CN114723789A - Python-based LED spectrometer calibration method, system and platform - Google Patents
Python-based LED spectrometer calibration method, system and platform Download PDFInfo
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Abstract
The invention belongs to the technical field of spectrometer calibration, and particularly relates to a Python-based LED spectrometer calibration method, system and platform. The method comprises the steps of obtaining original image positioning parameters through the method, and generating a first original configuration file in real time according to original configuration file data; generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data; generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient; the second configuration file is generated in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data, and the system and the platform corresponding to the method can realize that all parameters needing to be modified are in the configuration file modified by the calibration system, reduce the steps of manually modifying the parameters, effectively prevent quality accidents caused by inputting wrong parameters, reduce the workload of workers, effectively record the calibration data and the configuration file each time through the calibration system, and effectively analyze the data and trace back the source.
Description
Technical Field
The invention belongs to the technical field of spectrometer calibration, and particularly relates to a Python-based LED spectrometer calibration method, system and platform.
Background
With the development of industrial 4.0 intelligent factories, equipment in a production workshop continuously needs intelligent production, and currently, calibration of an LED light splitting machine is realized by manually inputting calibration coefficients and setting parameters, so that the conditions of input errors of workers and the like frequently occur in the operation process, and the risk of outflow of defective products is caused; and the efficiency is low in the process of analyzing data and tracing the source; that is to say, the calibration operation process of traditional LED light splitting machine is loaded down with trivial details, and staff's work load is big, and then leads to production efficiency low, moreover to the retrospective nature of data low.
Therefore, aiming at the technical problems that the traditional calibration operation process of the LED sorting machine is complicated, the workload of workers is large, the production efficiency is low, and the traceability of data is low, the Python-based calibration method, system and platform for the LED sorting machine are urgently needed to be designed and developed.
Disclosure of Invention
The first purpose of the invention is to provide a calibration method of an LED light splitter based on Python;
the second purpose of the invention is to provide a calibration system of an LED spectrometer based on Python;
the third purpose of the invention is to provide a Python-based LED spectrometer calibration platform;
the first object of the present invention is achieved by: the method specifically comprises the following steps:
acquiring original image positioning parameters, and generating a first original configuration file in real time according to original configuration file data;
generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data;
generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient;
and generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data.
Further, the standard part is a product tested by the standard machine;
the method comprises the following steps that before the positioning parameters of the original image are obtained and the first original configuration file is generated in real time according to the data of the original configuration file, the method also comprises the following steps:
and acquiring the data information of the work order and generating the original image positioning parameters.
Further, the step of obtaining the original image positioning parameters and generating the first original configuration file in real time according to the original configuration file data further comprises the following steps:
and judging whether the work order number data and the configuration template data are consistent, if so, executing the next step, and otherwise, returning to the previous step.
Further, the first original configuration file is specifically a configuration file for modifying image positioning parameters, voltage and current; the first original configuration file is specifically a configuration file without a computer difference coefficient.
Further, the step of generating the model error coefficient in real time by combining the first original configuration file and the test standard data further includes the following steps:
and acquiring standard part data, and calculating the machine difference between the standard part data tested by the spectrometer and the standard part data tested by the standard machine in real time.
Further, the step of obtaining the standard part data and calculating the machine difference between the standard part data tested by the spectrometer and the standard part data tested by the standard machine in real time further comprises the following steps:
calibrating the work order number data and the configuration template data;
and judging whether the standard coefficient in the standard piece data is qualified, if so, executing the next step, otherwise, returning to the previous step.
Further, the second configuration file is specifically a configuration file for modifying and increasing the calibration coefficient and the card control parameter;
the step of generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data further comprises the following steps:
acquiring an organic difference coefficient configuration file, test standard part data and standard part data;
and judging whether the difference between the standard component data tested by the spectrometer and the standard component data tested by the standard machine is within a standard threshold range, if so, executing the next step, and otherwise, returning to the previous step.
The second object of the present invention is achieved by: the system specifically comprises:
the first generation unit is used for acquiring the positioning parameters of the original image and generating a first original configuration file in real time according to the data of the original configuration file;
the second generating unit is used for generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data;
the third generating unit is used for generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient;
and the fourth generating unit is used for generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data.
Further, the standard part is a product tested by the standard machine; the first original configuration file is specifically a configuration file for modifying image positioning parameters, voltage and current; the first original configuration file is specifically a configuration file without a computer difference coefficient; the second configuration file is specifically a configuration file for modifying and increasing the calibration coefficient and the card control parameter;
the system is also provided with:
the first generation module is used for acquiring the data information of the work order and generating the positioning parameters of the original image;
the first generation unit is also provided with:
the first judging module is used for judging whether the worksheet number data and the configuration template data are consistent;
the second generation unit is also provided with:
the calculation processing module is used for acquiring the data of the standard component and calculating the machine difference between the data of the standard component tested by the spectrometer and the data of the standard component tested by the standard machine in real time;
the calibration module is used for calibrating the worksheet number data and the configuration template data;
the second judging module is used for judging whether the standard coefficient in the standard piece data is qualified or not;
the fourth generation unit is further provided with:
the acquisition module is used for acquiring an organic difference coefficient configuration file, test standard part data and standard part data;
and the third judging module is used for judging whether the difference between the standard component data of the spectrometer and the standard component data of the standard machine is within the standard threshold range.
The third object of the present invention is achieved by: the method comprises the following steps: the device comprises a processor, a memory and a Python-based LED spectrometer calibration platform control program;
the Python-based LED spectrometer calibration platform control program is executed by the processor, stored in the memory, and implements the Python-based LED spectrometer calibration method.
According to the method, the original image positioning parameters are obtained, and a first original configuration file is generated in real time according to original configuration file data; generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data; generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient; the second configuration file is generated in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data, and the system and the platform corresponding to the method can realize that all parameters needing to be modified are in the configuration file modified by the calibration system, reduce the steps of modifying the parameters manually, effectively prevent quality accidents caused by inputting wrong parameters, reduce the workload of workers, and effectively record the calibration data and the configuration file each time through the calibration system, thereby effectively analyzing the data and tracing the source.
That is to say, the scheme of the invention can eliminate the step of manually inputting parameters, simplify the operation requirements on workers, improve the production efficiency, increase and save historical calibration coefficients and files through the calibration system, and improve the traceability of data.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a Python-based LED spectrometer calibration method of the present invention;
FIG. 2 is a schematic flow chart illustrating an embodiment of a Python-based LED spectrometer calibration method according to the present invention;
fig. 3 is a schematic diagram of an architecture of a Python-based LED spectrometer calibration system according to the present invention;
fig. 4 is a schematic diagram of an architecture of a Python-based LED spectrometer calibration platform according to the present invention;
FIG. 5 is a block diagram of a computer-readable storage medium according to an embodiment of the present invention;
the objects, features and advantages of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
For better understanding of the objects, aspects and advantages of the present invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings, and other advantages and capabilities of the present invention will become apparent to those skilled in the art from the description.
The invention is capable of other and different embodiments and its several details are capable of modification in various other respects, all without departing from the spirit and scope of the present invention.
It should be noted that, if directional indications (such as up, down, left, right, front, and back … …) are involved in the embodiment of the present invention, the directional indications are only used to explain the relative positional relationship between the components, the movement situation, and the like in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indications are changed accordingly.
In addition, if there is a description of "first", "second", etc. in an embodiment of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Secondly, the technical solutions in the embodiments can be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not be within the protection scope of the present invention.
Preferably, the Python-based LED splitter calibration method of the present invention is applied to one or more terminals or servers. The terminal is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The terminal can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The terminal can be in man-machine interaction with a client in a keyboard mode, a mouse mode, a remote controller mode, a touch panel mode or a voice control device mode.
The invention discloses a Python-based LED spectrometer calibration method, a Python-based LED spectrometer calibration system, a Python-based LED spectrometer calibration platform and a storage medium.
Fig. 1 is a flowchart of a Python-based LED spectrometer calibration method according to an embodiment of the present invention.
In this embodiment, the Python-based LED spectrometer calibration method may be applied to a terminal or a fixed terminal having a display function, where the terminal is not limited to a personal computer, a smart phone, a tablet computer, a desktop or all-in-one machine with a camera, and the like.
The Python-based LED spectrometer calibration method can also be applied to a hardware environment formed by a terminal and a server connected with the terminal through a network. Networks include, but are not limited to: a wide area network, a metropolitan area network, or a local area network. The calibration method for the Python-based LED spectrometer can be executed by a server, a terminal or both.
For example, for a terminal which needs to perform Python-based LED spectrometer calibration, the Python-based LED spectrometer calibration function provided by the method of the present invention may be directly integrated on the terminal, or a client for implementing the method of the present invention may be installed. For another example, the method provided by the present invention may further run on a device such as a server in the form of a Software Development Kit (SDK), an interface of the Python-based LED spectrometer calibration function is provided in the form of an SDK, and the terminal or other device may implement the Python-based LED spectrometer calibration function through the provided interface.
The invention is further elucidated with reference to the drawing.
As shown in fig. 1-2, the invention provides a Python-based LED spectrometer calibration method, which specifically includes the following steps:
s1, acquiring original image positioning parameters, and generating a first original configuration file in real time according to original configuration file data;
s2, generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data;
s3, generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient;
and S4, generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data.
The standard part is a product tested by a standard machine;
the method comprises the following steps that before the positioning parameters of the original image are obtained and the first original configuration file is generated in real time according to the data of the original configuration file, the method also comprises the following steps:
and S01, acquiring the data information of the work order and generating the original image positioning parameters.
The step of obtaining the original image positioning parameters and generating a first original configuration file in real time according to the original configuration file data further comprises the following steps:
and S11, judging whether the work order number data and the configuration template data are consistent, if so, executing the next step, otherwise, returning to the previous step.
The first original configuration file is specifically a configuration file for modifying image positioning parameters, voltage and current; the first original configuration file is specifically a configuration file without a computer difference coefficient.
The step of generating the machine error coefficient in real time by combining the first original configuration file and the data of the test standard component further comprises the following steps:
and S21, acquiring the data of the standard component, and calculating the machine difference between the data of the standard component tested by the spectrometer and the data of the standard component tested by the standard machine in real time.
The step of obtaining the standard part data and calculating the machine difference between the standard part data tested by the spectrometer and the standard part data tested by the standard machine in real time further comprises the following steps:
s211, calibrating the worksheet number data and configuring the template data;
s212, judging whether the standard coefficient in the standard piece data is qualified, if so, executing the next step, otherwise, returning to the previous step.
The second configuration file is specifically a configuration file for modifying and increasing the calibration coefficient and the card control parameter;
the step of generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data further comprises the following steps:
s41, acquiring an organic difference coefficient configuration file, test standard part data and standard part data;
and S42, judging whether the difference between the spectrometer test standard part data and the standard machine test standard part data is within a standard threshold range, if so, executing the next step, otherwise, returning to the previous step.
Specifically, in the embodiment of the present invention, the solution is implemented according to the following technical solutions:
step 1: uploading an original configuration file to export an image positioning parameter, and importing the parameter into a specified original configuration file;
step 2: according to the input work order number, importing the corresponding setting parameters of the specified original configuration file, and downloading to generate the configuration file;
and step 3: importing a generating configuration file by the light splitting machine, testing standard part data and uploading the standard part data;
and 4, step 4: calculating the machine difference between the standard part data of the spectrometer and the standard part data of the standard machine, generating a machine difference coefficient, downloading and generating an organic difference coefficient configuration file
And 5: importing an organic difference coefficient configuration file into the light splitting machine, testing data of the standard part, and uploading the data of the standard part;
step 6: and judging whether the difference between the data of the standard component tested by the spectrometer and the data of the standard component tested by the standard machine is within the range or not, and importing the standard of the card control spectrometer according to the work order number.
And 7: downloading the final configuration file;
the method has the advantages that all parameters needing to be modified are in the configuration files modified by the calibration system, so that the steps of manually modifying the parameters are reduced, quality accidents caused by inputting wrong parameters are effectively prevented, and the workload of workers is reduced. The calibration system effectively records each calibration data and configuration file, and can effectively analyze the data and trace back the source.
That is to say, as shown in fig. 2, the present embodiment provides a calibration method for a spectrometer based on Python, which includes the following steps:
b1: and inputting a work order number, uploading an original configuration file and exporting image positioning parameters.
Specifically, the work order number includes setting parameters and card control standards for obtaining the product, the original configuration file is uploaded to read image positioning parameters, and the image positioning parameters are used for positioning the product grabbing position by the light splitting machine.
B2: according to the input work order number, importing the corresponding setting parameters of the specified original configuration file, and importing the parameters into the specified original configuration file;
specifically, the configuration file queries voltage and current parameters through a work order number, and modifies the original configuration file according to the parameters.
B3: downloading and generating a configuration file;
specifically, the download generation configuration file is a configuration file for modifying image positioning parameters and voltages and currents
B4: importing a generating configuration file by the light splitting machine, testing standard part data and uploading the standard part data;
specifically, the import generation configuration file is a configuration file without a computer difference coefficient, and the standard component is a product tested by a standard machine where the standard component is located. And the spectrometer tests the standard part data under the condition of no mechanical difference adding coefficient, and the standard part data is uploaded through a Web page of a Python flash framework.
B5: and calculating the machine difference between the data of the standard component tested by the spectrometer and the data of the standard component tested by the standard machine to generate a machine difference coefficient, specifically, inquiring the corresponding data of the original standard component by the data of the standard component tested by the spectrometer through the work order number, and calculating the difference value between the data of the standard component tested by the spectrometer and the data of the standard component tested by the standard machine, wherein the machine difference coefficient is the difference between the data of the standard component tested by the spectrometer and the data of the standard component tested by the standard machine.
B6: downloading and generating organic difference coefficient configuration file
Specifically, the generating organic difference coefficient profile is a profile for modifying and adding calibration coefficients;
b7: importing an organic difference coefficient configuration file into the light splitting machine, testing data of the standard part, and uploading the data of the standard part;
specifically, the imported organic difference coefficient configuration file is a configuration file of a computer difference coefficient, the spectrometer tests the standard piece data under the condition of adding the computer difference coefficient, and the standard piece data is uploaded through a Web page of a Python flash framework.
B8: and judging whether the difference between the standard component data tested by the spectrometer and the standard component data tested by the standard machine is within the range or not, and importing the standard data into the card control spectrometer according to the work order number.
Specifically, the spectrometer test standard piece data is standard piece test data after a machine difference coefficient is added, the standard piece test data is compared with standard machine test standard piece data to judge whether the standard piece test data meets a machine difference range or not, an error prompt is not reported through a calibration system, the configuration file is judged to be added with a client difference coefficient in the original calibration coefficient through judgment, and the card control spectrometer standard inquired according to the work order number is modified.
B9: downloading the final configuration file;
specifically, the final configuration file is a configuration file and a card control parameter which are modified and increased by the calibration coefficient.
In order to achieve the above object, the present invention further provides a calibration system for a Python-based LED spectrometer, as shown in fig. 3, the calibration system specifically includes:
the first generation unit is used for acquiring the positioning parameters of the original image and generating a first original configuration file in real time according to the data of the original configuration file;
the second generating unit is used for generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data;
the third generating unit is used for generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient;
and the fourth generating unit is used for generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data.
The standard part is a product tested by a standard machine; the first original configuration file is specifically a configuration file for modifying image positioning parameters, voltage and current; the first original configuration file is specifically a configuration file without a computer difference coefficient; the second configuration file is specifically a configuration file for modifying and increasing the calibration coefficient and the card control parameter;
the system is also provided with:
the first generation module is used for acquiring the data information of the work order and generating the positioning parameters of the original image;
the first generation unit is also provided with:
the first generation unit is also provided with:
the first judging module is used for judging whether the worksheet number data and the configuration template data are consistent;
the second generation unit is also provided with:
the calculation processing module is used for acquiring the data of the standard component and calculating the machine difference between the data of the standard component tested by the spectrometer and the data of the standard component tested by the standard machine in real time;
the calibration module is used for calibrating the worksheet number data and the configuration template data;
the second judging module is used for judging whether the standard coefficient in the standard piece data is qualified or not;
the fourth generation unit is further provided with:
the acquisition module is used for acquiring an organic difference coefficient configuration file, test standard part data and standard part data;
and the third judging module is used for judging whether the difference between the standard component data of the spectrometer and the standard component data of the standard machine is within the standard threshold range.
In the embodiment of the system scheme of the present invention, specific details of the method steps involved in the calibration of the Python-based LED spectrometer are already described above, and are not described herein again.
In order to achieve the above object, the present invention further provides a calibration platform for a Python-based LED spectrometer, as shown in fig. 4, including: the device comprises a processor, a memory and a Python-based LED spectrometer calibration platform control program;
the Python-based LED spectrometer calibration platform control program is executed by the processor, and is stored in the memory, and implements the Python-based LED spectrometer calibration method, for example:
s1, acquiring original image positioning parameters, and generating a first original configuration file in real time according to original configuration file data;
s2, generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data;
s3, generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient;
and S4, generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data.
The details of the steps have been set forth above and will not be described herein.
In an embodiment of the present invention, the Python-based LED spectrometer calibration platform built-in processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, and a combination of various control chips. The processor acquires each component by using various interfaces and line connections, and executes various functions and processes data of the Python-based LED light splitting machine by running or executing programs or units stored in the memory and calling data stored in the memory;
the memory is used for storing program codes and various data, is installed in the Python-based LED spectrometer calibration platform, and realizes high-speed and automatic program or data access in the operation process.
The Memory includes Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically Erasable rewritable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM) or other optical Disc Memory, magnetic disk Memory, tape Memory, or any other medium readable by a computer that can be used to carry or store data.
In order to achieve the above object, the present invention further provides a computer readable storage medium, as shown in fig. 5, where the computer readable storage medium stores a Python-based LED spectrometer calibration platform control program, and the Python-based LED spectrometer calibration platform control program implements the steps of the Python-based LED spectrometer calibration method, for example:
s1, acquiring original image positioning parameters, and generating a first original configuration file in real time according to original configuration file data;
s2, generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data;
s3, generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient;
and S4, generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data.
The details of the steps have been set forth above and will not be described herein.
In describing embodiments of the present invention, it should be noted that any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and that the scope of the preferred embodiments of the present invention includes additional implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processing module-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM).
Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
In an embodiment of the present invention, to achieve the above object, the present invention further provides a chip system, where the chip system includes at least one processor, and when program instructions are executed in the at least one processor, the chip system is enabled to execute the steps of the Python-based LED splitter calibration method, for example:
s1, acquiring original image positioning parameters, and generating a first original configuration file in real time according to original configuration file data;
s2, generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data;
s3, generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient;
and S4, generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data.
The details of the steps have been set forth above and will not be described herein.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The method comprises the steps of obtaining original image positioning parameters through the method, and generating a first original configuration file in real time according to original configuration file data; generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data; generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient; the second configuration file is generated in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data, and the system and the platform corresponding to the method can realize that all parameters needing to be modified are in the configuration file modified by the calibration system, reduce the steps of modifying the parameters manually, effectively prevent quality accidents caused by inputting wrong parameters, reduce the workload of workers, and effectively record the calibration data and the configuration file each time through the calibration system, thereby effectively analyzing the data and tracing the source.
That is to say, the scheme of the invention can eliminate the step of manually inputting parameters, simplify the operation requirements on workers, improve the production efficiency, increase and save historical calibration coefficients and files through the calibration system, and improve the traceability of data.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A Python-based LED spectrometer calibration method is characterized by specifically comprising the following steps:
acquiring original image positioning parameters, and generating a first original configuration file in real time according to original configuration file data;
generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data;
generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient;
and generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data.
2. The calibration method according to claim 1, wherein the standard component is a product tested by a standard machine;
the method comprises the following steps that before the positioning parameters of the original image are obtained and the first original configuration file is generated in real time according to the data of the original configuration file, the method also comprises the following steps:
and acquiring the data information of the work order and generating the original image positioning parameters.
3. The calibration method for the Python-based LED spectrometer according to claim 1 or 2, wherein the step of obtaining the original image positioning parameters and generating the first original configuration file in real time according to the original configuration file data further comprises the following steps:
and judging whether the work order number data and the configuration template data are consistent, if so, executing the next step, and otherwise, returning to the previous step.
4. The calibration method according to claim 3, wherein the first original configuration file is a configuration file for modifying image positioning parameters, voltage and current; the first original configuration file is specifically a configuration file without a computer difference coefficient.
5. The calibration method according to claim 1, wherein the step of generating the machine error coefficient in real time in combination with the first original configuration file and the test standard data further comprises the steps of:
and acquiring standard part data, and calculating the machine difference between the standard part data tested by the spectrometer and the standard part data tested by the standard machine in real time.
6. The Python-based LED spectrometer calibration method according to claim 5, wherein the step of obtaining the standard part data and calculating the machine difference between the spectrometer test standard part data and the standard machine test standard part data in real time further comprises the steps of:
calibrating the work order number data and the configuration template data;
and judging whether the standard coefficient in the standard piece data is qualified, if so, executing the next step, otherwise, returning to the previous step.
7. The calibration method according to claim 1, wherein the second configuration file is a configuration file for modifying and increasing calibration coefficients and card control parameters;
the step of generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data further comprises the following steps:
acquiring an organic difference coefficient configuration file, test standard part data and standard part data;
and judging whether the difference between the standard component data tested by the spectrometer and the standard component data tested by the standard machine is within a standard threshold range, if so, executing the next step, and otherwise, returning to the previous step.
8. The utility model provides a LED divides light machine calbiration system based on Python which characterized in that the system specifically includes:
the first generation unit is used for acquiring the positioning parameters of the original image and generating a first original configuration file in real time according to the data of the original configuration file;
the second generating unit is used for generating a machine error coefficient in real time by combining the first original configuration file and the test standard part data;
the third generating unit is used for generating a configuration file containing the opportunity difference coefficient in real time according to the opportunity difference coefficient;
and the fourth generating unit is used for generating a second configuration file in real time according to the organic difference coefficient configuration file, the test standard part data and the standard part data.
9. The calibration system according to claim 8, wherein the standard component is a product tested by a standard machine; the first original configuration file is specifically a configuration file for modifying image positioning parameters, voltage and current; the first original configuration file is specifically a configuration file without a computer difference coefficient; the second configuration file is specifically a configuration file for modifying and increasing the calibration coefficient and the card control parameter;
the system is also provided with:
the first generation module is used for acquiring the data information of the work order and generating the positioning parameters of the original image;
the first generation unit is also provided with:
the first judging module is used for judging whether the worksheet number data and the configuration template data are consistent;
the second generation unit is also provided with:
the calculation processing module is used for acquiring the data of the standard component and calculating the machine difference between the data of the standard component tested by the light splitting machine and the data of the standard component tested by the standard machine in real time;
the calibration module is used for calibrating the worksheet number data and the configuration template data;
the second judging module is used for judging whether the standard coefficient in the standard piece data is qualified or not;
the fourth generation unit is further provided with:
the acquisition module is used for acquiring an organic difference coefficient configuration file, test standard part data and standard part data;
and the third judging module is used for judging whether the difference between the standard component data of the spectrometer and the standard component data of the standard machine is within the standard threshold range.
10. The utility model provides a LED divides light machine calibration platform based on Python which characterized in that includes: the device comprises a processor, a memory and a Python-based LED spectrometer calibration platform control program;
wherein the Python-based LED-spectrometer calibration platform control program is executed by the processor, the Python-based LED-spectrometer calibration platform control program being stored in the memory, the Python-based LED-spectrometer calibration platform control program implementing the Python-based LED-spectrometer calibration method steps of any one of claims 1 to 7.
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