CN108694268B - Intelligent ring-throwing cross-repairing allocation system based on big data analysis - Google Patents
Intelligent ring-throwing cross-repairing allocation system based on big data analysis Download PDFInfo
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Abstract
The invention discloses a loop-throwing intelligent cross-repairing allocating system based on big data analysis, which has the following structural schematic diagram and comprises a surface shape data acquisition module, a cross-repairing matching analysis module and an intelligent access allocating module; the surface shape data acquisition module comprises a standing constant temperature unit and a surface shape detection unit, and acquires data such as the full-aperture reflection surface shape, the transmission surface shape and the material non-uniform distribution of the element through a digital interferometer; the cross repairing matching analysis module comprises a data classification unit, an index extraction unit, a data analysis unit and an analysis result driving unit, and has the functions of processing, analyzing and judging the acquired surface shape data to finally form an optimal cross repairing matching scheme; the intelligent cross-repairing allocating module comprises an element storage unit and an automatic transmission unit, and the elements are automatically accessed and allocated according to a cross-repairing matching scheme. The intelligent cross-repairing allocating system is applied to the batch processing process of the multi-station ring polishing machine group, the diversified first surface shape distribution and the diversified polishing die surface shape change characteristics are utilized, the matching decision is carried out based on the big data analysis, the whole process is intelligently controlled, the cross-repairing hit rate and the surface shape control efficiency are effectively improved, and the system has strong practicability.
Description
Technical Field
The invention belongs to the field of optical cold machining, and particularly relates to batch ring polishing machining of large-caliber neodymium glass elements.
Background
In a high power laser system, to achieve ideal beam focusing quality, the precision optical elements need to meet stringent full-band wavefront error and surface quality requirements. The ring polishing technology has excellent low, medium and high frequency error control and multi-station batch processing capability, and becomes a main technical approach for processing high-precision large-caliber planar elements.
The large-caliber neodymium glass plate element is a working substance of a laser system and is one of key elements in an ICF laser driver. The difficulty of ring polishing processing of the neodymium glass element comes from the overlarge length-width ratio, the overlarge radius-thickness ratio and the large thermal expansion coefficient, and the element is easy to deform in the processing process, so that local surface shape errors such as astigmatism, edge collapse and the like occur; meanwhile, the surface shape change of the polishing mould is influenced by a plurality of factors such as environment temperature and humidity fluctuation, polishing mould temperature sensitivity, batch stability and the like, and has the characteristic of uncertainty. The existing ring polishing process is mainly technically characterized by dynamic polishing, namely, qualified products are 'grabbed' in the dynamic change process of the surface shape of an element from high to low or from low to high. This process directly results in periodic variations in the component profile during processing.
In addition, considering that the thickness of the neodymium glass element is 40mm, the transmission wavefront distribution PV value caused by the material nonuniformity of 4ppm is 0.303 lambda (57 DEG incidence), which is already equivalent to the transmission wavefront index requirement 1/3 lambda. According to the uniformity control level of the current neodymium glass material, the transmission wavefront distribution caused by the uniformity is difficult to control to a negligible degree, and qualified neodymium glass elements can be processed only by surface shape matching compensation of an upper surface and a lower surface. The required lower surface shapes are different for the upper surface shapes and the material uniformity distribution with different distributions.
The current situation of the process increases the difficulty of controlling the surface shape of the neodymium glass element, so that the processing efficiency of a single device is difficult to greatly improve. Can not meet the finish machining requirement of thousands of neodymium glass elements in a high-power laser system.
Disclosure of Invention
The invention provides an intelligent ring polishing cross repairing and allocating system based on big data analysis, which utilizes diversified polishing disc surface shape trends to automatically generate an optimal cross repairing and processing scheme between elements and stations according to big data analysis results under the condition that a piece to be processed is sufficient and multi-station batch processing is carried out, and can greatly improve the surface shape batch processing efficiency of neodymium glass elements.
The technical solution of the invention is as follows: an intelligent cross-repairing and allocating system of a ring throwing machine group based on big data analysis comprises a surface shape data acquisition module, a cross-repairing matching analysis module and an intelligent access allocating module.
The data acquisition module acquires the full-aperture surface shape data of the element after being processed by a laser plane interferometer; the cross repairing matching analysis module comprises a data classification unit, an index extraction unit, a data analysis unit and an analysis result driving unit; the data classification unit classifies the acquired surface shape data according to the file name of the surface shape data to form a classification database, the index extraction unit extracts peak-to-valley value (PV), defocus (P), astigmatism (A) and residual peak-to-valley value (PVr) indexes from the surface shape data, and storing in corresponding classification database to form surface shape index database, the data analysis unit judges whether the element is qualified or not according to the surface shape index, and analyzing the surface shape data of the unqualified elements to generate a cross-repairing matching scheme, wherein the analysis result driving unit converts the cross-repairing matching scheme into an executable instruction by using a computer, and the data is transmitted to the intelligent access allocating module through the Ethernet, and the intelligent access allocating module is driven to finish the automatic allocation of the elements among processing, detecting and storing devices.
Furthermore, the surface shape data acquisition module comprises a standing constant temperature unit and a surface shape detection unit, wherein the standing constant temperature unit is used for eliminating residual thermal stress after the element is processed, and meanwhile, the multi-station design meets the requirement of detection rhythm; the surface shape detection unit is used for acquiring surface shape data after the element is processed, and when necessary, full-caliber surface shape detection of the large-caliber element can be realized by utilizing the sub-caliber movable splicing device.
Furthermore, the surface shape data is divided into a single-surface processing condition and a transmission processing condition, and under the single-surface processing condition, only the reflection surface shape of the processing surface is needed to be obtained; under the condition of transmission processing, the position of the element needs to be kept still, and the transmission surface shape of the element is continuously obtained after the reflection surface shape of the processing surface is obtained; during the first detection, the position of the element needs to be kept, and the data of the front surface reflection surface shape, the back surface reflection surface shape, the transmission surface shape and the cavity are acquired in sequence to calculate the uniformity distribution data of the element.
Further, the data classification unit classifies the facial shape data according to the facial shape data file name, and the naming rule of the file name is as follows: the system comprises five fields, wherein the first field distinguishes stations and is represented by the numbers 1,2,3, … and M, and M is the total number of the stations; the second field is element number, distinguishing element; the third field distinguishes the processing surface, the processing surface is the surface with the side number pointing in the positive direction and is marked as U, and the surface with the side number pointing in the negative direction is marked as L; the fourth field distinguishes the surface shape data type, the reflection surface shape is recorded as R, the transmission surface shape is recorded as T, and the back reflection surface shape is recorded as B; the fifth field checks the date and time to the nearest minute.
Further, the classification database includes a station surface shape database and an element surface shape database, the station surface shape database is a database subdivided according to stations, the processing surface reflection surface shape data of the elements processed by each station are smoothly arranged according to time, and the element surface shape database is a database subdivided according to element numbers and includes element non-uniformity distribution data, and processing surface reflection surface shape and transmission surface shape data arranged according to time sequence.
Further, the data analysis unit judges whether the element is qualified or not through the surface shape index, and the criterion whether the single-side surface shape is qualified or not is as follows: PV value is 1 lambda-2 lambda, and residual peak-to-valley value (PV)r) Values better than 0.33 λ; whether the transmission surface shape is qualified or not is determined according to the specific index requirements of the element processing.
Further, the data analysis unit mainly selects the components to perform matching analysis under the condition that the sheets to be processed are sufficient, until the suitable components are found to perform processing.
Furthermore, the analysis result driving unit converts the cross-repairing matching scheme into an executable instruction by using an industrial computer, and transmits the executable instruction to the element storage unit and the automatic transmission unit through an industrial Ethernet to drive the element storage unit and the automatic transmission unit.
Furthermore, the intelligent access allocation module comprises an element storage unit and an automatic transmission unit, wherein the element storage unit is a buffer area for the elements to enter and exit or wait, and the automatic transmission unit finishes the automatic transportation of the elements among processing, detection and storage.
Furthermore, the element storage unit adopts an intelligent three-dimensional library, and is managed and controlled by a PLC (programmable logic controller), so that the elements to be processed can be automatically stored and taken according to instructions.
Furthermore, the automatic transmission unit comprises 1 industrial robot with 6 shafts and a guide rail, the industrial robot is provided with a front-end grabbing mechanism and a visual identification system, in-situ grabbing of the element on the ring polishing station is achieved, and then automatic transmission of the element between ring polishing processing, surface shape detection and storage is achieved. Under a certain condition, the circular polishing equipment has the function of automatically loading and unloading elements, and the automatic transmission unit can be composed of a magnetic navigation AGV trolley, so that the elements can be automatically transmitted among circular polishing processing, surface shape detection and storage.
Further, a ring-throwing intelligent cross-repairing allocation method based on big data analysis comprises the following steps:
step 1) acquiring surface shape data of the element after processing;
step 2) classifying the acquired surface shape data to form a classification database;
step 3) extracting peak-to-valley value (PV), defocusing (P), astigmatism (A) and residual peak-to-valley value (PV) from surface shape datar) Indexes are stored in a corresponding classification database to form a surface-shaped index database;
step 4) judging whether the elements are qualified or not, and classifying and warehousing;
step 5) performing surface shape matching degree cross analysis on the workpiece and the station to generate a cross repairing matching scheme;
and 6) converting the cross-repairing matching scheme into an executable instruction, and transmitting the executable instruction to the intelligent access allocation module through the Ethernet to finish automatic allocation of the elements among processing, detection and storage.
Further, in the step 5), cross analysis is performed on the matching degree of the workpiece and the station surface shape to generate a cross repairing matching scheme, and the specific steps are as follows:
step 5.1) selecting a station to be analyzed, and extracting the surface shape of the latest station from the station database;
step 5.2) determining the residual peak-to-valley value (PV)r) Whether or not condition 1 is satisfied: residual peak-to-valley value (PV)r) The working position is better than 0.33 lambda, if the working position meets the requirement, the working position is suitable for transmission surface shape processing, otherwise, the working position is suitable for single-side processing;
step 5.3) selecting the element to be processed from the element inventory record, and extracting the latest surface shape detection data;
step 5.4) judging whether the current processing state of the element is a single-side processing stage or a transmission processing stage according to the file name of the surface data;
when the element is in the single-side processing stage, if the judgment result in the step 5.2 is that the station is suitable for single-side surface processing, according to the formula 1: Δ S ═ S2-S1Calculating the expected single-sided profile, wherein S1For the shape of the last station face, S2Processing the surface reflection surface shape, and entering the step 5.5);
when the element is in the single-side processing stage, if the judgment result in the step 5.2 is that the station is suitable for the transmission surface shape processing, returning to the step 5.3);
when the element is in the transmission processing stage, if the judgment result in the step 5.2 is that the station is suitable for transmission surface shape processing, according to the formula 2: t ═ Δ n × d- (n-1) (S)1+S2) Calculating the predicted transmission surface shape, wherein T is the predicted transmission surface shape of the element, n is the optical refractive index of the element to be processed, d is the thickness of the element, and delta n isMaterial refractive index non-uniformity profile, S1For the shape of the last station face, S2Is the upper surface reflection surface shape; and go to step 5.7);
when the element is in the transmission processing stage, if the judgment result in the step 5.2 is that the station is suitable for processing the single-side surface shape, returning to the step 5.3);
step 5.5) judging whether the single-sided surface shape meets the condition 2: defocusing P is less than 5 lambda, if the defocusing P meets the requirement, the step 5.6 is carried out, and if the defocusing P does not meet the requirement, the step 5.3 is carried out;
step 5.6) returning the number of the matched element, and ending;
step 5.7) judging whether the transmission surface shape meets the condition 3: astigmatism is better than 0.44 lambda, if yes, step 5.8) is carried out, otherwise, step 5.3) is carried out;
step 5.8) extracting the work station surface shape defocusing index in the latest scraping disc period from the work station surface shape database;
step 5.9) judging whether the defocusing trend meets the condition 4: when the defocusing trend of the station is gradually increased, the predicted transmission surface defocusing needs to meet the condition that P is more than 0; when the defocusing trend of the station is gradually reduced, predicting that the defocusing of the transmission surface shape needs to meet the condition that P is less than 0; when the station defocusing trend fluctuates up and down, the transmission surface defocusing is expected to meet the condition that-0.3 lambda is more than P and less than 0.3 lambda, the step 5.10 is carried out, and if not, the step is returned to 5.3);
and 5.10) returning the number of the matching element, storing the expected transmission surface shape and finishing.
The invention has the technical effects that: compared with the prior art, the method can obtain the surface shape evolution law of the station through big data analysis on the basis of counting a large amount of historical surface shape data, further optimize the matching degree of elements and the station surface shape, automatically generate the optimal cross repairing scheme, and effectively improve the cross repairing hit rate and further improve the surface shape qualified efficiency by applying under the conditions of sufficient pieces to be polished and multi-station batch processing.
Drawings
FIG. 1 is a schematic diagram of an organization structure of a ring-throwing intelligent cross-repairing allocation system based on big data analysis
FIG. 2 is a cross-repairing matching analysis algorithm of a ring-throwing intelligent cross-repairing allocation system based on big data analysis
Detailed Description
The present invention will be described in detail with reference to examples.
Example 1
Aiming at a ring polishing machine group consisting of 8 phi 3m ring polishing machines, an intelligent cross repairing and allocating system based on big data analysis is designed, and a processing object is a neodymium glass element with the size of 810mm 460mm 40 mm.
The intelligent cross-repairing and allocating system of the ring throwing machine group based on big data analysis comprises a surface shape data acquisition module, a cross-repairing matching analysis module and an intelligent access allocating module. The surface shape data acquisition module consists of a standing constant temperature unit and a surface shape detection unit; the cross repairing matching analysis module consists of a data classification unit, an index extraction unit and a data analysis unit; the intelligent access allocating module comprises an element storage unit and an automatic transmission unit.
More specifically, the surface shape data acquisition module meets the full-caliber surface shape detection requirement of a neodymium glass element with the size of 810mm x 460mm x 40mm through digital interference of 2 stations with the caliber of 600mm and the sub-caliber moving and splicing function; each interferometer is provided with a standing constant temperature unit with 8-station standing capability.
More specifically, the element storage unit adopts 2 intelligent three-dimensional storage libraries, and achieves the storage capacity of about 100 pieces.
More specifically, under a certain condition, the ring polishing equipment is arranged in a line, the automatic transmission unit consists of 1 6-axis industrial robot and a guide rail, and the industrial robot is provided with a front-end grabbing mechanism and a visual identification system to realize in-situ grabbing of the components on the ring polishing station and further realize automatic transmission of the components among ring polishing processing, surface shape detection and storage. Under a certain condition, the circular polishing equipment has the function of automatically loading and unloading elements, and the automatic transmission unit can be composed of a magnetic navigation AGV trolley, so that the elements can be automatically transmitted among circular polishing processing, surface shape detection and storage.
More specifically, the cross-repairing matching analysis module is mainly composed of 1 industrial computer and performs data communication with modules such as an industrial Ethernet and a surface shape detection unit, an element storage unit, an automatic transmission unit and the like.
The system comprises the following working steps:
step 1) placing an element to be detected in a standing constant temperature unit for sufficient standing, and releasing surface shape change caused by thermal stress;
step 2) acquiring surface shape data of the processed element by combining a laser plane interferometer with sub-aperture moving splicing, and storing the surface shape data as a DAT format file;
and 3) transmitting the acquired surface shape data to a cross-repairing matching analysis module by using the industrial Ethernet.
Step 4), the data classification unit classifies the acquired surface shape data to respectively form a station surface shape database and an element surface shape database;
step 5), the index extraction unit extracts peak-to-valley value (PV), defocusing (P), astigmatism (A) and residual peak-to-valley value (PV) from the surface shape data DAT filer) Indexes are stored in a corresponding classification database to form a surface-shaped index database;
step 6) judging whether the elements are qualified or not, and classifying and warehousing;
step 7) under the condition that the sheets to be processed are sufficient, mainly using the station, selecting elements to carry out matching degree analysis until suitable elements are found to carry out processing, and generating a cross repairing matching scheme;
and 8) converting the cross-repairing matching scheme into an executable instruction, and transmitting the executable instruction to the intelligent access allocation module through the Ethernet to finish automatic allocation of the elements among processing, detection and storage.
More specifically, in step 7), cross analysis is performed on the matching degree between the workpiece and the station surface shape to generate a cross-repairing matching scheme, and the specific steps are as follows:
step 7.1) selecting a station to be analyzed, and extracting the surface shape of the latest station from the station database;
step 7.2) determining the residual peak-to-valley value (PV)r) Whether or not condition 1 is satisfied: residual peak-to-valley value (PVr) The working position is better than 0.33 lambda, if the working position meets the requirement, the working position is suitable for transmission surface shape processing, otherwise, the working position is suitable for single-side processing;
step 7.3) selecting the element to be processed from the element inventory record, and extracting the latest surface shape detection data;
step 7.4) judging whether the current processing state of the element is a single-side processing stage or a transmission processing stage according to the file name of the surface data;
when the element is in the single-side processing stage, if the judgment result in the step 7.2 is that the station is suitable for single-side surface processing, according to the formula 1: Δ S ═ S2-S1Calculating the expected single-sided profile, wherein S1For the shape of the last station face, S2Processing the surface reflection surface shape, and entering the step 7.5);
when the element is in the single-side processing stage, if the judgment result in the step 7.2 is that the station is suitable for the transmission surface shape processing, returning to the step 7.3);
when the element is in the transmission processing stage, if the judgment result in the step 7.2 is that the station is suitable for transmission surface shape processing, according to the formula 2: t ═ Δ n × d- (n-1) (S)1+S2) Calculating a predicted transmission surface shape, wherein T is the predicted transmission surface shape of the element, n is the optical refractive index of the element to be processed, d is the thickness of the element, delta n is the non-uniform distribution of the refractive index of the material, and S1For the shape of the last station face, S2Is the upper surface reflection surface shape; and go to step 7.7);
when the element is in the transmission processing stage, if the judgment result in the step 7.2 is that the station is suitable for processing the single-side surface shape, returning to the step 7.3);
step 7.5) judging whether the single-sided surface shape meets the condition 2: defocusing P is less than 5 lambda, if the defocusing P meets the requirement, the step 7.6 is carried out, and if the defocusing P does not meet the requirement, the step 7.3 is carried out;
step 7.6) returning the number of the matched element, and ending;
step 7.7) judging whether the transmission surface shape meets the condition 3: astigmatism better than 0.44 λ, if satisfied, go to step 7.8), otherwise, return to step 7.3);
step 7.8) extracting the work station surface shape defocusing index in the latest scraping disc period from the work station surface shape database;
step 7.9) judging whether the defocusing trend meets the condition 4: when the defocusing trend of the station is gradually increased, the predicted transmission surface defocusing needs to meet the condition that P is more than 0; when the defocusing trend of the station is gradually reduced, predicting that the defocusing of the transmission surface shape needs to meet the condition that P is less than 0; when the station defocusing trend fluctuates up and down, the transmission surface defocusing is expected to meet the condition that-0.3 lambda is more than P and less than 0.3 lambda, the step 7.10 is carried out, and otherwise, the step is returned to 7.3);
step 7.10) returns the matching element number and stores the expected transmission surface shape, and the process is finished.
Claims (9)
1. An intelligent loop-casting cross-repairing allocation system based on big data analysis is characterized by comprising a surface shape data acquisition module, a cross-repairing matching analysis module and an intelligent access allocation module; the surface shape data acquisition module acquires the surface shape data of the element after processing through a laser plane interferometer; the cross repairing matching analysis module comprises a data classification unit, an index extraction unit, a data analysis unit and an analysis result driving unit; the data classification unit classifies the acquired surface shape data according to the file name of the surface shape data to form a classification database, and the index extraction unit extracts a peak-to-valley value PV, a defocus P, an astigmatism A and a residual peak-to-valley value PV from the surface shape datarIndexes are stored in corresponding classification databases to form a surface shape index database, the data analysis unit judges whether the elements are qualified or not through the surface shape indexes and analyzes surface shape data of the unqualified elements to generate a cross repair matching scheme, the analysis result driving unit converts the cross repair matching scheme into an executable instruction by using a computer, transmits the executable instruction to the intelligent access allocating module through Ethernet and drives the intelligent access allocating module to finish automatic allocation of the elements among processing, detecting and storing devices;
the cross-repairing matching scheme comprises the following specific steps:
step 1) selecting a station to be analyzed, and extracting the surface shape of the latest station from a station database;
step 2) judging residual peak-to-valley PVrWhether or not condition 1 is satisfied: residual peak-to-valley value PVrThe working position is better than 0.33 lambda, if the working position meets the requirement, the working position is suitable for transmission surface shape processing, otherwise, the working position is suitable for single-side processing;
step 3) selecting a component to be processed from the component inventory record, and extracting the latest surface shape detection data;
step 4) judging whether the current processing state of the element is a single-side processing stage or a transmission processing stage according to the file name of the surface data;
when the element is in the single-side processing stage, if the judgment result in the step 2 is that the station is suitable for single-side surface shape processing, according to the formula 1: Δ S ═ S2-S1Calculating the expected single-sided profile, wherein S1For the shape of the last station face, S2Processing the surface reflection surface shape, and entering the step 5);
when the element is in the single-side processing stage, if the judgment result in the step 2 is that the station is suitable for the transmission surface shape processing, returning to the step 3);
when the element is in the transmission processing stage, if the judgment result in the step 2 is that the station is suitable for transmission surface shape processing, according to the formula 2: t ═ Δ n × d- (n-1) (S)1+S2) Calculating a predicted transmission surface shape, wherein T is the predicted transmission surface shape of the element, n is the optical refractive index of the element to be processed, d is the thickness of the element, delta n is the non-uniform distribution of the refractive index of the material, and S1For the shape of the last station face, S2Is the upper surface reflection surface shape; and entering step 7);
when the element is in the transmission processing stage, if the judgment result in the step 2 is that the station is suitable for processing the single-side surface shape, returning to the step 3);
step 5) judging whether the single-sided surface shape meets the condition 2: defocusing P is less than 5 lambda, if the defocusing P meets the requirement, the step 6) is carried out, and if the defocusing P does not meet the requirement, the step 3 is carried out;
step 6), returning the number of the matched element, and ending;
step 7) judging whether the transmission surface shape meets the condition 3: astigmatism is better than 0.44 lambda, if the astigmatism is satisfied, the step 8) is carried out, otherwise, the step 3) is carried out;
step 8) extracting the defocusing index of the station surface shape in the latest scraping disc period from the station surface shape database;
step 9) judging whether the defocusing trend meets the condition 4: when the station defocusing trend is gradually increased, the transmission surface defocusing is expected to satisfy P & gt 0, when the station defocusing trend is gradually reduced, the transmission surface defocusing is expected to satisfy P & lt 0, and when the station defocusing trend fluctuates up and down, the transmission surface defocusing is expected to satisfy-0.3 lambda & lt P & lt 0.3 lambda; if yes, entering the step 10), otherwise, returning to the step 3);
and step 10) returning the number of the matching element, storing the expected transmission surface shape, and ending.
2. The intelligent loop-throwing cross-repairing allocating system based on big data analysis according to claim 1, wherein the surface shape data acquisition module comprises a standing constant temperature unit and a surface shape detection unit, the standing constant temperature unit is used for eliminating residual thermal stress after element processing, and meanwhile, a multi-station design meets the requirement of detection beat; the surface shape detection unit is used for acquiring surface shape data of the element after processing.
3. The intelligent ring-throwing cross-repairing allocating system based on big data analysis according to claim 1 or 2, characterized in that the surface shape data is divided into two situations of single-surface processing and transmission processing, and under the condition of single-surface processing, only the reflection surface shape of the processing surface is needed to be obtained; under the condition of transmission processing, the position of the element needs to be kept still, and the transmission surface shape of the element is continuously obtained after the reflection surface shape of the processing surface is obtained; during the first detection, the position of the element needs to be kept, and the data of the front surface reflection surface shape, the back surface reflection surface shape, the transmission surface shape and the cavity are acquired in sequence to calculate the uniformity distribution data of the element.
4. The intelligent loop-throwing cross-repairing transferring system based on big data analysis as claimed in claim 1, wherein the data classification unit classifies the facial morphology data according to the facial morphology data file name, and the naming rule of the file name is as follows: the system comprises five fields, wherein the first field distinguishes stations and is represented by the numbers 1,2,3, … and M, and M is the total number of the stations; the second field is element number, distinguishing element; the third field distinguishes the processing surface, the processing surface is the surface with the side number pointing in the positive direction and is marked as U, and the surface with the side number pointing in the negative direction is marked as L; the fourth field distinguishes the surface shape data type, the reflection surface shape is recorded as R, the transmission surface shape is recorded as T, and the back reflection surface shape is recorded as B; the fifth field checks the date and time to the nearest minute.
5. The intelligent ring polishing cross-repairing allocating system based on big data analysis according to claim 1 or 4, characterized in that the classification database comprises a station surface shape database and an element surface shape database, the station surface shape database is a database subdivided according to stations, the processing surface reflection surface shape data of the elements processed for each station are smoothly arranged according to time, and the element surface shape database is a database subdivided according to element numbers and comprises element non-uniformity distribution data, and processing surface reflection surface shape and transmission surface shape data arranged according to time sequence.
6. The intelligent loop-throwing cross-repairing allocating system based on big data analysis of claim 1, wherein the data analysis unit judges whether the element is qualified or not through surface shape indexes, and the criterion of whether the single-side surface shape is qualified or not is as follows: the PV value is 1 lambda-2 lambda, and the residual PV value is better than 0.33 lambda; whether the transmission surface shape is qualified or not is determined according to the specific index requirements of the element processing.
7. The intelligent loop-throwing cross-repairing allocating system based on big data analysis as claimed in claim 1 or 6, wherein the data analysis unit, under the condition that the piece to be processed is sufficient, mainly uses the station, selects the component to perform matching analysis until a proper component is found to perform processing.
8. The intelligent loop-throwing cross-repairing dispatching system based on big data analysis as claimed in claim 1, wherein the intelligent accessing dispatching module comprises a component storage unit and an automatic transmission unit, the component storage unit is a buffer area for component entering and exiting or waiting, and the automatic transmission unit completes automatic handling of components between processing, detection and storage.
9. A loop-casting intelligent cross-repairing allocation method based on big data analysis is characterized by comprising the following steps:
step 1) acquiring surface shape data of the element after processing;
step 2) classifying the acquired surface shape data to form a classification database;
step 3) extracting peak-to-valley value PV, defocusing P, astigmatism A and residual peak-to-valley value PV from surface shape datarIndexes are stored in a corresponding classification database to form a surface-shaped index database;
step 4) judging whether the elements are qualified or not, and classifying and warehousing;
step 5) performing surface shape matching degree cross analysis on the workpiece and the station to generate a cross repairing matching scheme, which comprises the following specific steps:
step 5.1) selecting a station to be analyzed, and extracting the surface shape of the latest station from the station database;
step 5.2) determining residual peak-to-valley PVrWhether or not condition 1 is satisfied: residual peak-to-valley value PVrThe working position is better than 0.33 lambda, if the working position meets the requirement, the working position is suitable for transmission surface shape processing, otherwise, the working position is suitable for single-side processing;
step 5.3) selecting the element to be processed from the element inventory record, and extracting the latest surface shape detection data;
step 5.4) judging whether the current processing state of the element is a single-side processing stage or a transmission processing stage according to the file name of the surface data;
when the element is in the single-side processing stage, if the judgment result in the step 5.2 is that the station is suitable for single-side surface processing, according to the formula 1: Δ S ═ S2-S1Calculating the expected single-sided profile, wherein S1For the shape of the last station face, S2To process the surface reflection profile, and proceed to step 5.5)；
When the element is in the single-side processing stage, if the judgment result in the step 5.2 is that the station is suitable for the transmission surface shape processing, returning to the step 5.3);
when the element is in the transmission processing stage, if the judgment result in the step 5.2 is that the station is suitable for transmission surface shape processing, according to the formula 2: t ═ Δ n × d- (n-1) (S)1+S2) Calculating a predicted transmission surface shape, wherein T is the predicted transmission surface shape of the element, n is the optical refractive index of the element to be processed, d is the thickness of the element, delta n is the non-uniform distribution of the refractive index of the material, and S1For the shape of the last station face, S2Is the upper surface reflection surface shape; and go to step 5.7);
when the element is in the transmission processing stage, if the judgment result in the step 5.2 is that the station is suitable for processing the single-side surface shape, returning to the step 5.3);
step 5.5) judging whether the single-sided surface shape meets the condition 2: defocusing P is less than 5 lambda, if the defocusing P meets the requirement, the step 5.6 is carried out, and if the defocusing P does not meet the requirement, the step 5.3 is carried out;
step 5.6) returning the number of the matched element, and ending;
step 5.7) judging whether the transmission surface shape meets the condition 3: astigmatism is better than 0.44 lambda, if yes, step 5.8) is carried out, otherwise, step 5.3) is carried out;
step 5.8) extracting the work station surface shape defocusing index in the latest scraping disc period from the work station surface shape database;
step 5.9) judging whether the defocusing trend meets the condition 4: when the station defocusing trend is gradually increased, the transmission surface defocusing is expected to satisfy P & gt 0, when the station defocusing trend is gradually reduced, the transmission surface defocusing is expected to satisfy P & lt 0, and when the station defocusing trend fluctuates up and down, the transmission surface defocusing is expected to satisfy-0.3 lambda & lt P & lt 0.3 lambda; if yes, entering step 5.10), otherwise, returning to step 5.3);
step 5.10) returning the number of the matching element, storing the expected transmission surface shape, and ending;
and 6) converting the cross-repairing matching scheme into an executable instruction, and transmitting the executable instruction to the intelligent access allocation module through the Ethernet to finish automatic allocation of the elements among processing, detection and storage.
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