CN116107281B - Thermal-insulated oil casing performance test tube accuse system based on data analysis - Google Patents
Thermal-insulated oil casing performance test tube accuse system based on data analysis Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
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- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a heat-insulating oil sleeve performance test tube control system based on data analysis, which relates to the technical field of heat-insulating oil sleeve performance test management, and comprises a performance test unit, a management and control server, an information storage unit, a quality analysis unit, a display recommendation unit, a cost monitoring terminal and a cost acquisition unit; generating a process performance set and an optimal performance set through heat insulation capability information analysis, generating a plurality of cost characteristic values through cost information analysis, extracting the cost characteristic values smaller than a preset cost value, and combining the cost characteristic values with a manufacturing process to construct a cost characteristic set; and step-by-step screening analysis is performed through the collection to obtain a feedback warning text or a preferred recommending process, on one hand, the making process is subjected to anticipated measurement and early warning, and on the other hand, staff is assisted in making rules for mass production and realizing the performance recommendation of maximizing energy conservation and optimization.
Description
Technical Field
The invention relates to the technical field of performance test management of heat-insulating oil sleeves, in particular to a heat-insulating oil sleeve performance test tube control system based on data analysis.
Background
With the continuous development of the petroleum exploitation industry and the continuous increase of petroleum consumption, the exploitation of thick oil has become a main yield source of petroleum companies at home and abroad, but the exploitation of thick oil generally adopts two processes of steam huff and puff and steam drive, and the two processes all need to use a heat insulation oil pipe and a sleeve as a steam injection oil extraction tool, wherein the heat insulation oil sleeve consists of a coupling, a heat insulation lining, a liner pipe, an outer pipe, an inner pipe, a heat insulation layer, a centralizing block, a hydrogen absorber and the like, and the heat insulation layer has the structure that: the inner pipe is wound by a plurality of layers of aluminum foils and glass screens at intervals, the annular space is vacuumized, and the hydrogen absorbing agent is added, so that the heat insulation oil sleeve produced by the manufacturing process has good apparent heat conductivity coefficient, and plays a positive role in protecting the oil well sleeve, reducing the heat loss of a shaft and improving the injection and production effect.
However, the method has some defects in the process of making process standards, and the problems that the method cannot realize the maximum energy conservation and optimal performance recommendation because the method cannot collect relevant parameters, perform expected measurement and early warning on the manufacturing process and assist workers in making rules for mass production.
Disclosure of Invention
The invention aims at: on one hand, the method carries out expected measurement and early warning on the manufacturing process, and on the other hand, assists staff in making rules for mass production and realizes the maximum energy conservation and optimal performance recommendation.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the system comprises a performance test unit, wherein the performance test unit is in signal connection with a management and control server, the management and control server comprises an information storage unit and a quality analysis unit, and the management and control server is in signal connection with a display recommendation unit;
the performance test unit is used for testing the heat insulation oil sleeves under different standard manufacturing processes, generating corresponding heat insulation capability information, combining the heat insulation capability information with the corresponding manufacturing process to generate process tag information, and transmitting the process tag information to the information storage unit of the management and control server;
the information storage unit is used for receiving and storing a plurality of process label information;
the quality analysis unit is used for acquiring the number of the process tag information in the information storage unit in real time, extracting all the process tag information in the information storage unit when the number of the process tag information is larger than a preset number value, quantitatively analyzing all the process tag information to obtain a plurality of performance evaluation factors, and comparing and screening the performance evaluation factors with preset evaluation values respectively to generate a process performance set; the generated process performance set is also sent to a display recommendation unit;
the display recommending unit is used for receiving the process performance set, extracting performance evaluation factors in the process performance set, sorting the manufacturing processes in the process performance set from large to small according to the size of the performance evaluation factors, obtaining the manufacturing process of the front 10 in the process performance set after sorting, extracting and constructing the manufacturing process to generate an optimal performance set, and sending the optimal performance set to a visual terminal of a worker after the optimal performance set is generated so as to assist the worker to select the optimal process from the aspect of performance quality.
Further, the management and control server is also connected with a cost monitoring terminal in a signal mode, and the cost monitoring terminal is connected with a cost acquisition unit in a signal mode;
the cost acquisition unit is used for acquiring cost information under different standard manufacturing processes; and transmitting the cost information to the cost monitoring terminal; the cost information of the manufacturing process comprises a total manpower cost value, a total equipment cost value and a total material cost value;
the cost monitoring terminal is used for receiving the cost information, multiplying the data in the cost information with the weight parameters I corresponding to the data in the cost information respectively, adding the multiplied products to obtain a plurality of cost characteristic values, extracting the cost characteristic values smaller than the preset cost values, and combining the cost characteristic values with the manufacturing process to construct a cost characteristic set; and sending the cost feature set to a display recommendation unit;
the display recommendation unit is used for receiving the cost feature set, sorting the manufacturing processes in the cost feature set from small to large according to the size of the cost feature value, obtaining the manufacturing processes in the first three of the sorting to construct a cost optimization set, and sending the cost optimization set to a visual terminal of a worker for assisting the worker to select the optimal process from the cost angle.
Further, the display recommendation unit performs step-by-step screening analysis on the cost optimization set, the optimal performance set, the process performance set and the cost feature set to obtain a preferred recommendation process, edits a preferred recommendation text after the preferred recommendation process is generated, and sends the preferred recommendation text to a visual terminal of a worker for display, so that the worker is assisted in selecting the optimal production process from multiple angles.
Further, the heat insulating capability information comprises an outer surface temperature-time curve of the heat insulating oil sleeve and an inner surface temperature-time curve of the heat insulating oil sleeve;
the specific process of generating the outer surface temperature-time curve is as follows: the method comprises the steps of constantly heating the inside of the heat-insulating oil sleeve to a preset temperature, collecting the outer surface temperature of the heat-insulating oil sleeve through a temperature sensor, and then combining the collected outer surface temperature with a time sequence to generate an outer surface temperature-time curve;
the specific process of generating the inner surface temperature-time curve is as follows: and (3) heating the heat-insulating oil sleeve to a preset temperature constantly, collecting the inner surface temperature of the heat-insulating oil sleeve through a temperature sensor, and then combining the collected inner surface temperature with a time sequence to generate an inner surface temperature-time curve.
Further, the mass analysis unit performs the following steps:
placing the outer surface temperature-time curve and the inner surface temperature-time curve into a curvature calculation model to respectively calculate an outer surface fluctuation coefficient and an inner surface fluctuation coefficient; the lower the fluctuation coefficient of the outer surface and the fluctuation coefficient of the inner surface, the stronger the heat insulation stability of the heat insulation oil sleeve is shown;
the outer surface temperature-time and the inner surface temperature-time curves are put into a growth calculation model and are calculated to generate an outer surface growth rate and an inner surface growth rate; the lower the outer surface growth rate and the inner surface growth rate, the stronger the heat insulation capability of the heat insulation oil sleeve;
and obtaining the performance evaluation factor by normalizing the outer surface fluctuation coefficient, the inner surface fluctuation coefficient, the outer surface growth rate and the inner surface growth rate.
Further, the specific calculation process of the curvature calculation model is as follows:
for any point on the curve, calculating the curvature radius R of the point, and then calculating the average value R of the curvature radius of all points on the curve; for each point, calculating the fluctuation coefficient b of the point, wherein b=r/R, and calculating the standard deviation sigma of the fluctuation coefficients b of all points, namely the fluctuation coefficient of the curve: σ=sqrt {1/n×Σ [ b-mean (b) ]Σ2) }, where n represents the total number of points on the curve, mean (b) represents the average value of the fluctuation coefficients b of all points, and Σ represents the summation of the fluctuation coefficients b of all points.
Further, the specific operation process of the growth calculation model is as follows:
sa: selecting any point of the curve, determining the abscissa x0 of the point, and calculating the ordinate y0 at the point;
sb: selecting an adjacent point from x0, wherein the abscissa is x1, and calculating the ordinate y1 at the point;
sc: the average growth rate m of the curve between two points is recalculated: m= (y 1-yo)/(x 1-x 0);
sd: the above calculation is repeated through two consecutive points to obtain a number of average growth rates, which are averaged to obtain the growth rate.
Further, the specific steps of comparative screening are as follows:
the performance evaluation factors are respectively compared with preset evaluation values, when the performance evaluation factors are larger than the preset evaluation values, a marking signal is generated, and when the performance evaluation factors are smaller than or equal to the preset evaluation values, no signal is generated; and obtaining a performance evaluation factor corresponding to the marking signal and a manufacturing process construction to generate a process performance set.
Further, the progressive screening analysis process of the display recommendation unit is as follows:
marking a cost optimization set, a cost feature set, an optimal performance set and a process performance set as alpha 1, beta 1, alpha 2 and beta 2 respectively;
when α1n α2+.phi; extracting subsets under the same manufacturing process, constructing a superposition set, acquiring the number of the subsets in the superposition set, marking the number as n, and marking the inner subset as a preferred recommended process when n=1;
when α1n α2=Φ, the cost feature set and the process performance set are analyzed:
when β1n β2=phi, generating a feedback alarm signal, editing a feedback warning text after generating the feedback alarm signal, sending the generated feedback warning text to a controllable terminal of a worker for display,
when β1n_β2 is not equal to phi, extracting a subset of the same manufacturing process and constructing and generating an intermediate set theta;
and multiplying the data corresponding to the manufacturing process in the middle set theta by the weight parameters II respectively, adding the multiplied results to obtain a push-push factor, sorting the manufacturing process of the middle set theta from small to large by the push-push factor, obtaining the manufacturing process with the first sorting, marking the manufacturing process and generating the preferred recommended process.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
the invention collects the corresponding heat insulation capability information through testing the heat insulation oil sleeves under different standard manufacturing processes, then generates process tag information with the corresponding manufacturing processes and stores the process tag information, determines whether to carry out quantitative work through the number of the process tag information, obtains a plurality of performance evaluation factors through quantitative analysis of all the process tag information, screens and eliminates the performance evaluation factors, directly eliminates the worse process, constructs the better process to generate a process performance set, sequences the process performance set to extract the optimal 10 manufacturing processes to generate the optimal performance set, is used for assisting staff to select the optimal process from the aspect of performance quality, also generates a plurality of cost characteristic values through collecting the cost information under the different standard manufacturing processes, extracts the cost characteristic values smaller than the preset cost value, and combines the cost characteristic values with the manufacturing processes to generate the cost characteristic set; and step-by-step screening analysis is performed through the collection to obtain a feedback warning text or a preferred recommending process, on one hand, the making process is subjected to anticipated measurement and early warning, and on the other hand, staff is assisted in making rules for mass production and realizing the performance recommendation of maximizing energy conservation and optimization.
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FIG. 1 illustrates a first flow diagram of the present invention;
fig. 2 shows a second flow diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: 1-2, a thermal insulation oil sleeve performance test tube control system based on data analysis comprises a performance test unit, wherein the performance test unit is connected with a control server in a signal manner, the control server comprises an information storage unit and a quality analysis unit, and the control server is connected with a display recommendation unit in a signal manner; the management and control server is also connected with a cost monitoring terminal in a signal way, and the cost monitoring terminal is connected with a cost acquisition unit in a signal way;
working principle:
the performance test unit is used for testing the heat insulation oil sleeves under different standard manufacturing processes, generating corresponding heat insulation capability information, combining the heat insulation capability information with the corresponding manufacturing process to generate process tag information, and transmitting the process tag information to the information storage unit of the management and control server; wherein the heat insulating capability information comprises an outer surface temperature-time curve of the heat insulating oil sleeve and an inner surface temperature-time curve of the heat insulating oil sleeve;
the specific process of generating the outer surface temperature-time curve is as follows: the method comprises the steps of constantly heating the inside of the heat-insulating oil sleeve to a preset temperature, collecting the outer surface temperature of the heat-insulating oil sleeve through a temperature sensor, and then combining the collected outer surface temperature with a time sequence to generate an outer surface temperature-time curve;
the specific process of generating the inner surface temperature-time curve is as follows: and (3) heating the heat-insulating oil sleeve to a preset temperature constantly, collecting the inner surface temperature of the heat-insulating oil sleeve through a temperature sensor, and then combining the collected inner surface temperature with a time sequence to generate an inner surface temperature-time curve.
The information storage unit is used for receiving and storing a plurality of process label information;
the quality analysis unit is used for acquiring the number of the process label information in the information storage unit in real time, when the number of the process label information is larger than a preset number value, extracting all the process label information in the information storage unit, and performing quantitative analysis on all the process label information to obtain a plurality of performance evaluation factors, wherein the quantitative analysis of the quality analysis unit comprises the following specific processes:
placing the outer surface temperature-time curve and the inner surface temperature-time curve into a curvature calculation model to respectively calculate an outer surface fluctuation coefficient and an inner surface fluctuation coefficient; the lower the fluctuation coefficient of the outer surface and the fluctuation coefficient of the inner surface, the stronger the heat insulation stability of the heat insulation oil sleeve is shown;
the specific calculation process of the curvature calculation model is as follows:
for any point on the curve, calculating the curvature radius R of the point, and then calculating the average value R of the curvature radius of all points on the curve; for each point, calculating the fluctuation coefficient b of the point, wherein b=r/R, and calculating the standard deviation sigma of the fluctuation coefficients b of all points, namely the fluctuation coefficient of the curve: σ=sqrt {1/n×Σ [ b-mean (b) ]Σ2) }, where n represents the total number of points on the curve, mean (b) represents the average value of the fluctuation coefficients b of all points, and Σ represents the summation of the fluctuation coefficients b of all points.
The outer surface temperature-time and the inner surface temperature-time curves are put into a growth calculation model and are calculated to generate an outer surface growth rate and an inner surface growth rate; the lower the outer surface growth rate and the inner surface growth rate, the stronger the heat insulation capability of the heat insulation oil sleeve;
the specific operation process of the growth calculation model is as follows:
sa: selecting any point of the curve, determining the abscissa x0 of the point, and calculating the ordinate y0 at the point;
sb: selecting an adjacent point from x0, wherein the abscissa is x1, and calculating the ordinate y1 at the point;
sc: the average growth rate m of the curve between two points is recalculated: m= (y 1-yo)/(x 1-x 0);
sd: repeating the calculation through two continuous points to obtain a plurality of average growth rates, and averaging the average growth rates to obtain the growth rate;
sc: the performance evaluation factor is obtained by normalizing the surface fluctuation coefficient, the inner surface fluctuation coefficient, the surface growth rate and the inner surface growth rate by a normalization formula, and the normalization processing process is specifically as follows:
marking the external surface fluctuation coefficient, the internal surface fluctuation coefficient, the external surface growth rate and the internal surface growth rate as W1, W2, L1 and L2 respectively byThe performance evaluation factor a is obtained, wherein e1, e2, e3, e4 and e5 are all conversion coefficients, and the conversion coefficients enable the calculated result to be more approximate to a true value, e1+e2+e3+e4+e5=15.6, and e2 > e1 > e3 > e5 > e4.
Comparing and screening a plurality of performance evaluation factors with preset evaluation values respectively to generate a process performance set; the generated process performance set is also sent to a display recommendation unit;
the specific steps of comparative screening are as follows:
the performance evaluation factors are respectively compared with preset evaluation values, when the performance evaluation factors are larger than the preset evaluation values, a marking signal is generated, and when the performance evaluation factors are smaller than or equal to the preset evaluation values, no signal is generated; acquiring a performance evaluation factor corresponding to the marking signal and constructing and generating a process performance set by a manufacturing process;
the display recommending unit is used for receiving the process performance set, extracting performance evaluation factors in the process performance set, sorting the manufacturing processes in the process performance set from large to small according to the size of the performance evaluation factors, obtaining the manufacturing process of the front 10 in the process performance set after sorting, extracting and constructing the manufacturing process to generate an optimal performance set, and sending the optimal performance set to a visual terminal of a worker after the optimal performance set is generated so as to assist the worker to select the optimal process from the aspect of performance quality;
the cost acquisition unit is used for acquiring cost information under different standard manufacturing processes; and transmitting the cost information to the cost monitoring terminal; the cost information of the manufacturing process comprises a total manpower cost value, a total equipment cost value and a total material cost value;
the cost monitoring terminal is used for receiving the cost information, multiplying the data in the cost information with the weight parameters I corresponding to the data in the cost information respectively, adding the multiplied products to obtain a plurality of cost characteristic values, extracting the cost characteristic values smaller than the preset cost values, and combining the cost characteristic values with the manufacturing process to construct a cost characteristic set; and sending the cost feature set to a display recommendation unit;
the display recommendation unit is used for receiving the cost feature set, sorting the manufacturing processes in the cost feature set from small to large according to the size of the cost feature value, obtaining the manufacturing processes in the first three of the sorting to construct a cost optimization set, and sending the cost optimization set to a visual terminal of a worker for assisting the worker to select the optimal process from the cost angle.
The display recommendation unit performs step-by-step screening analysis on the cost optimization set, the optimal performance set, the process performance set and the cost feature set to obtain a preferred recommendation process, edits a preferred recommendation text after the preferred recommendation process is generated, and sends the preferred recommendation text to a visual terminal of a worker for display, so as to assist the worker to select an optimal production process from multiple angles;
the progressive screening analysis process of the display recommendation unit is as follows:
marking a cost optimization set, a cost feature set, an optimal performance set and a process performance set as alpha 1, beta 1, alpha 2 and beta 2 respectively;
when α1n α2+.phi; extracting subsets under the same manufacturing process, constructing a superposition set, acquiring the number of the subsets in the superposition set, marking the number as n, and marking the inner subset as a preferred recommended process when n=1;
when α1n α2=Φ, the cost feature set and the process performance set are analyzed:
when β1 n beta 2 = phi, generating a feedback alarm signal, editing a feedback warning text after generating the feedback alarm signal, and sending the generated feedback warning text to a controllable terminal of a worker for display so as to assist the worker to reform a manufacturing process, wherein the performance of the experimental process is up to the requirement;
when β1n_β2 is not equal to phi, extracting a subset of the same manufacturing process and constructing and generating an intermediate set theta;
multiplying the data corresponding to the manufacturing process in the middle set theta with the weight parameter II respectively, adding the multiplied results to obtain a push-push factor, sorting the manufacturing process of the middle set theta from small to large through the push-push factor, obtaining the manufacturing process of the first sorting, marking the first sorting and generating a preferred recommending process; the optimization recommendation process is used for assisting workers in making rules of mass production so as to achieve the maximum energy conservation and optimal performance recommendation, wherein weight parameters and conversion coefficients are generated through mass data simulation;
the technical scheme is summarized as follows: the invention collects the corresponding heat insulation capability information through testing the heat insulation oil sleeves under different standard manufacturing processes, then generates process tag information with the corresponding manufacturing processes and stores the process tag information, determines whether to carry out quantitative work through the number of the process tag information, obtains a plurality of performance evaluation factors through quantitative analysis of all the process tag information, screens and eliminates the performance evaluation factors, directly eliminates the worse process, constructs the better process to generate a process performance set, sequences the process performance set to extract the optimal 10 manufacturing processes to generate the optimal performance set, is used for assisting staff to select the optimal process from the aspect of performance quality, also generates a plurality of cost characteristic values through collecting the cost information under the different standard manufacturing processes, extracts the cost characteristic values smaller than the preset cost value, and combines the cost characteristic values with the manufacturing processes to generate the cost characteristic set; and step-by-step screening analysis is performed through the collection to obtain a feedback warning text or a preferred recommending process, on one hand, the making process is subjected to anticipated measurement and early warning, and on the other hand, staff is assisted in making rules for mass production and realizing the performance recommendation of maximizing energy conservation and optimization.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (5)
1. The heat-insulating oil sleeve performance test tube control system based on data analysis comprises a performance test unit, wherein the performance test unit is in signal connection with a control server, and the heat-insulating oil sleeve performance test tube control system is characterized in that the control server comprises an information storage unit and a quality analysis unit, and the control server is in signal connection with a display recommendation unit;
the performance test unit is used for testing the heat insulation oil sleeves under different standard manufacturing processes, generating corresponding heat insulation capability information, combining the heat insulation capability information with the corresponding manufacturing process to generate process tag information, and transmitting the process tag information to the information storage unit of the management and control server;
the information storage unit is used for receiving and storing a plurality of process label information;
the quality analysis unit is used for acquiring the number of the process tag information in the information storage unit in real time, extracting all the process tag information in the information storage unit when the number of the process tag information is larger than a preset number value, quantitatively analyzing all the process tag information to obtain a plurality of performance evaluation factors, and comparing and screening the performance evaluation factors with preset evaluation values respectively to generate a process performance set; the generated process performance set is also sent to a display recommendation unit;
the display recommending unit is used for receiving the process performance set, extracting performance evaluation factors in the process performance set, sorting the manufacturing processes in the process performance set from large to small according to the size of the performance evaluation factors, obtaining the manufacturing process of the front 10 in the process performance set after sorting, extracting and constructing the manufacturing process to generate an optimal performance set, and sending the optimal performance set to a visual terminal of a worker after the optimal performance set is generated so as to assist the worker to select the optimal process from the aspect of performance quality;
the heat insulating capacity information comprises an outer surface temperature-time curve of the heat insulating oil sleeve and an inner surface temperature-time curve of the heat insulating oil sleeve;
the specific process of generating the outer surface temperature-time curve is as follows: the method comprises the steps of constantly heating the inside of the heat-insulating oil sleeve to a preset temperature, collecting the outer surface temperature of the heat-insulating oil sleeve through a temperature sensor, and then combining the collected outer surface temperature with a time sequence to generate an outer surface temperature-time curve;
the specific process of generating the inner surface temperature-time curve is as follows: the method comprises the steps of constantly heating the outside of a heat-insulating oil sleeve to a preset temperature, collecting the inner surface temperature of the heat-insulating oil sleeve through a temperature sensor, and then combining the collected inner surface temperature with a time sequence to generate an inner surface temperature-time curve;
the quantitative analysis of the mass analysis unit comprises the following specific processes:
placing the outer surface temperature-time curve and the inner surface temperature-time curve into a curvature calculation model to respectively calculate an outer surface fluctuation coefficient and an inner surface fluctuation coefficient; the lower the fluctuation coefficient of the outer surface and the fluctuation coefficient of the inner surface, the stronger the heat insulation stability of the heat insulation oil sleeve is shown;
the outer surface temperature-time and the inner surface temperature-time curves are put into a growth calculation model and are calculated to generate an outer surface growth rate and an inner surface growth rate; the lower the outer surface growth rate and the inner surface growth rate, the stronger the heat insulation capability of the heat insulation oil sleeve;
obtaining performance evaluation factors through normalization formulas by using the outer surface fluctuation coefficient, the inner surface fluctuation coefficient, the outer surface growth rate and the inner surface growth rate;
the specific calculation process of the curvature calculation model is as follows:
for any point on the curve, calculating the curvature radius R of the point, and then calculating the average value R of the curvature radius of all points on the curve; for each point, calculating the fluctuation coefficient b of the point, wherein b=r/R, and calculating the standard deviation sigma of the fluctuation coefficients b of all points, namely the fluctuation coefficient of the curve: sigma=sqrt {1/n×Σ [ b-mean (b) ]Σ2) }, where n represents the total number of points on the curve, mean (b) represents the average value of the fluctuation coefficients b of all points, Σ represents the summation of the fluctuation coefficients b of all points;
the specific operation process of the growth calculation model is as follows:
sa: selecting any point of the curve, determining the abscissa x0 of the point, and calculating the ordinate y0 at the point;
sb: selecting an adjacent point from x0, wherein the abscissa is x1, and calculating the ordinate y1 at the point;
sc: the average growth rate m of the curve between two points is recalculated: m= (y 1-yo)/(x 1-x 0);
sd: the above calculation is repeated through two consecutive points to obtain a plurality of average growth rates, and the plurality of average growth rates are averaged to obtain the growth rate.
2. The heat-insulating oil casing performance test tube control system based on data analysis according to claim 1, wherein the control server is further connected with a cost monitoring terminal in signal connection with a cost acquisition unit;
the cost acquisition unit is used for acquiring cost information under different standard manufacturing processes; and transmitting the cost information to the cost monitoring terminal; the cost information of the manufacturing process comprises a total manpower cost value, a total equipment cost value and a total material cost value;
the cost monitoring terminal is used for receiving the cost information, multiplying the data in the cost information with the weight parameters I corresponding to the data in the cost information respectively, adding the multiplied products to obtain a plurality of cost characteristic values, extracting the cost characteristic values smaller than the preset cost values, and combining the cost characteristic values with the manufacturing process to construct a cost characteristic set; and sending the cost feature set to a display recommendation unit;
the display recommendation unit is used for receiving the cost feature set, sorting the manufacturing processes in the cost feature set from small to large according to the size of the cost feature value, obtaining the manufacturing processes in the first three of the sorting to construct a cost optimization set, and sending the cost optimization set to a visual terminal of a worker for assisting the worker to select the optimal process from the cost angle.
3. The system for testing and controlling the performance of the thermal insulation oil sleeve based on the data analysis according to claim 2, wherein the display recommending unit performs step-by-step screening analysis on the cost optimizing set, the optimal performance set, the process performance set and the cost characteristic set to obtain a preferred recommending process, edits a preferred recommending text after the preferred recommending process is generated, and sends the preferred recommending text to a visual terminal of a worker for displaying, so as to assist the worker to select the optimal production process from multiple angles.
4. The system for testing and controlling the performance of a thermal insulation oil sleeve based on data analysis according to claim 1, wherein the specific steps of comparing and screening are as follows:
the performance evaluation factors are respectively compared with preset evaluation values, when the performance evaluation factors are larger than the preset evaluation values, marking signals are generated, and when the performance evaluation factors are smaller than or equal to the preset evaluation values, no signals are generated; and obtaining a performance evaluation factor corresponding to the marking signal and a manufacturing process construction to generate a process performance set.
5. A thermal oil casing performance test and control system based on data analysis according to claim 3, wherein the progressive screening analysis process of the display recommendation unit is as follows:
marking a cost optimization set, a cost feature set, an optimal performance set and a process performance set as alpha 1, beta 1, alpha 2 and beta 2 respectively;
when α1n α2+.phi; extracting subsets under the same manufacturing process, constructing a superposition set, acquiring the number of the subsets in the superposition set, marking the number as n, and marking the inner subset as a preferred recommended process when n=1;
when α1n α2=Φ, the cost feature set and the process performance set are analyzed:
when β1 n beta 2 = phi, generating a feedback alarm signal, editing a feedback warning text after generating the feedback alarm signal, and sending the generated feedback warning text to a controllable terminal of a worker for display;
when β1n_β2 is not equal to phi, extracting a subset of the same manufacturing process and constructing and generating an intermediate set theta;
and multiplying the data corresponding to the manufacturing process in the middle set theta by the weight parameters II respectively, adding the multiplied results to obtain a push-push factor, sorting the manufacturing process of the middle set theta from small to large by the push-push factor, obtaining the manufacturing process with the first sorting, marking the manufacturing process and generating the preferred recommended process.
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