipe heating environment error value in the current processing environment according to the difference between the real-time pipe heating temperature and the standard branch pipe heating temperature:
collecting the temperature of the pipe under the theoretical value of the medium-frequency heating power in real time, and recording the temperature data at the moment as the actual temperature of the pipe when the feedback temperature of a plurality of continuous set times is not changed;
acquiring actual pipe temperature data of a plurality of pipes;
performing outlier rejection on the actual temperature data of the pipes;
averaging non-outliers in the actual temperature data of the plurality of pipes, and taking the average as a calculated value of the actual temperature of the pipes;
and (4) subtracting the calculated value of the actual temperature of the pipe from the standard heating temperature of the branch pipe, and solving the error value of the pipe heating environment in the current processing environment.
Preferably, the outlier rejection of the actual temperature data of the pipes specifically comprises the following steps:
arranging the actual temperature data of the plurality of pipes from small to large;
setting a detection level alpha and obtaining a critical value bp (n) of kurtosis detection;
calculating a kurtosis inspection value bk (n) of actual temperature data of each pipe;
judging whether the kurtosis inspection value bk (n) of the actual temperature data of the pipe is larger than a critical value bp (n) of the kurtosis inspection, if so, judging that the actual temperature data of the pipe is an outlier, eliminating the actual temperature data of the pipe, and if not, not responding.
Preferably, the calculation formula of the kurtosis test value bk (n) of the actual temperature data of the pipe is as follows:
in the formula, n is the ranking number of the actual temperature data of the pipe from small to large;
Preferably, the step of calculating the actual value of the intermediate-frequency heating power comprises the following steps:
substituting the pipe heating environment error value into a branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model to obtain an intermediate frequency heating frequency correction value;
and the corrected value of the intermediate frequency heating frequency is summed with the theoretical value of the intermediate frequency heating power to obtain the actual value of the intermediate frequency heating power.
Preferably, the heating of the pipe according to the actual value of the medium-frequency heating power, the real-time detection of the temperature of the pipe heating area, and the judgment of whether the temperature meets the standard heating temperature of the branch pipe specifically include the following steps:
detecting the temperature of a pipe heating area in real time to obtain branch pipe processing real-time temperature data;
comparing the branch pipe processing real-time temperature data with the standard heating temperature of the branch pipe, and judging whether the branch pipe processing real-time temperature data meets the processing requirements or not;
if yes, a temperature qualified signal is fed back, if not, a temperature unqualified signal is fed back, real-time deviation temperature data are calculated, deviation correction power is calculated according to the real-time deviation temperature data and a branch pipe temperature-intermediate frequency heating power and a pipe wall thickness parameter regression model, and the deviation correction power is output.
Further, a real-time temperature monitoring and feedback system for branch pipe forming is provided, which is used for implementing the above method for real-time temperature monitoring and feedback for branch pipe forming, and comprises:
the control module is used for outputting control signals to each component to realize control of each component;
the temperature monitoring module is electrically connected with the control module and is used for detecting the real-time temperature in the branch pipe forming and processing process and feeding the real-time temperature back to the control module;
the data processing module is electrically connected with the control module and is used for calculating branch pipe temperature-intermediate frequency heating power, a pipe wall thickness parameter regression model, an intermediate frequency heating power theoretical value and an intermediate frequency heating power actual value;
the data storage module is electrically connected with the control module and the data processing module and is used for storing a branch pipe temperature-intermediate frequency heating power and a pipe wall thickness parameter regression model;
and the signal output module is electrically connected with the control module and the data processing module and is used for outputting signals.
Further, a storage medium is provided, on which a computer program is stored, and when the computer program is called to run, the branch pipe forming real-time temperature monitoring feedback method is executed.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, a branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model is established based on the pipe wall thickness and intermediate frequency heating power which have determining factors for the branch pipe heating temperature in the branch pipe forming process, and in the actual processing, according to the linear regression relationship existing in the branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model, the intermediate frequency heating power calculated by the branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model is rapidly corrected by collecting the heating temperature at the current environment temperature, so that the stability of the pipe heating temperature in the actual branch pipe processing process is effectively ensured, the situation that the inner and outer surfaces of the extruded branch pipe are cracked due to the fact that the nickel-based alloy can separate out sigma-phase brittle tissues when the extrusion temperature is too low in the hot extrusion forming process is effectively avoided, and the forming quality of the branch pipe is ensured.
Drawings
FIG. 1 is a flow chart of steps S100-S700 of a branch pipe forming real-time temperature monitoring feedback method provided by the invention;
FIG. 2 is a flow chart of steps S101-S104 of a branch pipe forming real-time temperature monitoring feedback method provided by the invention;
FIG. 3 is a flow chart of steps S401-S405 of a branch pipe forming real-time temperature monitoring feedback method according to the present invention;
FIG. 4 is a flow chart of steps S406-S409 of a branch pipe forming real-time temperature monitoring feedback method according to the present invention;
FIG. 5 is a flow chart of steps S601-S602 of a branch pipe forming real-time temperature monitoring feedback method according to the present invention;
FIG. 6 is a flow chart of steps S701-S703 of a branch pipe forming real-time temperature monitoring feedback method provided by the present invention;
FIG. 7 is a block diagram of a branch pipe forming real-time temperature monitoring feedback system according to the present invention;
FIG. 8 is a schematic view of a manifold forming heating arrangement.
Detailed Description
The following description is provided to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art.
Referring to fig. 1, a method for real-time temperature monitoring and feedback of branch pipe forming includes:
s100, according to a currently processed branch pipe forming process, a branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model is established in advance, and the branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model is stored in a database;
s200, obtaining branch pipe forming parameters, and determining standard heating temperature of the branch pipe according to the branch pipe forming parameters;
s300, calculating the intermediate frequency heating power under the current processing technology according to branch pipe heating temperature information, a branch pipe temperature-intermediate frequency heating power and a pipe wall thickness parameter regression model to obtain an intermediate frequency heating power theoretical value;
s400, heating the pipe according to the medium-frequency heating power theoretical value, detecting the temperature of a pipe heating area in real time to obtain the pipe heating real-time temperature, and calculating the pipe heating environment error value in the current processing environment according to the difference between the pipe heating real-time temperature and the standard heating temperature of the branch pipe;
s500, calculating according to the error value of the pipe heating environment to obtain a medium-frequency heating frequency correction value;
s600, calculating the intermediate frequency heating power in the current processing environment state according to the intermediate frequency heating frequency correction value to obtain an actual value of the intermediate frequency heating power;
s700, heating the pipe according to the actual value of the medium-frequency heating power, detecting the temperature of a pipe heating area in real time, judging whether the pipe heating area meets the standard heating temperature of the branch pipe, if so, feeding back a temperature qualified signal, and if not, feeding back a temperature unqualified signal.
In the scheme, a branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model is established in advance aiming at a currently processed branch pipe forming process, and then the intermediate frequency heating frequency in the elbow pipe processing process can be quickly obtained by substituting the processing process parameters into the branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model during actual branch pipe forming processing;
because the environmental information in the calculation process of the regression model of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter and the environmental information in actual processing possibly have deviation, the scheme introduces the calculation of the pipe heating environmental error value on the basis of the regression model of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter and calculates the intermediate frequency heating frequency correction value according to the pipe heating environmental error value, thereby realizing the targeted adjustment aiming at different processing environments and effectively ensuring the stability of the branch pipe forming temperature.
Referring to fig. 2, the establishing of the regression model of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameters specifically includes the following steps:
s101, respectively obtaining pipes with different wall thicknesses according to a preset gradient;
s102, respectively heating pipes with different wall thicknesses according to a set intermediate frequency heating power gradient, detecting the stable temperature of the pipes, and acquiring temperature sample data of a plurality of branch pipes;
s103, establishing a mapping relation between the temperature sample data of each branch pipe and the intermediate-frequency heating power and the wall thickness of the pipe to obtain a plurality of groups of branch pipe temperature sample mapping data;
s104, performing regression calculation on coefficients of a regression model of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter according to the branch pipe temperature sample mapping data to obtain a regression model of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter.
The regression calculation steps of the regression model coefficients of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter are as follows:
first, the following model was established:
equation 1
In the formula (I), wherein
，
Wherein, the first and the second end of the pipe are connected with each other,
heating temperature for the branch pipe;
is provided with
Is estimated as
And then:
then the multiple regression equation of equation 1 is:
equation 2
Wherein C satisfies:
equation 1
Solving equation 1 to obtain regression equation coefficient
Least squares estimation of (c).
Technical personnel in the field are well known, the heating temperature of branch pipe in forming process is relevant with the wall thickness of intermediate frequency heating power and tubular product, concretely, intermediate frequency heating power is big, then the heating temperature of branch pipe in forming process, the wall thickness of tubular product is thicker, under the same intermediate frequency heating power, the actual heating temperature of branch pipe in forming process is low, based on this, this scheme establishes branch pipe temperature-intermediate frequency heating power, tubular product wall thickness parameter regression model, in-service processing, through inputting this time the fashioned heating temperature of branch pipe and tubular product wall thickness can be quick calculate present intermediate frequency heating power theoretical value, very big reduction intermediate frequency heating power's calculation complexity, can effectual improvement branch pipe shaping parameter design efficiency.
Referring to fig. 3, heating a pipe according to a theoretical value of medium-frequency heating power, detecting a temperature of a pipe heating area in real time to obtain a real-time pipe heating temperature, and calculating an error value of a pipe heating environment in a current processing environment by subtracting the real-time pipe heating temperature from a standard branch pipe heating temperature, specifically including the following steps:
s401, collecting the temperature of the pipe under the theoretical value of medium-frequency heating power in real time, and recording the temperature data at the moment as the actual temperature of the pipe when the feedback temperature of a plurality of continuous set times is not changed;
s402, collecting the actual temperature of the pipes to obtain actual temperature data of the pipes;
s403, outlier rejection is carried out on the actual temperature data of the pipes;
s404, averaging non-outliers in the actual temperature data of the pipes, and taking the average as a calculated value of the actual temperature of the pipes;
s405, subtracting the calculated value of the actual temperature of the pipe from the standard heating temperature of the branch pipe, and calculating the error value of the pipe heating environment in the current processing environment.
Specifically, referring to fig. 4, the outlier rejection of the actual temperature data of the pipes specifically includes the following steps:
s406, arranging the actual temperature data of the pipes from small to large;
s407, setting the detection level alpha and obtaining a critical value bp (n) of the kurtosis test;
s408, calculating a kurtosis inspection value bk (n) of actual temperature data of each pipe;
s409, judging whether the kurtosis inspection value bk (n) of the actual temperature data of the pipe is larger than a critical value bp (n) of the kurtosis inspection, if so, judging that the actual temperature data of the pipe is an outlier, rejecting the actual temperature data of the pipe, and if not, not responding.
The calculation formula of the kurtosis inspection value bk (n) of the actual temperature data of the pipe is as follows:
in the formula, n is the ranking number of the actual temperature data of the pipe from small to large;
According to the scheme, data processing is carried out on a plurality of actual pipe temperature data heated according to a medium-frequency heating power theoretical value, on the basis of a kurtosis inspection method, after bilateral outliers in the actual pipe temperature data are removed, the actual pipe temperature data capable of accurately reflecting the current environment are reserved, the average value is obtained, and the obtained calculated value of the actual pipe temperature can accurately reflect the current pipe heating temperature state;
it will be understood by those skilled in the art that the detection level α represents the confidence level of the current data, which is usually between 0.01 and 0.1, and may be set smaller when the requirement is strict, or larger when the requirement is not strict, and in this embodiment, the detection level α is set to 0.05.
Referring to fig. 5, the actual value of the intermediate frequency heating power is calculated by the following steps:
s601, substituting the pipe heating environment error value into a branch pipe temperature-medium frequency heating power and pipe wall thickness parameter regression model to obtain a medium frequency heating frequency correction value;
and S602, summing the corrected value of the intermediate frequency heating frequency and the theoretical value of the intermediate frequency heating power to obtain an actual value of the intermediate frequency heating power.
According to a linear regression relation existing in a regression model of branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameters, a pipe heating environment error value is substituted into a theoretical value of intermediate frequency heating power and a corrected value of intermediate frequency heating frequency obtained in the regression model of branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameters, and then an actual value of intermediate frequency heating power is obtained, the pipe is heated by the actual value of intermediate frequency heating power, the requirement of the forming temperature of the branch pipe can be effectively met, and the forming quality of the branch pipe is guaranteed.
Referring to fig. 6, heating the pipe according to the actual value of the intermediate frequency heating power, detecting the temperature of the heating area of the pipe in real time, and determining whether the temperature meets the standard heating temperature of the branch pipe specifically includes the following steps:
s701, detecting the temperature of a pipe heating area in real time to obtain real-time temperature data for branch pipe machining;
s702, comparing the real-time temperature data of branch pipe machining with the standard heating temperature of the branch pipe, and judging whether the real-time temperature data of branch pipe machining meets the machining requirement or not;
and S703, if so, feeding back a temperature qualified signal, otherwise, feeding back a temperature unqualified signal, calculating real-time deviation temperature data, calculating deviation correction power according to the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter regression model according to the real-time deviation temperature data, and outputting the deviation correction power.
Through the real-time detection of the temperature of the material heating area, when temperature deviation occurs, deviation correction power can be quickly carried out through a branch pipe temperature-intermediate frequency heating power and a pipe wall thickness parameter regression model to adjust the heating of the pipe, and the stability of the branch pipe heating temperature in the branch pipe forming process is greatly guaranteed.
Referring to fig. 7, further, in combination with the branch pipe forming real-time temperature monitoring feedback method, a branch pipe forming real-time temperature monitoring feedback system is provided, which includes:
the control module is used for outputting control signals to each component to realize control on each component;
the temperature monitoring module is electrically connected with the control module and used for detecting the real-time temperature in the branch pipe forming and processing process and feeding the real-time temperature back to the control module;
the data processing module is electrically connected with the control module and is used for calculating branch pipe temperature-intermediate frequency heating power, a pipe wall thickness parameter regression model, an intermediate frequency heating power theoretical value and an intermediate frequency heating power actual value;
the data storage module is electrically connected with the control module and the data processing module and is used for storing a regression model of branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameters;
and the signal output module is electrically connected with the control module and the data processing module and is used for outputting signals.
The temperature monitoring feedback system comprises the following steps:
firstly, establishing a branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model, detecting the temperature of a sample branch pipe in the model establishment experiment by a temperature monitoring module, inputting a plurality of groups of branch pipe temperature sample mapping data into a data processing module for analysis and processing, obtaining branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model, and storing the branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model into a data storage module;
inputting current branch pipe forming parameters, and controlling a data processing module by a control module according to the branch pipe forming parameters to calculate a theoretical value of medium-frequency heating power;
thirdly, the signal output module outputs a theoretical value of medium-frequency heating power to heat the branch pipe, the temperature monitoring module monitors the temperature of the pipe and feeds the temperature back to the control module, and the control module transmits real-time temperature data of the pipe to the data processing module to calculate an actual value of the medium-frequency heating power;
and step four, the signal output module outputs an actual value of the intermediate frequency heating power to heat the branch pipe, the temperature monitoring module monitors the temperature of the pipe and feeds the temperature back to the control system, the control system judges that the standard heating temperature of the branch pipe is met, if so, the signal output module feeds back a qualified temperature signal, if not, the signal output module feeds back an unqualified temperature signal, the data processing module calculates real-time deviation temperature data, the deviation correction power is calculated according to the real-time deviation temperature data according to the branch pipe temperature-intermediate frequency heating power and a pipe wall thickness parameter regression model, and the signal output module outputs the deviation correction power.
Furthermore, the present invention also provides a storage medium, on which a computer program is stored, and when the computer program is called to run, the branch pipe forming real-time temperature monitoring feedback method is executed.
It is understood that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape; optical media such as DVD; or semiconductor media such as solid state disk SolidStateDisk, SSD, etc.
In summary, the invention has the advantages that: a regression model of branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameters is established, and the stability of pipe heating temperature in the actual branch pipe machining process is effectively guaranteed.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the invention, but various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. A real-time temperature monitoring and feedback method for branch pipe forming is characterized by comprising the following steps:
according to the current branch pipe forming process, a branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model is established in advance, and the branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model is stored in a database;
obtaining branch pipe forming parameters, and determining standard heating temperature of the branch pipe according to the branch pipe forming parameters;
calculating the medium-frequency heating power under the current processing technology according to the branch pipe heating temperature information, the branch pipe temperature-medium-frequency heating power and the pipe wall thickness parameter regression model to obtain a medium-frequency heating power theoretical value;
heating the pipe according to the theoretical value of the medium-frequency heating power, detecting the temperature of a pipe heating area in real time to obtain the real-time heating temperature of the pipe, and calculating the error value of the pipe heating environment in the current processing environment according to the difference between the real-time heating temperature of the pipe and the standard heating temperature of the branch pipe;
calculating to obtain a medium-frequency heating frequency correction value according to the pipe heating environment error value;
calculating the medium-frequency heating power in the current processing environment state according to the medium-frequency heating frequency corrected value to obtain a medium-frequency heating power actual value;
heating the pipe according to the actual value of the medium-frequency heating power, detecting the temperature of a pipe heating area in real time, judging whether the pipe heating area meets the standard heating temperature of the branch pipe, if so, feeding back a temperature qualified signal, and if not, feeding back a temperature unqualified signal;
the method specifically comprises the following steps of establishing a regression model of branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameters:
respectively obtaining pipes with different wall thicknesses according to a preset gradient;
respectively heating the pipes with different wall thicknesses according to a set intermediate frequency heating power gradient, detecting the stable temperature of the pipes, and acquiring temperature sample data of a plurality of branch pipes;
establishing a mapping relation between the temperature sample data of each branch pipe and the intermediate-frequency heating power and the wall thickness of the pipe to obtain a plurality of groups of branch pipe temperature sample mapping data;
performing regression calculation on coefficients of a regression model of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter according to the branch pipe temperature sample mapping data to obtain a regression model of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter;
the regression calculation steps of the regression model coefficients of the branch pipe temperature-intermediate frequency heating power and the pipe wall thickness parameter are as follows:
first, the following model was established:
equation 1
In the formula (I), wherein
，
Wherein, the first and the second end of the pipe are connected with each other,
heating temperature for the branch pipe;
is provided with
Is estimated as
Then:
then the multiple regression equation of equation 1 is:
equation 2
Wherein C satisfies:
equation 1
Solving equation 1 to obtain regression equation coefficient
Least squares estimation of (c).
2. The method for monitoring and feeding back the real-time temperature of branch pipe forming as claimed in claim 1, wherein the method comprises the steps of heating the pipe according to a theoretical value of medium-frequency heating power, detecting the temperature of a heating area of the pipe in real time to obtain a real-time heating temperature of the pipe, and calculating an error value of the pipe heating environment in the current processing environment by subtracting the real-time heating temperature of the pipe from a standard heating temperature of the branch pipe, and specifically comprises the following steps:
collecting the temperature of the pipe under the theoretical value of the medium-frequency heating power in real time, and recording the temperature data at the moment as the actual temperature of the pipe when the feedback temperature of a plurality of continuous set times is not changed;
acquiring the actual temperature of the pipes to obtain actual temperature data of the pipes;
removing outliers from the actual temperature data of the pipes;
averaging non-outliers in the actual temperature data of the plurality of pipes, and taking the average as a calculated value of the actual temperature of the pipes;
and (4) subtracting the calculated value of the actual temperature of the pipe from the standard heating temperature of the branch pipe to obtain an error value of the pipe heating environment in the current processing environment.
3. The method for monitoring and feeding back the real-time temperature for branch pipe forming as claimed in claim 2, wherein the step of performing outlier rejection on the actual temperature data of the plurality of pipes specifically comprises the following steps:
arranging the actual temperature data of the pipes from small to large;
setting a detection level alpha, and obtaining a critical value bp (n) of kurtosis detection;
calculating a kurtosis inspection value bk (n) of actual temperature data of each pipe;
judging whether the kurtosis inspection value bk (n) of the actual temperature data of the pipe is larger than a critical value bp (n) of the kurtosis inspection, if so, judging that the actual temperature data of the pipe is an outlier, eliminating the actual temperature data of the pipe, and if not, not responding.
4. The method for monitoring and feeding back the real-time temperature of the branch pipe forming as claimed in claim 3, wherein the kurtosis test value bk (n) of the actual temperature data of the pipe is calculated by the formula:
in the formula, n is the ranking number of the actual temperature data of the pipe from small to large;
5. The method for real-time temperature monitoring and feedback of branch pipe forming according to claim 4, wherein the step of calculating the actual value of the intermediate frequency heating power comprises:
substituting the pipe heating environment error value into a branch pipe temperature-intermediate frequency heating power and pipe wall thickness parameter regression model to obtain an intermediate frequency heating frequency correction value;
and the corrected value of the intermediate frequency heating frequency is summed with the theoretical value of the intermediate frequency heating power to obtain the actual value of the intermediate frequency heating power.
6. The method for monitoring and feeding back the real-time temperature of branch pipe forming according to claim 5, wherein the steps of heating the pipe according to the actual value of the medium-frequency heating power, detecting the temperature of the heating area of the pipe in real time, and judging whether the temperature meets the standard heating temperature of the branch pipe specifically comprise:
detecting the temperature of a pipe heating area in real time to obtain branch pipe processing real-time temperature data;
comparing the branch pipe processing real-time temperature data with the standard heating temperature of the branch pipe, and judging whether the branch pipe processing real-time temperature data meets the processing requirement or not;
if yes, a temperature qualified signal is fed back, if not, a temperature unqualified signal is fed back, real-time deviation temperature data are calculated, deviation correction power is calculated according to the real-time deviation temperature data and a branch pipe temperature-intermediate frequency heating power and a pipe wall thickness parameter regression model, and the deviation correction power is output.
7. A branch pipe forming real-time temperature monitoring feedback system for implementing the branch pipe forming real-time temperature monitoring feedback method according to any one of claims 1 to 6, comprising:
the control module is used for outputting control signals to each component to realize control of each component;
the temperature monitoring module is electrically connected with the control module and is used for detecting the real-time temperature in the branch pipe forming and processing process and feeding the real-time temperature back to the control module;
the data processing module is electrically connected with the control module and is used for calculating branch pipe temperature-intermediate frequency heating power, a pipe wall thickness parameter regression model, an intermediate frequency heating power theoretical value and an intermediate frequency heating power actual value;
the data storage module is electrically connected with the control module and the data processing module and is used for storing a branch pipe temperature-intermediate frequency heating power and a pipe wall thickness parameter regression model;
and the signal output module is electrically connected with the control module and the data processing module and is used for outputting signals.
8. A storage medium having a computer program stored thereon, wherein the computer program, when invoked for execution, performs a branch pipe forming real-time temperature monitoring feedback method according to any one of claims 1-6.
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