Data collation method and system for core full-fluence diagram measurement test based on Python

Abstract

The present invention relates to a Python-based data collation method and system for a full-core fluence diagram measurement test. After completing the full-core fluence diagram measurement test, the data collation method performs the following steps: obtaining an output file from the post-processing software of the RIC system, extracting the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type from the output file; determining whether the extracted evaluation content meets the corresponding acceptance criteria and obtaining the acceptance results; and sequentially filling the extracted evaluation content and its corresponding acceptance criteria and acceptance results into the corresponding positions of a pre-established Prettytable database to generate an evaluation table. Implementing the technical solution of the present invention shortens personnel data processing time, improves personnel processing efficiency, and ensures the effectiveness and reliability of core evaluation in the full-core fluence diagram test.

Description

Technical Field

[0001] The present invention relates to the field of nuclear power, and in particular to a Python-based data collation method and system for a core full fluence diagram measurement test. Background Art

[0002] Currently, CPR1000 nuclear power plant units perform core full-fluence diagram (FFD) measurements every 30 EFPDs (equivalent full-power days) as required by the supervision program. After the core neutron flux measurement is completed by the RIC system (core measurement system) and the corresponding output files are generated using the accompanying dedicated post-processing software (CEDRIC / CARIN / ETALONG), physics test personnel are required to evaluate the core parameters in the output files as required. This involves a series of operations, including data point selection, evaluation acceptance criteria calculation, and corresponding correction coefficient calculation. The data points are then entered into an Excel spreadsheet to form an evaluation table, completing the core evaluation. However, this process takes 15 minutes per person, and when combined with the approval process, the average process takes about an hour. Furthermore, the selection process primarily relies on visual inspection, which increases the potential for errors. This can even lead to inaccurate evaluation results, compromising the accuracy of the core evaluation. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a Python-based data collation method and system for the core full fluence diagram measurement test in response to the above-mentioned defects of low efficiency and high human error rate in the prior art.

[0004] The technical solution adopted by the present invention to solve the technical problem is to construct a data sorting method for the core full fluence map measurement test based on Python. After completing the core full fluence map measurement test, the following steps are performed:

[0005] Step S10. Obtaining an output file from the post-processing software of the RIC system, and extracting the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type from the output file, wherein the core evaluation type includes: core power distribution in each quadrant, tilt value between each quadrant, core enthalpy rise factor and power peak factor, and display deviation of the external nuclear instrument system;

[0006] Step S20: Determine whether the extracted evaluation content meets the corresponding acceptance criteria and obtain the acceptance result;

[0007] Step S30: Fill the extracted evaluation content and its corresponding acceptance criteria and acceptance results into the corresponding positions of the pre-established Prettytable database in sequence to generate an evaluation form.

[0008] Preferably, it also includes:

[0009] Step S40. Extract the hot pipe temperature, cold pipe temperature and main circuit pressure value of each quadrant from the output file;

[0010] Step S50. Calculate the power deviation value between each quadrant based on the hot pipe temperature, cold pipe temperature, and main circuit pressure value of each quadrant, and normalize the power deviation value between each quadrant to obtain the tilt value of each quadrant;

[0011] Step S60. Calculate the correction coefficient of each quadrant based on the tilt value of each quadrant obtained in step S50 and the tilt value of each quadrant obtained in step S10, so that the RIC system displays the corrected tilt value of each quadrant based on the correction coefficient of each quadrant, wherein the tilt value of each quadrant obtained in step S10 is obtained by processing the measurement data of the neutron flux in the pile by the post-processing software.

[0012] Preferably, in step S60, after calculating the correction coefficient of each quadrant, the following steps are further included:

[0013] The correction coefficients of the quadrants are verified.

[0014] Preferably, extracting the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type from the output file includes:

[0015] Step S11. Scanning the output file line by line based on the pre-set keywords corresponding to each core evaluation type to locate the content of the corresponding core evaluation type;

[0016] Step S12. According to the pre-set conditions of the corresponding core evaluation type, content that meets the conditions is filtered out from the located content to serve as the evaluation content corresponding to the core evaluation type;

[0017] Step S13. Generate the first txt file corresponding to each core evaluation type based on the evaluation content corresponding to each core evaluation type;

[0018] Step S14: Summarize and perform secondary extraction on the first txt file corresponding to each core evaluation type to obtain the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type, and generate a second txt file.

[0019] Preferably, after step S14, the method further includes:

[0020] The second txt file is checked according to the first txt files corresponding to each core evaluation type.

[0021] The present invention also constructs a data collating system for a core full fluence diagram measurement test based on Python, comprising:

[0022] A first extraction module is configured to obtain an output file from the post-processing software of the RIC system and extract the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type from the output file, wherein the core evaluation content includes: core power distribution in each quadrant, tilt values ​​between quadrants, core enthalpy rise factor and power peak factor, and display deviation of the external nuclear instrument system;

[0023] The judgment module is used to judge whether the extracted evaluation content meets the corresponding acceptance criteria and obtain the acceptance result;

[0024] The storage module is used to fill the extracted evaluation content and its corresponding acceptance criteria and acceptance results into the corresponding positions of the pre-established Prettytable database in sequence to generate an evaluation form.

[0025] Preferably, it also includes:

[0026] a second extraction module, configured to extract the hot pipe temperature, the cold pipe temperature, and the main circuit pressure value of each quadrant from the output file;

[0027] a first calculation module, configured to calculate a power deviation value between each quadrant based on the hot pipe temperature, the cold pipe temperature, and the main circuit pressure value of each quadrant, and normalize the power deviation value between each quadrant to obtain a tilt value for each quadrant;

[0028] The second calculation module is used to calculate the correction coefficient of each quadrant based on the tilt value of each quadrant obtained by the first calculation module and the tilt value of each quadrant obtained by the first extraction module, so that the RIC system displays the corrected tilt value of each quadrant according to the correction coefficient of each quadrant, wherein the tilt value of each quadrant obtained by the first extraction module is obtained by the post-processing software processing the measurement data of the neutron flux in the pile.

[0029] Preferably, it also includes:

[0030] The verification module is used to verify the correction coefficient of each quadrant after the second calculation module calculates the correction coefficient of each quadrant.

[0031] Preferably, the first extraction module includes:

[0032] a scanning unit, configured to scan the output file line by line according to the preset keywords corresponding to each core evaluation type, so as to locate the content of the corresponding core evaluation type;

[0033] A screening unit, configured to screen out content that meets the conditions from the located content according to pre-set conditions of the corresponding core evaluation type, to serve as evaluation content corresponding to the core evaluation type;

[0034] The first generating unit is used to generate the first txt file corresponding to the corresponding core evaluation type from the evaluation content corresponding to each core evaluation type;

[0035] The second generating unit is used to summarize and perform secondary extraction on the first txt file corresponding to each core evaluation type to obtain the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type, and generate a second txt file.

[0036] Preferably, it also includes:

[0037] The verification module is used to verify the second txt file according to the first txt files corresponding to each core evaluation type.

[0038] The technical solution provided by the present invention, based on Python code and Prettytable database, can automatically generate a core evaluation summary result page in about 10 seconds by running Python code, greatly shortening the data processing time of personnel and improving personnel processing efficiency. At the same time, due to the accuracy and speed of Python software operation, parameter evaluation avoids the possibility of higher risk of errors such as visual errors by personnel, accurately extracts core evaluation data, and can transfer personnel energy and time from data preprocessing to parameter verification and result evaluation, greatly improving personnel attention and ensuring the effectiveness and reliability of core evaluation of the full core fluence diagram test. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without inventive efforts. In the drawings:

[0040] Figure 1 This is a flow chart of a first embodiment of a data collating method for a core full fluence diagram measurement test based on Python of the present invention;

[0041] Figure 2 This is a logical structure diagram of a first embodiment of a data collating system for a core full-fluence diagram measurement test based on Python according to the present invention. DETAILED DESCRIPTION

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] Figure 1 This is a flow chart of a first embodiment of a data collation method for a core full fluence diagram measurement test based on Python of the present invention. After completing the core full fluence diagram measurement test, the data collation method of this embodiment performs the following steps:

[0044] Step S10. Obtaining an output file from the post-processing software of the RIC system, and extracting the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type from the output file, wherein the core evaluation type includes: core power distribution in each quadrant, tilt value between each quadrant, core enthalpy rise factor and power peak factor, and display deviation of the external nuclear instrument system;

[0045] In this step, after obtaining the output files (CEDFMxxx.OUT / CARFMxxx.OUT / ETACAxxx.OUT, xxx is the test number of each time) from the post-processing software (CEDRIC / CARIN / ETALONG) of the RIC system, in order to obtain the evaluation content of the core power distribution, the inclination value between each quadrant, the core enthalpy rise factor and the power peak factor, and the display deviation of the RPN system (extra-core nuclear instrument system) in the file, the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type can be extracted based on Python software.

[0046] Step S20: Determine whether the extracted evaluation content meets the corresponding acceptance criteria and obtain the acceptance result;

[0047] Step S30: Fill the extracted evaluation content and its corresponding acceptance criteria and acceptance results into the corresponding positions of the pre-established Prettytable database in sequence to generate an evaluation form.

[0048] In this step, the extracted information is entered into the Prettytable database (a custom Prettytable database contains evaluation type, measurement results, component or core location information, acceptance criteria, and whether the evaluation meets the acceptance criteria, etc.) to form an evaluation table, which corresponds to the evaluation table in the procedure.

[0049] The technical solution of this embodiment, based on Python code and Prettytable database, can automatically generate a core evaluation summary result page in about 10 seconds by running Python code, greatly shortening the data processing time of personnel and improving personnel processing efficiency. At the same time, due to the accuracy and speed of Python software operation, parameter evaluation avoids the possibility of higher risk of errors such as visual errors of personnel, accurately extracts core evaluation data, and can shift personnel energy and time from data preprocessing to parameter verification and result evaluation, greatly improving personnel attention and ensuring the effectiveness and reliability of core evaluation of the full core fluence diagram test.

[0050] In an optional embodiment, the data sorting method of the core full fluence diagram measurement test based on Python of the present invention further includes:

[0051] Step S40. Extract the hot pipe temperature, cold pipe temperature and main circuit pressure value of each quadrant from the output file;

[0052] In this step, it should be noted that each of the eight quadrants of the core distribution is equipped with a thermocouple probe to measure the hot and cold pipe temperatures in the corresponding quadrant. The Python software of the present invention extracts the hot and cold pipe temperatures and the main circuit pressure values ​​for each quadrant from the output file (CEDFMxxx.OUT) of the post-processing software.

[0053] Step S50. Calculate the power deviation value between each quadrant based on the hot pipe temperature, cold pipe temperature, and main circuit pressure value of each quadrant, and normalize the power deviation value between each quadrant to obtain the tilt value of each quadrant;

[0054] In this step, after extracting the hot pipe temperature, cold pipe temperature and main circuit pressure value of each quadrant, the power deviation value between the quadrants can be calculated according to the empirical formula for enthalpy rise calculation in the regulations, and then normalized to obtain the tilt value of each quadrant.

[0055] Step S60. Calculate the correction coefficient of each quadrant based on the tilt value of each quadrant obtained in step S50 and the tilt value of each quadrant obtained in step S10, so that the RIC system displays the corrected tilt value of each quadrant based on the correction coefficient of each quadrant, wherein the tilt value of each quadrant obtained in step S10 is obtained by processing the measurement data of the neutron flux in the pile by the post-processing software.

[0056] In this step, it should be noted that the tilt values ​​between each quadrant contained in the output file of the post-processing software are obtained by processing the in-pile neutron flux measurement data, that is, by the in-pile data measurement method; while the tilt values ​​for each quadrant in step S50 are obtained by calculating the measurement data of the ex-pile thermocouples, that is, by the ex-pile data measurement method. Moreover, the in-pile measurement data is obtained through regular testing and is more accurate; while the ex-pile measurement data is obtained in real time and, because it is obtained through estimation, is less accurate than the in-pile data measurement method. After the tilt values ​​for each quadrant are obtained using these two methods, the correction coefficients for each quadrant can be calculated. Once these correction coefficients for each quadrant are input into the RIC system by the engineer, the RIC system corrects the tilt values ​​for each quadrant obtained using the ex-pile measurement data according to these correction coefficients and displays them.

[0057] Compared with the above embodiment, the technical solution of this embodiment adds the calculation of the correction coefficient Ci of the inclination of each quadrant, thereby meeting the requirements of on-site use.

[0058] Furthermore, after calculating the correction coefficients for each quadrant in step S60, the method further includes verifying the correction coefficients for each quadrant. In this embodiment, the correction coefficients for each quadrant (rounded to four decimal places) are substituted into the deviation values ​​between the quadrants to calculate the corrected tilt values ​​for each quadrant of the RIC system. The accuracy of the correction coefficients can be verified by comparing the deviations.

[0059] In an optional embodiment, in step S10, the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type are extracted from the output file, including:

[0060] Step S11. Scanning the output file line by line based on the pre-set keywords corresponding to each core evaluation type to locate the content of the corresponding core evaluation type;

[0061] Step S12. According to the pre-set conditions of the corresponding core evaluation type, content that meets the conditions is filtered out from the located content to serve as the evaluation content corresponding to the core evaluation type;

[0062] Step S13. Generate the first txt file corresponding to each core evaluation type based on the evaluation content corresponding to each core evaluation type;

[0063] Step S14: Summarize and perform secondary extraction on the first txt file corresponding to each core evaluation type to obtain the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type, and generate a second txt file.

[0064] In this embodiment, the output file is scanned line by line using a Python "for" command according to pre-set keywords (for example, the keyword corresponding to core power distribution is "RADIAL MAP OF 3D POWER") to locate the required core power distribution. Then, according to pre-set conditions (for example, the conditions corresponding to core power distribution are "sum" and "total"), the "if" command (conditional selection) in Python is used to filter out content that meets the conditions from the located content. The code then generates a first txt file corresponding to each core evaluation type based on the evaluation content corresponding to the corresponding core evaluation type. This is the raw data for the evaluation file required for the full-core fluence map measurement test. After generating the first txt files corresponding to each required core evaluation type, the code extracts and summarizes information from each first txt file, extracts information at specific locations in each first txt file, and performs conditional selection ("if" command) on the information. Through secondary processing, the evaluation content and corresponding acceptance criteria in the evaluation file are refined and extracted, and the summary result is a second txt file. Since conditional selection (if instruction) can obtain the maximum value at different heights of the core, it is necessary to evaluate and capture information one by one. Python can obtain all data points that need to be evaluated by adding a loop (for instruction), avoiding the omission of content that needs to be evaluated by visual means.

[0065] Furthermore, after step S14, the process further includes: verifying the second txt file according to the first txt files corresponding to each core evaluation type. Since the first txt files required for the evaluation have been retained, engineers can verify and reverse calculate the contents of the second txt files by comparing them with the first txt files.

[0066] Figure 2 It is a logical structure diagram of the first embodiment of the data collation system for the core full-flux diagram measurement test based on Python of the present invention. The data collation system of this embodiment includes: a first extraction module 10, a judgment module 20 and a storage module 30, wherein the first extraction module 10 is used to obtain the output file from the post-processing software of the RIC system, and extract the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type from the output file, wherein the core evaluation type includes: core power distribution of each quadrant, tilt value between each quadrant, core enthalpy rise factor and power peak factor, display deviation of the off-core nuclear instrument system; the judgment module 20 is used to judge whether the extracted evaluation content meets the corresponding acceptance criteria and obtain the acceptance result; the storage module 30 is used to fill the extracted evaluation content and its corresponding acceptance criteria and acceptance result into the corresponding positions of the pre-established Prettytable database in sequence to generate an evaluation form.

[0067] Furthermore, the data collation system of the core full fluence diagram measurement test based on Python of the present invention also includes: a second extraction module, a first calculation module, and a second calculation module, wherein the second extraction module is used to extract the hot pipe temperature, cold pipe temperature and main loop pressure value of each quadrant from the output file; the first calculation module is used to calculate the power deviation value between each quadrant based on the hot pipe temperature, cold pipe temperature and main loop pressure value of each quadrant, and normalize the power deviation value between each quadrant to obtain the tilt value of each quadrant; the second calculation module is used to calculate the correction coefficient of each quadrant based on the tilt value of each quadrant obtained by the first calculation module and the tilt value of each quadrant obtained by the first extraction module, so that the RIC system displays the corrected tilt value of each quadrant according to the correction coefficient of each quadrant, wherein the tilt value of each quadrant obtained by the first extraction module is obtained by the post-processing software processing the measurement data of the neutron flux in the pile.

[0068] Furthermore, the data collation system of the core full fluence diagram measurement test based on Python of the present invention also includes a verification module, which is used to verify the correction coefficient of each quadrant after the second calculation module calculates the correction coefficient of each quadrant.

[0069] Furthermore, the first extraction module includes: a scanning unit, a screening unit, a first generating unit and a second generating unit, wherein the scanning unit is used to scan the output file line by line according to the pre-set keywords corresponding to each core evaluation type to locate the content of the corresponding core evaluation type; the screening unit is used to screen out the content that meets the conditions from the located content according to the pre-set conditions of the corresponding core evaluation type, as the evaluation content corresponding to the core evaluation type; the first generating unit is used to generate the first txt file corresponding to the corresponding core evaluation type for the evaluation content corresponding to each core evaluation type; the second generating unit is used to summarize and perform secondary extraction on the first txt file corresponding to each core evaluation type to obtain the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type, and generate a second txt file.

[0070] Furthermore, the data collation system of the core full fluence diagram measurement test based on Python of the present invention also includes a verification module, which is used to verify the second txt file according to the first txt file corresponding to each core evaluation type.

[0071] This invention simplifies and optimizes the on-site core evaluation process, improving data processing efficiency. The current on-site data processing time is shortened from 15 minutes to 10 seconds, significantly saving personnel time and improving their focus during evaluation. This avoids human errors caused by time pressure and stress, ensuring the effectiveness and accuracy of full-core fluence diagram test result evaluation. This approach improves personnel processing efficiency, reduces the risk of human error, and increases the convenience and effectiveness of data processing.

[0072] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be encompassed within the scope of the claims.

Claims

1. A data collation method for core full fluence diagram measurement test based on Python, characterized in that: After completing the core full fluence map measurement test, perform the following steps: Step S10. Obtaining an output file from the post-processing software of the RIC system, and extracting the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type from the output file, wherein the core evaluation type includes: core power distribution in each quadrant, tilt value between each quadrant, core enthalpy rise factor and power peak factor, and display deviation of the external nuclear instrument system; Step S20: Determine whether the extracted evaluation content meets the corresponding acceptance criteria and obtain the acceptance result; Step S30. Fill the extracted evaluation content and its corresponding acceptance criteria and acceptance results into the corresponding positions of a pre-established Prettytable database to generate an evaluation table, wherein the Prettytable database contains evaluation type, measurement results, component or core location information, acceptance criteria, and evaluation results; Step S40. Extract the hot pipe temperature, cold pipe temperature and main circuit pressure value of each quadrant from the output file; Step S50. Calculate the power deviation value between each quadrant based on the hot pipe temperature, cold pipe temperature, and main circuit pressure value of each quadrant, and normalize the power deviation value between each quadrant to obtain the tilt value of each quadrant; Step S60. Calculate the correction coefficient of each quadrant based on the tilt value of each quadrant obtained in real time in step S50 and the tilt value of each quadrant obtained periodically in step S10, so that the RIC system corrects the tilt value of each quadrant obtained in step S50 according to the correction coefficient of each quadrant, and displays the corrected tilt value of each quadrant, wherein the tilt value of each quadrant obtained in step S10 is obtained by the post-processing software processing the measurement data of the neutron flux in the pile.

2. The data sorting method for the core full fluence map measurement test based on Python according to claim 1 is characterized in that: In the step S60, after calculating the correction coefficient of each quadrant, the following steps are further included: The correction coefficients of the quadrants are verified.

3. The data sorting method for the core full fluence map measurement test based on Python according to claim 1 is characterized in that: Extracting the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type from the output file includes: Step S11. Scanning the output file line by line based on the pre-set keywords corresponding to each core evaluation type to locate the content of the corresponding core evaluation type; Step S12. According to the pre-set conditions of the corresponding core evaluation type, content that meets the conditions is filtered out from the located content to serve as the evaluation content corresponding to the core evaluation type; Step S13. Generate the first txt file corresponding to each core evaluation type based on the evaluation content corresponding to each core evaluation type; Step S14: Summarize and perform secondary extraction on the first txt file corresponding to each core evaluation type to obtain the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type, and generate a second txt file.

4. The data sorting method for the core full fluence map measurement test based on Python according to claim 3 is characterized in that: After step S14, the method further includes: The second txt file is checked according to the first txt files corresponding to each core evaluation type.

5. A data collation system for core full fluence diagram measurement test based on Python, characterized in that: include: A first extraction module is configured to obtain an output file from the post-processing software of the RIC system and extract the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type from the output file, wherein the core evaluation content includes: core power distribution in each quadrant, tilt values ​​between quadrants, core enthalpy rise factor and power peak factor, and display deviation of the external nuclear instrument system; The judgment module is used to judge whether the extracted evaluation content meets the corresponding acceptance criteria and obtain the acceptance result; a storage module for sequentially filling the extracted evaluation content and its corresponding acceptance criteria and acceptance results into corresponding locations of a pre-established Prettytable database to generate an evaluation table, wherein the Prettytable database includes evaluation type, measurement results, component or core location information, acceptance criteria, and evaluation results; a second extraction module, configured to extract the hot pipe temperature, the cold pipe temperature, and the main circuit pressure value of each quadrant from the output file; a first calculation module, configured to calculate a power deviation value between each quadrant based on the hot pipe temperature, the cold pipe temperature, and the main circuit pressure value of each quadrant, and normalize the power deviation value between each quadrant to obtain a tilt value for each quadrant; The second calculation module is used to calculate the correction coefficient of each quadrant based on the tilt value of each quadrant obtained in real time by the first calculation module and the tilt value of each quadrant obtained periodically by the first extraction module, so that the RIC system corrects the tilt value of each quadrant obtained by the first calculation module according to the correction coefficient of each quadrant and displays the corrected tilt value of each quadrant, wherein the tilt value of each quadrant obtained by the first extraction module is obtained by the post-processing software processing the measurement data of the neutron flux in the pile.

6. The data collating system for the core full fluence map measurement test based on Python according to claim 5 is characterized in that: Also includes: The verification module is used to verify the correction coefficient of each quadrant after the second calculation module calculates the correction coefficient of each quadrant.

7. The data collating system for the core full fluence map measurement test based on Python according to claim 5 is characterized in that: The first extraction module includes: a scanning unit, configured to scan the output file line by line according to the preset keywords corresponding to each core evaluation type, so as to locate the content of the corresponding core evaluation type; A screening unit, configured to screen out content that meets the conditions from the located content according to pre-set conditions of the corresponding core evaluation type, to serve as evaluation content corresponding to the core evaluation type; The first generating unit is used to generate the first txt file corresponding to the corresponding core evaluation type from the evaluation content corresponding to each core evaluation type; The second generating unit is used to summarize and perform secondary extraction on the first txt file corresponding to each core evaluation type to obtain the evaluation content and corresponding acceptance criteria corresponding to each core evaluation type, and generate a second txt file.

8. The data collating system for the core full fluence map measurement test based on Python according to claim 7 is characterized in that: Also includes: The verification module is used to verify the second txt file according to the first txt files corresponding to each core evaluation type.

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