Method and device for testing transport vibration adaptability of pathological section scanner and readable storage medium thereof

Abstract

The invention provides a pathological section scanner transportation vibration adaptability testing method and device and a readable storage medium thereof. The device comprises a three-axis electromagnetic vibration table, a rapid detection module, an evaluation system, a vibration-temperature coupling control module and a self-adaptive clamp. A composite vibration spectrum containing random impact and temperature change is generated through a three-axis electromagnetic vibration table, and a real transportation environment is simulated; a mechanical detection unit (a laser displacement sensor and an acoustic emission sensor) and an optical detection unit (a standard test slide and an image analysis algorithm) are used for synchronously acquiring multi-dimensional data; and fusing data based on a neural network model and outputting a fault evaluation index, and judging a test result in combination with three-dimensional qualified thresholds of displacement, image sharpness loss and temperature rise. According to the method, the test time is shortened from 48 hours to 2 hours, the frequency spectrum reduction degree reaches 98%, the fault detection rate is increased to 99.5%, and the test precision and efficiency are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical device reliability detection, and in particular to a method and device for testing the transport vibration adaptability of a pathological slice scanner and a readable storage medium thereof. Background Art

[0002] In the development and production of pathology slide scanners, the adaptability to the transport vibration environment is a key factor affecting product reliability. The existing technology has the following problems: (1) Vibration simulation distortion: Traditional vibration tables can only generate sinusoidal waves or fixed-frequency vibrations and cannot reproduce random shocks (such as bumps and sudden braking) in road transportation, resulting in significant deviations between the test results and the actual transportation environment (spectrum reproduction ≤ 70%). (2) Single detection dimension: Existing methods rely only on acceleration sensors to monitor mechanical vibration, ignoring the impact of vibration on optical imaging quality (such as image blur and positioning offset), and are unable to fully evaluate product performance degradation; (3) Low testing efficiency: The complete testing process requires disassembling the equipment and connecting external instruments, which takes more than 48 hours and is difficult to meet the rapid sampling requirements of the production process.

[0003] For example, although the ISTA 3A standard provides a vibration spectrum reference, traditional equipment cannot synchronously couple temperature changes and lacks the ability to superimpose multi-axis vibrations, resulting in distortion of the simulated environment. Therefore, a high-precision, multi-dimensional, and efficient pathology slide scanner transportation vibration adaptability test method, device, and readable storage medium thereof are urgently needed. Summary of the invention

[0004] The embodiments of the present invention provide a method, device and readable storage medium for testing the transport vibration adaptability of a pathology slice scanner, which aims to address the problems of vibration simulation distortion, single detection dimension and low test efficiency in current technologies, which lead to insufficient accuracy of test results and inability to meet rapid detection requirements.

[0005] The core technology of this invention is mainly to achieve high-precision simulation of the transportation vibration environment and comprehensive quantitative evaluation of product performance through multi-mode vibration simulation (three-axis electromagnetic drive + temperature coupling), multi-dimensional synchronous detection (mechanical micro-displacement + optical imaging analysis) and intelligent evaluation system (neural network data fusion).

[0006] In a first aspect, the present invention provides a method for testing the transport vibration adaptability of a pathology slide scanner, the method comprising the following steps: S00, composite vibration spectrum generation: Fix the device under test on the three-axis electromagnetic vibration table through an adaptive fixture to trigger the pressure feedback threshold; Load the preset transport vibration spectrum and simultaneously apply temperature changes to simulate the environment; A composite vibration spectrum containing random shock and preset temperature gradient is generated by a three-axis electromagnetic vibration table, which supports non-orthogonal vibration superposition of X / Y / Z axes; S10, Multi-dimensional synchronous detection: Vibration data is collected through a mechanical detection unit, and imaging performance data is obtained through an optical detection unit using an image analysis algorithm; S20, Intelligent Assessment and Judgment: Based on the neural network model, multi-dimensional data including vibration data and imaging performance data are integrated to output fault assessment indicators; The test result is determined based on the pre-stored three-dimensional qualified thresholds of displacement, image sharpness loss, and temperature rise. If the test fails, the cause of the fault is located.

[0007] Furthermore, in step S00, the composite vibration spectrum generation includes: Based on the standard vibration spectrum superimposed with random shock, the amplitude distribution and occurrence probability of random shock are configurable; The synchronization of vibration and temperature is achieved through the vibration-temperature coupling control algorithm, and the synchronization error is less than the preset threshold.

[0008] Furthermore, in step S10, the mechanical detection unit includes a laser displacement sensor and an acoustic emission sensor. The accuracy of the laser displacement sensor is in the micron level, and the acoustic emission sensor is configured to capture the characteristic frequency of screw loosening.

[0009] Furthermore, in step S10, the image analysis algorithm includes the following steps: Preprocess low-contrast images to improve feature extraction stability; The scale-invariant feature transformation algorithm is used to calculate the feature point offset.

[0010] Furthermore, in step S00, the preset transport vibration spectrum includes road, air or sea transport spectrum, the air transport spectrum simulates high frequency vibration, and the sea transport spectrum simulates low frequency composite vibration.

[0011] Furthermore, in step S20, the neural network model is a three-layer feedforward neural network, and the input parameters include vibration frequency, amplitude and image offset.

[0012] Furthermore, in step S30, the fault cause is located based on a fault characteristic frequency database, and the fault characteristic frequency database includes frequency spectrum characteristics of loose screws and motor wear.

[0013] In a second aspect, the present invention provides a pathology slice scanner transport vibration adaptability testing device, comprising: (a) A three-axis electromagnetic vibration table, configured to simulate non-orthogonal vibration superposition of X / Y / Z axes, with a built-in vibration spectrum generation module for generating a composite vibration spectrum containing random shock and preset temperature change gradient; (b) Detection module, including: i. Mechanical detection unit, used to measure the vibration displacement of the equipment and capture the characteristic frequency of mechanical failure to collect vibration data; ii. an optical detection unit configured with a standard test slide and an image analysis algorithm for evaluating image quality degradation to obtain imaging performance data; (c) an evaluation system configured to fuse multi-dimensional data including vibration data and imaging performance data based on a neural network model to output a fault evaluation index; determine the test result based on pre-stored three-dimensional qualified thresholds of displacement, image sharpness loss, and temperature rise, and locate the cause of the fault if the test fails; (d) Vibration-temperature coupling control module, which dynamically and synchronously adjusts the vibration spectrum and temperature changes through a closed-loop control algorithm; (e) Adaptive clamp, configured as a pressure feedback device, compatible with equipment of different weights and enabling quick clamping.

[0014] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the above-mentioned pathology slice scanner transportation vibration adaptability test method.

[0015] In a fourth aspect, the present invention provides a readable storage medium storing a computer program, wherein the computer program includes a program code for controlling a process to execute a process, wherein the process includes the above-mentioned pathology slice scanner transport vibration adaptability test method.

[0016] The main contributions and innovations of the present invention are as follows: 1. Improved vibration simulation accuracy The three-axis electromagnetic vibration table supports non-orthogonal vibration superposition and synchronizes the temperature gradient (5℃ / min), achieving a vibration spectrum restoration rate of 98% (traditional ≤70%), truly simulating complex transportation environments such as road, air, and sea transportation.

[0017] The random impact parameters (amplitude distribution, occurrence probability) are configurable to cover scenarios such as bumps and sudden braking in actual transportation.

[0018] 2. Enhanced comprehensiveness of detection Mechanical detection: 0.1μm-level laser displacement sensor monitors micro-displacement, and acoustic emission sensor captures the characteristic frequency of screw loosening (800-1200Hz) to locate mechanical faults.

[0019] Optical detection: Analyze image sharpness and feature point offset based on SIFT algorithm, identify 1μm positioning deviation (50% higher than industry standard), and quantify the impact of vibration on imaging quality.

[0020] 3. Test efficiency optimization The entire process takes ≤ 2 hours (traditional 48 hours), supports one-click automated testing and cloud data management, and meets the needs of rapid sampling in the production process.

[0021] The U-shaped adaptive clamp is compatible with 15-50kg equipment, the clamping time is less than 30 seconds, and the pressure feedback device ensures stability.

[0022] 4. Intelligent fault diagnosis The neural network model integrates parameters such as vibration frequency, amplitude, and image offset, and outputs a fault assessment index F, increasing the fault detection rate from 85% to 99.5%.

[0023] Based on the fault characteristic frequency database (such as screw loosening 50-100Hz), the cause of the fault can be accurately located to guide product improvement.

[0024] 5. Environmental adaptability expansion It supports the generation of vibration spectra for multiple scenarios such as air transport (high frequency band) and sea transport (low frequency composite vibration) to meet the testing requirements of different transportation modes.

[0025] The vibration-temperature coupling control algorithm achieves a synchronization error of <0.1s, simulating an extreme temperature change environment (such as -20℃ to 60℃).

[0026] The details of one or more embodiments of the invention are set forth in the following drawings and description so that other features, objects, and advantages of the invention are more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flow chart of a method for testing the transport vibration adaptability of a pathology slice scanner according to an embodiment of the present invention; Figure 2 is an operation flow chart of a device for testing the transport vibration adaptability of a pathological slice scanner according to an embodiment of the present invention; Figure 3 4 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0029] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0030] Existing technologies have fundamental defects in vibration simulation accuracy (low spectrum restoration), detection comprehensiveness (mechanical detection only) and test efficiency (too long cycle), and cannot meet the evaluation needs of the transportation reliability of pathology slide scanners.

[0031] Based on this, the present invention solves the problems existing in the prior art based on multi-mode vibration simulation (three-axis electromagnetic drive + temperature coupling), multi-dimensional synchronous detection (mechanical micro-displacement + optical imaging analysis) and intelligent evaluation system (neural network data fusion).

[0032] Embodiment 1 The present invention aims to propose a method for testing the transport vibration adaptability of a pathology slide scanner. Specifically, referring to Figure 1 , the method comprises the following steps: S00, composite vibration spectrum generation: Fix the device under test on the three-axis electromagnetic vibration table through an adaptive fixture to trigger the pressure feedback threshold; Load the preset transport vibration spectrum and simultaneously apply temperature changes to simulate the environment; A composite vibration spectrum containing random shock and preset temperature gradient is generated by a three-axis electromagnetic vibration table, which supports non-orthogonal vibration superposition of X / Y / Z axes; In this embodiment, the pressure feedback threshold setting range is: Different pressure feedback threshold ranges are set according to the weight of the equipment. For 15-50kg equipment, the pressure range is set to 50-200N. This range is determined by clamping tests on equipment of different weights to ensure that the equipment will not loosen during vibration and will not cause damage to the equipment.

[0033] Among them, the three-axis electromagnetic vibration table adopts three-axis electromagnetic drive technology, and the X / Y / Z axes all support ±5mm displacement. The non-orthogonal vibration superposition algorithm is used to simulate multi-directional coupled vibration (such as compound vibration when the phase difference between the X-axis and the Y-axis is 90°). The three-axis electromagnetic vibration table has a built-in standard vibration spectrum library (such as ISTA3A) and superimposed random shock (amplitude normal distribution, mean μ=0.5mm, standard deviation σ=0.2mm, probability of occurrence 5%).

[0034] The temperature change gradient is 5℃ / min, and the synchronization error is less than 0.1s through the vibration-temperature coupling control algorithm (PID parameter setting: Kp=0.6Kp_cr, Ki=1.2Kp_cr / Tcr, Kd=0.075Kp_cr・Tcr).

[0035] Preferably, examples of vibration spectrum generation for different modes of transport are: Road transport: # Highway transport profile generation example def generate_highway_profile(): base = ista_3a_profile()# Load the ISTA standard profile add_random_impulse(base)# Add random impulse (simulate turbulence) return add_temp_sync(base)# Coupled temperature change (5℃ / min gradient) Among them, the random parameters of the add_random_impulse() function are defined as follows: the amplitude distribution adopts a normal distribution with a mean of μ and a standard deviation of σ, where μ and σ are determined according to the amplitude statistics of random shocks in actual transportation; the probability of occurrence is determined according to the frequency of random shocks during transportation. For example, if there are 5 random shocks in every 100 vibration cycles, the probability of occurrence is 5%.

[0036] Air transport: According to the characteristics of air transport, the vibration spectrum is mainly concentrated in the high frequency band (100-500Hz) and the amplitude is small (0.01-0.1mm). By collecting and analyzing the vibration data during air transport, a vibration spectrum that conforms to the characteristics of air transport is generated.

[0037] Shipping: The vibration spectrum of shipping is relatively complex, including vibrations in the low frequency band (1-10Hz) and the medium frequency band (10-100Hz), with a relatively large amplitude (0.1-10mm). Considering the influence of factors such as waves and ship engines, it is necessary to comprehensively consider multiple factors when simulating the vibration spectrum of shipping.

[0038] S10, Multi-dimensional synchronous detection: Vibration data is collected through a mechanical detection unit, and imaging performance data is obtained through an optical detection unit using an image analysis algorithm; The mechanical detection unit includes a laser displacement sensor and an acoustic emission sensor. The laser displacement sensor has a micron-level accuracy and the acoustic emission sensor is configured to capture the characteristic frequency of screw loosening. For example: Laser displacement sensor: 0.1μm accuracy, real-time monitoring of scanning table micro-displacement (such as XYZ axis displacement).

[0039] Acoustic emission sensor: Captures the characteristic frequency of screw loosening between 800-1200Hz and identifies faults through FFT spectrum analysis.

[0040] The optical detection unit uses a standard glass slide with a 2μm grid, and the image acquisition frequency is set to 3-5 times the vibration frequency (e.g., 60Hz acquisition when vibrating at 20Hz).

[0041] Preferably, the image analysis algorithm of the optical detection unit comprises: Perform histogram equalization preprocessing on low-contrast images; The SIFT (Scale Invariant Feature Transform) algorithm is used to calculate the feature point offset (accuracy 1μm) and Laplace variance sharpness value.

[0042] Among them, the adaptability of SIFT algorithm in low-contrast scenes: when the image contrast is reduced due to glass slide contamination, the image is preprocessed using methods such as histogram equalization and adaptive filtering to improve the image contrast. Then the SIFT algorithm is applied for feature extraction and analysis. At the same time, a contrast threshold is set in the algorithm, and when the image contrast is lower than the threshold, the preprocessing operation is automatically triggered.

[0043] Preferably, the matching relationship between the image acquisition frequency and the vibration frequency is also included: according to the Nyquist sampling theorem, the image acquisition frequency fs should be at least twice the vibration frequency f, that is, fs ≥ 2 × f. In practical applications, in order to ensure that sufficient image information is collected, the image acquisition frequency is set to 3-5 times the vibration frequency. For example, when the vibration frequency is 20 Hz, the image acquisition frequency is set to 60-100 Hz.

[0044] S20, Intelligent Assessment and Judgment: Based on the neural network model, multi-dimensional data including vibration data and imaging performance data are integrated to output fault assessment indicators; The test result is determined based on the pre-stored three-dimensional qualified thresholds of displacement, image sharpness loss, and temperature rise. If the test fails, the cause of the fault is located.

[0045] Fault assessment indicators include:

[0046] Among them, the multimodal data fusion model (neural network model) includes: Input / output parameter definition: Input parameters include vibration frequency f, amplitude A, image offset x, displacement d, sharpness s, and temperature rise T. The output parameter is the equipment fault assessment index F. For example, the mathematical relationship between vibration frequency and image offset can be obtained by fitting experimental data: x=a×f×f+b×f+c, where a, b, and c are fitting coefficients.

[0047] Data fusion method: A neural network structure is used for data fusion. A three-layer feedforward neural network is constructed. The number of input layer nodes is determined according to the number of input parameters, the number of hidden layer nodes is determined through multiple experiments and cross-validation, and the number of output layer nodes is 1 (i.e., the fault assessment index F). The training of the neural network uses the back propagation algorithm, and by continuously adjusting the weights and biases of the network, the output of the network is as close as possible to the actual fault assessment results.

[0048] Characteristics of training data set: The sample size of the training data set is determined according to the actual situation, and is generally not less than 1,000 samples. Data collection conditions include different vibration frequencies, amplitudes, temperature changes, etc., to cover various situations that the equipment may encounter during transportation. For example, the vibration frequency range is 1-100Hz, the amplitude range is 0.1-10mm, and the temperature range is -20℃-60℃.

[0049] In this embodiment, the qualified threshold is determined based on the following: The qualified threshold (such as ≤3μm displacement) is obtained through accelerated life testing. In the accelerated life test, the pathology slide scanner is subjected to vibration and temperature stress higher than normal transportation conditions, and the displacement value when the equipment fails is recorded. After multiple tests and data analysis, it is determined that when the displacement is ≤3μm, the reliability of the equipment under normal transportation conditions can meet the requirements. At the same time, the qualified threshold also refers to the relevant requirements for transportation vibration testing of similar equipment in industry standards (such as ISTA3A), and is appropriately adjusted according to the characteristics of the pathology slide scanner.

[0050] In this embodiment, fault location matches the characteristic frequency of the database through spectrum analysis and outputs the confidence of the fault type. For example, the typical characteristic values ​​of different fault types are recorded in the fault characteristic frequency database. For example, the specific frequency band of screw loosening is 50-100Hz, which is obtained by spectrum analysis of vibration signals of a large number of screw loosening fault cases. In actual testing, by monitoring the spectrum of the vibration signal, it is determined whether there is a fault such as loose screws.

[0051] Preferably, after design and testing, the maximum size range of the device that can be detected by this test system is 1000 mm in length, 800 mm in width, and 600 mm in height. This size range is determined based on the load capacity of the vibration table and the test space.

[0052] In order to facilitate understanding of the technical effects of the present invention, the following is a comparison of the spectrum restoration of the present invention and the traditional vibration table: 1. Test Preparation 1. Vibration spectrum selection Vibration spectrum A: simulates the aviation transportation environment. Its spectrum characteristics are mainly concentrated in the high frequency band (100 - 500 Hz) with a small amplitude (0.01 - 0.1 mm). This high-frequency and small-amplitude vibration is usually caused by the operation of the aircraft engine and airflow disturbances.

[0053] Vibration spectrum B: Corresponding to road transportation conditions, the spectrum is distributed in the medium and low frequency bands (10 - 100 Hz), and the amplitude range is 0.1 - 5 mm. The vibration in this frequency band mainly comes from the unevenness of the road surface and the vibration of the engine during vehicle driving.

[0054] Vibration spectrum C: simulates the shipping environment. The spectrum is more complex, covering low frequency bands (1 - 10 Hz) and medium frequency bands (10 -100 Hz). The amplitude is relatively large (0.1 - 10 mm). This is the result of the combined effect of multiple factors such as the undulating waves and the operation of the ship's engine.

[0055] 2. Test equipment Traditional vibration table: A common single-axis electric vibration table on the market is selected. Its control accuracy and spectrum simulation capabilities represent the general level of traditional vibration test equipment.

[0056] The vibration table of the present invention: a multi-mode vibration table driven by three axes of electromagnetics, capable of simulating complex transport vibration spectra (including random shock + temperature coupling).

[0057] 3. Test method: Under the same test environment and conditions, the above three vibration spectra are simulated using a traditional vibration table and a vibration table of the present invention. A high-precision vibration sensor is installed on the test platform of the vibration table, and the sampling frequency of the sensor is set to 1000 Hz to ensure that the vibration signals of each frequency band can be accurately captured. The actual vibration signal collected is subjected to spectrum analysis, and then compared with the preset standard vibration spectrum, and the spectrum restoration degree is evaluated by calculating the correlation coefficient of the spectrum.

[0058] 2. Test Results

[0059] 3. Results Analysis It can be seen from the test data that for different types of vibration spectra, the spectrum restoration degree of the vibration table of the present invention is significantly higher than that of the traditional vibration table. When simulating the vibration spectrum A of air transportation, the spectrum restoration degree of the traditional vibration table is 0.70, while that of the vibration table of the present invention reaches 0.90; in the vibration spectrum B simulating road transportation, the restoration degree of the traditional vibration table is 0.65, while that of the vibration table of the present invention is 0.85; for the complex vibration spectrum C simulating sea transportation, the restoration degree of the traditional vibration table is only 0.60, while that of the vibration table of the present invention reaches 0.80.

[0060] After calculation, the average spectrum restoration degree of the vibration table of the present invention in these three vibration spectrum tests is 0.85, and the average spectrum restoration degree of the traditional vibration table is 0.65. The spectrum restoration degree of the vibration table of the present invention is 20% higher than that of the traditional vibration table on average. This fully proves that the vibration table of the present invention has significant advantages in simulating complex vibration spectra, can more accurately restore the vibration conditions in the actual transportation process, and provide a more reliable test environment for transportation vibration tests of equipment such as pathological slice scanners.

[0061] To this end, a comparison test of the fault detection rate between single detection and multi-modal detection was also conducted: 100 pathology slide scanners were tested, using single detection (such as mechanical detection only) and multimodal detection (mechanical detection + optical detection) methods. The number of faults detected by the two detection methods was recorded, and the fault detection rate was calculated. The test results showed that the fault detection rate of multimodal detection was 15% higher than that of single detection.

[0062] The influence curve of different clamping time on the test results was also tested: Set 5 different clamping times (5s, 10s, 15s, 20s, 25s) and test the same batch of equipment. Record the test results (such as displacement, sharpness, etc.) under different clamping times, and draw a curve between clamping time and test results. Test data shows that when the clamping time is 15-20s, the stability of the test results is the best.

[0063] Embodiment 2 Based on the same concept, the present invention also proposes a pathology slice scanner transport vibration adaptability testing device, comprising: (a) A three-axis electromagnetic vibration table, configured to simulate non-orthogonal vibration superposition of X / Y / Z axes, with a built-in vibration spectrum generation module for generating a composite vibration spectrum containing random shock and preset temperature change gradient; In this embodiment, multi-directional coupled vibration, i.e., non-orthogonal vibration superposition of XYZ axes, is considered in the vibration simulation. By establishing a multi-axis vibration model, the mutual influence between different axes is simulated. For example, when the X-axis and Y-axis vibrate at the same time, the phase difference and coupling coefficient between them are considered, so that the vibration simulation more realistically reflects the actual transportation situation.

[0064] (b) Detection module, including: i. Mechanical detection unit, used to measure the vibration displacement of the equipment and capture the characteristic frequency of mechanical failure to collect vibration data; The mechanical detection unit uses a laser displacement sensor with an accuracy of 0.1μm to measure the micro-displacement of the scanning table, which can accurately monitor the small displacement changes of the scanning table during vibration and promptly discover potential mechanical stability problems. At the same time, the acoustic emission sensor captures the characteristic frequency of screw loosening of 800-1200Hz, which can effectively detect whether the equipment has mechanical faults such as screw loosening during vibration.

[0065] ii. an optical detection unit configured with a standard test slide and an image analysis algorithm for evaluating image quality degradation to obtain imaging performance data; Among them, optical detection: using a standard test slide containing a 2μm grid and simulated cell patterns, the image analysis algorithm automatically calculates relevant indicators. For example: def calc_quality(img): # Calculate key indicators sharpness = cv2.Laplacian(img).var()# Image sharpness offset = sift_match(img_pre, img_post) # feature point offset return (sharpness, offset) (c) an evaluation system configured to fuse multi-dimensional data including vibration data and imaging performance data based on a neural network model to output a fault evaluation index; determine the test result based on pre-stored three-dimensional qualified thresholds of displacement, image sharpness loss, and temperature rise, and locate the cause of the fault if the test fails; (d) Vibration-temperature coupling control module, which dynamically and synchronously adjusts the vibration spectrum and temperature changes through a closed-loop control algorithm; In this embodiment, after a large number of experiments and data analysis, the association rules between frequency, amplitude and temperature rise rate are established. The relationship between frequency f, amplitude A and temperature rise rate ΔT / Δt can be approximately expressed as: ΔT / Δt=k1×f+k2×A+k3×f×A, where k1, k2, and k3 are coefficients obtained by experimental fitting. For example, the temperature changes under different frequencies and amplitudes are recorded in multiple experiments, and the values ​​of these coefficients are fitted using the least squares method.

[0066] Among them, the closed-loop control algorithm adopts the PID (proportional-integral-differential) control algorithm. The PID controller calculates the control amount to adjust the vibration table and temperature control system based on the error between the set temperature and vibration parameters and the actual measured values. The proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd are adjusted by the Ziegler-Nichols method. First, set the integral and differential effects to zero, gradually increase the proportional coefficient Kp until the system has a critically stable equal-amplitude oscillation, record the proportional coefficient Kp_cr and oscillation period Tcr at this time, and then calculate the PID parameters according to the empirical formula Kp=0.6×Kp_cr, Ki=1.2×Kp_cr / Tcr, Kd=0.075×Kp_cr×Tcr.

[0067] The synchronization error is measured by using a timestamp alignment strategy. High-precision clocks are set in the vibration table and temperature control system to record the start timestamp of each control cycle. The synchronization error is calculated by comparing the timestamps of the vibration and temperature control signals. In order to improve the measurement accuracy, the synchronization errors of multiple control cycles are averaged.

[0068] (e) Adaptive clamp, configured as a pressure feedback device, compatible with equipment of different weights and enabling quick clamping.

[0069] In this embodiment, the adaptive clamp can also be equipped with an overload protection mechanism. For example, when the weight of the equipment exceeds 50kg, the clamp system automatically detects the pressure overload and triggers the overload protection mechanism. First, the clamp stops further clamping action, and then prompts the operator through sound and light alarms. At the same time, the system records the overload situation for subsequent analysis and processing.

[0070] Preferably, under extreme temperature conditions (such as -20℃ low-temperature transportation), a temperature box is used to control the test environment. The temperature in the temperature box is lowered to -20℃ through the refrigeration system and kept stable. At the same time, a vibration table is installed in the temperature box to perform vibration tests to simulate the transportation vibration in a low-temperature environment.

[0071] Preferably, the flow chart for testing using the device is as follows: 1. Start: The starting point of the entire testing process.

[0072] 2. Install the pathology slide scanner on the multi-mode vibration table (fixed with a U-shaped adaptive clamp): Install the pathology slide scanner to be tested on the multi-mode vibration table and fix it with a U-shaped adaptive clamp. The clamp is compatible with devices of different weights, has a short clamping time and is equipped with a pressure feedback device to ensure stable clamping of the device.

[0073] 3. Start the vibration table and vibrate according to the preset vibration spectrum (synchronous temperature changes): Start the multi-mode vibration table and make it vibrate according to the preset vibration spectrum. At the same time, the vibration spectrum and temperature changes are accurately synchronized through the vibration-temperature coupling control algorithm to simulate the real transportation environment.

[0074] 4. Rapid detection module performs mechanical detection (laser displacement sensor measures micro-displacement, acoustic emission sensor captures characteristic frequency): During the vibration process, the laser displacement sensor in the rapid detection module measures the micro-displacement of the scanning table, and the acoustic emission sensor captures the characteristic frequency of screw loosening to monitor mechanical stability and whether there is a mechanical failure.

[0075] 5. Rapid detection module for optical detection (using standard test slides and image analysis algorithm to calculate indicators): using standard test slides containing specific patterns, image analysis algorithms are used to calculate indicators such as image sharpness and feature point offset to detect the impact of vibration on scanning imaging quality.

[0076] 6. The evaluation system analyzes data based on the judgment criteria: The evaluation system analyzes the data collected from mechanical and optical inspections based on preset judgment criteria.

[0077] 7. Whether the test is passed: Determine whether the pathology slide scanner passes the test based on the analysis results of the evaluation system.

[0078] 8. Generate test report and end: If the test passes, a test report is generated and the entire test process ends.

[0079] 9. Analyze the cause of the failure using a multimodal data fusion model: If the test fails, use the multimodal data fusion model to analyze the quantitative relationship between the mechanical vibration data and the image quality loss to locate the cause of the failure.

[0080] 10. Provide a basis for product improvement: According to the results of fault cause analysis, provide a basis for product improvement, then reinstall the equipment for testing to form a closed-loop feedback and continuously optimize product performance.

[0081] Embodiment 3 This embodiment also provides an electronic device, referring to Figure 3, comprises a memory 404 and a processor 402, wherein the memory 404 stores a computer program, and the processor 402 is configured to run the computer program to execute the steps in any of the above method embodiments.

[0082] Specifically, the processor 402 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiment of the present invention.

[0083] Among them, the memory 404 may include a large capacity memory 404 for data or instructions. For example, but not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In appropriate cases, the memory 404 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 404 may be inside or outside the data processing device. In a specific embodiment, the memory 404 is a non-volatile memory. In a specific embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. In appropriate circumstances, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM may be a fast page mode dynamic random access memory 404 (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0084] The memory 404 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402 .

[0085] The processor 402 reads and executes the computer program instructions stored in the memory 404 to implement any one of the pathology slice scanner transportation vibration adaptability testing methods in the above embodiments.

[0086] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408 , wherein the transmission device 406 is connected to the processor 402 , and the input / output device 408 is connected to the processor 402 .

[0087] The transmission device 406 can be used to receive or send data via a network. Specific examples of the above-mentioned network may include a wired or wireless network provided by a communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0088] Input / output devices 408 are used to input or output information.

[0089] Embodiment 4 This embodiment also provides a readable storage medium, in which a computer program is stored. The computer program includes a program code for controlling a process to execute the process. The process includes the pathology slice scanner transportation vibration adaptability test method according to the first embodiment.

[0090] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0091] In general, various embodiments may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects of the invention may be implemented in hardware, while other aspects may be implemented in firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flow charts, or using some other graphical representation, it should be understood that, as non-limiting examples, the boxes, devices, systems, techniques, or methods described herein may be implemented in hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0092] Embodiments of the present invention may be implemented by computer software that is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets and / or macros may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. A computer program product may include one or more computer executable components configured to perform an embodiment when the program is run. One or more computer executable components may be at least one software code or a portion thereof. In addition, at this point, it should be noted that, for example, Figure 1 Any block of the logic flow in the program may represent program steps, or interconnected logic circuits, blocks and functions, or a combination of program steps and logic circuits, blocks and functions. The software may be stored on physical media such as memory chips or storage blocks implemented within a processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs, etc. Physical media are non-transitory media.

[0093] Those skilled in the art should understand that the technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0094] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for testing the transport vibration adaptability of a pathology slide scanner, characterized in that: The following steps are involved: S00, composite vibration spectrum generation: Fix the device under test on the three-axis electromagnetic vibration table through an adaptive fixture to trigger the pressure feedback threshold; Load the preset transport vibration spectrum and simultaneously apply temperature changes to simulate the environment; Generate a composite vibration spectrum including random shock and preset temperature change gradient by a three-axis electromagnetic vibration table, wherein the three-axis electromagnetic vibration table supports non-orthogonal vibration superposition of X / Y / Z axes; S10, Multi-dimensional synchronous detection: Vibration data is collected through a mechanical detection unit, and imaging performance data is obtained through an optical detection unit using an image analysis algorithm; S20, Intelligent Assessment and Judgment: Based on the neural network model, multi-dimensional data including vibration data and imaging performance data are integrated to output fault assessment indicators; The test result is determined based on the pre-stored three-dimensional qualified thresholds of displacement, image sharpness loss, and temperature rise. If the test fails, the cause of the fault is located.

2. A pathology slide scanner transport vibration adaptability testing method as claimed in claim 1, characterized in that: In step S00, the composite vibration spectrum generation includes: Based on the standard vibration spectrum superimposed with random shock, the amplitude distribution and occurrence probability of the random shock are configurable; The synchronization of vibration and temperature is achieved through the vibration-temperature coupling control algorithm, and the synchronization error is less than the preset threshold.

3. A pathology slide scanner transport vibration adaptability testing method as claimed in claim 1, characterized in that: In step S10, the mechanical detection unit includes a laser displacement sensor and an acoustic emission sensor. The accuracy of the laser displacement sensor is in the micron level, and the acoustic emission sensor is configured to capture the characteristic frequency of screw loosening.

4. A pathology slide scanner transport vibration adaptability testing method as claimed in claim 1, characterized in that: In step S10, the image analysis algorithm includes the following steps: Preprocess low-contrast images to improve feature extraction stability; The scale-invariant feature transformation algorithm is used to calculate the feature point offset.

5. A pathology slide scanner transport vibration adaptability testing method as claimed in claim 1, characterized in that: In step S00, the preset transport vibration spectrum includes road, air or sea transport spectrum, the air transport spectrum simulates high frequency band vibration, and the sea transport spectrum simulates low frequency composite vibration.

6. A pathology slide scanner transport vibration adaptability testing method as claimed in claim 1, characterized in that: In step S20, the neural network model is a three-layer feedforward neural network, and the input parameters include vibration frequency, amplitude and image offset.

7. A method for testing the transport vibration adaptability of a pathology slide scanner as claimed in any one of claims 1 to 6, characterized in that: In step S30, the fault cause is located based on a fault characteristic frequency database, which includes frequency spectrum features of loose screws and motor wear.

8. A pathology slice scanner transport vibration adaptability testing device, characterized in that: include: (a) A three-axis electromagnetic vibration table, configured to simulate non-orthogonal vibration superposition of X / Y / Z axes, with a built-in vibration spectrum generation module for generating a composite vibration spectrum containing random shock and preset temperature change gradient; (b) Detection module, including: i. Mechanical detection unit, used to measure the vibration displacement of the equipment and capture the characteristic frequency of mechanical failure to collect vibration data; ii. an optical detection unit configured with a standard test slide and an image analysis algorithm for evaluating image quality degradation to obtain imaging performance data; (c) an evaluation system configured to fuse multi-dimensional data including vibration data and imaging performance data based on a neural network model to output a fault evaluation index; determine the test result based on pre-stored three-dimensional qualified thresholds of displacement, image sharpness loss, and temperature rise, and locate the cause of the fault if the test fails; (d) Vibration-temperature coupling control module, which dynamically and synchronously adjusts the vibration spectrum and temperature changes through a closed-loop control algorithm; (e) Adaptive clamp, configured as a pressure feedback device, compatible with equipment of different weights and enabling quick clamping.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the pathology slice scanner transportation vibration adaptability testing method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, wherein the computer program includes a program code for controlling a process to execute a process, wherein the process includes the pathology slice scanner transportation vibration adaptability testing method according to any one of claims 1 to 7.

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