A portable isometric path libs laser detection device

Abstract

The application relates to the technical field of new material related services, in particular to a portable same-light-path Libs laser detection device, which comprises a laser generator, a spectrometer, a detection module, a display screen and a battery, and the detection module comprises a detection data acquisition unit, a detection data processing unit, a detection priority division unit and a detection storage decision unit. Laser processing data reflecting the element and material characteristics of new materials is obtained according to the laser detection data obtained when the new materials are detected, and priority division is carried out according to the laser processing data, so that high-quality data is preferentially processed, the efficiency of data processing when the new materials are detected is improved, the storage decision is real-timely optimized, the data processing calculation amount and the memory occupation are reduced, and therefore the utilization rate of storage resources and the efficiency of data storage when the new materials are detected are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new material related services, and particularly relates to a portable same-optical-path Libs laser detection device. BACKGROUND

[0002] The computing unit power of the portable detection equipment is usually low due to the limitation of volume, power consumption and cost, the data processing task generated by Libs detection is extremely heavy, the existing portable detection device lacks effective data priority identification and processing mechanism, all data are processed according to the same process, so that the new material detection data cannot obtain a suitable data processing mode in the case that the information richness is different, thereby affecting the timeliness and accuracy of the detection result, the storage space of the portable equipment is also limited, with the extension of the detection time and the increase of the number of detection samples, the storage space will soon be filled, when the storage space is insufficient, the traditional device usually simply deletes the earliest data without distinguishing the importance of the data, cannot make flexible adjustment to the data storage, thereby causing the loss of key data.

[0003] Chinese patent application publication No. CN116183585A discloses a Libs-LIF spectrum detection device and method of a laser, which comprises a pulsed laser, a beam splitter, a dye laser, an optical fiber, a collimating lens, a first frequency doubling crystal, a focusing lens, a photodiode, an oscilloscope, a mirror, a second frequency doubling crystal, an objective lens, an optical collection system of light radiation, a photomultiplier tube, a monochromator or a spectrometer, a three-dimensional motion platform, and a three-dimensional motion platform and a sample. This scheme still has serious deficiencies in the flexibility of data priority processing and storage space management of the Libs laser detection device, it is difficult to process data according to data quality, resulting in low data processing efficiency, which cannot meet the complex and variable detection requirements when detecting new materials, and lacks flexible allocation of storage space, resulting in low utilization rate of storage resources. SUMMARY

[0004] Therefore, the present application provides a portable same-optical-path Libs laser detection device to overcome the serious deficiencies in the flexibility of data priority processing and storage space management of the portable detection equipment in the prior art, which makes it difficult to process data according to data quality, resulting in low data processing efficiency, which cannot meet the complex and variable detection requirements when detecting new materials, and lacks flexible allocation of storage space, resulting in low utilization rate of storage resources.

[0005] To achieve the above-mentioned purpose, the present application provides a portable same-optical-path Libs laser detection device, which comprises:

[0006] A laser generator connected with the spectrometer for emitting a laser beam;

[0007] A spectrometer connected with the laser generator and the detection module, used for collecting laser detection data;

[0008] A detection module connected with the spectrometer, the display screen and the battery, used for controlling the portable on-axis LIBS laser detection device;

[0009] A display screen connected with the detection module, used for displaying laser processing data;

[0010] A battery connected with the detection module, used for providing power;

[0011] The detection module comprises:

[0012] A detection data acquisition unit configured to acquire laser detection data;

[0013] A detection data processing unit configured to perform data processing on the laser detection data according to a detection data processing method, to obtain laser processing data, and to push the laser processing data to the display screen;

[0014] A detection priority division unit configured to perform priority division on the laser processing data according to a priority division method, to obtain a priority division result, and further configured to perform priority adjustment on the priority division result according to battery power data, and further configured to perform priority update on the adjustment process of the priority adjustment according to a task urgency index;

[0015] A detection storage decision unit configured to generate a storage decision according to the laser processing data by a storage decision generation method, and further configured to perform storage adjustment on an output process of the storage decision according to data required storage space, and further configured to perform storage update on the data required storage space according to the priority division result;

[0016] The detection storage decision unit is further configured to perform decision adjustment on the storage decision according to a storage decision tuning method;

[0017] The detection data processing unit performs data processing on the laser detection data according to a detection data processing method, to obtain laser processing data, and pushes the laser processing data to the display screen, and the detection data processing method comprises:

[0018] Step C01, calculating a detection data point baseline value y j ` according to a wavelength x and a number of baseline data points jk, to obtain a detection data point baseline value y jk , wherein a0, a1, a2, …, an are prediction coefficient values, and y j = a0 + a1x + a2x 2 + … + anx jk-1 + a jk-1 + a jk×x jk ;

[0019] Step C02, according to the detection data point baseline value y j `and the baseline inner detection data value y j The error sum of squares S is calculated, and the error sum of squares S is set as: ;

[0020] Step C03, according to the spectrum data value y i and the detection data point baseline value y j `to calculate the corrected laser detection data yp, and set yp=y i -y j `;

[0021] Step C04, according to the corrected laser detection data yp, the sliding window value N and the total number of laser detection data points ik, to calculate the processed laser detection data yp`to get the processed laser detection data yp`, set ;

[0022] Step C05, output the processed laser detection data yp`as laser processing data to get laser processing data;

[0023] The detection priority division unit divides the laser processing data according to the priority division method, and the priority division method includes:

[0024] Step E01, according to the spectrum data value y i , the detection data point baseline value y j `, the total number of laser detection data points ik, and the number of baseline inner data points jk, to calculate the noise level bz, and set ;

[0025] Step E02, according to the spectrum intensity value fq, the spectral line width pk, the noise level bz, the spectrum intensity value weight coefficient wf, the spectral line width weight coefficient wpand the noise level weight coefficient wb, to calculate the comprehensive priority score P, and set P=wf×fq+wp×pk+wb / bz;

[0026] Step E03, compare the comprehensive priority score P with the first preset priority score P1 and the second preset priority score P2, wherein P1=0.58, P2=0.71, judge the priority degree of the comprehensive priority score P according to the comparison result, and output the priority division result according to the judgment result, and adjust the value of the sliding window value N according to the judgment result, wherein:

[0027] When P>P2, the detection priority classification unit determines that the priority degree of the comprehensive priority score P is high, outputs the first priority as the priority classification result, adjusts the sliding window value N, sets u1=1-(P-P2) / P through the first window adjustment coefficient u1, adjusts the sliding window value N, obtains the first adjusted sliding window value N1, sets N1=u1×N, replaces the sliding window value N with the first adjusted sliding window value N1, and recalculates the processed laser detection data yp` according to the first adjusted sliding window value N1;

[0028] When P1≤P≤P2, the detection priority classification unit determines that the priority degree of the comprehensive priority score P is moderate, outputs the second priority as the priority classification result, and does not adjust the sliding window value N;

[0029] When P

[0030] Further, when the detection priority classification unit adjusts the priority classification result according to the battery power data, the battery power data Ws is compared with the preset power data Ws0, 30%≤Ws0≤70% is set, the battery power sufficiency is judged according to the comparison result, and the first preset priority score P1 and the second preset priority score P2 are adjusted according to the judgment result, wherein:

[0031] When Ws≥Ws0, the detection priority classification unit determines that the battery power sufficiency is sufficient, and does not adjust the first preset priority score P1 and the second preset priority score P2;

[0032] When Ws < Ws0, the detection priority division unit determines that the battery power is insufficient, adjusts the first preset priority score P1, obtains the adjusted first preset priority score P1`, sets P1` = P1 + Ws0 / Ws, replaces the first preset priority score P1 with the adjusted first preset priority score P1`, adjusts the second preset priority score P2, obtains the adjusted second preset priority score P2`, sets P2` = P2 + Ws0 / Ws, replaces the second preset priority score P2 with the adjusted second preset priority score P2`, and recompares the comprehensive priority score P with the adjusted first preset priority score P1` and the adjusted second preset priority score P2`.

[0033] Further, when the detection priority division unit updates the priority of the adjustment process according to the task urgency index, the task urgency index Sr is compared with the preset urgency index Sr0, 0.62 ≤ Sr0 ≤ 0.78 is set, the task urgency is judged according to the comparison result, and the battery power data Ws is updated according to the judgment result, wherein:

[0034] When Sr ≤ Sr0, the detection priority division unit determines that the task urgency is not urgent, and does not update the priority of the battery power data Ws.

[0035] When Sr > Sr0, the detection priority division unit determines that the task urgency is urgent, updates the priority of the battery power data Ws, updates the priority of the battery power data Ws by the power update coefficient q, q = Sr0 / Sr, obtains the updated battery power data Ws`, sets Ws` = Ws × q, compares the updated battery power data Ws` with the preset power data Ws0, and rejudges the state of the battery power data Ws.

[0036] Further, the detection storage decision unit constructs the task load model according to the task load model construction method, and the task load model construction method comprises:

[0037] Step H01, dividing the historical task load data set into 70% task training set, 20% task validation set and 10% task test set;

[0038] Step H02, initializing the task parameters of the convolutional neural network model;

[0039] Step H03, input the task training set into the initialized convolutional neural network model for training, input the task validation set into the trained convolutional neural network model, optimize the task parameters of the trained convolutional neural network model, input the task test set into the parameter-optimized convolutional neural network model for testing, and output the test accuracy;

[0040] Step H04, output the parameter-optimized convolutional neural network model with an accuracy of 90% as the task load model;

[0041] The detection storage decision unit generates a storage decision according to the laser processing data by a storage decision generation method, and the storage decision generation method comprises:

[0042] Step K01, input the task into the task load model by the portable isoplanatic Libs laser detection device to obtain a sample data amount Ns;

[0043] Step K02, compare the sample data amount Ns with a preset sample data amount Ns0, set 12MB≤Ns0≤21MB, judge the sample data amount state according to the comparison result, and output the storage decision according to the judgment result, wherein:

[0044] When Ns≤Ns0, the detection storage decision unit determines that the sample data amount state is normal, and outputs the storage decision: storing the laser processing data in the device storage space;

[0045] When Ns>N0, the detection storage decision unit determines that the sample data amount state is abnormal, and outputs the storage decision: after sample segmentation of the laser detection data, obtaining segmented laser detection data, processing the segmented laser detection data to obtain segmented laser processing data, and storing the segmented laser processing data

[0046] in the device storage space.

[0047] Further, when the detection storage decision unit adjusts the output process of the storage decision according to the data required storage space, the detection storage decision unit compares the data required storage space Yc with the preset data required storage space Yc0, sets 12MB≤Yc0≤21MB, judges the data storage space requirement according to the comparison result, and adjusts the preset sample data amount Ns0 according to the judgment result, wherein:

[0048] When Yc≥Yc0, the detection storage decision unit determines that the data storage space requirement is normal, and does not adjust the preset sample data amount Ns0;

[0049] When Yc < Yc0, the detection storage decision unit determines that the demand condition of the data storage space is abnormal demand, performs storage adjustment on the preset sample data amount Ns0, sets the storage coefficient β, and sets The preset sample data amount Ns0 is adjusted to obtain an adjusted preset sample data amount Ns0', Ns0' = β × Ns0, the preset sample data amount Ns0 is replaced by the adjusted preset sample data amount Ns0', and the sample data amount Ns is compared with the adjusted preset sample data amount Ns0' again.

[0050] Further, the detection storage decision unit performs storage update on the data required storage space Yc according to the priority division result, wherein:

[0051] When the priority division result is the first priority, the data required storage space Yc is updated by a first storage update coefficient z1, z1 = (P / P2), the data required storage space Yc is updated to obtain a first updated data required storage space Yc1, Yc1 = z1 × Yc, the first updated data required storage space Yc1 is compared with the preset data required storage space Yc0, and the demand condition of the data storage space is re-judged;

[0052] When the priority division result is the second priority, the data required storage space Yc is not updated;

[0053] When the priority division result is the third priority, the data required storage space Yc is updated by a first storage update coefficient z2, z2 = P1-P / P1, the data required storage space Yc is updated to obtain a second updated data required storage space Yc2, Yc2 = z2 × Yc, the second updated data required storage space Yc2 is compared with the preset data required storage space Yc0, and the demand condition of the data storage space is re-judged.

[0054] Further, the detection storage decision unit performs decision adjustment on the storage decision according to a storage decision adjustment method, and the storage decision adjustment method comprises:

[0055] In step S01, the required storage space proportion Yb is calculated according to the data required storage space Yc and the device storage space Y to obtain the required storage space proportion Yb, Yb = Yc / Y;

[0056] In step S02, the margin storage space proportion Ys is calculated according to the device storage space margin Yf and the device storage space Y to obtain the margin storage space proportion Ys, Ys = Yf / Y;

[0057] Step S03, compare the required storage space ratio Yb with the remaining storage space ratio Ys, set 30%≤Ys≤50%, judge the device storage space sufficiency according to the comparison result, and make storage decision optimization according to the judgment result, wherein:

[0058] When Yb≤Ys, the detection storage optimization unit determines that the device storage space sufficiency is sufficient, and does not perform storage decision optimization;

[0059] When Yb>Ys, the detection storage optimization unit determines that the device storage space sufficiency is not sufficient, and performs storage decision optimization: decision adjustment is performed on the spectral intensity value weight coefficient wf, the spectral intensity optimization coefficient v is set as v=Yb / Ys, the decision adjustment is performed on the spectral intensity value weight coefficient wf, the adjusted spectral intensity value weight coefficient wf` is obtained, wf` is set as wf×v, the comprehensive priority score P is recalculated according to the adjusted spectral intensity value weight coefficient wf`, and the second priority and third priority laser processing data are stored in the cloud.

[0060] Compared with the prior art, the beneficial effects of the present application are that the laser processing data reflecting the characteristics of new material elements and substances can be obtained from the laser detection data obtained when the new material is detected, and the priority is divided according to the laser processing data, so as to process high-quality data preferentially, improve the efficiency of data processing when the new material is detected, and at the same time, the storage decision is optimized in real time, the data processing calculation amount and memory occupation are reduced, and the efficiency of data storage and the utilization rate of storage resources when the new material is detected are improved. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 It is a structural schematic diagram of the portable same light path Libs laser detection device of the embodiment;

[0062] Figure 2 It is a structural schematic diagram of the detection module of the embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose and advantages of the present application more clear and explicit, the present application will be further described below in combination with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the protection scope of the present application.

[0064] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application, and are not used to limit the protection scope of the present application.

[0065] It should be noted that in the description of the present application, the terms of direction or position relationship such as "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or position relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0066] In addition, it should be noted that in the description of the present application, unless otherwise specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected, it can be mechanically connected, or it can be electrically connected, it can be directly connected, or it can be indirectly connected through an intermediate medium, it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0067] Please refer to Figure 1 As shown in the structure schematic diagram of the portable isoplanatic Libs laser detection device of the embodiment, the device comprises:

[0068] The laser generator 1 is connected with the spectrometer 2, and is used for emitting laser beam;

[0069] The spectrometer 2 is connected with the laser generator 1 and the detection module 3, and is used for collecting laser detection data;

[0070] The detection module 3 is connected with the spectrometer 2, the display screen 4 and the battery 5, and is used for controlling the portable isoplanatic Libs laser detection device;

[0071] The display screen 4 is connected with the detection module 3, and is used for displaying laser processing data;

[0072] The battery 5 is connected with the detection module 3, and is used for providing power.

[0073] Specifically, the portable homophotal Libs laser detection device is applied to a portable material detection equipment. The portable homophotal Libs laser detection device processes and displays laser detection data obtained by detecting new materials through the cooperation of a laser generator, a spectrometer, a detection module, a display screen and a battery, so as to adapt to the needs of complex detection environments, thereby improving detection efficiency, data reliability and equipment resource utilization. The laser generator emits stable laser beams to generate stable light radiation when detecting new materials, thereby improving detection accuracy. The spectrometer accurately collects light radiation generated after laser irradiation of new materials to determine the elements and material properties of new materials, thereby obtaining accurate laser detection data and improving detection accuracy. The detection module integrates various units to control the portable homophotal Libs laser detection device to flexibly cope with different detection tasks and equipment states when detecting new materials, thereby improving the adaptive ability of the portable homophotal Libs laser detection device for detecting new materials. The display screen displays laser processing data in real time, so that the operator can quickly obtain the detection processing result and improve the detection interaction efficiency. The battery integrates a capacitance sensor to collect power data, provide power information support for priority adjustment and storage decision, and continuously power the portable homophotal Libs laser detection device, so as to ensure the stable operation of the portable homophotal Libs laser detection device for detecting new materials and avoid interruption of detection due to power problems, thereby improving the continuity and reliability of the portable homophotal Libs laser detection device.

[0074] Specifically, the spectrometer 2 collects laser detection data.

[0075] Specifically, the laser detection data refers to light radiation data obtained after the laser generator 1 emits a laser beam to irradiate the surface of new materials, which reflects the elements and material properties contained in the new materials.

[0076] Specifically, the spectrometer 2 determines the elements and material properties contained in the new materials according to the light radiation generated after the laser beam emitted by the laser generator 1 irradiates the surface of the object, thereby accurately collecting laser detection data for subsequent data processing.

[0077] Specifically, the battery 5 integrates a capacitance sensor to collect battery power data.

[0078] Specifically, the capacitance sensor is a component that measures the battery power by measuring the capacitance between the plates, and the battery power data refers to the numerical information of the remaining power content in the battery.

[0079] Specifically, the battery 5 collects battery power data, so as to adjust the priority of the priority division result, thereby improving the accuracy and effectiveness of detection.

[0080] Referring to Figure 2 As shown in the figure, it is a structural schematic diagram of the detection module of the embodiment, and the detection module comprises:

[0081] The detection data acquisition unit is configured to acquire the laser detection data.

[0082] The detection data processing unit is configured to perform data processing on the laser detection data according to a detection data processing method, to obtain laser processing data, and to push the laser processing data to the display screen. The detection data processing unit is connected with the detection data acquisition unit.

[0083] The detection priority division unit is configured to perform priority division on the laser processing data according to a priority division method, to obtain a priority division result, and to adjust the priority of the priority division result according to battery power data. The detection priority division unit is also configured to update the priority of the adjustment process according to a task emergency index, and is connected with the detection data processing unit.

[0084] The detection storage decision unit is configured to generate a storage decision according to the laser processing data by a storage decision generation method, and to adjust the output process of the storage decision according to a data required storage space, and to update the data required storage space according to the priority division result. The detection storage decision unit is connected with the detection priority division unit.

[0085] The detection storage tuning unit is also configured to adjust the storage decision according to a storage decision tuning method. The detection storage tuning unit is connected with the detection storage decision unit.

[0086] Specifically, the detection module is applied to a portable same-light-path Libs laser detection device, and the detection module comprehensively controls the operation of the portable same-light-path Libs laser detection device through operations such as acquisition, processing, priority division, and storage optimization of laser detection data, so as to realize real-time optimization of priority division and storage decision of laser processing data, thereby improving the efficiency and accuracy of laser detection data processing and the efficiency and utilization rate of storage resources when detecting new materials. The detection module acquires laser detection data through a detection data acquisition unit, so as to ensure the accuracy of the data source when detecting new materials, thereby improving the reliability of data processing. The detection module processes laser detection data through a detection data processing unit, so as to remove baseline drift interference and high-frequency noise, thereby improving the smoothness and stability of laser detection data, improving the quality of laser detection data, and making the laser processing data accurately reflect the material properties of new materials. The detection module divides the priority of laser processing data through a detection priority division unit, preferentially processes data with rich information and high quality, and adjusts the sliding window value according to the comprehensive priority score, so as to dynamically allocate resources, avoid data feature loss, and improve the accuracy and reliability of data processing when detecting new materials. The detection module generates and adjusts a storage decision through a detection storage decision unit, so as to flexibly adjust the storage strategy, reduce the data processing calculation amount and memory occupation, and improve the utilization rate of storage resources and the efficiency of data storage when detecting new materials.

[0087] Specifically, the detection data acquisition unit acquires laser detection data through a spectrometer.

[0088] Specifically, the detection data acquisition unit acquires laser detection data, so as to ensure the accuracy of the data source when detecting new materials, thereby improving the reliability of data processing.

[0089] Specifically, the detection data processing unit processes laser detection data according to a detection data processing method to obtain laser processing data, and pushes the laser processing data to a display screen. The detection data processing method includes:

[0090] Step C01: calculating a detection data point baseline value y j ` according to a wavelength x and a number jk of baseline data points, to obtain a detection data point baseline value y jk , wherein a0, a1, a2, …, a j are prediction coefficient values, y 2 = a0 + a1x + a2x jk-1 + … + a jk-1 x + ajk ×x jk ;

[0091] Step C02: Based on the baseline value y of the detection data point j ` and baseline detection data value y j Calculate the error sum of squares S to get the error sum of squares S, and set: ;

[0092] Step C03, according to the spectrum data value y i and the baseline value y of the detection data point j `Calculate the corrected laser detection data yp to obtain the corrected laser detection data yp, set yp=y i -y j `;

[0093] Step C04: Calculate the processed laser detection data yp' based on the corrected laser detection data yp, the sliding window value N and the total number of laser detection data points ik to obtain the processed laser detection data yp'. Set ;

[0094] Step C05: outputting the processed laser detection data yp` as laser processing data to obtain laser processing data.

[0095] Specifically, the wavelength x refers to the value y detected in the baseline j The length of the complete fluctuation of the corresponding j-th data point, the detected data value y within the baseline j It refers to the detection data value corresponding to the jth data point in the baseline of the laser detection data, wherein j is the order of the detection data value in the baseline, j is a positive integer, and the maximum value of j is the total number of data points of the laser detection data included in the baseline. The number of data points in the baseline jk refers to the total number of data points of the laser detection data included in the baseline. The baseline refers to the baseline without obvious spectral characteristic peaks in the laser detection data for reflecting the background signal related to non-new materials. The spectral characteristic peaks refer to the peaks and valleys with wavelength position and intensity in the spectrum of the laser detection data for reflecting the information of new materials. The lack of obvious spectral characteristic peaks refers to the fact that the laser detection data does not present a band with wavelength position, peak and valley. This embodiment does not limit the method of selecting the baseline. Relevant technicians in this field can freely choose according to actual needs, and only need to meet the need of reflecting the background signal related to non-new materials, such as manual selection. The prediction coefficient value refers to the baseline value y of the detection data point j `Calculate the fitting value and solve the prediction coefficient value by minimizing the sum of squared errors, where the sum of squared errors S refers to the value of the detection data y in the baseline. j and the baseline value y of the detection data point jThe embodiment does not limit the calculation method of minimizing the error sum of squares, and a person skilled in the art can freely select the calculation method according to actual needs, as long as the calculation of minimizing the error sum of squares is met, for example, the calculation is performed by programming software, and the corrected laser detection data yp is the laser detection data y j The predicted value obtained after processing the spectral data value y i is the spectral data value corresponding to the i-th data point in the laser detection data, wherein i is the order of the spectral data value, i is a positive integer, and the maximum value of i is the total number of data points in the laser detection data, the sliding window value N is the number of data points taken before and after a single data point in the corrected laser detection data yp, for example, assuming that the single data point in the corrected laser detection data yp is b, b-1 is the data point before the single data point b, and b+1 is the data point after the single data point b, when N=3, b-1, b and b+1 are taken as the sliding window value N, the total number of laser detection data points ik is the total number of data points in the laser detection data, and the processed laser detection data yp' is the value obtained by processing the corrected laser detection data yp by the step C03, and the embodiment does not limit the method of pushing the laser processing data to the display screen, and a person skilled in the art can freely select the method according to actual needs, as long as the laser processing data is pushed to the display screen, for example, the software window is pushed.

[0096] Specifically, the detection data processing unit acquires the corrected laser detection data yp, so that the fitted detection data point baseline value y j ` can accurately reflect the overall trend of the laser detection data, thereby removing the interference factors of baseline drift in the spectrum, and the detection data processing unit acquires the processed laser detection data yp', so that the laser detection data removes high-frequency noise interference, thereby improving the data smoothness and stability of the laser detection data.

[0097] Specifically, the detection priority division unit divides the laser processing data according to a priority division method, and the priority division method comprises:

[0098] Step E01, calculating the noise level bz according to the spectral data value y i , the detection data point baseline value y j , the total number of laser detection data points ik, and the number of baseline data points jk, to obtain the noise level bz, and it is assumed that ;

[0099] Step E02, calculating the comprehensive priority score P according to the spectral intensity value fq, the spectral line width pk, the noise level bz, the spectral intensity value weight coefficient wf, the spectral line width weight coefficient wp and the noise level weight coefficient wb, obtaining the comprehensive priority score P, setting P = wf x fq + wp x pk + wb / bz;

[0100] Step E03, comparing the comprehensive priority score P with the first preset priority score P1 and the second preset priority score P2, wherein P1 = 0.58, P2 = 0.71, judging the priority degree of the comprehensive priority score P according to the comparison result, outputting the priority division result according to the judgment result, and adjusting the sliding window value N according to the judgment result, wherein:

[0101] When P>P2, the detection priority division unit determines that the priority degree of the comprehensive priority score P is high, outputs the first priority as the priority division result, adjusts the sliding window value N, sets u1 = 1-(P-P2) / P through the first window adjustment coefficient u1, adjusts the sliding window value N, obtains the first adjusted sliding window value N1, sets N1 = u1 x N, replaces the sliding window value N with the first adjusted sliding window value N1, and recalculates the processed laser detection data yp` according to the first adjusted sliding window value N1;

[0102] When P1≤P≤P2, the detection priority division unit determines that the priority degree of the comprehensive priority score P is moderate, outputs the second priority as the priority division result, and does not adjust the sliding window value N;

[0103] When P<P1, the detection priority division unit determines that the priority degree of the comprehensive priority score P is low, outputs the third priority as the priority division result, and adjusts the sliding window value N, sets u2 = 1+(P1-P) / P1 through the second window adjustment coefficient u2, adjusts the sliding window value N, obtains the second adjusted sliding window value N2, sets N2 = u2 x N, replaces the sliding window value N with the second adjusted sliding window value N2, and recalculates the processed laser detection data yp` according to the second adjusted sliding window value N2.

[0104] Specifically, the noise level bz refers to a value used to represent the degree of noise influence in the laser detection data, the smaller the noise level bz, the smaller the degree of noise influence in the laser detection data, and the better the quality of the laser detection data, the comprehensive priority score P refers to a value used to measure the richness of new material information included in the laser processing data, the spectral intensity value fq refers to the intensity of light of a corresponding wavelength of the laser detection data, the spectral line width pk refers to the wavelength interval width at half of the peak intensity of the laser detection data, and the peak intensity of the laser detection data refers to the maximum intensity value reached by the data signal in the laser detection data. The present embodiment does not limit the acquisition method of the spectral intensity value fq and the spectral line width pk, which are acquired by a spectrometer in the present embodiment. The spectral intensity value weight coefficient wf refers to a value used to measure the importance of the spectral intensity value in calculating the comprehensive priority score P, the spectral line width weight coefficient wp refers to a value used to measure the importance of the spectral line width in calculating the comprehensive priority score P, and the noise level weight coefficient wb refers to a value used to measure the importance of the noise level in calculating the comprehensive priority score P. The present embodiment does not limit the setting of the spectral intensity value weight coefficient, the spectral line width weight coefficient, and the noise level weight coefficient, and a person skilled in the art can freely select them according to actual needs, as long as wf+wp+wb=1 is met. For example, the present embodiment sets wf=0.5, wp=0.2, and wb=0.3, the first preset priority score P1 refers to a preset lower limit value used to judge the priority of the comprehensive priority score P, the second preset priority score P2 refers to a preset upper limit value used to judge the priority of the comprehensive priority score P, the priority of the comprehensive priority score P refers to the richness of new material information included in the laser processing data represented by the comprehensive priority score P, the higher the richness of new material information included in the laser processing data, the higher the priority of the comprehensive priority score P, the priority of the comprehensive priority score P includes high priority of the comprehensive priority score P, medium priority of the comprehensive priority score P and low priority of the comprehensive priority score P, the priority division result refers to the result output after the priority level of the laser detection data is determined by judging the priority of the comprehensive priority score P, the priority division result includes first priority, second priority and third priority, the first priority refers to the priority division result of the laser detection data in the priority level determination of the laser detection data, when the comprehensive priority score calculated according to the laser detection data is greater than the second preset priority score, the second priority refers to the priority division result of the laser detection data in the priority level determination of the laser detection data, when the comprehensive priority score calculated according to the laser detection data is greater than the first preset priority score and less than the second preset priority score, and the third priority refers to the priority division result of the laser detection data in the priority level determination of the laser detection data, when the comprehensive priority score calculated according to the laser detection data is less than the first preset priority score.

[0105] Specifically, the detection priority division unit divides the priority of the laser processing data by judging the priority of the comprehensive priority score P, and processes the laser processing data with rich and high-quality information preferentially, so as to adopt different processing strategies for the laser processing data in different levels, dynamically allocate resources, and improve the efficiency and effect of detection. The detection priority division unit also adjusts the value of the sliding window N, so that when the priority of the comprehensive priority score P is high, the value of the sliding window N decreases with the increase of the comprehensive priority score P, so as to increase the number of iterations during data processing, thereby improving the data processing effect, and when the priority of the comprehensive priority score P is low, the value of the sliding window N increases with the decrease of the comprehensive priority score P, so as to reduce the number of iterations during data processing, avoid excessive processing leading to loss of data characteristics, and thereby improve the accuracy and reliability of data processing.

[0106] Specifically, when the detection priority classification unit adjusts the priority of the priority classification result according to the battery power data, the battery power data Ws is compared with the preset power data Ws0, 30%≤Ws0≤70% is set, the battery power sufficiency is judged according to the comparison result, and the first preset priority score P1 and the second preset priority score P2 are adjusted according to the judgment result, wherein:

[0107] When Ws≥Ws0, the detection priority classification unit determines that the battery power sufficiency is sufficient, and does not adjust the first preset priority score P1 and the second preset priority score P2;

[0108] When Ws<Ws0, the detection priority classification unit determines that the battery power sufficiency is insufficient, adjusts the first preset priority score P1 to obtain the adjusted first preset priority score P1`, sets P1`=P1+Ws0 / Ws, replaces the first preset priority score P1 with the adjusted first preset priority score P1`, adjusts the second preset priority score P2 to obtain the adjusted second preset priority score P2`, sets P2`=P2+Ws0 / Ws, replaces the second preset priority score P2 with the adjusted second preset priority score P2`, and recompares the comprehensive priority score P with the adjusted first preset priority score P1` and the adjusted second preset priority score P2`.

[0109] Specifically, the preset power data Ws0 refers to a preset value for judging the battery power sufficiency, the battery power sufficiency refers to the sufficiency of the power contained in the battery according to the comparison of the battery power data and the preset power data, and the battery power sufficiency includes the cases that the battery power sufficiency is sufficient and the battery power sufficiency is insufficient.

[0110] Specifically, the detection priority classification unit judges the state of the battery power data Ws to make the first preset priority score P1 and the second preset priority score P2 increase with the decrease of the battery power data Ws, so as to increase the processing amount of high-priority data by increasing the priority score threshold in the low-power scenario, avoid the situation that excessive power consumption leads to early power depletion and forces task interruption, and thus improve the accuracy and safety of detection.

[0111] Specifically, when the detection priority classification unit updates the adjustment process of priority adjustment according to the task urgency index, the task urgency index Sr is compared with the preset urgency index Sr0, 0.62≤Sr0≤0.78 is set, the task urgency is judged according to the comparison result, and the battery power data Ws is updated according to the judgment result, wherein:

[0112] When Sr≤Sr0, the detection priority classification unit determines that the task urgency is not urgent, and does not update the battery power data Ws in priority;

[0113] When Sr> Sr0, the detection priority classification unit determines that the task urgency is urgent, and updates the battery power data Ws in priority, updates the battery power data Ws in priority by a power update coefficient q, q=Sr0 / Sr, obtains updated battery power data Ws', sets Ws'=Ws x q, and compares the updated battery power data Ws' with preset power data Ws0 to re-judge the state of the battery power data Ws.

[0114] Specifically, the task urgency index Sr is a digital index reflecting the importance of the current detection task. The embodiment does not limit the way of obtaining the task urgency index Sr, such as expert evaluation method. The expert evaluation method refers to that an expert user who has the ability to set the task urgency index Sr sets the value of the task urgency index Sr. The embodiment does not limit the way of setting the task urgency index Sr by the expert evaluation method, such as using an Internet expert terminal to obtain the task urgency index Sr. The preset urgency index Sr0 is a preset value for judging the task urgency. The task urgency refers to an index measuring the importance and urgency of a task. The task urgency includes low degree and high degree.

[0115] Specifically, the detection priority classification unit judges the task urgency to make the battery power data Ws decrease with the increase of the task urgency index Sr, so as to ensure that the urgent task obtains the computing resource in priority, realize intelligent matching of power, task demand and data processing priority, flexibly allocate resources, and keep the detection system in a high-efficiency and stable running state.

[0116] Specifically, when the detection storage decision unit stores and optimizes the laser processing data according to the data storage optimization method, the detection storage decision unit constructs a task load model according to a task load model construction method. The task load model construction method includes:

[0117] Step H01, dividing the historical task load data set into 70% task training set, 20% task validation set and 10% task test set;

[0118] Step H02, initializing the task parameters of the convolutional neural network model;

[0119] Step H03, input the task training set into the initialized convolutional neural network model for training, input the task validation set into the trained convolutional neural network model, optimize the task parameters of the trained convolutional neural network model, input the task test set into the parameter-optimized convolutional neural network model for testing, and output the test accuracy;

[0120] Step H04, output the parameter-optimized convolutional neural network model with an accuracy of 90% as the task load model.

[0121] Specifically, the historical task load data set refers to the historical portable same-light-path Libs laser detection device detection task and the corresponding data sample quantity of the portable same-light-path Libs laser detection device detection task. The portable same-light-path Libs laser detection device detection task is taken as the input data of the task load model, and the data sample quantity corresponding to the portable same-light-path Libs laser detection device detection task is taken as the output data of the task load model. The portable same-light-path Libs laser detection device detection task refers to the task of detecting new materials performed by the portable same-light-path Libs laser detection device. The task training set refers to the data set in the historical task load data set used to train the convolutional neural network model. The task validation set refers to the data set used to adjust the parameters of the convolutional neural network model during training. The task test set refers to the data set used to evaluate the final performance of the convolutional neural network model after the task parameter optimization of the convolutional neural network model is completed. The convolutional neural network model refers to a statistical model used to describe the linear relationship between the portable same-light-path Libs laser detection device detection task and the data sample quantity corresponding to the portable same-light-path Libs laser detection device detection task. The task parameter initialization refers to the process of setting initial values for the task weights and task biases of the convolutional neural network model when constructing the task load model. The task weight refers to a parameter in the convolutional neural network model used to measure the connection strength between neurons. The task bias refers to a parameter in the convolutional neural network model that is output when the input is zero. This embodiment does not limit the task parameter initialization method, such as random initialization. The task parameter optimization refers to the process of adjusting the task weights and task biases of the convolutional neural network model to make the performance of the convolutional neural network model on the task validation set optimal when the task validation set is used to evaluate the trained convolutional neural network model. This embodiment does not limit the adjustment method of the task weights and task biases of the convolutional neural network model. Those skilled in the art can freely choose according to actual needs, as long as the task weights and task biases of the convolutional neural network model are adjusted to make the performance of the convolutional neural network model on the task validation set optimal, such as adjustment by a calculation software.

[0122] Specifically, the detection storage decision unit constructs a task load model to accurately store and optimize laser processing data, thereby improving detection efficiency.

[0123] Specifically, the detection storage decision unit generates a storage decision from laser processing data according to a storage decision generation method, which includes:

[0124] Step K01, inputting a task into a task load model by using a portable same-light-path Libs laser detection device to obtain sample data volume Ns;

[0125] Step K02, comparing the sample data volume Ns with a preset sample data volume Ns0, setting 12MB≤Ns0≤21MB, judging the sample data volume state according to the comparison result, and outputting a storage decision according to the judgment result, wherein:

[0126] When Ns≤Ns0, the detection storage decision unit determines that the sample data volume state is normal, and outputs a storage decision that the laser processing data is stored in the device storage space;

[0127] When Ns>N0, the detection storage decision unit determines that the sample data volume state is abnormal, and outputs a storage decision that the laser detection data is segmented to obtain segmented laser detection data, the segmented laser detection data is processed to obtain segmented laser processing data, and the segmented laser processing data is stored in the device storage space.

[0128] Specifically, the portable homocentric path Libs laser detection device detection task refers to the task of detecting new materials performed by the portable homocentric path Libs laser detection device. The embodiment does not limit the acquisition method of the portable homocentric path Libs laser detection device detection task, and a person skilled in the art can freely choose according to actual needs, as long as the requirement of acquiring the portable homocentric path Libs laser detection device detection task is met, such as manual setting. The sample data amount Ns refers to the amount of laser detection data obtained by the portable homocentric path Libs laser detection device when detecting samples. The preset sample data amount Ns0 refers to a preset value used to judge the state of the sample data amount Ns. The state of the sample data amount Ns refers to the quantity state of the data contained in the sample data amount Ns. The state of the sample data amount Ns includes a normal state and an abnormal state. The device storage space refers to the local storage space in the portable homocentric path Libs laser detection device for storing laser processing data. The sample segmentation refers to the process of segmenting the sample data amount Ns according to the preset number np for data processing, such as calculating the segmented data Nsp according to the sample data amount Ns and the preset number np, setting Nsp=Nsp / np, and then processing the segmented data Nsp. The embodiment does not limit the preset number np, and a person skilled in the art can freely choose according to actual needs, as long as the requirement of limiting the preset number np is met, such as setting np=20.

[0129] Specifically, the detection storage decision unit judges the state of the sample data amount Ns to output a storage decision, so as to flexibly adjust the data amount of each data processing according to the size of the sample data amount, reduce the calculation amount of data processing, thereby reducing the memory occupation and improving the resource utilization.

[0130] Specifically, when the detection storage decision unit adjusts the output process of the storage decision according to the data required storage space Yc and the preset data required storage space Yc0, it is set that 12MB≤Yc0≤21MB. According to the comparison result, the demand situation of the data storage space is judged, and the preset sample data amount Ns0 is adjusted according to the judgment result, wherein:

[0131] When Yc≥Yc0, the detection storage decision unit determines that the demand situation of the data storage space is normal demand, and does not adjust the preset sample data amount Ns0;

[0132] When Yc < Yc0, the detection storage decision unit determines that the data storage space requirement is an abnormal requirement, adjusts the preset sample data amount Ns0, sets the preset sample data amount Ns0 through a storage coefficient β, that is, Ns0 = β × Ns0, replaces the preset sample data amount Ns0 with the adjusted preset sample data amount Ns0, and recompares the sample data amount Ns with the adjusted preset sample data amount Ns0. , adjusts the preset sample data amount Ns0, obtains an adjusted preset sample data amount Ns0, sets Ns0 = β × Ns0, replaces the preset sample data amount Ns0 with the adjusted preset sample data amount Ns0, and recompares the sample data amount Ns with the adjusted preset sample data amount Ns0.

[0133] Specifically, the data required storage space Yc refers to the storage space required for storing laser processing data. The embodiment does not limit the acquisition method of the data required storage space Yc. A person skilled in the art can freely select according to actual needs, as long as the requirement of acquiring the data required storage space Yc is met, such as acquiring through software calculation. The preset data required storage space Yc0 refers to a preset value for judging the data storage space requirement. The data storage space requirement refers to the degree of requirement for storage space of laser processing data according to the judgment of data required storage space and preset data required storage space. The data storage space requirement includes normal requirement and abnormal requirement.

[0134] Specifically, the detection storage decision unit judges the data storage space requirement, so that the preset sample data amount Ns0 decreases with the decrease of the data required storage space Yc, so as to store the laser processing data in the device storage space in the case of small data required storage space, thereby improving the efficiency of data storage.

[0135] Specifically, the detection storage decision unit updates the data required storage space Yc according to the priority division result, wherein:

[0136] When the priority division result is the first priority, the data required storage space Yc is updated, the first storage update coefficient z1 is set as z1 = (P / P2), the data required storage space Yc is updated, the first updated data required storage space Yc1 is obtained, Yc1 = z1 × Yc is set, the first updated data required storage space Yc1 is compared with the preset data required storage space Yc0, and the data storage space requirement is rejudged;

[0137] When the priority division result is the second priority, the data required storage space Yc is not updated;

[0138] When the priority classification result is the third priority, the required storage space Yc of the data is updated, the first storage update coefficient z2 is set as z2=P1-P / P1, the required storage space Yc of the data is updated, the second updated required storage space Yc2 of the data is obtained, Yc2 is set as z2*Yc, the second updated required storage space Yc2 of the data is compared with the preset required storage space Yc0 of the data, and the requirement of the data storage space is re-judged.

[0139] Specifically, the detection storage decision unit updates the required storage space Yc of the data, so that the laser processing data is stored in stages. When the laser processing data is the first priority, the required storage space Yc of the data is increased to optimize the storage of the laser processing data, thereby reducing the occupation of the device storage space. When the laser processing data is the second priority, the required storage space Yc of the data is reduced, and the data is stored in the device storage space, thereby improving the efficiency of data storage.

[0140] Specifically, the detection storage decision unit updates the required storage space Yc of the data, so that the laser processing data is stored in stages. When the laser processing data is the first priority, the required storage space Yc of the data is increased to optimize the storage of the laser processing data, thereby reducing the occupation of the device storage space. When the laser processing data is the second priority, the required storage space Yc of the data is reduced, and the data is stored in the device storage space, thereby improving the efficiency of data storage.

[0141] Step S01, the required storage space ratio Yb is calculated according to the required storage space Yc of the data and the device storage space Y, and the required storage space ratio Yb is obtained, and Yb=Yc / Y is set.

[0142] Step S02, the margin storage space ratio Ys is calculated according to the device storage space margin Yf and the device storage space Y, and the margin storage space ratio Ys is obtained, and Ys=Yf / Y is set.

[0143] Step S03, the required storage space ratio Yb is compared with the margin storage space ratio Ys, 30%≤Ys≤50% is set, the device storage space sufficiency is judged according to the comparison result, and the storage decision optimization is performed according to the judgment result, wherein:

[0144] When Yb≤Ys, the detection storage optimization unit determines that the device storage space sufficiency is sufficient, and does not perform storage decision optimization;

[0145] When Yb > Ys, the detection storage tuning unit determines that the device storage space is not sufficient, and performs storage decision tuning: the spectral intensity value weight coefficient wf is adjusted by decision, and the spectral intensity tuning coefficient v is set as v = Yb / Ys, the spectral intensity value weight coefficient wf is adjusted by decision, the adjusted spectral intensity value weight coefficient wf' is obtained, wf' is set as wf x v, the comprehensive priority score P is recalculated according to the adjusted spectral intensity value weight coefficient wf', and the second priority and third priority laser processing data are stored in the cloud.

[0146] Specifically, the required storage space proportion Yb refers to the proportion of the data required storage space Yc in the device storage space, which is used to represent the occupation degree of the stored laser processing data to the device storage space, the device storage space margin Yf refers to the remaining storage capacity in the device storage space, which is set as Yf = Y-Yc, the margin storage space proportion Ys refers to the proportion of the device storage space margin Yf in the device storage space, which is used to represent the current storage capacity of the device storage space for storing laser processing data, the device storage space sufficiency degree refers to the degree of the device storage space margin meeting the laser processing data storage demand according to the required storage space proportion and the margin storage space proportion, the device storage space sufficiency degree includes the device storage space sufficiency degree as sufficient and the device storage space sufficiency degree as insufficient, and the cloud refers to a remote network storage system for storing the second priority and third priority laser processing data.

[0147] Specifically, the detection storage tuning unit adjusts the storage decision by judging the device storage space sufficiency degree, so that when the device storage space margin is insufficient, the spectral intensity value weight coefficient wf increases with the increase of the device storage space margin Yf, thereby improving the weight of the laser processing data in the storage decision, ensuring that the key data is stored in the local device first, ensuring the accuracy and integrity of data storage, and storing the second priority and third priority laser processing data in the cloud, so as to increase the device storage space margin, relieve the local storage pressure, realize the flexible allocation of local storage and cloud storage resources, thereby improving the utilization rate of storage resources and the efficiency and reliability of data storage.

[0148] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to related technical features without departing from the principles of the present application, and the technical solutions after these changes or replacements will fall within the protection scope of the present application.

Claims

1. A portable same-light path Libs laser detection device, characterized in that: The device comprises: a laser generator connected to the spectrometer and configured to emit a laser beam; A spectrometer connected to the laser generator and the detection module for collecting laser detection data; The detection module is connected to the spectrometer, display screen and battery, and is used to control the portable same-light path Libs laser detection device; A display screen, connected to the detection module, for displaying laser processing data; A battery connected to the detection module for providing power; The detection module includes: A detection data acquisition unit, used to acquire laser detection data; A detection data processing unit is used to process the laser detection data according to the detection data processing method to obtain laser processing data, and push the laser processing data to the display screen; a detection priority division unit, configured to prioritize the laser processing data according to the priority division method to obtain a priority division result, further configured to adjust the priority of the priority division result according to the battery power data, and further configured to update the priority of the priority adjustment process according to the task urgency index; a detection storage decision unit, configured to generate a storage decision based on the laser processing data using a storage decision generation method, to adjust the storage of the output process of the storage decision based on the storage space required for the data, and to update the storage space required for the data based on a priority classification result; The detection storage tuning unit is also used to adjust the storage decision according to the storage decision tuning method; The detection data processing unit processes the laser detection data according to the detection data processing method to obtain laser processing data, and pushes the laser processing data to the display screen. The detection data processing method includes: Step C01, calculate the detection data point baseline value yj' according to the wavelength x and the number of data points jk in the baseline to obtain the detection data point baseline value y j `, where a0, a1, a2...a jk is the prediction coefficient value, set y j `=a0+a1×x+a2×x 2 +……a jk-1 ×x jk-1 +a jk ×x jk ; Step C02: Based on the baseline value y of the detection data point j ` and baseline detection data value y j Calculate the error sum of squares S to get the error sum of squares S, and set: ; Step C03, according to the spectrum data value y i and the baseline value y of the detection data point j `Calculate the corrected laser detection data yp to obtain the corrected laser detection data yp, set yp=y i -y j `; Step C04: Calculate the processed laser detection data yp' based on the corrected laser detection data yp, the sliding window value N and the total number of laser detection data points ik to obtain the processed laser detection data yp'. Set ; Step C05, outputting the processed laser detection data yp' as laser processing data to obtain laser processing data; The detection priority division unit divides the laser processing data into priorities according to a priority division method, wherein the priority division method includes: Step E01, according to the spectrum data value y i , detection data point baseline value y j `, the total number of laser detection data points ik, the number of data points in the baseline jk are used to calculate the noise level bz, and the noise level bz is set. ; Step E02: Calculate the comprehensive priority score P based on the spectral intensity value fq, the spectral line width pk, the noise level bz, the spectral intensity value weight coefficient wf, the spectral line width weight coefficient wp, and the noise level weight coefficient wb to obtain the comprehensive priority score P, setting P=wf×fq+wp×pk+wb / bz; In step E03, the comprehensive priority score P is compared with the first preset priority score P1 and the second preset priority score P2, where P1 = 0.58 and P2 = 0.

71. The priority of the comprehensive priority score P is determined based on the comparison result, and the priority division result is output based on the determination result. The sliding window value N is also adjusted based on the determination result, where: When P>P2, the detection priority division unit determines that the priority of the comprehensive priority score P is high, outputs the first priority as the priority division result, adjusts the sliding window value N, sets u1=1-(P-P2) / P by the first window adjustment coefficient u1, adjusts the sliding window value N to obtain a first adjusted sliding window value N1, sets N1=u1×N, replaces the sliding window value N with the first adjusted sliding window value N1, and recalculates the processed laser detection data yp` based on the first adjusted sliding window value N1; When P1≤P≤P2, the detection priority division unit determines that the priority of the comprehensive priority score P is medium, outputs the second priority as the priority division result, and does not adjust the sliding window value N; When P<P1, the detection priority division unit determines that the priority of the comprehensive priority score P is low, outputs the third priority as the priority division result, adjusts the sliding window value N, and uses the second window adjustment coefficient u2 to set u2=1+(P1-P) / P1, adjusts the sliding window value N to obtain the second adjusted sliding window value N2, sets N2=u2×N, replaces the sliding window value N with the second adjusted sliding window value N2, and recalculates the processed laser detection data yp` according to the second adjusted sliding window value N2.

2. The portable same-light path Libs laser detection device according to claim 1, characterized in that: When the detection priority division unit adjusts the priority division result according to the battery power data, the battery power data Ws is compared with the preset power data Ws0, 30%≤Ws0≤70% is set, the battery power sufficiency is judged according to the comparison result, and the first preset priority score P1 and the second preset priority score P2 are adjusted according to the judgment result, wherein: When Ws≥Ws0, the detection priority division unit determines that the battery power is sufficient, and does not adjust the priority of the first preset priority score P1 and the second preset priority score P2; When Ws<Ws0, the detection priority division unit determines that the battery power is insufficient, adjusts the priority of the first preset priority score P1 to obtain the adjusted first preset priority score P1`, sets P1`=P1+Ws0 / Ws, replaces the first preset priority score P1 with the adjusted first preset priority score P1`, adjusts the priority of the second preset priority score P2 to obtain the adjusted second preset priority score P2`, sets P2`=P2+Ws0 / Ws, replaces the second preset priority score P2 with the adjusted second preset priority score P2`, and re-compares the comprehensive priority score P with the adjusted first preset priority score P1` and the adjusted second preset priority score P2`.

3. The portable same-light path Libs laser detection device according to claim 2, characterized in that: When the detection priority division unit performs a priority update on the priority adjustment process according to the task urgency index, the task urgency index Sr is compared with the preset urgency index Sr0, and 0.62≤Sr0≤0.78 is set. The urgency of the task is judged according to the comparison result, and the priority of the battery power data Ws is updated according to the judgment result, wherein: When Sr≤Sr0, the detection priority division unit determines that the task urgency is not urgent and does not update the priority of the battery power data Ws; When Sr>Sr0, the detection priority division unit determines that the task urgency is urgent, updates the priority of the battery power data Ws, and updates the priority of the battery power data Ws through the power update coefficient q, q=Sr0 / Sr, to obtain the updated battery power data Ws`, sets Ws`=Ws×q, compares the updated battery power data Ws` with the preset power data Ws0, and re-judges the status of the battery power data Ws.

4. The portable same-light-path Libs laser detection device according to claim 3, characterized in that: The detection storage decision unit constructs the task load model according to the task load model construction method, and the task load model construction method includes: Step H01, divide the historical task load dataset into 70% task training set, 20% task validation set and 10% task test set; Step H02, initializing the task parameters of the convolutional neural network model; Step H03: Input the task training set into the parameter-initialized convolutional neural network model for training, input the task verification set into the trained convolutional neural network model, optimize the task parameters of the trained convolutional neural network model, and then input the task test set into the parameter-optimized convolutional neural network model for testing, and output the test accuracy; Step H04: Output the parameter-optimized convolutional neural network model with an accuracy of 90% as the task load model; The detection storage decision unit generates a storage decision according to the laser processing data by using a storage decision generation method, wherein the storage decision generation method includes: Step K01, input the detection task of the portable same-light path Libs laser detection device into the task load model to obtain the sample data volume Ns; Step K02: compare the sample data size Ns with the preset sample data size Ns0, set 12MB≤Ns0≤21MB, judge the sample data size status based on the comparison result, and output the storage decision based on the judgment result, where: When Ns≤Ns0, the detection storage decision unit determines that the sample data quantity state is normal and outputs a storage decision: storing the laser processing data in the device storage space; When Ns>Ns0, the detection storage decision unit determines that the sample data quantity state is abnormal and outputs the storage decision: after the laser detection data is sample segmented, segmented laser detection data is obtained, and data processing is performed on the segmented laser detection data to obtain segmented laser processing data, and the segmented laser processing data is stored in the device storage space.

5. The portable same-light-path Libs laser detection device according to claim 4, characterized in that: When the detection storage decision unit performs storage adjustment on the output process of the storage decision according to the storage space required for the data, the storage space required for the data Yc is compared with the preset storage space required for the data Yc0, and 12 megabytes ≤ Yc0 ≤ 21 megabytes is set. The data storage space requirement is judged based on the comparison result, and the preset sample data amount Ns0 is adjusted based on the judgment result, wherein: When Yc≥Yc0, the detection storage decision unit determines that the demand for data storage space is normal and does not perform storage adjustment on the preset sample data amount Ns0; When Yc<Yc0, the detection storage decision unit determines that the demand for data storage space is abnormal, and adjusts the storage of the preset sample data volume Ns0 by setting the storage coefficient β. , the preset sample data volume Ns0 is stored and adjusted to obtain the adjusted preset sample data volume Ns0`, Ns0`=β×Ns0 is set, the preset sample data volume Ns0 is replaced with the adjusted preset sample data volume Ns0`, and the sample data volume Ns is re-compared with the adjusted preset sample data volume Ns0`.

6. The portable same-light-path Libs laser detection device according to claim 5, characterized in that: The detection storage decision unit updates the storage space Yc required for the data according to the priority classification result, wherein: When the priority classification result is the first priority, the storage space Yc required for the data is updated. By using the first storage update coefficient z1, z1=(P / P2), the storage space Yc required for the data is updated to obtain the first updated data storage space Yc1, and Yc1=z1×Yc. The storage space Yc1 required for the first updated data is compared with the preset data storage space Yc0 to re-evaluate the data storage space demand. When the priority classification result is the second priority, the storage space Yc required for the data is not updated; When the priority division result is the third priority, the storage space Yc required for the data is updated. Through the first storage update coefficient z2, z2=P1-P / P1 is set, and the storage space Yc required for the data is updated to obtain the second updated storage space Yc2 required for the data. Yc2=z2×Yc is set, and the storage space Yc2 required for the second updated data is compared with the storage space Yc0 required for the preset data to re-judge the demand for data storage space.

7. The portable same-light-path Libs laser detection device according to claim 6, characterized in that: The detection storage optimization unit adjusts the storage decision according to the storage decision optimization method, and the storage decision optimization method includes: Step S01, according to the data storage space required Yc and the device storage space Y for the required storage space ratio Yb is calculated to obtain the required storage space ratio Yb, set Yb = Yc / Y; Step S02: Calculate the remaining storage space proportion Ys according to the device storage space remaining Yf and the device storage space Y to obtain the remaining storage space proportion Ys, and set Ys=Yf / Y; Step S03: Compare the required storage space ratio Yb with the remaining storage space ratio Ys, set 30%≤Ys≤50%, judge the device storage space adequacy based on the comparison result, and perform storage decision optimization based on the judgment result, wherein: When Yb≤Ys, the storage optimization unit determines that the storage space of the device is sufficient and does not perform storage optimization; When Yb>Ys, the detection storage tuning unit determines that the storage space of the device is insufficient, and performs storage decision tuning: the spectral intensity value weight coefficient wf is adjusted by decision, and v=Yb / Ys is set through the spectral intensity tuning coefficient v. The spectral intensity value weight coefficient wf is adjusted by decision to obtain the adjusted spectral intensity value weight coefficient wf`, and wf`=wf×v is set. The comprehensive priority score P is recalculated according to the adjusted spectral intensity value weight coefficient wf`, and the second priority and third priority laser processing data are stored in the cloud.

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