Medical refrigerator box manufacturing detection system and method

Abstract

The present invention relates to the field of quality inspection technology, specifically to a medical refrigerator box manufacturing inspection system and method. The system includes a coating reflection detection module, an acoustic wave response measurement module, an optical signal fluctuation analysis module, a surface defect recognition module, and a structural feature state assessment module. In the present invention, by comparing the reflection amplitude gradient changes of adjacent scanning areas with the continuity of the spectral curve, the perception accuracy of the continuity of the surface reflection distribution is improved, and the recognition ability of weak anomalies is enhanced. Combined with the acoustic wave echo time of specific parts and structural stability analysis, internal potential defects are accurately captured. Based on the acoustic wave analysis results, a light intensity gradient model is constructed to identify the light intensity jump areas of splicing seams and recessed areas, and further extract abnormal forms such as scratches and bubbles and mark their coordinates. The entire process relies on dynamic feedback and multi-signal linkage calibration to improve recognition accuracy and coverage, and achieve full-domain and efficient detection of medical box quality.

Description

Technical Field

[0001] The present invention relates to the technical field of quality inspection, and in particular to a medical refrigerator box manufacturing inspection system and method. Background Art

[0002] The field of quality inspection technology encompasses a range of methods used to ensure that products or systems meet specified standards and requirements. The core of this technology involves measuring, evaluating, and verifying the performance, size, shape, quality, and other attributes of items or equipment. Quality inspection involves the application of various measuring tools and testing equipment, such as dimensional measuring instruments, surface inspection, and functional testing, to ensure that products in the production process meet quality standards. Quality inspection technology is not only widely used in the manufacturing industry, but also plays a vital role in many industries, including medical, aviation, automotive, and food, ensuring the reliability, safety, and effectiveness of each product or system.

[0003] The Medical Refrigerator Cabinet Manufacturing Inspection System is designed for quality inspection during the production process. It primarily addresses the challenges of inspecting dimensions, structural integrity, and other key quality indicators during the manufacturing process. By combining measurement technology and automation, the system accurately inspects various cabinet indicators and ensures compliance with medical standards. The system covers technical aspects such as cabinet dimensional measurement and material surface testing. Specific testing standards and equipment are used to ensure the cabinet's manufacturing quality meets safety and standardization requirements.

[0004] Existing technologies mainly rely on dimensional measuring instruments and surface imaging tools for parameter detection and structural evaluation. When faced with multi-structure, multi-material complexes such as medical refrigerator cabinets, it is difficult to fully reflect the stability and integrity of the internal structure. Conventional optical detection methods have insufficient recognition capabilities when faced with uneven spectral distribution or slight surface reflection differences, and are prone to image misjudgment or missed abnormalities, affecting the accuracy and credibility of the detection results. Acoustic wave testing methods are mostly fixed-point collection. The sound wave propagation path is significantly affected by changes in material density, and there is a lack of a linkage comparison mechanism with other detection signals. It is difficult to accurately judge the integrity of the internal structure, and potential cracks and looseness hazards in key parts are missed. Optical recognition is often limited to static image comparison and ignores abnormal behavior under dynamic changes in light intensity. In complex morphological structures such as joints and edge transition areas, the recognition ability is significantly reduced, affecting the detection performance of complex curved surfaces and detailed areas. For example, reflective dead corners are easily formed at corners. Traditional imaging technology is limited to single-angle shooting and cannot effectively extract complete image information, resulting in detection blind spots and the risk of missing defects. Technical limitations directly affect the precise control of key quality indicators of medical refrigerator cabinets, increasing safety hazards and performance degradation risks during subsequent use. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a medical refrigerator box manufacturing detection system and method.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] Medical refrigerator cabinet manufacturing and inspection system, including:

[0008] The coating reflection detection module extracts multi-band reflection spectrum data from the front door and side panels based on the external data of the medical refrigerator. It compares the reflection amplitude gradient differences and spectral curve changes of adjacent scanning areas to determine the continuity of the reflection distribution of the material surface in the scanning path and obtain the reflection continuous distribution characteristics.

[0009] The acoustic wave response measurement module collects the ultrasonic emission echo time values of the bottom corner reinforcement and the side wall embedded layer of the medical refrigerator body based on the reflection continuous distribution characteristics, compares the time interval difference of the emission points with the structural echo stability, identifies the number of abnormal points in the conduction process, and obtains the abnormal acoustic wave response distribution;

[0010] An optical signal fluctuation analysis module records the light intensity gradient changes in the optical scanning path between the joint seam and the recessed area on the top of the medical refrigerator based on the abnormal acoustic wave response distribution, selects jump points, and analyzes the distribution interval span to obtain a light intensity jump distribution segment;

[0011] The surface defect recognition module calls the light intensity jump distribution segment to identify the locations of scratches, bubbles, and detached areas on the panels and corners of the medical refrigerator body, determines the boundary morphological similarity of multiple defects in the scanned image, and marks the position coordinate group to obtain surface anomaly positioning data.

[0012] As a further solution of the present invention, the reflection continuous distribution characteristics include reflection consistency index, spectral change trend characteristics, and local reflection difference distribution; the acoustic wave abnormal response distribution includes emission echo time abnormal value, structural interference intensity level, and abnormal echo concentration area; the light intensity jump distribution segment includes light intensity mutation amplitude value, jump point spacing interval, and light intensity discontinuous section; the surface abnormality positioning data includes the center coordinates of the defect area, boundary contour morphological parameters, and position distribution density.

[0013] As a further solution of the present invention, the coating reflection detection module includes:

[0014] The reflectance spectrum acquisition submodule collects the multi-band reflectivity of each point along the scanning path of the front door panel and side panel based on the external data of the medical refrigerator body, records the band position and corresponding reflection value, and obtains a multi-band reflection value sequence;

[0015] The amplitude gradient analysis submodule calls the multi-band reflection value sequence, identifies the band reflection amplitude difference between adjacent scanning point positions, analyzes the positive and negative changes of the difference and the amplitude fluctuation, and obtains the reflection gradient change trend;

[0016] The curve continuity judgment submodule compares the smoothness and jump difference of the reflection values of consecutive points according to the reflection gradient change trend, and quantifies the difference between the reflection value and the gradient value and the cumulative amount of trend change using the formula:

[0017] ;

[0018] Calculate the reflection consistency value between continuous segments, make judgments based on the reflection fluctuation level, and obtain the reflection continuous distribution characteristics;

[0019] in, Representative The band reflectance value of each point, Representative The band reflectance value of each point, Representative The reflection gradient value corresponding to the point, Representative The reflection gradient value corresponding to the point, Indicates the reflection consistency value between consecutive segments, is the total number of points in the scan path.

[0020] As a further solution of the present invention, the acoustic wave response measurement module includes:

[0021] The acoustic wave time analysis submodule collects the ultrasonic emission and echo time values of the bottom corner reinforcement and the side wall embedded layer of the medical refrigerator body based on the continuous distribution characteristics of the reflection, records the propagation speed and path changes, and obtains the acoustic wave propagation time data;

[0022] The structural echo stability detection submodule compares the acoustic wave echo time value with the original stability data and analyzes the fluctuation of the echo time using the formula:

[0023] ;

[0024] Obtain echo stability index;

[0025] in, is the echo stability index, For the The echo time of the measurement, is the original average echo time, is the total number of measurements;

[0026] The outlier identification submodule compares the echo stability index with a preset threshold, identifies the number of outliers, locates the problem area in the sound wave propagation, and obtains the sound wave abnormal response distribution.

[0027] As a further solution of the present invention, the optical signal fluctuation analysis module includes:

[0028] The light intensity gradient acquisition submodule monitors and records the changes in light intensity based on the abnormal response distribution of the acoustic wave and the optical scanning path of the joint seam and the recessed area on the top of the medical refrigerator to obtain light intensity gradient change data;

[0029] A jumping point screening submodule screens the light intensity gradient change data, detects and marks jumping points, screens abnormal points that meet a set threshold, and obtains a jumping point data set;

[0030] The light intensity jump distribution analysis submodule analyzes the distribution interval span of the jump points based on the jump point data set, identifies the associated distribution segments, and uses the formula:

[0031] ;

[0032] Calculate the light intensity change value to obtain the light intensity jump distribution segment;

[0033] in, Represents the light intensity value of the jth sampling point, Representative The light intensity value at the sampling point, represents the average light intensity, Represents the total number of sampling points, is the light intensity change value.

[0034] As a further solution of the present invention, the surface defect recognition module includes:

[0035] The light intensity jump data application submodule calls the light intensity jump distribution segment, identifies the jump point data in each interval and determines the existing defect area. By comparing the similarity between the light intensity change and the scratch, bubble, and detachment area, it screens the potential defect area and obtains the defect area data set;

[0036] The defect morphology recognition submodule determines the boundary morphology similarity of defects in the region based on the defect region dataset, identifies the graphic features of scratches, bubbles, and detached regions, and compares them with the preset defect template using the formula:

[0037] ;

[0038] Calculate the defect boundary similarity and obtain defect morphological feature data;

[0039] in, Representative The defect boundary coordinate value of the sampling point, Representative The preset template boundary coordinate value of the sampling point, Representative The mean of the defect boundary coordinate values at the sampling points, Represents the total number of sampling points, is the defect boundary similarity;

[0040] The defect position marking submodule marks the position coordinates of the defect in the scanned image based on the defect morphological feature data and the defect type, identifies the defect position coordinate group, and obtains surface anomaly positioning data.

[0041] As a further solution of the present invention, the system further includes a structural feature status assessment module:

[0042] The structural feature status assessment module determines the association between structural connection point defects and acoustic wave interference based on the surface anomaly location data and the acoustic wave path changes and reflection differences between the connection between the cold storage compartment and the evaporator area of the medical refrigerator, identifies the number of offset path intervals, and obtains the refrigerator cabinet structural defect interference block;

[0043] The refrigerator body structural defect interference block includes an abnormal path section of the connection part, an abnormal reflection wave offset area, and a structural response imbalance area.

[0044] As a further solution of the present invention, the structural feature state assessment module includes:

[0045] The anomaly positioning submodule extracts the coordinates of multiple points in the cold storage connection based on the surface anomaly positioning data, selects regional point groups with a change rate exceeding the anomaly detection threshold, and counts the number of anomaly points per unit area and their continuous distribution trend to obtain an anomaly density interval value;

[0046] The acoustic path determination submodule calls the abnormal density interval value, compares the acoustic wave reflection data from the connection part to the evaporator area, analyzes the reflection angle deviation rate, and screens the path segments exceeding the interference threshold. The reflection path deviation coefficient is obtained by combining the abnormal area difference and the reflection attenuation.

[0047] The path interval identification submodule calls the reflection path deviation coefficient, identifies the segments where the deviation amplitude exceeds the stability threshold based on the path segment length and propagation speed, extracts the number of deviation segments between the starting and ending nodes, and obtains the path deviation interval amount;

[0048] The interference block extraction submodule calls the path offset interval, compares the number of offset segments with the overlap of abnormal dense areas, and screens the spatial range where the overlap exceeds the structural overlap threshold to obtain the interference block of the refrigerator body structure defect.

[0049] The present invention also provides a medical refrigerator cabinet manufacturing and testing method, which is based on the above-mentioned medical refrigerator cabinet manufacturing and testing system and includes the following steps:

[0050] S1: Based on the external data of the medical refrigerator, the reflectance spectrum data of the front door panel and side panel area are extracted. The reflection amplitude gradient differences of adjacent scanning areas are analyzed, the continuity of the band changes is judged, the change trends of adjacent areas are compared, the curve jump values are identified and the reflection gradient deviation range is determined. The discontinuous change segments are screened and a set of reflection distribution continuity indicators is established.

[0051] S2: Based on the reflection distribution continuity index set, collect the ultrasonic emission echo time values of the bottom corner reinforcement position and the side wall embedded layer, compare the echo time interval difference and amplitude stability value, identify the abnormal point position, and obtain the abnormal acoustic wave response distribution area;

[0052] S3: Based on the abnormal acoustic wave response distribution area, record the light intensity gradient change value in the optical scanning path of the top joint seam and the recessed area, determine the position and span of the light intensity jump point, and establish a light intensity jump distribution segment sequence;

[0053] S4: Based on the light intensity jump distribution segment sequence, extract the boundary morphological feature values in the panel and corner area image scanning data, identify the similarity of the defect boundary, mark the spatial coordinates, and obtain the surface anomaly positioning data set;

[0054] S5: Based on the surface anomaly positioning data set, extract the acoustic wave path data of the cold storage connection part and the evaporator area, match the defect coordinates with the path change position, count the number of path offset segments, and obtain the refrigerator body structure defect interference block.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] In the present invention, by comparing the reflection amplitude gradient changes and spectral curve continuity of adjacent scanning areas, the dynamic perception capability of the continuity of the reflection distribution of the material surface is enhanced, and the recognition accuracy of weak surface anomalies is significantly improved. Based on the reflection continuity results, the ultrasonic emission echo time of specific parts of the structure is collected, and the time interval difference between the emission points is compared with the stability of the structural echo to form an accurate discrimination mechanism for the propagation consistency in the sound wave path, effectively capturing potential defect areas inside the structure. With the support of the acoustic wave response analysis results, an optical scanning light intensity gradient model of the top joint seam and the recessed area is constructed, and with the help of the jump point and distribution interval span extraction strategy, the rapid positioning of the area with drastic changes in surface light intensity is achieved. Based on the jump light intensity segment, the morphological analysis and position marking of surface abnormal features such as scratches, bubbles, and shedding are further carried out, and the coordinate output of the defect area is achieved by combining the boundary similarity comparison algorithm. In this testing process, each step is based on the data from the previous stage, forming a dynamic feedback and linkage calibration mechanism, which enhances detection accuracy and the ability to identify local anomalies. It breaks through the limitations of traditional reliance on a single signal source for judgment, and strengthens the comprehensiveness and stability of complex structural defect identification, thereby ensuring full coverage of quality indicators in all areas of the box and establishing a highly repeatable and automated data link for box production quality control under medical standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a system flow chart of the present invention;

[0058] Figure 2 This is a flow chart of the coating reflection detection module in the present invention;

[0059] Figure 3 This is a flow chart of the acoustic wave response measurement module in the present invention;

[0060] Figure 4 This is a flow chart of the optical signal fluctuation analysis module in the present invention;

[0061] Figure 5 This is a flow chart of the surface defect recognition module in the present invention;

[0062] Figure 6 This is a flow chart of the structural feature state evaluation module in the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] See also Figure 1 The present invention provides a technical solution: a medical refrigerator box manufacturing and detection system includes:

[0065] The coating reflection detection module extracts multi-band reflection spectrum data from the front door and side panels based on the external data of the medical refrigerator. It compares the reflection amplitude gradient differences and spectral curve changes of adjacent scanning areas to determine the continuity of the reflection distribution of the material surface in the scanning path and obtain the reflection continuous distribution characteristics.

[0066] The spectral data of the front door and side panels of a medical refrigerator are analyzed. The reflectance spectrum of the area is collected. A spectrum analyzer is used to scan light at multiple wavelengths and record the reflectance at different wavelengths. Assuming the scan is performed in the infrared band, the collected wavelengths are 700nm to 1000nm, and the reflectance data is between 0.5 and 0.75. The reflection amplitude gradient of adjacent scan areas is then calculated. This requires differentiating the reflectance of two consecutive scan points to obtain a gradient difference value. For example, if the reflectances of adjacent scan points are 0.7 and 0.72, the gradient difference is 0.02. The gradient differences are then compared to determine whether there is a significant reflectance change. The criterion for a significant change is a gradient difference value exceeding 0.05. Finally, the coherence of the spectral curve is analyzed to check whether areas with smaller gradient differences show continuous reflectance changes. Continuity is determined by setting a threshold for the gradient difference value. For example, if the threshold is set to 0.03, if the gradient difference value is less than this threshold, the reflection distribution is considered continuous, thus obtaining the continuity characteristic of the reflection distribution of the material surface along the scanning path.

[0067] The acoustic wave response measurement module, based on the continuous distribution characteristics of reflections, collects the ultrasonic emission echo time values of the bottom corner reinforcement and the side wall embedded layer of the medical refrigerator body, compares the time interval difference between the emission points with the structural echo stability, identifies the number of abnormal points in the conduction process, and obtains the abnormal acoustic wave response distribution;

[0068] An acoustic wave transmitter is installed at the bottom corner reinforcement and the side wall embedded layer of the medical refrigerator, and ultrasonic waves are emitted. The echo time is recorded using an acoustic wave detector. Assuming that the frequency of the emitted acoustic wave is 40 kHz and the emission time interval is 100 milliseconds, the echo time recorded by the acoustic wave detector is 20 to 30 milliseconds. Then, the time interval difference between different emission points is calculated. This time interval difference is obtained by comparing the echo time difference of two consecutive acoustic wave emissions. Assuming that the two consecutive echo times are 25 milliseconds and 27 milliseconds respectively, the time interval difference is 2 milliseconds. Then, the time interval difference is compared, and a threshold is set to determine whether there is an abnormality. For example, if the time interval difference exceeds 5 milliseconds, it is marked as an abnormal point. By counting the number of all marked abnormal points, the number of abnormal points in the conduction process is identified, and the distribution of abnormal acoustic wave responses is obtained.

[0069] The optical signal fluctuation analysis module records the light intensity gradient changes in the optical scanning path of the joint seam and the recessed area on the top of the medical refrigerator based on the abnormal response distribution of the acoustic wave, selects the jump points and analyzes the distribution interval span to obtain the light intensity jump distribution segment;

[0070] An optical scanner is set to scan along the joints and recessed areas on the top of the medical refrigerator, and the light intensity value of each scanning point is recorded. It is assumed that the light intensity value range during the scanning process is 200 to 300 light units. Then, the gradient change of the light intensity is calculated. The light intensity gradient is obtained by differentiating the light intensity of consecutive scanning points. For example, the light intensity values of consecutive scanning points are 250 and 260, and the light intensity gradient is 10 light units. Then, those points where the light intensity gradient suddenly jumps are screened, and the jump threshold is set to 15 light units. Only when the light intensity gradient exceeds the threshold is it marked as a jump point. Finally, the distribution interval span of these jump points is analyzed. For example, if the span of consecutive jump points exceeds 5 scanning points, the recorded segment is a light intensity jump distribution segment, and the light intensity jump distribution segment is obtained.

[0071] The surface defect recognition module uses the light intensity jump distribution segment to identify the locations of scratches, bubbles, and detached areas on the panels and corners of the medical refrigerator body. It determines the boundary morphology similarity of multiple defects in the scanned image, annotates the position coordinate groups, and obtains surface anomaly positioning data.

[0072] The data of the light intensity jump distribution segment is analyzed by image processing software to find the area with significant changes compared with the normal light intensity distribution. It is assumed that the light intensity jump area obtained by analysis is in the upper right corner of the medical refrigerator body, with the specific coordinates of (200, 150). Then, a detailed image analysis is performed on the area. By comparing the light intensity values of the surrounding areas, the specific locations of scratches, bubbles or peeling are identified. It is assumed that the bubble position is at (205, 155) and the scratch is at (210, 160). Then, the boundary morphological similarity of these defects is judged. By calculating the boundary shape parameters of different defect areas, such as circularity, boundary roughness, etc., and comparing them with the known defect morphological parameters, their types are determined. Finally, the position coordinate group of the defects is marked, and the data is marked on the structural diagram of the medical refrigerator to obtain surface abnormality positioning data.

[0073] The structural feature status assessment module uses surface anomaly location data, combined with the acoustic path changes and reflection differences between the connection between the medical refrigerator's cold storage compartment and the evaporator area, to determine the correlation between structural connection point defects and acoustic interference, identify the number of offset path intervals, and obtain the refrigerator cabinet structural defect interference block;

[0074] The acoustic wave paths of the cold storage connection and the evaporator area are measured. An acoustic wave detector is used to emit acoustic waves in these areas, and the propagation time of the acoustic waves is recorded. It is assumed that the propagation time of the acoustic wave in the cold storage connection is 15 milliseconds, and the propagation time in the evaporator area is 18 milliseconds. Then the time difference of the acoustic wave paths in the two areas is calculated, and the difference is 3 milliseconds. Then the time difference is evaluated, and the time difference threshold is set to 5 milliseconds. If it is less than the threshold, it is judged that the structure is well connected. If it is greater than the threshold, it is marked as a possible defect point. Then the difference in acoustic wave reflection is analyzed. By comparing the acoustic wave reflection data of different areas, such as reflection intensity, reflection time, etc., it is assumed that the reflection intensity of the evaporator area is 0.8, and the reflection intensity of the cold storage connection is 0.75, with a difference of 0.05. The defects of the structural connection points are analyzed and identified, and the interference block of the refrigerator body structure defect is obtained.

[0075] The continuous distribution characteristics of reflection include reflection consistency index, spectral change trend characteristics, and local reflection difference distribution. The abnormal response distribution of acoustic waves includes abnormal values of emission echo time, structural interference intensity level, and abnormal echo concentration area. The light intensity jump distribution segment includes light intensity mutation amplitude value, jump point spacing interval, and light intensity discontinuity section. The surface anomaly positioning data includes the center coordinates of the defect area, boundary contour morphological parameters, and position distribution density. The refrigerator body structure defect interference block includes abnormal path segment of the connection part, abnormal reflection wave offset area, and structural response imbalance area.

[0076] See also Figure 2 , coating reflection detection module includes:

[0077] The reflectance spectrum acquisition submodule collects the multi-band reflectivity of each point along the scanning path of the front door panel and side panel based on the external data of the medical refrigerator body, records the band position and corresponding reflection value, and obtains a multi-band reflection value sequence;

[0078] Collect reflectance information from the front door and side panels in multiple wavelength bands to construct a reflectance spectrum sequence for each region at consecutive points along the scanning path. To implement this, first establish a point numbering system for each part of the vehicle to identify the spatial relationship of each measurement location. For example, the front door is divided into P1 to P10, and the side panels are divided into S1 to S10. Then, use a spectrometer to measure the reflectance of each point at multiple wavelengths, such as 400nm, 500nm, and 600nm. For example, at position P3, the reflectance is recorded as 0.42, 0.37, and 0.34, while at position S5, the reflectance is recorded as 0.39, 0.36, and 0.32, respectively. During the acquisition process, ensure that each point is scanned at a uniform spacing and illuminated with the same light source angle to ensure comparability between data. The collected data must be uniformly converted into a reflectance value curve with the wavelength as the horizontal axis and the reflectance as the vertical axis. The data is then stored in a structured table with index numbers, ultimately forming a multi-band reflectance value sequence arranged in path order.

[0079] The amplitude gradient analysis submodule calls the multi-band reflection value sequence, identifies the band reflection amplitude difference between adjacent scanning point positions, analyzes the positive and negative changes of the difference and the amplitude fluctuation, and obtains the reflection gradient change trend;

[0080] The reflectivity changes between adjacent scanning points in each band are calculated by difference. For example, if the reflectivity of point P3 is 0.44 at 500nm and that of P4 in the same band is 0.48, then the difference in their reflection amplitudes is 0.04. A reflection difference sequence can be formed by performing similar calculations on all pairs of points in the entire sequence. The continuous differences in each band are processed by absolute value to obtain their gradient intensity and compare them with the standard gradient range. If the absolute value of the gradient difference is between 0.02 and 0.05, it is considered a slow-changing interval, and above 0.05, it is a sudden-changing interval. Then, the distribution ratio of different types of areas in the overall reflection path is counted and normalized to form a reflection gradient curve in each band. The stability of the reflection difference sequence of the measuring points must also be considered during the calculation process. If the differences of three consecutive points are in the same direction and the amplitude changes are all less than 0.01, they are defined as a reflection stable segment. The final output is summarized and organized to form the reflection gradient change trend.

[0081] The curve continuity judgment submodule compares the smoothness and jump difference of the reflection values of consecutive points according to the reflection gradient change trend, and quantifies the difference between the reflection value and the gradient value and the cumulative amount of trend change using the formula:

[0082] ;

[0083] Calculate the reflection consistency value between continuous segments, make judgments based on the reflection fluctuation level, and obtain the reflection continuous distribution characteristics;

[0084] in, Representative The band reflectance value of each point, Representative The band reflectance value of each point, Representative The reflection gradient value corresponding to the point, Representative The reflection gradient value corresponding to the point, Indicates the reflection consistency value between consecutive segments, is the total number of points in the scanning path;

[0085] Quantitatively judge the smoothness and jump status of the curve of the reflection value of the continuous points in each scanning segment. In the specific implementation process, first call the reflectivity data of 10 consecutive scanning points (recorded as P1 to P10) measured at a wavelength of 500nm , whose values are directly collected by the on-site spectrometer and recorded as: , the reflectivity itself is a dimensionless quantity and does not require additional processing;

[0086] Call the reflection gradient value calculated from the reflection gradient change trend in the previous step , this gradient value is the dimensionless value obtained by dividing the reflectivity difference of adjacent points by the distance between adjacent points, and is also directly recorded as: ;

[0087] Perform difference calculation and gradient combination calculation on each pair of consecutive points according to the scanning path. For example, from point P1 to P2, the reflectivity difference is , corresponding to the gradient value Substituting the gradient combination operation into , the product of the two items is the local difference product 0.02×0.1281=0.00256;

[0088] The difference product between points P2 and P3 is calculated to be 0.00224. The difference product between points P9 and P10 is calculated to be 0.00188. All local difference products are added one by one to obtain the sum of the numerator terms:

[0089] ;

[0090] Sum the reflectivity of all points to obtain the denominator of the first part:

[0091] ;

[0092] Then, the absolute value of the gradient difference is accumulated to obtain the denominator of the second part:

[0093] ;

[0094] Combine the two denominator terms and add them together to get the total denominator: ;

[0095] Substitute the numerator and denominator values calculated above into the formula to obtain the final consistency value The calculation formula is as follows:

[0096] ;

[0097] The consistency value The results show that the reflection change trend of the coating on the medical refrigerator body under the current scanning path is relatively continuous at a wavelength of 500nm, and the reflection curve jump is weak. The obtained value can directly correspond to the continuous distribution characteristics of the reflection of the coating surface in a specific band.

[0098] Indicates the The reflectance data collected at each scanning point (such as P1, P2...P10) at a specified wavelength (500nm in this case) is dimensionless and is directly collected by the spectrometer;

[0099] Indicates the The reflection gradient value corresponding to each point is obtained by dividing the reflectivity difference of adjacent scanning points by the spatial spacing. The value is dimensionless and is calculated as follows: , where the spatial spacing d is a fixed value, uniformly taken as 1;

[0100] Indicates that the above difference product calculation and gradient difference accumulation are performed one by one for each pair of adjacent points on the continuous scanning path, where is the total number of scan points;

[0101] is the reflection consistency value between continuous segments. The closer its value is to 0, the more stable the reflection change on the scanning path is and the better the continuity is.

[0102] By combining the dual data features of reflectivity difference and gradient change to jointly judge the continuity of the coating surface spectral curve, the characterization is made more comprehensive and precise. Through normalization processing, a unified numerical continuity evaluation is achieved, which can effectively reflect the uniformity of material distribution on the coating surface of medical refrigerators during the manufacturing process.

[0103] See also Figure 3 , the acoustic wave response measurement module includes:

[0104] The acoustic wave time analysis submodule collects the ultrasonic emission and echo time values of the bottom corner reinforcement and side wall embedded layer of the medical refrigerator body based on the continuous distribution characteristics of reflection, records the propagation speed and path changes, and obtains the acoustic wave propagation time data;

[0105] The ultrasonic emission and echo times of the medical refrigerator's bottom corner reinforcements and sidewall embedded layers are precisely collected. High-precision sensors are used to collect data at multiple points across key locations within the refrigerator, ensuring representative data at each measurement point. For example, sensors are placed at two sampling points, one on the bottom and one on the sidewall, to record the emission and echo times of the sound wave. Each set of data collected includes the start time of the sound wave emission and the return time of the echo. By measuring these time values, the delay of the sound wave propagation within the cabinet can be further analyzed. The sensors record the echo time with millisecond accuracy, ensuring the accuracy of time differences. For example, in a real-world scenario, the emission time at acquisition point A is 2.5 milliseconds and the echo time is 3.0 milliseconds, while the emission time at acquisition point B is 2.6 milliseconds and the echo time is 3.2 milliseconds. This method allows the emission and echo time values of each sampling point to be determined. This data can then be used in subsequent analysis to compare the response characteristics of different areas and obtain sound wave propagation time data for each sampling point.

[0106] The structural echo stability detection submodule compares the acoustic echo time value with the original stability data and analyzes the fluctuation of the echo time using the formula:

[0107] ;

[0108] Obtain echo stability index;

[0109] in, is the echo stability index, For the The echo time of the measurement, is the original average echo time, is the total number of measurements;

[0110] Each echo time is compared with the average echo time of the original data to analyze its fluctuation. The key to the process is to ensure the accuracy and consistency of the echo time data used. In actual implementation, the echo time of each sampling point is measured and a standard value is obtained based on the original data (for example, the average echo time of the original stability data). The echo time can be obtained by measuring the timing data of the reflected echo and compared with the original standard to calculate the fluctuation value. Assuming that the current echo time is measured to be 3.0 milliseconds, 3.2 milliseconds, and 3.3 milliseconds, the original echo time average is 3.1 milliseconds.

[0111] in, is the current echo time value, is the original echo time mean, is the number of sampling times. Specifically, assuming there are 3 sampling points, the echo time values are millisecond, millisecond, milliseconds, while the original mean is milliseconds, then volatility The calculation process is as follows:

[0112] Take the absolute value of the difference between each echo time and the original mean:

[0113] ;

[0114] ;

[0115] ;

[0116] Compute the mean of the differences: ;

[0117] Get echo stability index milliseconds, indicating the current fluctuation of the echo signal, calculated The unit is milliseconds (ms), which indicates the average deviation between the current echo time and the original mean. The volatility value is a measure of the echo stability. The larger the value, the more abnormal the echo signal in this area is. If the fluctuation exceeds the set threshold (for example, the threshold is set to 0.15 milliseconds) during the measurement process, the echo signal is considered abnormal.

[0118] The outlier identification submodule compares the echo stability index with the preset threshold, identifies the number of outliers, locates the problem area in the sound wave propagation, and obtains the distribution of abnormal sound wave responses;

[0119] The echo stability index is compared with the preset threshold to identify abnormal points. In actual operation, an echo stability threshold is set, for example 0.15 milliseconds, which means that when the volatility is greater than the threshold, the echo signal is abnormal, which means that there is a potential problem with the structure of the cabinet. When the volatility value of the echo time is 0.133 milliseconds, it is lower than the threshold, indicating that the current echo signal is stable. If the volatility reaches 0.16 milliseconds, it means that the echo signal has abnormal fluctuations. The abnormal points are recorded and their positions are marked. In this way, structural damage to the refrigerator cabinet during use can be automatically identified, which helps maintenance personnel quickly locate the problem area. The distribution of abnormal points will generate an abnormal sound wave response distribution, which is convenient for subsequent maintenance and repair work.

[0120] See also Figure 4 , the optical signal fluctuation analysis module includes:

[0121] The light intensity gradient acquisition submodule monitors and records changes in light intensity based on the abnormal response distribution of acoustic waves and the optical scanning path of the joint seam and recessed area on the top of the medical refrigerator to obtain light intensity gradient change data;

[0122] Light intensity data is acquired through an optical sensor based on the optical scanning path of the joints and recessed areas on the top of the medical refrigerator. The optical sensor's scanning accuracy should reach 0.01 Lux. The light intensity data is collected every 1 second, and 100 data points are collected at each location to ensure data stability and accuracy. For example, a sensor installed on the top of the refrigerator will gradually acquire light intensity values at each location according to the established scanning path. The sensor's acquisition frequency can record changes in light intensity second by second. The recorded light intensity data will reflect the light intensity fluctuations between different locations, thereby obtaining more accurate light intensity gradient information. For example, at a certain location, the recorded light intensity values of 5 Lux and 4.8 Lux indicate a slight fluctuation in the light intensity gradient. The change process is recorded until the light intensity change stabilizes. The collected data will be used to calculate the light intensity gradient change in subsequent processing and ultimately generate the corresponding light intensity gradient change data.

[0123] The jump point screening submodule screens the light intensity gradient change data, detects and marks the jump points, screens the abnormal points that meet the set threshold, and obtains the jump point data set;

[0124] The collected light intensity gradient change data are screened. The screening process adopts the threshold setting method, and the fluctuation points that exceed the set threshold are marked as jump points. When setting the threshold, it is experimentally concluded that the threshold is 0.2Lux / second. When the light intensity change rate exceeds this value, it is considered that a jump has occurred. In actual operation, if the light intensity change rate at a certain position is 0.3Lux / second, and this value is higher than the set threshold of 0.2Lux / second, this point will be marked as a jump point. During the screening process, the data needs to be judged for continuity to ensure that there is no single abnormal fluctuation point that is misjudged. By setting the threshold screening method, noise fluctuations caused by external factors or equipment failures can be eliminated, and the jump point data set can be accurately obtained. The data provides a basis for subsequent light intensity jump distribution analysis.

[0125] The light intensity jump distribution analysis submodule analyzes the distribution interval span of the jump point based on the jump point data set and identifies the associated distribution segment using the formula:

[0126] ;

[0127] Calculate the light intensity change value to obtain the light intensity jump distribution segment;

[0128] in, Represents the light intensity value of the jth sampling point, Representative The light intensity value at the sampling point, represents the average light intensity, Represents the total number of sampling points, is the light intensity change value;

[0129] By screening and processing the jump point data, the jump point intervals are calculated and divided according to the size and distribution of the light intensity jump points. The difference between the previous and next values of the light intensity jump point (i.e., the rate of change of light intensity) is used as the standard for judging whether the interval belongs to the "large span" interval. By comparing the light intensity difference, a threshold of 0.4Lux is set as the benchmark value for the interval span. When the light intensity change value is greater than this threshold, it is considered that there is a large jump in the interval. Based on this, the jump point data of the light intensity change will be divided into multiple intervals. For example, assuming that the jump point data collected in a certain area are 5Lux and 4.2Lux, the difference is calculated and the light intensity jump difference is 0.8Lux. Since this difference is greater than the set threshold of 0.4Lux, the span of this interval is identified as a "large span jump segment". After multiple calculations, the intervals of all jump point distributions are finally divided;

[0130] The light intensity value of each sampling point is obtained through the aforementioned light intensity gradient change data, and the data is summed. Assuming that the light intensity value data of 5 sampling points is [5.0, 4.5, 4.8, 5.2, 5.1] Lux, the light intensity difference of the calculated value can be obtained according to the terms in the formula:

[0131] Calculate the light intensity difference:

[0132] ;

[0133] Calculate the mean light intensity:

[0134] ;

[0135] Calculate the squared difference in light intensity:

[0136] ;

[0137] Calculate the final light intensity change value: ;

[0138] The obtained light intensity change value is 0.1803 Lux, which represents the amplitude of the light intensity change in the interval. Since this change value is less than the set threshold of 0.4 Lux, the interval will not be identified as a large span jump segment. However, if the light intensity change value of a certain interval exceeds 0.4 Lux (for example, the difference in a certain interval is 0.6 Lux), the interval will be classified as a large jump segment. This can more accurately analyze the light intensity fluctuations of medical refrigerators, thereby effectively monitoring the performance changes of refrigerators in actual scenarios.

[0139] See also Figure 5, the surface defect recognition module includes:

[0140] The light intensity jump data application submodule calls the light intensity jump distribution segment, identifies the jump point data in each interval and determines the existing defect areas. By comparing the similarity between the light intensity change and the scratches, bubbles, and detachment areas, it screens the potential defect areas and obtains the defect area data set.

[0141] The light intensity jump distribution segments obtained by the optical scanning device reflect the light intensity changes in different areas. By matching the light intensity data with various parts of the refrigerator body and combining the physical structure information of the refrigerator body, the candidate positions of the surface defect areas are determined. If the light intensity fluctuates greatly and the local light intensity changes have obvious "jumps" in the scanning path, then the position is considered to be a potential defect position. For example, when the light intensity value at one position changes more dramatically and the light intensity fluctuations in the adjacent area are small, this indicates that the area has scratches or falls off. By using the light intensity data to identify the jump segments, the potential defect area can be preliminarily located, and then the defect morphology identification and positioning stage is entered to obtain a defect area data set.

[0142] The defect morphology recognition submodule determines the boundary morphology similarity of defects in the region based on the defect region dataset, identifies the graphic features of scratches, bubbles, and detached areas, and compares them with the preset defect template using the formula:

[0143] ;

[0144] Calculate the defect boundary similarity and obtain defect morphological feature data;

[0145] in, Representative The defect boundary coordinate value of the sampling point, Representative The preset template boundary coordinate value of the sampling point, Representative The mean of the defect boundary coordinate values at the sampling points, Represents the total number of sampling points, is the defect boundary similarity;

[0146] The boundary morphology of the candidate defect area is identified. The boundary features of each defect area are extracted through image processing technology to obtain the boundary coordinate points. The boundary coordinate points are then matched with the preset defect template (such as scratches, bubbles, and detached areas). By matching the boundary morphological features, it is determined whether the candidate area meets a certain type of defect. To achieve this process, the coordinate values of each area are normalized to ensure that the scale of all defect area data is consistent, which is convenient for subsequent morphological matching calculations. For example, assuming that the boundary coordinates of a region are (5, 10, 15, 20) and the boundary coordinates of the preset template are (5, 12, 14, 18), the coordinate values are normalized to convert them into a unified scale value to facilitate morphological matching. When performing morphological matching, the defect boundary similarity is calculated;

[0147] Assume that the boundary coordinates of a region are (5, 10, 15, 20) and the boundary coordinates of the preset template are (5, 12, 14, 18). To eliminate the scale difference, the coordinates are normalized using the following formula:

[0148] ;

[0149] For example, for (5, 10, 15, 20), take the minimum value , maximum value , then the normalized coordinate value is:

[0150] ;

[0151] Similarly, the preset template coordinates (5, 12, 14, 18) are normalized to:

[0152] ;

[0153] Calculate similarity: Use the normalized coordinate values above to calculate the defect morphology similarity using the formula:

[0154] ;

[0155] Determine defect type: Based on the calculated similarity Value, if If the value is greater than the set threshold (e.g. 0.7), the area is judged to be consistent with a certain defect type (e.g. scratch), otherwise it is judged as a type or no defect. Through this calculation, the defect boundary similarity is obtained. =0.697. Since this similarity is close to the set threshold (for example, 0.7), the area is a scratch. This process provides a basis for further defect location. Next, we will enter the stage of defect location annotation, which can accurately identify the morphology and classify the type of the defect area, thereby ensuring that each defect is correctly handled in subsequent processing.

[0156] The defect location marking submodule marks the position coordinates of the defect in the scanned image based on the defect morphological feature data and the defect type, identifies the defect position coordinate group, and obtains the surface anomaly location data;

[0157] Determine the type of each identified defect, then accurately calculate the position coordinates of each defect in the image and mark the corresponding coordinate points. For example, when a defect is determined to be a scratch, the defect position in the scanned image will be marked as a straight line, and the bubble will be marked as a circular area. By recording the center position and boundary points of each defect, the specific coordinates of each defect are calibrated, and basic data is provided for subsequent defect repair work. The coordinate data is finally summarized into surface anomaly positioning data, and the data is output to the visualization interface or subsequent processing module for later maintenance and quality inspection to ensure accurate repair and monitoring of defective parts in actual production.

[0158] See also Figure 6 , the structural characteristic status assessment module includes:

[0159] The anomaly location submodule extracts the coordinates of multiple points in the cold storage connection based on the surface anomaly location data, selects regional point groups whose change rates exceed the anomaly detection threshold, and counts the number of anomaly points per unit area and their continuous distribution trend to obtain the anomaly density interval value.

[0160] First, a detailed surface scan of the cold compartment connection of a medical refrigerator is performed to identify abnormal point groups whose coordinate change rate exceeds a preset threshold. Abnormal point groups indicate potential structural defects or wear issues. In practical applications, such as refrigerators used in hospitals or research institutes, the connection parts are prone to rapid wear due to frequent door opening and closing. By analyzing the specific location and distribution of regional point groups, the number of abnormal points per unit area and the continuous distribution trend of the points can be calculated. This involves statistical analysis of the spatial distribution of points and comparing the magnitude and frequency of changes in each point group through set operations. The magnitude of change is calculated by averaging the coordinate change amplitude of the abnormal points in each point group and then comparing it with the structural abnormality baseline value. This can determine which point groups have abnormalities outside the normal range and obtain an abnormality density interval value. This value is obtained through calculation. For example, if the structural abnormality baseline value is set to a change amplitude of 0.5 cm, and the average change amplitude of a point group is 0.7 cm, the abnormality density interval value of this point group will be marked as a high abnormality interval, which helps subsequent maintenance and repair personnel quickly locate and resolve the problem, thereby maintaining the safe and effective operation of the refrigerator.

[0161] The acoustic path judgment submodule calls the abnormal density interval value, compares the acoustic wave reflection data from the connection to the evaporator area, analyzes the reflection angle deviation rate, and filters the path segments that exceed the interference threshold. It combines the abnormal area difference with the reflection attenuation to obtain the reflection path deviation coefficient;

[0162] Using the anomaly density interval value helps understand which areas affect sound wave propagation. The sound wave propagation path is critical in practical applications, especially in medical devices, where the integrity of sound wave propagation directly affects the device's diagnostic accuracy. By analyzing the sound wave reflection data from the connection to the evaporator, the incident and reflection angles of the sound wave can be calculated. If the reflection angle deviates significantly, the reflection angle deviation rate exceeds the set sound wave reflection interference threshold. This involves a numerical calculation comparing the measured sound wave angle with the theoretical angle. This calculation helps identify structural problems or sound wave propagation obstacles. For example, if the theoretical reflection angle is 30 degrees and the measured reflection angle is 35 degrees, the reflection angle deviation rate can be calculated as (35-30) / 30=16.7%. If the interference threshold is set to 10%, the sound wave path in this area will be marked as abnormal. Combined with the reflection intensity attenuation, the reflection path deviation coefficient can be calculated. This coefficient provides a quantitative basis for subsequent adjustments and optimizations, ensuring that the medical device can operate under the correct sound wave path and ensuring the accuracy of diagnosis and research.

[0163] The path interval identification submodule calls the reflection path deviation coefficient and identifies the segments where the deviation amplitude exceeds the stability threshold based on the path segment length and propagation speed. It then extracts the number of deviation segments between the start and end nodes to obtain the path deviation interval.

[0164] The length of the acoustic wave path segments and the acoustic wave propagation velocity in the evaporator area will be analyzed, which is very critical in practice. For example, in the daily maintenance of medical refrigerators, ensuring the correctness of the acoustic wave path can avoid incorrect temperature readings and drug preservation problems. By identifying and classifying all acoustic wave path segments, especially those path segments with offset amplitudes exceeding the stability change threshold, the equipment can be accurately adjusted to ensure the correct propagation of the sound waves. By calculating the offset coefficient of the path segment and comparing it with the stability change threshold, the path interval that requires special attention can be identified. For example, if the acoustic wave propagation velocity varies within the range of ±5% of the theoretical speed, and the offset coefficient of a certain path segment shows a deviation of 10%, this segment will be marked and a path offset interval value will be generated, which helps technicians quickly locate the problem and make necessary adjustments.

[0165] The interference block extraction submodule calls the path offset interval, compares the number of offset segments with the overlap of abnormally dense areas, and selects the spatial range where the overlap exceeds the structural overlap threshold to obtain the interference blocks of the refrigerator body structure defects;

[0166] Using the path offset interval as a reference, by comparing the number of offset segments in detail with the overlap of areas with dense anomaly points, such as in environments such as hospitals, it is ensured that every part of the refrigerator is not subject to undetected structural interference. This is especially true when storing sensitive materials such as vaccines and other important medicines. By screening areas where the overlap value exceeds the interference structure overlap threshold, blocks in the refrigerator that require further inspection can be marked. For example, if the calculated overlap value of a block is 30%, and the interference overlap threshold is set to 25%, this block will be considered a structural defect interference block. The information is aggregated and marked, and finally the refrigerator body structural defect interference block is obtained. This helps the maintenance team to carry out targeted repairs or replacements to ensure the normal operation of the refrigerator and the stability of the internal storage environment.

[0167] The medical refrigerator cabinet manufacturing and testing method is based on the above-mentioned medical refrigerator cabinet manufacturing and testing system and includes the following steps:

[0168] S1: Based on the external data of the medical refrigerator, the reflectance spectrum data of the front door panel and side panel area are extracted. The reflection amplitude gradient differences of adjacent scanning areas are analyzed, the continuity of the band changes is judged, the change trends of adjacent areas are compared, the curve jump values are identified and the reflection gradient deviation range is determined. The discontinuous change segments are screened and a set of reflection distribution continuity indicators is established.

[0169] S2: Based on the reflection distribution continuity index set, the ultrasonic echo time values of the bottom corner reinforcement position and the side wall embedded layer are collected, and the echo time interval difference and amplitude stability value are compared to identify the abnormal point location and obtain the abnormal acoustic response distribution area;

[0170] S3: Based on the distribution area of abnormal acoustic wave response, record the light intensity gradient change value in the optical scanning path of the top joint seam and the concave area, determine the position and span of the light intensity jump point, and establish a light intensity jump distribution segment sequence;

[0171] S4: Based on the light intensity jump distribution segment sequence, the boundary morphological feature values in the panel and corner area image scanning data are extracted, the similarity of the defect boundary is identified, the spatial coordinates are marked, and the surface anomaly positioning data set is obtained;

[0172] S5: Based on the surface anomaly positioning dataset, the acoustic wave path data of the cold storage connection and the evaporator area are extracted, the defect coordinates are matched with the path change position, the number of path offset segments is counted, and the refrigerator body structure defect interference block is obtained.

[0173] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. Medical refrigerator box manufacturing and testing system, characterized by: The system comprises: The coating reflection detection module extracts multi-band reflection spectrum data from the front door and side panels based on the external data of the medical refrigerator. It compares the reflection amplitude gradient differences and spectral curve changes of adjacent scanning areas to determine the continuity of the reflection distribution of the material surface in the scanning path and obtain the reflection continuous distribution characteristics. The acoustic wave response measurement module collects the ultrasonic emission echo time values of the bottom corner reinforcement and the side wall embedded layer of the medical refrigerator body based on the reflection continuous distribution characteristics, compares the time interval difference of the emission points with the structural echo stability, identifies the number of abnormal points in the conduction process, and obtains the abnormal acoustic wave response distribution; The acoustic wave response measurement module includes: The acoustic wave time analysis submodule collects the ultrasonic emission and echo time values of the bottom corner reinforcement and the side wall embedded layer of the medical refrigerator body based on the continuous distribution characteristics of the reflection, records the propagation speed and path changes, and obtains the acoustic wave propagation time data; The structural echo stability detection submodule compares the acoustic echo time value with the original stability data and analyzes the fluctuation of the echo time using the formula: ； Obtain echo stability index; in, is the echo stability index, For the The echo time of the measurement, is the original average echo time, is the total number of measurements; The outlier identification submodule compares the echo stability index with a preset threshold, identifies the number of outliers, locates the problem area in the sound wave propagation, and obtains the sound wave abnormal response distribution; An optical signal fluctuation analysis module records the light intensity gradient changes in the optical scanning path between the joint seam and the recessed area on the top of the medical refrigerator based on the abnormal acoustic wave response distribution, selects jump points, and analyzes the distribution interval span to obtain a light intensity jump distribution segment; The optical signal fluctuation analysis module includes: The light intensity gradient acquisition submodule monitors and records the changes in light intensity based on the abnormal response distribution of the acoustic wave and the optical scanning path of the joint seam and the recessed area on the top of the medical refrigerator to obtain light intensity gradient change data; A jumping point screening submodule screens the light intensity gradient change data, detects and marks jumping points, screens abnormal points that meet a set threshold, and obtains a jumping point data set; The light intensity jump distribution analysis submodule analyzes the distribution interval span of the jump points based on the jump point data set, identifies the associated distribution segments, and uses the formula: ； Calculate the light intensity change value to obtain the light intensity jump distribution segment; in, Represents the light intensity value of the jth sampling point, Representative The light intensity value at the sampling point, represents the average light intensity, Represents the total number of sampling points, is the light intensity change value; The surface defect recognition module calls the light intensity jump distribution segment to identify the locations of scratches, bubbles, and detached areas on the panels and corners of the medical refrigerator body, determines the boundary morphological similarity of multiple defects in the scanned image, and marks the position coordinate group to obtain surface anomaly positioning data.

2. The medical refrigerator box manufacturing and detection system according to claim 1, characterized in that: The reflection continuous distribution characteristics include reflection consistency index, spectral change trend characteristics, and local reflection difference distribution; the acoustic wave abnormal response distribution includes emission echo time abnormal value, structural interference intensity level, and abnormal echo concentration area; the light intensity jump distribution segment includes light intensity mutation amplitude value, jump point spacing interval, and light intensity discontinuity section; the surface anomaly positioning data includes the center coordinates of the defect area, boundary contour morphological parameters, and position distribution density.

3. The medical refrigerator box manufacturing and detection system according to claim 1, characterized in that: The coating reflection detection module includes: The reflectance spectrum acquisition submodule collects the multi-band reflectivity of each point along the scanning path of the front door panel and side panel based on the external data of the medical refrigerator body, records the band position and corresponding reflection value, and obtains a multi-band reflection value sequence; The amplitude gradient analysis submodule calls the multi-band reflection value sequence, identifies the band reflection amplitude difference between adjacent scanning point positions, analyzes the positive and negative changes of the difference and the amplitude fluctuation, and obtains the reflection gradient change trend; The curve continuity judgment submodule compares the smoothness and jump difference of the reflection values of consecutive points according to the reflection gradient change trend, and quantifies the difference between the reflection value and the gradient value and the cumulative amount of trend change using the formula: ； Calculate the reflection consistency value between continuous segments, make judgments based on the reflection fluctuation level, and obtain the reflection continuous distribution characteristics; in, Representative The band reflectance value of each point, Representative The band reflectance value of each point, Representative The reflection gradient value corresponding to the point, Representative The reflection gradient value corresponding to the point, Indicates the reflection consistency value between consecutive segments, is the total number of points in the scan path.

4. The medical refrigerator box manufacturing and detection system according to claim 1, characterized in that: The surface defect recognition module includes: The light intensity jump data application submodule calls the light intensity jump distribution segment, identifies the jump point data in each interval and determines the existing defect area. By comparing the similarity between the light intensity change and the scratch, bubble, and detachment area, it screens the potential defect area and obtains the defect area data set; The defect morphology recognition submodule determines the boundary morphology similarity of defects in the region based on the defect region dataset, identifies the graphic features of scratches, bubbles, and detached regions, and compares them with the preset defect template using the formula: ； Calculate the defect boundary similarity and obtain defect morphological feature data; in, Representative The defect boundary coordinate value of the sampling point, Representative The preset template boundary coordinate value of the sampling point, Representative The mean of the defect boundary coordinate values at the sampling points, Represents the total number of sampling points, is the defect boundary similarity; The defect position marking submodule marks the position coordinates of the defect in the scanned image based on the defect morphological feature data and the defect type, identifies the defect position coordinate group, and obtains surface anomaly positioning data.

5. The medical refrigerator box manufacturing and detection system according to claim 4, characterized in that: The system also includes a structural feature status assessment module: The structural feature status assessment module determines the association between structural connection point defects and acoustic wave interference based on the surface anomaly location data and the acoustic wave path changes and reflection differences between the connection between the cold storage compartment and the evaporator area of the medical refrigerator, identifies the number of offset path intervals, and obtains the refrigerator cabinet structural defect interference block; The refrigerator body structural defect interference block includes an abnormal path section of the connection part, an abnormal reflection wave offset area, and a structural response imbalance area.

6. The medical refrigerator box manufacturing and detection system according to claim 5, characterized in that: The structural feature status assessment module includes: The anomaly positioning submodule extracts the coordinates of multiple points in the cold storage connection based on the surface anomaly positioning data, selects regional point groups with a change rate exceeding the anomaly detection threshold, and counts the number of anomaly points per unit area and their continuous distribution trend to obtain an anomaly density interval value; The acoustic path determination submodule calls the abnormal density interval value, compares the acoustic wave reflection data from the connection part to the evaporator area, analyzes the reflection angle deviation rate, and screens the path segments exceeding the interference threshold. The reflection path deviation coefficient is obtained by combining the abnormal area difference and the reflection attenuation. The path interval identification submodule calls the reflection path deviation coefficient, identifies the segments where the deviation amplitude exceeds the stability threshold based on the path segment length and propagation speed, extracts the number of deviation segments between the starting and ending nodes, and obtains the path deviation interval amount; The interference block extraction submodule calls the path offset interval, compares the number of offset segments with the overlap of abnormal dense areas, and screens the spatial range where the overlap exceeds the structural overlap threshold to obtain the interference block of the refrigerator body structure defect.

7. A method for manufacturing and inspecting a medical refrigerator housing, performed based on the medical refrigerator housing manufacturing and inspecting system according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1: Based on the external data of the medical refrigerator, the reflectance spectrum data of the front door panel and side panel area are extracted. The reflection amplitude gradient differences of adjacent scanning areas are analyzed, the continuity of the band changes is judged, the change trends of adjacent areas are compared, the curve jump values are identified and the reflection gradient deviation range is determined. The discontinuous change segments are screened and a set of reflection distribution continuity indicators is established. S2: Based on the reflection distribution continuity index set, collect the ultrasonic emission echo time values of the bottom corner reinforcement position and the side wall embedded layer, compare the echo time interval difference and amplitude stability value, identify the abnormal point position, and obtain the abnormal acoustic wave response distribution area; S3: Based on the abnormal acoustic wave response distribution area, record the light intensity gradient change value in the optical scanning path of the top joint seam and the recessed area, determine the position and span of the light intensity jump point, and establish a light intensity jump distribution segment sequence; S4: Based on the light intensity jump distribution segment sequence, extract the boundary morphological feature values in the panel and corner area image scanning data, identify the similarity of the defect boundary, mark the spatial coordinates, and obtain the surface anomaly positioning data set; S5: Based on the surface anomaly positioning data set, extract the acoustic wave path data of the cold storage connection part and the evaporator area, match the defect coordinates with the path change position, count the number of path offset segments, and obtain the refrigerator body structure defect interference block.

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