A big data intelligent processing method and system for density equivalent testing

Abstract

The present invention discloses a big data intelligent processing method and system for density equivalent testing, which belongs to the technical field of big data models, and comprises the following steps: recording historical test data, associating corresponding data in the historical test data with labels, wherein the labels include a variety of quality labels, normal density labels, vacuum density labels and density equivalent labels, generating a density equivalent test table using the historical test data through the labels, generating a density equivalent algorithm model, and performing a density equivalent test; receiving a detected weight value and a calculated sample density and equivalent value; substituting the received data into a density equivalent algorithm model after deep learning according to the labels to generate the closest function formula, selecting data with the largest difference from the function formula generated by the density equivalent algorithm model and displaying the corresponding label. The present invention has the function of automatically selecting the data most likely to fail in the test, and is convenient for checking the effect of the test data.

Description

Technical Field

[0001] The present invention relates to the field of big data models, and in particular to a big data intelligent processing method and system applied to density equivalent testing. Background Art

[0002] At present, the density equivalent tester is a high-precision measuring instrument, which is mainly used to measure the density of materials and plays an important role in specific fields such as aluminum alloy casting production. The density equivalent meter mainly calculates the density by measuring the mass and volume of the material, and is usually used to detect the density of materials with high density such as molten metal. The working principle of the density equivalent meter is based on the density calculation formula: density = mass / volume. First, use a balance or other equipment to measure the mass of the material; then, measure the volume of the material through various methods such as immersion method and volumetric method; finally, substitute the mass and volume data into the density calculation formula to obtain the density value.

[0003] The above-mentioned prior art solutions have the following defects: during the density equivalent test, since multiple sets of data need to be tested and multiple complex calculations need to be performed, individual data errors are prone to occur, but it is difficult for users to find erroneous data from a large amount of experimental data after the test is completed. Summary of the invention

[0004] In order to facilitate users to find erroneous data, the present application provides a big data intelligent processing method and system for density equivalent testing.

[0005] On the one hand, the present application provides a big data intelligent processing method for density equivalent testing using the following technical solutions:

[0006] A big data intelligent processing method applied to density equivalent testing comprises the following steps:

[0007] Record historical test data and associate corresponding data in the historical test data with labels, including various quality labels, normal density labels, vacuum density labels and density equivalent labels;

[0008] Generate density equivalent test tables using historical test data through tags;

[0009] Select some test data from the density equivalent test table, use the data corresponding to each label in the selected test data as a variable variable, use the density equivalent as an unknown variable to generate a function formula, and perform deep learning training on all function formulas to generate a density equivalent algorithm model;

[0010] The density equivalent algorithm model is deeply learned through the density equivalent test table;

[0011] Conduct density equivalence tests;

[0012] receiving the detected weight value and the calculated sample density and equivalent value and associating tags with the weight value, sample density and equivalent value according to the instruction;

[0013] Substitute the received data into the density equivalent algorithm model after deep learning according to the label to generate the closest function formula, select the data with the largest difference from the function formula generated by the density equivalent algorithm model and display the corresponding label.

[0014] By adopting the above solution, the user enters the test results into the system after the test is completed. The system automatically filters out the data generated by each test that has the largest difference from the predicted data through the data model. The user can quickly identify the erroneous data by checking the data.

[0015] Preferably, the step of "performing a density equivalent test" further comprises the following steps:

[0016] Switch the detection state to normal sample detection;

[0017] After receiving the instruction, the sample is tested;

[0018] Read the detected weight value as the preliminary mass in the air;

[0019] After receiving the instruction, the sample is tested;

[0020] Read the detected weight value as the preliminary mass in water;

[0021] Compare the preliminary mass in air with the preliminary mass in water;

[0022] If the initial mass in the air is less than the initial mass in the water, the weight value of the initial mass in the air is interchanged with the weight value of the initial mass in the water;

[0023] Calculate the normal sample density from the initial mass in air and the initial mass in water;

[0024] Switch the detection state to vacuum sample detection;

[0025] After receiving the instruction, the sample is tested;

[0026] Read the detected weight value as the mass in the secondary air;

[0027] After receiving the instruction, the sample is tested;

[0028] Read the detected weight value as the mass in secondary water;

[0029] Compare the mass in secondary air with the mass in secondary water;

[0030] If the mass in the secondary air is less than the mass in the secondary water, the weight value of the mass in the secondary air is interchanged with the weight value of the mass in the secondary water;

[0031] Calculate the density of the vacuum sample by the mass in the secondary air and the mass in the secondary water;

[0032] Compare the density of normal sample and the density of vacuum sample;

[0033] If the density of the normal sample is less than that of the vacuum sample, the density value of the normal sample density and the density value of the vacuum sample density are interchanged;

[0034] Calculate the equivalent value using the normal sample density and vacuum sample density and record it.

[0035] By adopting the above scheme, when performing calculations, the system will automatically check the data to be calculated to determine whether the two data have a wrong order. If it is found that the data is unreasonable, such as the weight detected in water is larger than the weight detected in the air, the two data will be exchanged to ensure that all data are not misplaced when the equivalent is finally calculated, which effectively improves the accuracy of the test, simplifies the test steps of the tester, and avoids the tester restarting the test after the detection order is wrong.

[0036] Preferably, the step of "calculating the normal sample density by the preliminary mass in air and the preliminary mass in water" further comprises:

[0037] Calculate the normal sample density using the formula , the formula is as follows:

[0038] ;

[0039] In the formula, is the initial air mass, is the initial water quality;

[0040] The step of "calculating the density of the vacuum sample by the mass in the secondary air and the mass in the secondary water" also includes:

[0041] Calculate the vacuum sample density using the formula , the formula is as follows:

[0042] ;

[0043] In the formula, is the mass in the secondary air, is the quality in secondary water;

[0044] The step of "calculating and recording the equivalent value by using the normal sample density and the vacuum sample density" also includes:

[0045] Calculate the equivalent value using the formula , the formula is as follows:

[0046] ;

[0047] Record equivalent value .

[0048] By adopting the above solution, the system calculates density, equivalent and other values ​​through the above formula, and the calculation is scientific and accurate.

[0049] Preferably, before the step of "calculating and recording the equivalent value by the normal sample density and the vacuum sample density", the step further includes:

[0050] The density value of the normal sample density and the density value of the vacuum sample density are exchanged as the second set of data;

[0051] Calculate the control equivalent value from the second set of data;

[0052] Compare equivalent values ​​with control equivalent values;

[0053] If the equivalent value is less than the control equivalent value, the density value of the normal sample density and the density value of the vacuum sample density are interchanged and the control equivalent value is recorded as the equivalent value.

[0054] By adopting the above solution, the system can also determine whether the test data entry sequence is correct by calculating a new equivalent value after exchanging the data and then comparing the two equivalent values.

[0055] Preferably, the method further comprises the following steps:

[0056] Preset form templates;

[0057] Fill in the initial mass in air, initial mass in water, secondary mass in air, secondary mass in water, normal sample density, vacuum sample density and equivalent value during calculation into the form template to generate a test record form;

[0058] Display the test record form and all historical data;

[0059] When receiving the adjustment instruction, replace the initial mass in air, initial mass in water, secondary mass in air, secondary mass in water, normal sample density or vacuum sample density in the test or test record form according to the adjustment instruction;

[0060] When the export instruction is received, the test record form is printed.

[0061] By adopting the above solution, the system can automatically organize historical test records and adjust them according to user requirements, and can also print the test record form.

[0062] Preferably, the method further comprises the following steps:

[0063] Set wind speed limits, standard air humidity ranges, and standard air pressure ranges;

[0064] Detect and display the ambient air humidity value and the ambient air pressure value, calculate the difference between the current ambient air humidity value and the middle value of the standard air humidity range as the humidity abnormality difference, calculate the difference between the current ambient air pressure value and the middle value of the standard air pressure range as the pressure abnormality difference;

[0065] Adjusting the ambient air humidity value and the ambient air pressure value to obtain different humidity anomaly differences or pressure anomaly differences, and proceeding to the next step under different humidity anomaly differences or pressure anomaly differences;

[0066] Taking fixed samples to detect the initial mass in the air, the initial mass in the water, the secondary mass in the air and the secondary mass in the water, and associating and storing the detection results with the abnormal humidity difference or the abnormal pressure difference;

[0067] Take the test data associated with different humidity anomaly values ​​and the test data with a humidity anomaly value of 0 to calculate the deviation ratio, draw a humidity anomaly data curve chart according to the deviation ratio, and store the humidity anomaly data curve chart;

[0068] Calculate the deviation ratio of the test data associated with different pressure anomaly values ​​and the test data with a pressure anomaly value of 0, draw a pressure anomaly data curve chart according to the deviation ratio, and store the pressure anomaly data curve chart;

[0069] Detect the ambient wind speed. If the ambient wind speed exceeds the wind speed limit, a wind speed warning will be displayed.

[0070] If the ambient wind speed is lower than the wind speed limit, the ambient air humidity value and the ambient air pressure value are detected;

[0071] If the ambient air humidity value is not within the standard air humidity range, the humidity anomaly difference is calculated;

[0072] Substitute the calculated humidity anomaly difference into the humidity anomaly data curve chart to obtain the deviation ratio of the test data, and correct the current test data according to the deviation ratio;

[0073] If the ambient air pressure value is not within the standard air pressure range, the abnormal pressure difference is calculated;

[0074] The calculated pressure anomaly difference is substituted into the pressure anomaly data curve chart to obtain the deviation ratio of the test data, and the current test data is corrected according to the deviation ratio.

[0075] By adopting the above solution, for tests that require accurate test results, the environmental wind speed, humidity, and air pressure will all affect the test results. In a fixed location, testers obtain multiple sets of test data by testing the same sample under different conditions. The system automatically generates humidity anomaly data curves and pressure anomaly data curves. When the testers conduct tests, the system automatically calibrates the test results according to the current environment.

[0076] Preferably, the method further comprises the following steps:

[0077] receiving and storing image information of the container;

[0078] Collect sampling image information of the instrument detection area, and retain the sampling image information with container image information;

[0079] The retained sampled image information is converted into a grayscale image and filtered, and the bubbles in the sampled image information are identified by an edge detection algorithm;

[0080] If bubbles are detected in the sampled image information, a bubble warning is issued.

[0081] By adopting the above solution, the image information system can automatically determine whether bubbles appear in the sample, thereby preventing the bubbles from affecting the test data.

[0082] On the other hand, the present application provides a big data intelligent processing system for density equivalent testing using the following technical solutions:

[0083] A big data intelligent processing system for density equivalent testing, comprising any of the above-mentioned density equivalent testing methods, including a data storage module, a model generation module, a deep learning module, and an error comparison module;

[0084] The data storage module receives and stores test data, historical test data and labels, associates corresponding data in the historical test data with labels, the labels include a variety of mass labels, normal density labels, vacuum density labels and density equivalent labels, and when the data storage module receives new test data, receives the detected weight value and the calculated sample density and equivalent value and associates labels with the weight value, sample density and equivalent value according to the instruction, and transmits the test data to the error comparison module;

[0085] The model generation module calls the historical test data and labels stored in the data storage module, generates a density equivalent test table using the historical test data through the labels, selects part of the test data from the density equivalent test table, uses the data corresponding to each label in the selected test data as a variable number variable, and uses the density equivalent as an unknown number variable to generate a function formula, performs deep learning training on all function formulas to generate a density equivalent algorithm model, and transmits the density equivalent test table and the density equivalent algorithm model to the deep learning module;

[0086] The deep learning module allows the density equivalent algorithm model to perform deep learning through the density equivalent test table, and transmits the density equivalent algorithm model after deep learning to the data storage module;

[0087] The error comparison module calls the density equivalent algorithm model of the data storage module, substitutes the received data into the density equivalent algorithm model after deep learning according to the label to generate the closest function formula, selects the data with the largest difference from the function formula generated by the density equivalent algorithm model and displays the corresponding label.

[0088] By adopting the above solution, the user enters the test results into the system after the test is completed. The system automatically filters out the data generated by each test that has the largest difference from the predicted data through the data model. The user can quickly identify the erroneous data by checking the data.

[0089] Preferably, it also includes a status update module, a detection execution module, a data calculation module, and a data verification module;

[0090] The state update module is preset with a detection state, the detection state includes normal sample detection and vacuum sample detection, and the detection state is switched according to the received instruction. When the detection state is switched to normal sample detection, a normal signal is transmitted to the detection execution module, and when the detection state is switched to vacuum sample detection, a vacuum signal is transmitted to the detection execution module;

[0091] When the detection execution module receives a normal signal and a first detection instruction, the control tester detects the sample and reads the detected weight value as the preliminary mass in the air. When the second detection instruction is received, the control tester detects the sample and reads the detected weight value as the preliminary mass in the water. When the detection execution module receives a vacuum signal and a first detection instruction, the control tester detects the sample and reads the detected weight value as the secondary mass in the air. When the second detection instruction is received, the control tester detects the sample and reads the detected weight value as the secondary mass in the water. The preliminary mass in the air, the preliminary mass in the water, the secondary mass in the air and the secondary mass in the water are transmitted to the data calculation module and the data verification module.

[0092] The data calculation module calculates the normal sample density according to the preliminary mass in the air and the preliminary mass in the water, calculates the vacuum sample density according to the secondary mass in the air and the secondary mass in the water, calculates the equivalent value according to the normal sample density and the vacuum sample density, transmits the normal sample density and the vacuum sample density to the data verification module, and transmits the preliminary mass in the air, the preliminary mass in the water, the secondary mass in the air, the secondary mass in the water, the normal sample density, the vacuum sample density and the equivalent value to the data storage module;

[0093] The data verification module receives the preliminary mass in the air and the preliminary mass in the water and compares them. If the preliminary mass in the air is less than the preliminary mass in the water, the weight value of the preliminary mass in the air is swapped with the weight value of the preliminary mass in the water, and the swapped preliminary mass in the air and the preliminary mass in the water are transmitted to the data calculation module and overwritten. The data verification module receives the secondary mass in the air and the secondary mass in the water and compares them. If the secondary mass in the air is less than the secondary mass in the water, the weight value of the secondary mass in the air is swapped with the weight value of the secondary mass in the water, and the swapped secondary mass in the air and the secondary mass in the water are transmitted to the data calculation module and overwritten. The data verification module receives the normal sample density and the vacuum sample density and compares them. If the normal sample density is less than the vacuum sample density, the density value of the normal sample density is swapped with the density value of the vacuum sample density, and the swapped normal sample density and the vacuum sample density are transmitted to the data calculation module and overwritten.

[0094] By adopting the above scheme, when performing calculations, the system will automatically check the data to be calculated to determine whether the two data have a wrong order. If it is found that the data is unreasonable, such as the weight detected in water is larger than the weight detected in the air, the two data will be exchanged to ensure that all data are not misplaced when the equivalent is finally calculated, which effectively improves the accuracy of the test, simplifies the test steps of the tester, and avoids the tester restarting the test after the detection order is wrong.

[0095] Preferably, the data calculation module interchanges the density value of the normal sample density and the density value of the vacuum sample density to calculate the control equivalent value, and transmits the equivalent value and the control equivalent value to the data verification module;

[0096] The data verification module receives the equivalent value and the control equivalent value and compares the equivalent value and the control equivalent value. If the equivalent value is smaller than the control equivalent value, the density value of the normal sample density and the density value of the vacuum sample density, and the swapped normal sample density and vacuum sample density are transmitted to the data calculation module and overwritten.

[0097] By adopting the above solution, the system can also determine whether the test data entry sequence is correct by calculating a new equivalent value after exchanging the data and then comparing the two equivalent values.

[0098] In summary, the present invention has the following beneficial effects:

[0099] After the test is completed, the user enters the test results into the system. The system automatically filters out the data generated by each test that has the largest difference from the predicted data through the data model. The user can quickly identify the erroneous data by checking the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 This is a system block diagram of the second embodiment of the present application.

[0101] Description of reference numerals:

[0102] 1. Status update module; 2. Detection execution module; 3. Data calculation module; 4. Data verification module; 41. Model generation module; 42. Deep learning module; 43. Error comparison module; 5. Data storage module; 6. Form generation module; 7. Environmental monitoring module; 8. Curve generation module; 9. Deviation correction module. DETAILED DESCRIPTION

[0103] Embodiment 1: This embodiment of the present application discloses a big data intelligent processing method applied to density equivalent test, and the specific steps are as follows:

[0104] The historical test data is recorded, and the corresponding data in the historical test data is associated with labels, including various quality labels, normal density labels, vacuum density labels, and density equivalent labels.

[0105] Generate density equivalent test tables using historical test data via tags.

[0106] Select some test data from the density equivalent test table, use the data corresponding to each label in the selected test data as a variable variable, and use the density equivalent as an unknown variable to generate a function formula, and perform deep learning training on all function formulas to generate a density equivalent algorithm model.

[0107] The density equivalent algorithm model is deeply learned through the density equivalent test table.

[0108] Perform a density equivalence test.

[0109] Pre-set form templates. Set wind speed limits, standard air humidity ranges, and standard air pressure ranges. Receive and store container image information.

[0110] The sampling image information of the instrument detection area is collected, and the sampling image information with the container image information is retained.

[0111] The retained sampled image information is converted into a grayscale image and filtered, and the bubbles in the sampled image information are identified by an edge detection algorithm.

[0112] If bubbles are detected in the sampled image information, a bubble warning is issued.

[0113] Detect and display the ambient air humidity value and ambient air pressure value, calculate the difference between the current ambient air humidity value and the middle value of the standard air humidity range as the humidity abnormality difference, calculate the difference between the current ambient air pressure value and the middle value of the standard air pressure range as the pressure abnormality difference.

[0114] Switch the detection state to normal sample detection.

[0115] After receiving the instruction, the sample is tested.

[0116] Read the detected weight value as the preliminary air mass.

[0117] After receiving the instruction, the sample is tested.

[0118] Read the detected weight value as the preliminary water mass.

[0119] Compare the preliminary mass in air with the preliminary mass in water.

[0120] If the initial mass in air is less than the initial mass in water, the weight value of the initial mass in air is interchanged with the weight value of the initial mass in water.

[0121] Calculate the normal sample density by using the initial mass in air and the initial mass in water. Calculate the normal sample density using the formula , the formula is as follows:

[0122] ;

[0123] In the formula, is the initial air mass, For the initial water quality.

[0124] Switch the detection state to vacuum sample detection.

[0125] After receiving the instruction, the sample is tested.

[0126] Read the detected weight value as the mass in the secondary air.

[0127] After receiving the instruction, the sample is tested.

[0128] The detected weight value is read as the secondary water mass.

[0129] Compare the mass in secondary air to the mass in secondary water.

[0130] If the mass in the secondary air is less than the mass in the secondary water, then the weight value of the mass in the secondary air is interchanged with the weight value of the mass in the secondary water.

[0131] Calculate the vacuum sample density by using the secondary air mass and the secondary water mass. Use the formula to calculate the vacuum sample density. , the formula is as follows:

[0132] ;

[0133] Where, is the mass of secondary air , is the mass in secondary water .

[0134] Compare the density of the normal sample and the density of the vacuum sample.

[0135] If the normal sample density is less than the vacuum sample density, the density value of the normal sample density and the density value of the vacuum sample density are interchanged.

[0136] The density value of the normal state sample density and the density value of the vacuum sample density are interchanged as the second set of data.

[0137] Calculate the equivalent value by using the normal sample density and the vacuum sample density. Use the formula to calculate the equivalent value , the formula is as follows:

[0138] ;

[0139] Record equivalent value .

[0140] The control equivalent value is calculated from the second set of data, and the equivalent value is compared with the control equivalent value.

[0141] If the equivalent value is less than the control equivalent value, the density value of the normal sample density and the density value of the vacuum sample density are interchanged and the control equivalent value is recorded as the equivalent value.

[0142] The ambient air humidity value and the ambient air pressure value are adjusted to obtain different humidity anomaly differences or pressure anomaly differences, and the next step is performed under different humidity anomaly differences or pressure anomaly differences.

[0143] Take fixed samples to detect the preliminary mass in the air, the preliminary mass in the water, the secondary mass in the air and the secondary mass in the water, and associate the test results with the abnormal humidity difference or the abnormal pressure difference and store them.

[0144] The test data associated with different humidity anomaly values ​​and the test data with a humidity anomaly value of 0 are taken to calculate the deviation ratio, a humidity anomaly data curve graph is drawn according to the deviation ratio, and the humidity anomaly data curve graph is stored.

[0145] The deviation ratio is calculated by taking the test data associated with different pressure abnormal values ​​and the test data with a pressure abnormal value of 0, drawing a pressure abnormal data curve graph according to the deviation ratio, and storing the pressure abnormal data curve graph.

[0146] Detect the ambient wind speed. If the ambient wind speed exceeds the wind speed limit, a wind speed warning will be displayed.

[0147] If the ambient wind speed is lower than the wind speed limit, the ambient air humidity value and the ambient air pressure value are detected.

[0148] If the ambient air humidity value is not within the standard air humidity range, the humidity anomaly difference is calculated.

[0149] Substitute the calculated humidity anomaly difference into the humidity anomaly data curve chart to obtain the deviation ratio of the test data, and correct the current test data according to the deviation ratio.

[0150] If the ambient air pressure value is not within the standard air pressure range, the abnormal pressure difference is calculated.

[0151] The calculated pressure anomaly difference is substituted into the pressure anomaly data curve chart to obtain the deviation ratio of the test data, and the current test data is corrected according to the deviation ratio.

[0152] Fill in the initial mass in air, initial mass in water, secondary mass in air, secondary mass in water, normal sample density, vacuum sample density and equivalent value during calculation into the form template to generate a test record form.

[0153] Displays the test record form and all historical data.

[0154] When an adjustment instruction is received, the preliminary mass in air, preliminary mass in water, secondary mass in air, secondary mass in water, normal sample density or vacuum sample density in the test or test record form are replaced according to the adjustment instruction.

[0155] When the export instruction is received, the test record form is printed.

[0156] Receive the detected weight value and the calculated sample density and equivalent value and associate tags with the weight value, sample density and equivalent value according to the instruction.

[0157] Substitute the received data into the density equivalent algorithm model after deep learning according to the label to generate the closest function formula, select the data with the largest difference from the function formula generated by the density equivalent algorithm model and display the corresponding label.

[0158] The implementation principle of a big data intelligent processing method applied to density equivalent testing in an embodiment of the present application is as follows: the user enters the test results into the system after the test is completed, and the system automatically filters out the data generated by each test that has the largest difference from the predicted data through a data model, and the user can quickly determine the erroneous data by checking the data.

[0159] When performing calculations, the system will automatically check the data to be calculated to determine whether the two data are in the wrong order. If unreasonable data are found, such as the weight detected in water is larger than the weight detected in the air, the two data will be exchanged to ensure that all data are not misplaced when the equivalent is finally calculated, which effectively improves the accuracy of the test, simplifies the test steps of the tester, and avoids the tester restarting the test after the detection order is wrong.

[0160] Embodiment 2: This embodiment of the present application discloses a big data intelligent processing system for density equivalent testing, such as Figure 1 As shown, it includes a state update module 1, a detection execution module 2, a data calculation module 3, a data verification module 4, a model generation module 41, a deep learning module 42, an error comparison module 43, a data storage module 5, a form generation module 6, an environmental monitoring module 7, a curve generation module 8 and a deviation correction module 9.

[0161] like Figure 1 As shown, the data storage module 5 receives and stores test data, historical test data and labels, and associates corresponding data in the historical test data with labels. The labels include various quality labels, normal density labels, vacuum density labels and density equivalent labels. When the data storage module receives new test data, it receives the detected weight value and the calculated sample density and equivalent value and associates labels with the weight value, sample density and equivalent value according to instructions, and transmits the test data to the error comparison module 43.

[0162] like Figure 1 As shown, the model generation module 41 calls the historical test data and labels stored in the data storage module, generates a density equivalent test table using the historical test data through the labels, selects part of the test data from the density equivalent test table, uses the data corresponding to each label in the selected test data as a variable variable, and uses the density equivalent as an unknown variable to generate a function formula, performs deep learning training on all function formulas to generate a density equivalent algorithm model, and transmits the density equivalent test table and the density equivalent algorithm model to the deep learning module 42.

[0163] like Figure 1As shown, the deep learning module 42 allows the density equivalent algorithm model to perform deep learning through the density equivalent test table, and transmits the density equivalent algorithm model after deep learning to the data storage module 5. The error comparison module 43 calls the density equivalent algorithm model of the data storage module, substitutes the received data into the density equivalent algorithm model after deep learning according to the label to generate the closest function formula, selects the data with the largest difference from the function formula generated by the density equivalent algorithm model and displays the corresponding label. After the test is completed, the user enters the test results into the system, and the system automatically screens out the data generated by each test with the largest difference from the predicted data through the data model, and the user can quickly determine the wrong data by checking the data.

[0164] like Figure 1 As shown, the state update module 1 is preset with a detection state, and the detection state includes normal sample detection and vacuum sample detection. The detection state is switched according to the received instruction. When the detection state is switched to normal sample detection, a normal signal is transmitted to the detection execution module 2. When the detection state is switched to vacuum sample detection, a vacuum signal is transmitted to the detection execution module 2.

[0165] like Figure 1 As shown, when the detection execution module 2 receives a normal signal and the first detection instruction, it controls the tester to detect the sample and reads the detected weight value as the preliminary mass in the air. When the second detection instruction is received, the tester controls the tester to detect the sample and reads the detected weight value as the preliminary mass in the water. When the detection execution module 2 receives a vacuum signal and the first detection instruction, it controls the tester to detect the sample and reads the detected weight value as the secondary mass in the air. When the second detection instruction is received, the tester controls the tester to detect the sample and reads the detected weight value as the secondary mass in the water. The preliminary mass in the air, the preliminary mass in the water, the secondary mass in the air and the secondary mass in the water are transmitted to the data calculation module 3 and the data verification module 4.

[0166] like Figure 1 As shown, the data calculation module 3 calculates the normal sample density according to the preliminary mass in air and the preliminary mass in water, calculates the vacuum sample density according to the secondary mass in air and the secondary mass in water, calculates the equivalent value according to the normal sample density and the vacuum sample density, transmits the normal sample density and the vacuum sample density to the data verification module 4, and transmits the preliminary mass in air, the preliminary mass in water, the secondary mass in air, the secondary mass in water, the normal sample density, the vacuum sample density and the equivalent value to the data storage module 5. The data calculation module 3 interchanges the density value of the normal sample density and the density value of the vacuum sample density to calculate the control equivalent value, and transmits the equivalent value and the control equivalent value to the data verification module 4.

[0167] like Figure 1As shown, the data verification module 4 receives the preliminary mass in the air and the preliminary mass in the water, and then compares the preliminary mass in the air and the preliminary mass in the water. If the preliminary mass in the air is less than the preliminary mass in the water, the weight value of the preliminary mass in the air is swapped with the weight value of the preliminary mass in the water, and the swapped preliminary mass in the air and the preliminary mass in the water are transmitted to the data calculation module 3 and covered. The data verification module 4 receives the secondary mass in the air and the secondary mass in the water, and then compares the secondary mass in the air and the secondary mass in the water. If the secondary mass in the air is less than the secondary mass in the water, the weight value of the secondary mass in the air is swapped with the weight value of the secondary mass in the water, and the swapped secondary mass in the air and the secondary mass in the water are transmitted to the data calculation module 3 and covered. The data verification module 4 receives the normal sample density and the vacuum sample density, and then compares the normal sample density and the vacuum sample density. If the normal sample density is less than the vacuum sample density, the density value of the normal sample density is swapped with the density value of the vacuum sample density, and the swapped normal sample density and the vacuum sample density are transmitted to the data calculation module 3 and covered. After receiving the equivalent value and the control equivalent value, the data verification module 4 compares the equivalent value and the control equivalent value. If the equivalent value is less than the control equivalent value, the density value of the normal sample density and the density value of the vacuum sample density are transmitted to the data calculation module 3 and overwritten. The system can also determine whether the test data entry sequence is correct by calculating a new equivalent value after data exchange and then comparing the two equivalent values.

[0168] like Figure 1 As shown, the data storage module 5 receives and stores data. The data storage module 5 is preset with a form template. The form generation module 6 calls the test data and form template stored in the data storage module 5, fills the preliminary mass in air, preliminary mass in water, secondary mass in air, secondary mass in water, normal sample density, vacuum sample density and equivalent value during calculation into the form template to generate a test record form, and when an adjustment instruction is received, the preliminary mass in air, preliminary mass in water, secondary mass in air, secondary mass in water, normal sample density or vacuum sample density in the test or test record form are replaced according to the adjustment instruction, and the test record form is output. The system can automatically organize historical test records and adjust them according to user requirements.

[0169] like Figure 1 As shown, the environmental monitoring module 7 is preset with a wind speed limit. The environmental monitoring module 7 detects the ambient wind speed, ambient air humidity value and ambient air pressure value, and transmits the ambient wind speed, ambient air humidity value and ambient air pressure value to the data storage module 5. When the ambient wind speed exceeds the wind speed limit, a wind speed warning is output.

[0170] like Figure 1As shown, the curve generation module 8 is preset with a standard air humidity range and a standard air pressure range. The curve generation module 8 calls the multiple air humidity values ​​and ambient air pressure values ​​stored in the data storage module 5, calculates the difference between the current ambient air humidity value and the middle value of the standard air humidity range as the humidity abnormality difference, calculates the difference between the current ambient air pressure value and the middle value of the standard air pressure range as the pressure abnormality difference, takes a fixed sample to detect the preliminary air mass, preliminary water mass, secondary air mass and secondary water mass, associates the detection result with the humidity abnormality difference or the pressure abnormality difference and stores it, takes the test data associated with different humidity abnormality values ​​and the test data with a humidity abnormality value of 0 to calculate the deviation ratio, draws a humidity abnormality data curve graph according to the deviation ratio, stores the humidity abnormality data curve graph, takes the test data associated with different pressure abnormality values ​​and the test data with a pressure abnormality value of 0 to calculate the deviation ratio, draws a pressure abnormality data curve graph according to the deviation ratio, stores the pressure abnormality data curve graph, and transmits the humidity abnormality data curve graph and the pressure abnormality data curve graph to the deviation correction module 9.

[0171] like Figure 1 As shown, the deviation correction module 9 calls the latest ambient air humidity value and ambient air pressure value stored in the data storage module 5. If the ambient air humidity value is not within the standard air humidity range, the current humidity anomaly difference is calculated, and the calculated humidity anomaly difference is substituted into the humidity anomaly data curve chart to obtain the deviation ratio of the test data. The current test data is corrected according to the deviation ratio. If the ambient air pressure value is not within the standard air pressure range, the current pressure anomaly difference is calculated. If the ambient air pressure value is not within the standard air pressure range, the pressure anomaly difference is calculated. For tests that require accurate test results, the ambient wind speed, humidity, and air pressure will all affect the test results. In a fixed site, the tester obtains multiple sets of test data by testing the same sample under different conditions. The system automatically generates a humidity anomaly data curve chart and a pressure anomaly data curve chart. When the tester performs the test, the system automatically calibrates the test results according to the current environment.

[0172] The embodiments of this specific implementation method are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A big data intelligent processing method for density equivalent testing, characterized in that: The following steps are involved: Record historical test data and associate corresponding data in the historical test data with labels, including various quality labels, normal density labels, vacuum density labels and density equivalent labels; Generate density equivalent test tables using historical test data through tags; Select some test data from the density equivalent test table, use the data corresponding to each label in the selected test data as a variable variable, use the density equivalent as an unknown variable to generate a function formula, and perform deep learning training on all function formulas to generate a density equivalent algorithm model; The density equivalent algorithm model is deeply learned through the density equivalent test table; Conduct density equivalence tests; receiving the detected weight value and the calculated sample density and equivalent value and associating tags with the weight value, sample density and equivalent value according to the instruction; Substitute the received data into the density equivalent algorithm model after deep learning according to the label to generate the closest function formula, select the data with the largest difference from the function formula generated by the density equivalent algorithm model and display the corresponding label.

2. The big data intelligent processing method for density equivalent testing according to claim 1 is characterized in that: The step of "performing a density equivalent test" also includes the following steps: Switch the detection state to normal sample detection; After receiving the instruction, the sample is tested; Read the detected weight value as the preliminary mass in the air; After receiving the instruction, the sample is tested; Read the detected weight value as the preliminary mass in water; Compare the preliminary mass in air with the preliminary mass in water; If the initial mass in the air is less than the initial mass in the water, the weight value of the initial mass in the air is interchanged with the weight value of the initial mass in the water; Calculate the normal sample density from the initial mass in air and the initial mass in water; Switch the detection state to vacuum sample detection; After receiving the instruction, the sample is tested; Read the detected weight value as the mass in the secondary air; After receiving the instruction, the sample is tested; Read the detected weight value as the mass in secondary water; Compare the mass in secondary air with the mass in secondary water; If the mass in the secondary air is less than the mass in the secondary water, the weight value of the mass in the secondary air is interchanged with the weight value of the mass in the secondary water; Calculate the density of the vacuum sample by the mass in the secondary air and the mass in the secondary water; Compare the density of normal sample and the density of vacuum sample; If the density of the normal sample is less than that of the vacuum sample, the density value of the normal sample density and the density value of the vacuum sample density are interchanged; Calculate the equivalent value using the normal sample density and vacuum sample density and record it.

3. The big data intelligent processing method for density equivalent testing according to claim 1 is characterized in that: The step of "calculating the normal sample density by the initial mass in air and the initial mass in water" also includes: Calculate the normal sample density using the formula , the formula is as follows: ； In the formula, is the initial air mass, is the initial water quality; The step of "calculating the density of the vacuum sample by the mass in the secondary air and the mass in the secondary water" also includes: Calculate the vacuum sample density using the formula , the formula is as follows: ； In the formula, is the mass in the secondary air, is the quality in secondary water; The step of "calculating and recording the equivalent value by the normal sample density and the vacuum sample density" also includes: Calculate equivalent value using the formula , the formula is as follows: ； Record equivalent value .

4. The big data intelligent processing method for density equivalent testing according to claim 1 is characterized in that: The step of "calculating and recording the equivalent value by the normal sample density and the vacuum sample density" also includes: The density value of the normal sample density and the density value of the vacuum sample density are exchanged as the second set of data; Calculate the control equivalent value from the second set of data; Compare equivalent values ​​with control equivalent values; If the equivalent value is less than the control equivalent value, the density value of the normal sample density and the density value of the vacuum sample density are interchanged and the control equivalent value is recorded as the equivalent value.

5. The big data intelligent processing method for density equivalent testing according to claim 1 is characterized in that: The following steps are also included: Preset form templates; Fill in the initial mass in air, initial mass in water, secondary mass in air, secondary mass in water, normal sample density, vacuum sample density and equivalent value during calculation into the form template to generate a test record form; Display the test record form and all historical data; When receiving the adjustment instruction, replace the initial mass in air, initial mass in water, secondary mass in air, secondary mass in water, normal sample density or vacuum sample density in the test or test record form according to the adjustment instruction; When the export instruction is received, the test record form is printed.

6. The big data intelligent processing method for density equivalent testing according to claim 1 is characterized in that: The following steps are also included: Set wind speed limits, standard air humidity ranges, and standard air pressure ranges; Detect and display the ambient air humidity value and the ambient air pressure value, calculate the difference between the current ambient air humidity value and the middle value of the standard air humidity range as the humidity abnormality difference, calculate the difference between the current ambient air pressure value and the middle value of the standard air pressure range as the pressure abnormality difference; Adjusting the ambient air humidity value and the ambient air pressure value to obtain different humidity anomaly differences or pressure anomaly differences, and proceeding to the next step under different humidity anomaly differences or pressure anomaly differences; Taking fixed samples to detect the initial mass in the air, the initial mass in the water, the secondary mass in the air and the secondary mass in the water, and associating and storing the detection results with the abnormal humidity difference or the abnormal pressure difference; Take the test data associated with different humidity anomaly values ​​and the test data with a humidity anomaly value of 0 to calculate the deviation ratio, draw a humidity anomaly data curve chart according to the deviation ratio, and store the humidity anomaly data curve chart; Calculate the deviation ratio of the test data associated with different pressure anomaly values ​​and the test data with a pressure anomaly value of 0, draw a pressure anomaly data curve chart according to the deviation ratio, and store the pressure anomaly data curve chart; Detect the ambient wind speed. If the ambient wind speed exceeds the wind speed limit, a wind speed warning will be displayed. If the ambient wind speed is lower than the wind speed limit, the ambient air humidity value and the ambient air pressure value are detected; If the ambient air humidity value is not within the standard air humidity range, the humidity anomaly difference is calculated; Substitute the calculated humidity anomaly difference into the humidity anomaly data curve chart to obtain the deviation ratio of the test data, and correct the current test data according to the deviation ratio; If the ambient air pressure value is not within the standard air pressure range, the abnormal pressure difference is calculated; The calculated pressure anomaly difference is substituted into the pressure anomaly data curve chart to obtain the deviation ratio of the test data, and the current test data is corrected according to the deviation ratio.

7. The big data intelligent processing method for density equivalent testing according to claim 1 is characterized in that: The following steps are also included: receiving and storing image information of the container; Collect sampling image information of the instrument detection area, and retain the sampling image information with container image information; The retained sampled image information is converted into a grayscale image and filtered, and the bubbles in the sampled image information are identified by an edge detection algorithm; If bubbles are detected in the sampled image information, a bubble warning is issued.

8. A big data intelligent processing system for density equivalent testing, comprising a density equivalent testing method according to any one of claims 1 to 6, characterized in that: It includes a data storage module (5), a model generation module (41), a deep learning module (42), and an error comparison module (43); The data storage module (5) receives and stores test data, historical test data and labels, associates corresponding data in the historical test data with labels, the labels including a plurality of mass labels, normal density labels, vacuum density labels and density equivalent labels, and when the data storage module receives new test data, receives the detected weight value and the calculated sample density and equivalent value, associates labels with the weight value, sample density and equivalent value according to instructions, and transmits the test data to the error comparison module (43); The model generation module (41) calls the historical test data and labels stored in the data storage module, generates a density equivalent test table using the historical test data through the labels, selects part of the test data in the density equivalent test table, uses the data corresponding to each label in the selected test data as a variable variable, and uses the density equivalent as an unknown variable to generate a function formula, performs deep learning training on all function formulas to generate a density equivalent algorithm model, and transmits the density equivalent test table and the density equivalent algorithm model to the deep learning module (42); The deep learning module (42) allows the density equivalent algorithm model to perform deep learning through a density equivalent test table, and transmits the density equivalent algorithm model after deep learning to the data storage module (5); The error comparison module (43) calls the density equivalent algorithm model of the data storage module (5), substitutes the received data into the density equivalent algorithm model after deep learning according to the label to generate the closest function formula, selects the data with the largest difference from the function formula generated by the density equivalent algorithm model and displays the corresponding label.

9. The big data intelligent processing system for density equivalent testing according to claim 8, characterized in that: It also includes a state updating module (1), a detection execution module (2), a data calculation module (3), and a data verification module (4); The state updating module (1) is preset with a detection state, the detection state including normal sample detection and vacuum sample detection, and switches the detection state according to the received instruction. When the detection state is switched to normal sample detection, a normal signal is transmitted to the detection execution module (2), and when the detection state is switched to vacuum sample detection, a vacuum signal is transmitted to the detection execution module (2); When the detection execution module (2) receives the normal signal and the first detection instruction is received, the tester is controlled to detect the sample and read the detected weight value as the preliminary mass in the air. When the second detection instruction is received, the tester is controlled to detect the sample and read the detected weight value as the preliminary mass in the water. When the detection execution module receives the vacuum signal and the first detection instruction is received, the tester is controlled to detect the sample and read the detected weight value as the secondary mass in the air. When the second detection instruction is received, the tester is controlled to detect the sample and read the detected weight value as the secondary mass in the water. The preliminary mass in the air, the preliminary mass in the water, the secondary mass in the air and the secondary mass in the water are transmitted to the data calculation module (3) and the data verification module (4); The data calculation module (3) calculates the normal sample density according to the preliminary mass in air and the preliminary mass in water, calculates the vacuum sample density according to the secondary mass in air and the secondary mass in water, calculates the equivalent value according to the normal sample density and the vacuum sample density, transmits the normal sample density and the vacuum sample density to the data verification module (4), and transmits the preliminary mass in air, the preliminary mass in water, the secondary mass in air, the secondary mass in water, the normal sample density, the vacuum sample density and the equivalent value to the data storage module (5); The data verification module (4) receives the preliminary mass in the air and the preliminary mass in the water and compares the preliminary mass in the air and the preliminary mass in the water. If the preliminary mass in the air is less than the preliminary mass in the water, the weight value of the preliminary mass in the air is exchanged with the weight value of the preliminary mass in the water. The exchanged preliminary mass in the air and the preliminary mass in the water are transmitted to the data calculation module (3) and covered. The data verification module (4) receives the secondary mass in the air and the secondary mass in the water and compares the secondary mass in the air and the secondary mass in the water. If the secondary mass in the air is less than the secondary mass in the water, the weight value of the secondary mass in the air is exchanged with the weight value of the secondary mass in the water. The exchanged secondary mass in the air and the secondary mass in the water are transmitted to the data calculation module (3) and covered. The data verification module (4) receives the normal sample density and the vacuum sample density and compares the normal sample density and the vacuum sample density. If the normal sample density is less than the vacuum sample density, the density value of the normal sample density is exchanged with the density value of the vacuum sample density. The exchanged normal sample density and the vacuum sample density are transmitted to the data calculation module (3) and covered.

10. The big data intelligent processing system for density equivalent testing according to claim 9, characterized in that: The data calculation module (3) interchanges the density value of the normal sample density and the density value of the vacuum sample density to calculate a comparison equivalent value, and transmits the equivalent value and the comparison equivalent value to the data verification module (4); The data verification module (4) receives the equivalent value and the control equivalent value and compares the equivalent value and the control equivalent value. If the equivalent value is smaller than the control equivalent value, the density value of the normal sample density and the density value of the vacuum sample density are transmitted to the data calculation module (3) and overwritten.

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