Detection method, device and equipment in RFID tag manufacturing process

Abstract

The invention discloses a detection method, device and equipment in an RFID tag manufacturing process, and the method comprises the steps: obtaining stress monitoring data of a plurality of sections of a manufacturing assembly line, and carrying out the preliminary screening; multi-dimensional optical and thermal detection is carried out on the suspected abnormal labels, and deformation feature points are determined; carrying out partitioned microwave side emission test and impedance mismatch analysis on the tag with the real deformation risk, and evaluating the radio frequency performance; performing intelligent section repairing processing on the to-be-repaired label; and obtaining data of each section, performing multi-dimensional fusion analysis, obtaining optimized parameters, and performing real-time adaptive adjustment. According to the technical scheme, the RFID tag manufacturing process can be comprehensively monitored, abnormal tags can be accurately identified and positioned, intelligent repair and process optimization are achieved, the product quality and the production efficiency are effectively improved, the defective product rate is reduced, and an innovative quality control solution is provided for RFID tag manufacturing.

Description

Technical Field

[0001] The present invention relates to the technical field of radio frequency identification (RFID) tag detection, and in particular to a detection method, device and equipment in a RFID tag manufacturing process. Background Art

[0002] RFID (Radio Frequency Identification) tags are an electronic tag technology that uses radio frequency signals to achieve automatic identification and data transmission. They are usually composed of a flexible substrate, a metal antenna, and a microchip. Compared with traditional barcodes or QR codes, RFID tags have the advantages of long-distance reading and writing, simultaneous identification of multiple tags, and no need for visual alignment. Therefore, they have been widely used in modern logistics, supply chain management, retail, manufacturing and other fields.

[0003] During the manufacturing and mounting process, existing RFID tags need to be attached to the surfaces of various packaging boxes at a very high frequency. Since the tag itself is extremely thin and flexible, the instantaneous impact applied by the robotic arm during high-speed operation and the local stress generated by the tiny bumps on the box surface will cause slight deformation of the antenna or bending of the metal traces. In traditional testing, the commonly used random sampling or simple read-write tests often fail to detect these "hidden deformations" in time, and their cumulative effects may cause subsequent read-write performance instability, and in severe cases, cause the tag to fail completely. Therefore, in order to maintain the reliability of RFID tag functions under high-speed assembly line conditions, a detection method is needed that can monitor in real time, accurately evaluate, and repair or adjust damaged tags when necessary, so as to effectively prevent or reduce the potential risks of micro-deformation of the antenna to large-scale manufacturing and application. Summary of the invention

[0004] The main purpose of the present invention is to solve the problem that it is difficult for existing RFID tags to timely capture and effectively repair tiny deformations of the antenna during high-speed mounting, resulting in tag performance degradation or failure and difficulty in tracing.

[0005] A first aspect of the present invention provides a detection method in a RFID tag manufacturing process, the detection method in the RFID tag manufacturing process comprising: Obtain stress monitoring data of multiple sections of the RFID tag manufacturing line, and perform preliminary screening of RFID tags based on the stress monitoring data to obtain suspected abnormal tags; Perform multi-dimensional optical and thermal inspections on the high-risk areas of the suspected abnormal labels to obtain contour measurement data and thermal diffusion data of the antenna sensitive domain, determine the deformation feature points of the antenna based on the contour measurement data and thermal diffusion data, and obtain labels with real deformation risks; Performing a partitioned microwave side-beam test and an impedance mismatch analysis on the tag with a real risk of deformation, obtaining the scattered signal, reflected signal, resonance peak position and impedance matching data of the antenna, evaluating the RF performance of the antenna based on the scattered signal, reflected signal, resonance peak position and impedance matching data, and obtaining the tag that needs to be repaired; Perform intelligent section repair processing on the label to be repaired, obtain deformation degree data of the repaired label, judge whether the repair is successful according to the deformation degree data, and obtain a repair success label and a repair failure label; The detection data, evaluation results and repair records of each section are obtained, and a multi-dimensional fusion analysis is performed on the obtained data to obtain the optimization parameters of the manufacturing process. According to the optimization parameters, each section of the RFID tag manufacturing line is adaptively adjusted in real time.

[0006] Optionally, the step of acquiring stress monitoring data of multiple sections of the RFID tag manufacturing line and preliminarily screening the RFID tags according to the stress monitoring data to obtain suspected abnormal tags includes: Obtaining the surface unevenness data and material property data of the packaging boxes in the preparatory area of ​​the RFID tag manufacturing line, and performing a fitting risk assessment on the packaging boxes based on the surface unevenness data and material property data of the packaging boxes to obtain high-risk boxes; Obtaining the robot arm mounting pressure data, mounting angle data and label characteristic data of the mounting execution area of ​​the RFID label manufacturing assembly line, analyzing the label bonding condition according to the robot arm mounting pressure data, mounting angle data and label characteristic data, and obtaining the label with abnormal bonding; Obtain radio frequency read / write test data of the preliminary detection area of ​​the RFID tag manufacturing line, and quickly screen the tags according to the radio frequency read / write test data to obtain the tags that failed to be read; A comprehensive analysis is performed on the labels on the high-risk boxes, the labels with abnormal fitting, and the labels with failed reading to obtain suspected abnormal labels.

[0007] Optionally, the step of acquiring the robot arm mounting pressure data, mounting angle data, and label characteristic data of the mounting execution area of ​​the RFID label manufacturing assembly line, analyzing the label fitting status according to the robot arm mounting pressure data, mounting angle data, and label characteristic data, and obtaining the label with abnormal fitting includes: Obtaining pressure change data, angle change data, and position change data of the robot arm during the label attaching process, and calculating the stress distribution during the label attaching process according to the pressure change data, angle change data, and position change data to obtain stress distribution data; Acquire antenna structure parameters and chip position data of the RFID tag, analyze antenna deformation degree and chip displacement risk according to the stress distribution data, and obtain tag structure risk data; Acquire instantaneous acceleration data of the robot arm, calculate the impact degree of the tag according to the instantaneous acceleration data, and obtain impact risk data; Acquire flexibility parameters of the label substrate, analyze the possibility of label performance degradation according to the label structure risk data, impact risk data and flexibility parameters, and obtain performance degradation risk data; A performance degradation threshold is set, the performance degradation risk data is evaluated, labels exceeding the performance degradation threshold are obtained, and the labels exceeding the performance degradation threshold are determined as performance abnormality labels to obtain fitted abnormality labels.

[0008] Optionally, performing multi-dimensional optical detection and thermal detection on the high-risk area of ​​the suspected abnormal label to obtain contour measurement data and thermal diffusion data of the antenna sensitive domain, determining the deformation feature points of the antenna according to the contour measurement data and thermal diffusion data, and obtaining a label with a real deformation risk, includes: Acquire antenna design parameters and operating frequency data of the RFID tag, calculate the antenna current distribution theoretical value according to the antenna design parameters and operating frequency data, and obtain the antenna sensitive area map; Gridding the antenna sensitive area map to obtain high-precision optical contour data, and comparing the high-precision optical contour data with a standard contour template to obtain micro-deformation area coordinates and contour measurement data; Acquiring thermal characteristic parameters of the label substrate, designing a pulse thermal excitation scheme according to the thermal characteristic parameters, performing directional thermal excitation on the coordinates of the micro-deformation region, and obtaining a thermal response curve and thermal diffusion data; Analyze the peak value, decay rate and steady-state temperature of the thermal response curve to obtain a thermal anomaly index, and obtain suspected internal structural abnormal points according to the matching degree between the thermal anomaly index and the coordinates of the micro-deformation area; Obtaining the nominal resonant frequency of the tag antenna, performing local high-frequency electromagnetic field excitation on the suspected internal structural abnormal point, and obtaining local impedance change data; The contour measurement data, heat diffusion data and local impedance change data are comprehensively analyzed to obtain a three-dimensional distribution map of deformation feature points, and the degree of antenna performance degradation is evaluated according to the three-dimensional distribution map to obtain a label with a real deformation risk.

[0009] Optionally, the acquiring of the nominal resonant frequency of the tag antenna and the local high-frequency electromagnetic field excitation of the suspected internal structural abnormal point to obtain local impedance change data include: Acquire the spatial distribution data of suspected internal structural abnormal points, design the adaptive electromagnetic field excitation probe array layout according to the spatial distribution data, and obtain multi-point precise excitation coordinates; Obtain the nominal resonant frequency of the tag antenna, generate a frequency scanning sequence including the frequency bands adjacent to the resonant frequency based on the nominal resonant frequency data, perform local high-frequency electromagnetic field excitation on the multi-point precise excitation coordinates, obtain the reflection coefficient spectrum of each excitation point, and obtain local electromagnetic response data; Analyzing the local electromagnetic response data, extracting the actual resonant frequency and bandwidth parameters of each excitation point, and comparing them with the nominal resonant frequency data to obtain the frequency offset and bandwidth change; According to the frequency offset and bandwidth variation, combined with the equivalent circuit model of the RFID tag antenna, the equivalent impedance variation of each excitation point is calculated to obtain a local impedance variation spectrum; The dynamic input impedance characteristics of the RFID chip are obtained, and based on the local impedance change spectrum and the chip dynamic input impedance characteristics, the change of impedance matching with frequency is calculated to obtain local impedance change data.

[0010] Optionally, performing a partitioned microwave side-beam test and an impedance mismatch analysis on the tag with a real deformation risk, obtaining a scattered signal, a reflected signal, a resonance peak position and impedance matching data of the antenna, and evaluating the RF performance of the antenna according to the scattered signal, the reflected signal, the resonance peak position and the impedance matching data to obtain the tag to be repaired, including: Obtain theoretical radiation pattern data of the RFID tag antenna, design a multi-angle microwave side-firing test scheme based on the theoretical radiation pattern data, and obtain a side-firing angle sequence; Performing microwave scanning on the tag with real deformation risk according to the side-firing angle sequence to obtain scattered signal intensity and phase data at different angles, reconstructing the actual radiation pattern according to the scattered signal intensity and phase data, and obtaining antenna scattering characteristic data; Obtaining the working frequency range of the RFID tag, performing frequency scanning within the working frequency range, obtaining the reflection coefficient spectrum of the tag antenna, extracting the resonance peak position and bandwidth data according to the reflection coefficient spectrum, and obtaining antenna reflection characteristic data; Based on the antenna scattering characteristic data and the antenna reflection characteristic data, the actual gain and directivity of the antenna are calculated, and compared with the standard antenna parameters to obtain antenna performance deviation data; Obtaining the nominal input impedance data of the RFID chip, calculating the input impedance of the antenna according to the antenna reflection characteristic data, performing a matching degree analysis with the nominal input impedance data of the chip, and obtaining impedance matching data; The antenna performance deviation data and impedance matching data are comprehensively analyzed to establish a radio frequency performance evaluation index for RFID tags, and the tags are graded according to the radio frequency performance evaluation index to obtain tags that need to be repaired.

[0011] Optionally, performing intelligent section repair processing on the label to be repaired, obtaining deformation degree data of the repaired label, judging whether the repair is successful according to the deformation degree data, and obtaining a repair success label and a repair failure label, includes: Determine the repair priority order according to the antenna performance deviation data and impedance matching data of the tag to be repaired, and obtain a hierarchical repair list; Perform antenna structure analysis on each tag in the hierarchical repair list, determine the area to be repaired according to the radio frequency performance evaluation index of the tag, and obtain a section repair plan; According to the section repair plan, the conductive material is precisely sprayed on the label to be repaired, real-time impedance change data is obtained during the spraying process, and the spraying parameters are dynamically adjusted according to the real-time impedance change data to obtain a preliminary repaired label; Performing local laser annealing on the preliminary repair label, obtaining temperature distribution data during the annealing process, and controlling the annealing intensity and time according to the temperature distribution data to obtain a reinforced repair label; Performing high-frequency electromagnetic field scanning on the enhanced repair tag to obtain local current distribution data after repair, calculating the current distribution uniformity according to the local current distribution data, and obtaining the deformation degree data after repair; A repair success threshold is set, and the repaired deformation degree data is compared with the repair success threshold. Labels with current distribution uniformity higher than the threshold are classified as repair success labels, and labels with current distribution uniformity lower than the threshold are classified as repair failure labels, thereby obtaining repair success labels and repair failure labels.

[0012] Optionally, the acquisition of detection data, evaluation results and repair records of each section, performing multi-dimensional fusion analysis on the acquired data to obtain optimization parameters of the manufacturing process, and performing real-time adaptive adjustment on each section of the RFID tag manufacturing line according to the optimization parameters, includes: Acquire the detection data of the placement preparation area, the placement execution area and the preliminary detection area, perform multi-parameter correlation analysis on the detection data, and obtain a manufacturing front-end abnormality correlation map; Acquire contour measurement data, thermal diffusion data, and deformation feature point data of the depth detection area, perform cluster analysis on the data, and obtain a label deformation type distribution map; Acquire scattered signals, reflected signals, resonance peak positions and impedance matching data in the radio frequency performance evaluation area, perform multi-parameter regression analysis on the data, and obtain performance evaluation model parameters; Acquire the repair plan data, the deformation degree data after repair and the repair success rate data of the repair treatment area, perform association rule mining on the data, and obtain the optimal repair strategy set; A multi-dimensional fusion analysis is performed on the manufacturing front-end abnormal association map, label deformation type distribution map, performance evaluation model parameters and optimal repair strategy set to obtain the optimization parameters of the manufacturing process, and real-time adaptive adjustment instructions for the RFID label manufacturing line are generated according to the optimization parameters, and real-time adaptive adjustment is performed on the mounting preparation area, mounting execution area, preliminary detection area, deep detection area, radio frequency performance evaluation area and repair processing area of ​​the RFID label manufacturing line.

[0013] A second aspect of the present invention provides a detection device in a RFID tag manufacturing process, the detection device in the RFID tag manufacturing process comprising: A preliminary screening module is used to obtain stress monitoring data of multiple sections of the RFID tag manufacturing line, and perform preliminary screening of RFID tags based on the stress monitoring data to obtain suspected abnormal tags; A multi-dimensional detection module is used to perform multi-dimensional optical detection and thermal detection on the high-risk area of ​​the suspected abnormal label, obtain contour measurement data and thermal diffusion data of the antenna sensitive domain, determine the deformation feature points of the antenna according to the contour measurement data and thermal diffusion data, and obtain a label with a real deformation risk; A radio frequency performance evaluation module is used to perform a partitioned microwave side-beam test and an impedance mismatch analysis on the tag with a real deformation risk, obtain the scattered signal, reflected signal, resonance peak position and impedance matching data of the antenna, evaluate the radio frequency performance of the antenna according to the scattered signal, reflected signal, resonance peak position and impedance matching data, and obtain the tag that needs to be repaired; An intelligent repair module is used to perform intelligent section repair processing on the label to be repaired, obtain deformation degree data of the repaired label, determine whether the repair is successful according to the deformation degree data, and obtain a repair success label and a repair failure label; The optimization and adjustment module is used to obtain the detection data, evaluation results and repair records of each section, perform multi-dimensional fusion analysis on the acquired data, obtain the optimization parameters of the manufacturing process, and perform real-time adaptive adjustment on each section of the RFID tag manufacturing line according to the optimization parameters.

[0014] A third aspect of the present invention provides a detection device in an RFID tag manufacturing process, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected via a line; and the at least one processor calls the instructions in the memory so that the detection device in the RFID tag manufacturing process performs the steps of the above-mentioned detection method in the RFID tag manufacturing process.

[0015] This solution first integrates a variety of stress and mounting status data in different sections of the manufacturing line to conduct preliminary screening of tags that may have slight antenna deformation, thereby accurately locking in key inspection objects in a high-speed mounting environment. Subsequently, the surface contour and local heat diffusion distribution of key parts of the antenna are double-detected by combining high-precision optical scanning with thermal excitation analysis. It is not difficult to understand that when there are slight bends or potential cracks in the metal routing, the results of optical interference or line scanning will show quantifiable deformation deviations in the characteristic coordinates, and the corresponding heat diffusion path will also change slightly with the material structure. By comparing these two types of information, local defects that are not visible to the naked eye but are sufficient to interfere with subsequent RF performance can be identified, and accurate deformation data foundations can be laid for the next step of RF analysis.

[0016] After completing the above deformation detection, this solution uses microwave side-beam and impedance analysis to comprehensively evaluate the scattering, reflection and matching of the antenna. When the equivalent circuit parameters deviate due to tiny cracks or excessive bending in the local routing, the scattered signal and reflection coefficient often show abnormalities in specific frequency bands or angles. By making a horizontal comparison with normal RF indicators, it is possible to quickly determine whether the safety margin of the antenna performance has been weakened. Once the detection judgment exceeds the tolerable threshold, the system immediately performs intelligent section repair, locally trims the damaged area by adding conductive materials or photothermal curing, and measures its deformation degree again to confirm the repair effect. At the same time, all inspection, evaluation and repair records in the manufacturing process will be integrated and analyzed for real-time optimization of key parameters such as mounting pressure, mounting speed and repair process, so as to maintain efficient production capacity and stable quality in mass production, and finally achieve the core goal of preventing micro deformation of the antenna and ensuring the reliability of RFID tags. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of an embodiment of a detection method in a RFID tag manufacturing process according to an embodiment of the present invention; Figure 2 A schematic diagram of an embodiment of a detection device in a RFID tag manufacturing process according to an embodiment of the present invention; Figure 3 It is a schematic diagram of an embodiment of a detection device in the RFID tag manufacturing process in an embodiment of the present invention.

[0019] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0022] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, which must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0023] An embodiment of the present application provides a detection method during the manufacturing process of an RFID tag. Figure 1 A flow chart of a detection method in the RFID tag manufacturing process provided in an embodiment of the present application. In this embodiment, the method includes: See also Figure 1 , obtaining stress monitoring data of multiple sections of the RFID tag manufacturing line, and preliminarily screening the RFID tags based on the stress monitoring data to obtain suspected abnormal tags; In one embodiment of the present invention, the stress monitoring data of multiple sections of the RFID tag manufacturing line is obtained, and the RFID tags are preliminarily screened according to the stress monitoring data to obtain suspected abnormal tags, including: obtaining the surface roughness data and material property data of the packaging box in the mounting preparation area of ​​the RFID tag manufacturing line, and performing a fitting risk assessment on the packaging box according to the surface roughness data and material property data of the packaging box to obtain a high-risk box; obtaining the robot arm mounting pressure data, mounting angle data and label characteristic data in the mounting execution area of ​​the RFID tag manufacturing line, analyzing the label fitting status according to the robot arm mounting pressure data, mounting angle data and label characteristic data to obtain a fitting abnormal label; obtaining the radio frequency reading and writing test data of the preliminary detection area of ​​the RFID tag manufacturing line, and quickly screening the tags according to the radio frequency reading and writing test data to obtain a read failure tag; and comprehensively analyzing the tags on the high-risk boxes, the fitting abnormal tags and the read failure tags to obtain suspected abnormal tags.

[0024] Specifically, in the mounting preparation area, the surface unevenness data and material property data of the packaging box are first collected. The detection device is set above or on the side of the conveyor belt, and the physical parameters such as the height difference and elongation coefficient of the packaging box are recorded through multi-angle scanning and hardness testing. The surface unevenness data is used to identify bulges, creases or depressions, and the material property data includes indicators such as fiber density and hardening degree. After comparing these data with the bonding safety threshold, it can be determined whether there are large areas of hard bulges or obvious defects on the surface of the packaging box. Once it is confirmed that the surface structure or material elasticity is difficult to adapt to the metal antenna, the packaging box is classified as a high-risk box. The mounting preparation area only needs to complete the scanning comparison and risk determination process. The scanning period is short and will not interfere with the rate of the entire production line. If a batch of paper boxes detects deep dents in the scanned image and the hardening coefficient is high, the system immediately evaluates that the batch of boxes is difficult to mount, so that the bonding speed of the robot arm or the bonding pressure is moderately slowed down in the subsequent mounting execution. The technical effect achieved by this method is that it can reduce the extreme stretching and torsion of the tag on high-risk surfaces, significantly reducing the probability of damage or warping of the antenna trace.

[0025] The mounting execution area then obtains the robot mounting pressure data, mounting angle data and label characteristic data, and inputs these data into the mechanical judgment algorithm to identify the abnormal bonding. The mechanical judgment algorithm can use a multi-parameter threshold comparison method to combine the pressure curve recorded by the force sensing component with the angle change value output by the robot arm posture sensor, and perform correlation analysis with the arrangement information of the metal trace of the tag antenna, the chip position and the thickness characteristics of the substrate. Once the pressure peak or the slope of the force curve exceeds the threshold set by the algorithm, or the robot arm deviates significantly in the spatial angle, the system determines that the bonding process exceeds the safe range and records the label as an abnormal bonding label. If the abnormal bonding labels are distributed near the chip, it is necessary to further detect whether the solder joints are loose or the traces are partially torn. The technical effect of the mechanical judgment algorithm is that it can achieve accurate real-time monitoring under high-speed assembly line conditions. Once the robot arm moves unsteadily or the mounting force is improper, the algorithm immediately identifies the label with local stress concentration and marks it as a potential risk, thereby effectively reducing the probability of excessive compression of the metal traces during mass mounting.

[0026] The preliminary inspection area performs radio frequency reading and writing tests on the tags that have been mounted. By sending high-frequency instructions to the tags and measuring the amplitude and integrity of the echo signal, the tags that failed to read are quickly screened out. The core of this step is to use the reader to complete data interaction in a very short time slot. If the chip and antenna of the tag are connected normally, the reader can collect a stable response; when the antenna is broken, the chip is displaced, or the impedance of the trace is mismatched, the reply signal will be significantly weakened or completely lost. In this process, multiple readers can be configured to work in parallel to cope with high-beat assembly lines. If a tag is not marked as abnormal in the mounting execution area, but cannot respond in the read and write test, it also means that the tag has hidden damage in the physical structure or electrical path. Through this step, the tags that failed to read and write are identified and recorded in a timely manner. The technical effect is that the tags that may fail in the subsequent logistics links can be eliminated in advance or more deeply detected, reducing the risk of large-scale identification difficulties in the back end.

[0027] When comprehensively analyzing labels on high-risk boxes, labels with abnormal fitting, and labels with failed reading, the system retrieves the data collected in the above sections, including the test results of the unevenness and material properties of the packaging box surface, the pressure and angle records of the mounting process, and the RF reading and writing feedback of the preliminary detection area, and cross-checks this information to determine whether the label has potential problems at both the physical and RF levels. If a label is located on a high-risk box, and a large pressure peak or angle deviation occurs during the mounting of the robot arm, and the reader does not receive a normal response, it can be determined as a suspected abnormal label. Logically speaking, these suspected abnormal labels need to receive special attention in the next stage of fine inspection or repair to prevent further aggravation of antenna breakage or chip contact failure. By comprehensively screening and correlating these three types of label data, the entire manufacturing line achieves hierarchical management and real-time monitoring, which can not only ensure that the efficiency of mass production is not affected, but also greatly reduce the hidden dangers of poor label recognition in subsequent circulation links, thereby improving the overall reliability of the RFID system.

[0028] In one embodiment of the present invention, the step of obtaining robot arm mounting pressure data, mounting angle data, and label characteristic data in a mounting execution area of ​​an RFID label manufacturing assembly line, analyzing the label fitting status according to the robot arm mounting pressure data, mounting angle data, and label characteristic data, and obtaining an abnormally fitted label includes: obtaining pressure change data, angle change data, and position change data of the robot arm during the label mounting process, calculating the stress distribution during the label mounting process according to the pressure change data, angle change data, and position change data, and obtaining stress distribution data; obtaining antenna structure parameters and chip position data of the RFID label, The antenna deformation degree and chip displacement risk are analyzed according to the stress distribution data to obtain the tag structure risk data; the instantaneous acceleration data of the robot arm is obtained, and the tag force impact degree is calculated according to the instantaneous acceleration data to obtain the impact risk data; the flexibility parameters of the tag substrate are obtained, and the possibility of tag performance attenuation is analyzed according to the tag structure risk data, impact risk data and flexibility parameters to obtain the performance attenuation risk data; a performance attenuation threshold is set, the performance attenuation risk data is evaluated, and the tags exceeding the performance attenuation threshold are obtained, and the tags exceeding the performance attenuation threshold are determined as performance abnormal tags to obtain fitting abnormal tags.

[0029] Specifically, during the label mounting process, the system first obtains the pressure change data, angle change data, and position change data applied by the robot arm on the label. This information mainly comes from the force sensing component at the end of the robot arm and the joint position solution module, which is used to describe the mechanical evolution process from initial contact to full bonding during mounting. The pressure change data records the vertical load and lateral thrust of the robot arm at different time nodes, the angle change data provides the posture offset of the robot arm in three-dimensional space, and the position change data further refines the real-time coordinates of each joint. After integrating the three, the system calculates the stress distribution during label bonding through multi-dimensional numerical fitting or finite element analysis to obtain stress distribution data. If the stress concentration is found to be significantly higher than the average level at the bonding corner or near the chip, it means that the area is more likely to produce metal wiring bending or solder joint loosening. Through this process, possible micro-deformation hazards can be located more accurately, instead of relying solely on a single peak pressure or a single robot arm angle information.

[0030] After obtaining the antenna structure parameters and chip position data of the RFID tag, the system combines this structural information with the stress distribution data to analyze the amount of antenna bending in the high stress area and the displacement amplitude of the chip relative to the antenna routing, and infer the tag structure risk data. Antenna structure parameters usually include the geometry of the routing, the number of bends and the metal thickness, and the chip position data describes the specific connection point between the chip and the routing. When the mechanical model detects a local stress peak and the area corresponds to the routing bend or the chip pin position, the actual deformation degree of the antenna and the chip displacement risk are calculated. If the bending radius is less than the safety limit or the chip position offset exceeds the allowable range, the tag structure risk is judged to be at a higher level, prompting the subsequent inspection area to prioritize it. Through this step, the twisted or over-stretched routing parts can be accurately identified, thereby reducing the probability of antenna breakage or chip contact failure after high-speed mounting.

[0031] Next, the system obtains the instantaneous acceleration data of the robot arm, which can be read in real time from the robot arm motion controller or acceleration sensor to characterize the acceleration and deceleration behavior during the mounting process. When the robot arm performs rapid bonding or position switching in a very short time, the label may be subjected to instantaneous impact, resulting in irreversible plastic deformation of the antenna surface or loosening of the chip solder joints. Based on the comparison between the instantaneous acceleration value and the load capacity of the robot arm itself, the system calculates the degree of force impact of the label and obtains the impact risk data. If the peak value of the acceleration is much higher than the dynamic range of normal operation of the robot arm, it means that the bonding action generates significant impact force in a certain period of time, which in turn causes additional damage to the metal wiring of the antenna. This impact risk data helps to distinguish between static overpressure problems and structural damage caused by instantaneous impact, and provides targeted decision-making references for downstream maintenance or label repair.

[0032] Based on the understanding of stress distribution, tag structure risk and impact risk, the system will obtain the flexibility parameters of the tag substrate to further evaluate whether the antenna performance is attenuated under extreme stress environments. The flexibility parameters of the substrate usually describe the elasticity and ductility of the tag material under different stress conditions. If this parameter value is low, it is more likely to have permanent bending or breakage under the same stress; if the flexibility parameter is high, the tag has a certain degree of self-recovery or buffering ability. The system comprehensively calculates the tag structure risk data, impact risk data and flexibility parameters to obtain performance attenuation risk data. For example, even if the mechanical analysis shows that there is a high stress peak in a certain area, but the substrate is sufficiently flexible, the performance attenuation risk may be assessed as relatively controllable; conversely, if the substrate itself lacks elasticity, severe routing deformation may occur under the same stress conditions, thereby affecting the RF performance.

[0033] Finally, in order to convert this comprehensive evaluation into a basis for judging abnormal labels, the system sets a performance attenuation threshold and compares all obtained performance attenuation risk data with this threshold. Labels that exceed this threshold are considered to be abnormal performance labels and enter the next step of more stringent in-depth inspection or repair process. The technical effect of this link is that through a quantitative threshold management mechanism, the labels that need the most attention can be quickly screened out without spending too much time inspecting all products in a high-speed mounting assembly line. At the same time, the corresponding mechanical characteristics, structural risks and impact analysis records will be retained during the marking process of abnormal performance labels, so that subsequent inspection areas or repair areas can be targeted and focused on routing corners, chip solder joints or other high-risk areas. Through this process, the entire system realizes the early capture and hierarchical management of potential antenna failure factors, greatly improving the reliability and service life of RFID tags in large-scale mounting environments.

[0034] Please continue reading Figure 1 , perform multi-dimensional optical detection and thermal detection on the high-risk area of ​​the suspected abnormal label, obtain contour measurement data and thermal diffusion data of the antenna sensitive domain, determine the deformation feature points of the antenna according to the contour measurement data and thermal diffusion data, and obtain a label with a real deformation risk; In one embodiment of the present invention, multi-dimensional optical detection and thermal detection are performed on the high-risk area of ​​the suspected abnormal label to obtain contour measurement data and thermal diffusion data of the antenna sensitive domain, and the deformation feature points of the antenna are determined according to the contour measurement data and thermal diffusion data to obtain a label with a real deformation risk, including: obtaining antenna design parameters and operating frequency data of the RFID tag, calculating the theoretical value of antenna current distribution according to the antenna design parameters and operating frequency data, and obtaining an antenna sensitive area map; gridding the antenna sensitive area map to obtain high-precision optical contour data, and comparing the high-precision optical contour data with a standard contour template to obtain micro-deformation area coordinates and contour measurement data; obtaining the label substrate The thermal characteristic parameters of the tag are obtained, a pulse thermal excitation scheme is designed according to the thermal characteristic parameters, and a directional thermal excitation is performed on the coordinates of the micro-deformation area to obtain a thermal response curve and thermal diffusion data; the peak value, decay rate and steady-state temperature of the thermal response curve are analyzed to obtain a thermal anomaly index, and the suspected internal structural abnormal points are obtained according to the matching degree between the thermal anomaly index and the coordinates of the micro-deformation area; the nominal resonant frequency of the tag antenna is obtained, and the suspected internal structural abnormal points are excited by a local high-frequency electromagnetic field to obtain local impedance change data; the contour measurement data, thermal diffusion data and local impedance change data are comprehensively analyzed to obtain a three-dimensional distribution map of the deformation feature points, and the degree of antenna performance degradation is evaluated according to the three-dimensional distribution map to obtain a tag with a real deformation risk.

[0035] Specifically, after obtaining the antenna design parameters and operating frequency data of the RFID tag, the system first calculates the theoretical value of the antenna current distribution based on this information, thereby generating an antenna sensitive area map. Antenna design parameters include trace shape, width, number of bends, and coupling position with the chip pad. The operating frequency data reflects the electromagnetic coupling characteristics of the tag under a certain RF band. The calculation process can be based on full-wave simulation or analytical approximation to digitize the current distribution of the antenna at the target operating frequency and mark key areas that are susceptible to external forces or environmental disturbances. Assuming that there are several corners and chip contacts on the antenna, the current density at these locations is often higher than that of the straight line segment, and the theoretical value calculation results will present them as sensitive areas. In this way, the potential vulnerable parts of the antenna can be estimated before actual physical testing is performed, and the antenna sensitive area map can be output in a visual form, which is conducive to subsequent more refined detection operations for these high-risk parts.

[0036] After obtaining the antenna sensitive area map, the system grids it and obtains high-precision optical profile data. Grid division refers to subdividing the sensitive area of ​​the entire antenna into multiple cells so that accurate positioning at the coordinate level can be achieved during the detection process. High-precision optical profile data comes from technical means such as line scanning or interferometry, and is compared with standard profile templates to determine the coordinates of the micro-deformation area and the profile measurement data. If the optical scanning result in a cell shows that the shape of the trace deviates from the original design contour, the coordinate is recorded as the micro-deformation point. Subsequently, the system obtains the thermal characteristic parameters of the label substrate, including specific heat capacity, thermal conductivity and material heating limit, and designs a pulse thermal excitation scheme based on these values. Directed thermal excitation refers to applying short-term high-energy pulsed light or heat to the grid unit where the micro-deformation area is located, so that the local temperature of the material is increased and the thermal diffusion behavior is observed. When there are local cracks or internal peeling in the trace, the local thermal impedance will be different from the normal area, resulting in abnormal peak value, decay rate and steady-state temperature of the thermal response curve. After calculating the thermal anomaly index of these thermal response data, it is matched with the coordinates of the micro-deformation area. If the two are highly overlapped, the point is marked as a suspected internal structural abnormality. This process can determine the location of hidden cracks or potential breaks by using the material's temperature change characteristics when optical deformation signs are not yet obvious, thereby improving detection reliability.

[0037] After completing the thermal inspection, the system will also obtain the nominal resonant frequency of the tag antenna, and perform local high-frequency electromagnetic field excitation on the coordinates that have been marked as suspected internal structural abnormalities to obtain local impedance change data. In this link, a microwave probe or a high-frequency resonant probe can be used to scan the frequency band of the routing around the coordinate point. If the metal structure or dielectric properties are distorted by impedance due to internal deformation, there will be obvious deviations in the local reflection or scattering characteristics. The impedance change data obtained in this way is comprehensively analyzed together with the contour measurement data and thermal diffusion data obtained previously, and the system finally generates a three-dimensional distribution map of the deformation feature points. This three-dimensional distribution map can not only show the surface deviation of the routing in the optical detection dimension, but also quantify the internal defects at the thermal and impedance levels, thereby intuitively reflecting the degree of degradation of the antenna performance. If a point shows obvious abnormalities in the three dimensions of optical deformation, thermal anomaly and impedance mismatch, it is identified as a tag with real deformation risk, and can subsequently enter a deeper repair process or be directly eliminated to avoid failures during subsequent supply chain use. Through this series of processes, high-precision, three-dimensional deformation detection and performance evaluation of key areas of the antenna can be completed without affecting the production rhythm, providing sufficient guarantee for the radio frequency communication quality of the RFID tag.

[0038] In one embodiment of the present invention, the step of obtaining the nominal resonant frequency of the tag antenna, performing local high-frequency electromagnetic field excitation on the suspected internal structural abnormal point, and obtaining local impedance change data includes: obtaining spatial distribution data of the suspected internal structural abnormal point, designing an adaptive electromagnetic field excitation probe array layout according to the spatial distribution data, and obtaining multi-point precise excitation coordinates; obtaining the nominal resonant frequency of the tag antenna, generating a frequency scanning sequence including a frequency band adjacent to the resonant frequency based on the nominal resonant frequency data, performing local high-frequency electromagnetic field excitation on the multi-point precise excitation coordinates, obtaining a reflection coefficient spectrum of each excitation point, and obtaining local electromagnetic response data; analyzing the local electromagnetic response data, extracting the actual resonant frequency and bandwidth parameters of each excitation point, and comparing them with the nominal resonant frequency data to obtain a frequency offset and a bandwidth change; calculating the equivalent impedance change of each excitation point according to the frequency offset and the bandwidth change, in combination with the equivalent circuit model of the RFID tag antenna, and obtaining a local impedance change spectrum; obtaining the dynamic input impedance characteristics of the RFID chip, and calculating the change of impedance matching with frequency based on the local impedance change spectrum and the dynamic input impedance characteristics of the chip, and obtaining local impedance change data.

[0039] Specifically, after confirming the spatial distribution data of suspected internal structural abnormalities, the system first designs the layout of the adaptive electromagnetic field excitation probe array based on these coordinate positions to obtain multi-point precise excitation coordinates. This design process will be based on the physical accessibility around each abnormal point on the tag, the direction of the antenna geometric routing, and the field of view of the probe itself, and will calculate the optimal excitation angle and distance to avoid unnecessary coupling between adjacent probes as much as possible. After completing the layout of the probe array, the system will read the nominal resonant frequency of the tag antenna and generate a frequency scanning sequence containing the frequency bands adjacent to the resonant frequency, which is used to implement local high-frequency electromagnetic field excitation at multiple excitation points. The technical purpose of doing this is: on the one hand, it can more accurately inject energy and detect key areas of the entire tag, and on the other hand, it can capture slight deviations caused by bending or breaking of metal routing by multiple scans near the resonant frequency, thereby further confirming the impact of potential deformation on antenna performance.

[0040] After importing each excitation point into the frequency scanning sequence, the system performs high-frequency pulse or continuous wave scanning on all excitation coordinates in the order of probe layout, and collects the reflection coefficient spectrum of each point to obtain local electromagnetic response data. For each excitation point, the reflection coefficient spectrum shows the energy reflection characteristics from low frequency to resonant frequency or even higher frequency bands. If there is excessive extrusion or metal cracking in a certain routing area, the current distribution in this area will be abnormal, resulting in a reflection coefficient curve that is very different from expectations. The system then conducts a detailed analysis of these local electromagnetic response data, and extracts the actual resonant frequency and bandwidth parameters of each excitation point. After comparing with the nominal resonant frequency data, the frequency offset and bandwidth change can be obtained. If the resonant peak of a certain excitation point is obviously shifted to the high-frequency or low-frequency side, and the bandwidth also fluctuates greatly, it can be preliminarily inferred that the metal conductor or medium in this area is deformed, thereby causing impedance mismatch.

[0041] Next, after mastering the frequency offset and bandwidth change of each excitation point, the system combines the equivalent circuit model of the RFID tag antenna to calculate the equivalent impedance change of each excitation point, and finally outputs the local impedance change spectrum. The equivalent circuit model usually contains elements such as inductance, capacitance and resistance, which are used to characterize the resonance characteristics of the antenna under the RF field. If the metal trace is subjected to mechanical stress in a certain area and causes geometric changes, it will be reflected in the parameter offset of this part of the circuit element. The local impedance change spectrum can help identify the most serious deformation parts and provide a more intuitive reference for subsequent repair or detection decisions. Finally, the system obtains the dynamic input impedance characteristics of the RFID chip and cross-compares it with the local impedance change spectrum to calculate the change of impedance matching with frequency, forming more in-depth local impedance change data. If it is found that the matching degree of a certain point drops sharply in the working frequency band, it indicates that the trace or medium properties in the area deviate from the normal level, which may seriously affect the energy coupling efficiency between the chip and the antenna. This link can not only quantify the damage to the RF performance caused by the deformation of the metal structure, but also indicate whether it is necessary to implement repair measures in this area to ensure that the tag maintains stable communication performance in actual application. Through comprehensive considerations from multi-point precise excitation coordinates, segmented frequency scanning to local impedance change maps and chip input impedance, the entire process achieves precise positioning and quantification of microscopic antenna damage, improving the depth and accuracy of overall RFID tag detection.

[0042] Please continue reading Figure 1 , performing a partitioned microwave side-beam test and an impedance mismatch analysis on the tag with a real deformation risk, obtaining the scattered signal, reflected signal, resonance peak position and impedance matching data of the antenna, evaluating the RF performance of the antenna based on the scattered signal, reflected signal, resonance peak position and impedance matching data, and obtaining the tag that needs to be repaired; In one embodiment of the present invention, the tag with real deformation risk is subjected to partitioned microwave side-beam test and impedance mismatch analysis to obtain the scattered signal, reflected signal, resonance peak position and impedance matching data of the antenna, and the RF performance of the antenna is evaluated according to the scattered signal, reflected signal, resonance peak position and impedance matching data to obtain the tag to be repaired, including: obtaining theoretical radiation pattern data of the RFID tag antenna, designing a multi-angle microwave side-beam test scheme according to the theoretical radiation pattern data, and obtaining a side-beam angle sequence; performing microwave scanning on the tag with real deformation risk according to the side-beam angle sequence to obtain scattered signal intensity and phase data at different angles, reconstructing the actual radiation pattern according to the scattered signal intensity and phase data, and obtaining antenna scattering characteristic data; obtaining RFID The working frequency band range of the tag is used to perform frequency scanning within the working frequency band range to obtain the reflection coefficient spectrum of the tag antenna, extract the resonance peak position and bandwidth data according to the reflection coefficient spectrum, and obtain the antenna reflection characteristic data; based on the antenna scattering characteristic data and the antenna reflection characteristic data, the actual gain and directivity of the antenna are calculated, and compared with the standard antenna parameters to obtain the antenna performance deviation data; the nominal input impedance data of the RFID chip is obtained, the input impedance of the antenna is calculated according to the antenna reflection characteristic data, and the matching degree is analyzed with the nominal input impedance data of the chip to obtain the impedance matching data; the antenna performance deviation data and the impedance matching data are comprehensively analyzed to establish the RFID tag radio frequency performance evaluation index, and the tags are graded according to the radio frequency performance evaluation index to obtain the tags that need to be repaired.

[0043] Specifically, after obtaining the theoretical radiation pattern data of the RFID tag antenna, the system will design a multi-angle microwave side-firing test plan for subsequent tests based on the pattern characteristics obtained by simulation or analytical calculation, and generate a side-firing angle sequence. The pattern data contains the theoretical gain or field strength distribution of the antenna at various azimuth and elevation angles. If a metal trace is bent or has material defects somewhere, the actual radiation characteristics will deviate from the theoretical values. By irradiating or receiving microwaves on the tag at several key angles, the system can capture abnormal points more efficiently and avoid invalid large-scale scanning.

[0044] After determining the side-firing angle sequence, the system performs microwave scanning on tags with real deformation risks in accordance with these angles, and records the scattered signal intensity and phase data at different angles. The high-dimensional information of signal strength and phase can reflect the reflection and scattering characteristics of the antenna under various incident directions. Based on these measurement results, the system reconstructs the actual radiation pattern and obtains the antenna scattering characteristic data. If the tag antenna produces excessive energy attenuation or phase distortion in a certain direction, there will be a considerable difference compared to the theoretical radiation pattern, indicating that there is potential bending, cracks or conductor delamination in the area.

[0045] After obtaining the scattering characteristic data, the system will perform a frequency scan on the antenna in combination with the working frequency band of the RFID tag to obtain the reflection coefficient spectrum. This reflection coefficient spectrum can be used to extract the resonance peak position and bandwidth data, and thus obtain the antenna reflection characteristic data. If the tag is deformed, resulting in a significant shift in the resonance point or a narrowing of the bandwidth, it means that the overall resonance properties of the antenna have been destroyed, thereby affecting the read and write performance of the tag. Subsequently, the system will fuse the antenna scattering characteristic data with the reflection characteristic data, calculate the actual gain and directivity of the antenna at multiple angles and multiple frequency bands, and compare it with the standard antenna parameters to obtain the antenna performance deviation data. If the gain attenuation or directivity distortion in the performance deviation data is large, it means that the physical structure of the antenna may be severely deformed.

[0046] Next, the system reads the nominal input impedance data of the RFID chip and calculates the input impedance of the antenna based on the antenna reflection characteristic data obtained previously. On this basis, the matching analysis is completed to determine the energy coupling between the antenna and the chip in the working frequency band, and then the impedance matching data is generated. If the impedance does not match, the read and write distance and stability will decrease. Finally, the system conducts a comprehensive evaluation of the antenna performance deviation data and the impedance matching data, and establishes corresponding RF performance evaluation indicators to grade the tags. If the tag deviates too much in gain or directivity, and the impedance matching is also in a high-risk range, it is identified as a tag that needs to be repaired so that the antenna deformation can be corrected or replaced in subsequent processes. In this way, the whole set of methods realizes the detection of the scattering and reflection characteristics of RFID tags at multiple angles and multiple frequency bands, and combines the actual gain, directivity and impedance matching to comprehensively determine the performance of the antenna, thereby ensuring that tags with serious degradation risks are quickly sorted out on high-speed production lines.

[0047] Please continue reading Figure 1 , perform intelligent section repair processing on the label to be repaired, obtain deformation degree data of the repaired label, judge whether the repair is successful according to the deformation degree data, and obtain a repair success label and a repair failure label; In one embodiment of the present invention, the intelligent segment repair processing is performed on the tag to be repaired, the deformation degree data of the tag after repair is obtained, and whether the repair is successful is determined according to the deformation degree data to obtain a successful repair tag and a failed repair tag, including: determining the repair priority according to the antenna performance deviation data and impedance matching data of the tag to be repaired, and obtaining a hierarchical repair list; performing antenna structure analysis on each tag in the hierarchical repair list, determining the area to be repaired according to the radio frequency performance evaluation index of the tag, and obtaining a segment repair plan; accurately spraying the conductive material on the tag to be repaired according to the segment repair plan, obtaining real-time impedance change data during the spraying process, and dynamically The method comprises the following steps: adjusting the spraying parameters in a state to obtain a preliminary repair label; performing local laser annealing on the preliminary repair label to obtain temperature distribution data during the annealing process, controlling the annealing intensity and time according to the temperature distribution data to obtain an enhanced repair label; performing high-frequency electromagnetic field scanning on the enhanced repair label to obtain local current distribution data after repair, calculating the current distribution uniformity according to the local current distribution data, and obtaining the deformation degree data after repair; setting a repair success threshold, comparing the deformation degree data after repair with the repair success threshold, classifying the label with a current distribution uniformity higher than the threshold as a repair success label, and classifying the label with a current distribution uniformity lower than the threshold as a repair failure label, thereby obtaining a repair success label and a repair failure label.

[0048] Specifically, after completing the analysis of the tag's RF performance deviation data and impedance matching data, the system first determines the repair priority based on these key indicators, and generates a hierarchical repair list. Since the gain attenuation, resonance point offset, bandwidth change, and chip matching of each tag have been quantitatively evaluated in the previous stage, the damage characteristics and severity between different tags will be significantly different. Therefore, those tags with the most significant performance deviations and the most serious impedance matching mismatches are placed at a high priority in the repair list to ensure that they can be treated first in the assembly line or back-end detection link. The technical effect of this is to ensure that the repair process with limited resources focuses on the objects that need correction most urgently, thereby reducing the risk of possible large-scale failures.

[0049] After the hierarchical repair list is generated, the system conducts a more in-depth analysis of the antenna structure of each tag, and locks the specific area to be repaired based on the established RF performance evaluation indicators, and then forms a segment repair plan. For example, if the gain attenuation of a tag mainly occurs in the high elevation direction, and the bandwidth is significantly narrowed to a high-frequency section, combined with the extreme impedance mismatch at the interface of the tag chip, it can be determined that there are serious bends or microcracks near the chip solder joints or the antenna bends. Therefore, the bend area will be marked as a heavy repair area in the repair plan, and additional data will remind the repair unit to pay attention to the potential fracture risks at the junction of the routing and the chip pins during operation. Through this directional determination based on RF performance evaluation indicators, the repair action can be more targeted, and there is no need to perform large-area processing on the entire antenna, saving time and material costs.

[0050] After determining the section repair plan, the system will implement precise conductive material spraying on the label to be repaired according to the plan. This process requires filling or thickening the existing metal routing defects on the label to reduce the current interruption caused by bending or cracks. To improve the spraying accuracy, the spray head usually cooperates with the visual or displacement sensor module to continuously calibrate the position, and injects conductive particles in small quantities in the spraying range several times to form a continuous and smooth metal thin layer. During the spraying process, the system will also collect impedance change data in real time to monitor whether the impedance of the local area of ​​the label is gradually restored to a safe range after being covered by the conductive material. If the impedance recovery effect is not good, the repair process will increase the amount of conductive material sprayed or extend the spraying time; if it is detected that the impedance curve fluctuates greatly due to excessive spraying, the spraying speed or spraying concentration will be reduced, and the routing thickness and surface uniformity will be consistent with the original design. After completing this step, the label is marked as a preliminary repair label, which means that the main routing or high-risk area has been strengthened with conductive materials, but there may still be a certain amount of residual stress in the internal metal lattice or adhesive layer, which needs to be further stabilized and strengthened.

[0051] To further improve the repair effect, the system will perform local laser annealing on the preliminary repair label. The purpose of annealing is to form a stronger metallurgical bond between the metal traces and the newly sprayed conductive material, while reducing the microcracks or pores in the metal layer caused by high-speed spraying. In this process, the system collects temperature distribution data of the annealing area through temperature sensors or thermal imaging equipment to control the laser power and action time in a local range. If the surface temperature of the trace is detected to rise too fast, the system will reduce the laser energy or shorten the annealing time to prevent overburning of the metal structure or ablation of the material; if the temperature rise rate is too slow or uneven, the laser irradiation intensity will be increased until a stable and moderate heating rate is achieved. The enhanced repair label obtained in this way can be more uniform in microstructure, the metal grains in the trace or spray area are denser, and the stress of the adhesive layer is relatively released. The technical effect of this link is to significantly improve the antenna's tolerance to external forces and temperature and humidity changes, and enhance the adhesion between the conductive thin layer and the original antenna material.

[0052] After completing the local laser annealing, the system performs a high-frequency electromagnetic field scan on the enhanced repair tag to obtain the local current distribution data after the repair. The scanning process is usually carried out in a small-scale RF environment. By applying a signal close to or the same as the original working frequency band to the tag, the current density distribution generated on the trace surface is detected. If the repair process is proper, there should be a smooth current transition between the metal layer formed after spraying and annealing and the antenna body, and there should be no obvious excessive concentration of current density at the trace corners. The system will measure the uniformity of the current distribution on the scanned current distribution diagram, and generate data on the degree of deformation after repair. If the current flow around the corners, bends or chip solder joints is significantly smooth, and the uniformity meets a certain threshold, it means that the deformation has been roughly corrected and the RF performance of the antenna is expected to be restored to a qualified level.

[0053] In order to further clarify the final repair quality, the system sets a repair success threshold and compares the deformation degree data after repair with the threshold. If the current distribution uniformity value is higher than the threshold, the tag is marked as a successfully repaired tag, indicating that the antenna has sufficient carrying and transmission capacity in subsequent application scenarios; if it is lower than the threshold, it is marked as a repair failure tag. The generation of repair failure labels may be due to cracks after spraying, holes in the metal layer, or poor heat distribution in the annealing process. At this time, the system will include it in the remediation list again, or in some cases directly determine it as an unrepairable product to avoid letting seriously defective tags flow into the subsequent supply chain.

[0054] Through the hierarchical repair and detection of the above series of links, the system can achieve accurate repair based on "antenna performance deviation data" and "impedance matching data", and make corresponding material supplements and local annealing operations for different degrees of deformation damage. The two key processes of conductive material spraying and laser annealing complement each other: the former directly repairs the broken or bent parts to restore the basic routing continuity, and the latter strengthens the bonding between the metal and the substrate and improves the local stress distribution. After the high-frequency electromagnetic field scan confirms the uniformity of the routing current, it is compared with the set repair success threshold, and the repair success label and repair failure label are output in a classified manner. The advantage of this is that it can not only carry out targeted treatment of micro-deformations caused by high-speed placement, but also collect sufficient data at each step to ensure the traceability of the repair process. Once a repair failure is found, it can also be traced back to the parameter monitoring value during spraying or annealing to quickly locate process deviations or material problems to improve the success rate of the next batch of repairs. Through this complete chain, RFID tags can maintain high quality standards during large-scale manufacturing and mounting processes, significantly reducing the probability of abnormal reading and writing or failure in the subsequent logistics stage, and providing multiple guarantees for improving production line efficiency and reducing scrap costs.

[0055] Please continue reading Figure 1 , obtain the detection data, evaluation results and repair records of each section, perform multi-dimensional fusion analysis on the acquired data, obtain the optimization parameters of the manufacturing process, and make real-time adaptive adjustments to each section of the RFID tag manufacturing line based on the optimization parameters.

[0056] In one embodiment of the present invention, the detection data, evaluation results and repair records of each section are obtained, and the obtained data are subjected to multi-dimensional fusion analysis to obtain the optimization parameters of the manufacturing process, and the various sections of the RFID tag manufacturing line are adjusted in real time and adaptively according to the optimization parameters, including: obtaining the detection data of the mounting preparation area, the mounting execution area and the preliminary detection area, performing multi-parameter correlation analysis on the detection data, and obtaining the manufacturing front-end abnormal correlation map; obtaining the contour measurement data, thermal diffusion data and deformation feature point data of the depth detection area, performing cluster analysis on the data, and obtaining the label deformation type distribution map; obtaining the scattered signal, reflection signal, resonance peak position and impedance matching of the radio frequency performance evaluation area The invention relates to a method for preparing a plurality of RFID tags and a plurality of RFID tags for manufacturing a plurality of RFID tags. The method comprises the steps of: obtaining the repair scheme data, the deformation degree data after repair and the repair success rate data of the repair processing area, mining the association rules of the data and obtaining the optimal repair strategy set; performing a multi-dimensional fusion analysis on the manufacturing front-end abnormal association map, the label deformation type distribution map, the performance evaluation model parameters and the optimal repair strategy set to obtain the optimization parameters of the manufacturing process, generating a real-time adaptive adjustment instruction of the RFID tag manufacturing line according to the optimization parameters, and performing real-time adaptive adjustment on the mounting preparation area, the mounting execution area, the preliminary detection area, the deep detection area, the radio frequency performance evaluation area and the repair processing area of ​​the RFID tag manufacturing line.

[0057] Specifically, after obtaining the detection data of the mounting preparation area, mounting execution area and preliminary inspection area, the system first performs multi-parameter correlation analysis on these multi-source information to form a manufacturing front-end abnormal correlation map. The mounting preparation area often contains the surface unevenness data of the packaging box, material characteristics and high-risk box determination information, the mounting execution area provides the pressure change, angle change, position change and label characteristic data of the robot arm, and the preliminary inspection area provides the success rate and signal strength of the simple RF read and write test. Multi-parameter correlation analysis usually uses regression, correlation coefficient matrix or aggregation methods to interactively compare these parameters. If the pressure peak abnormality and read and write failure occur repeatedly on a certain type of packaging box, then the manufacturing front-end abnormal correlation map will show an enhanced correlation at the corresponding node. Through this map, the potential coupling relationship between the packaging box characteristics and the mechanical abnormality during mounting and the failure of the initial RF inspection after mounting can be observed, so that the front-end factors that are more likely to cause antenna deformation or RF hidden dangers can be identified in the early stage of the overall production process. If it is found that the fibers of a batch of carton materials are too hard and the robot arm's operating acceleration is too high, and the corresponding labels frequently have poor signal reading and writing during the initial inspection, then the associated edges in the graph will appear densely connected, which means that process optimization may be required in the mounting preparation area or execution area to prevent large-scale defective products.

[0058] In the deep detection area, profile measurement data, heat diffusion data and deformation feature point data provide richer details for the microscopic deformation of the tag. Profile measurement data mainly comes from high-precision optical equipment or interferometers, which can mark the bends and undulations of the antenna line on a two-dimensional plane; heat diffusion data comes from pulse thermal excitation and temperature sensors, which detect internal dark cracks or peeling by observing local temperature rise and thermal conductivity; deformation feature point data combines optical detection and thermal detection to highlight the most suspicious damage points or crack coordinates. Cluster analysis of these data can divide the tag deformation into multiple types, such as single bends, complex cracks, interlayer separation, etc. These types often have their own characteristics in shape characteristics, thermal response patterns or fracture distribution. If some tags show interlayer peeling at the corners of the antenna, the clustering results will show that many tags in the same cluster have similar abnormal temperature curves under pulse thermal excitation, and the optical profile has concave deformation in the same area. Through the label deformation type distribution map, the common causes of problems in a batch or a certain working condition can be quickly located, and targeted input can be provided for subsequent repair strategies. The deep detection area thus plays the role of "deformation map induction", allowing the general anomalies collected by the front end to be effectively matched with the performance problems that may occur in the back end.

[0059] The RF performance evaluation area obtains scattered signals, reflected signals, resonance peak positions and impedance matching data in the multi-angle microwave side-shooting and reflection parameter collection, and then performs multi-parameter regression analysis on this information to generate performance evaluation model parameters. Scattering and reflection signals are used to determine whether there are abnormal paths of energy leakage or energy reflection in the routing. The resonance peak position and bandwidth can map whether the antenna maintains good resonance within the specified frequency band. The impedance matching data is related to the energy coupling efficiency between the antenna and the chip. At this stage, multi-parameter regression analysis often integrates quantitative indicators such as nominal input impedance, ideal gain pattern and actual measured return loss curve, and finds out which features can best determine the performance of the tag through fitting methods or neural network models. If some tags show significant resonance shift and sharp bandwidth contraction in microwave testing, the regression model will give poor performance evaluation model parameters, suggesting that there may be a large degree of deformation. By comparing the test results of different batches of tags under similar appearance or mounting conditions, the strong correlation between deformation and RF failure can also be revealed, thereby providing a more direct decision-making basis for repair area or production line optimization.

[0060] After the repair processing area performs precise conductive spraying, laser annealing and impedance correction, repair scheme data, repair deformation degree data and repair success rate data will be generated. The repair scheme data describes the specific repair path of each label, including the number of spraying times, annealing temperature curve and whether local glue filling has been performed. The deformation degree data after repair includes the optical scan, local current distribution or impedance recovery collected during re-testing, and the repair success rate data is used to count the proportion of labels that finally recover to qualified RF performance. Association rule mining of these data can reveal the optimal repair strategy set, such as whether similar fractures should be sprayed with a thicker conductive layer first and then annealed at a lower power, or whether rapid annealing should be used first and then the spraying details should be adjusted, which sequence has the highest success rate in a statistical sense. If certain repair methods repeatedly have a low success rate on labels of specific materials, the system will mark this result and prompt the operator or R&D department to replace the repair material, optimize the spraying accuracy or improve the annealing intensity to improve the process maturity. The optimal repair strategy set is thus formed, providing systematic and reusable repair path suggestions for subsequent similar faults or batch failures.

[0061] After completing the data analysis of the aforementioned multiple areas, the system conducts a multi-dimensional fusion analysis of the manufacturing front-end abnormal correlation map, the label deformation type distribution map, the performance evaluation model parameters, and the optimal repair strategy set, and finally obtains the optimization parameters of the manufacturing process. The multi-dimensional fusion analysis will comprehensively consider multiple factors such as the risk of packaging box materials, mounting pressure and angle anomalies, thermal diffusion clustering results, RF evaluation curve offset, and finished product yield after repair, and map them to a high-dimensional decision space. If it is detected that the mounting process has frequent mechanical anomalies in a certain period of time, and the depth detection area points to the same type of corner cracks, the data of the RF evaluation area confirms that the label resonance peak drifts significantly, and the repair strategy set The low repair success rate, the system will recommend that the robot arm acceleration or mounting angle setting of the mounting execution area be corrected to reduce the impact force, or additional pretreatment measures be taken for high-risk boxes. If the data merging analysis shows that certain repair processes are extremely effective on specific types of cracks and have a high success rate, then the system will inject this part of the optimal strategy into the mounting line operation instructions so that the subsequent repair processing area can quickly replicate the successful case, thereby improving the yield rate.

[0062] After obtaining the optimized parameters of the manufacturing process, the system will generate real-time adaptive adjustment instructions for the RFID tag manufacturing line, and synchronously schedule the mounting preparation area, mounting execution area, preliminary inspection area, deep inspection area, RF performance evaluation area, and repair processing area. The instructions received by the mounting preparation area may include adjusting the input rhythm of the packaging box, or more accurately screening vulnerable materials, so as to reduce the probability of large quantities of high-risk boxes pouring into the production line from the source; the mounting execution area adjusts the acceleration limit or real-time monitoring threshold of the robot arm according to the instructions to reduce the bending of the antenna caused by impact; the preliminary inspection area can add temporary encrypted read and write test steps after receiving the instructions. If the system finds that a large number of tags fail the initial inspection, it will automatically add a secondary test or slow down the conveyor belt speed; the deep inspection area and the RF performance evaluation area can give priority to checking the tags marked as high correlation risks or with multiple mounting anomalies under the guidance of the instructions; the repair processing area will also synchronously update the optimal repair strategy set, and execute the operation method with a higher verified success rate for the same type of damaged modules to further improve the repair efficiency and quality. Through this real-time adaptive adjustment, the entire production line can strike a balance between high-speed placement rhythm and high-quality requirements. It can not only respond quickly to short-term batch failures, but also gradually explore deeper manufacturing rules after accumulating a large amount of analytical data, thus laying an important foundation for large-scale and flexible production. The entire process greatly improves the product's qualification rate and reliability while ensuring production capacity, avoiding the failure of large quantities of RFID tags at the subsequent supply chain level, and greatly reducing the company's potential risks in logistics management and customer experience.

[0063] The above describes the detection method in the RFID tag manufacturing process according to the embodiment of the present invention. The following describes the detection device in the RFID tag manufacturing process according to the embodiment of the present invention. Figure 2 , an embodiment of a detection device in the RFID tag manufacturing process of the present invention includes: A preliminary screening module 101 is used to obtain stress monitoring data of multiple sections of the RFID tag manufacturing line, and perform preliminary screening of RFID tags based on the stress monitoring data to obtain suspected abnormal tags; The multi-dimensional detection module 102 is used to perform multi-dimensional optical detection and thermal detection on the high-risk area of ​​the suspected abnormal label, obtain the contour measurement data and thermal diffusion data of the antenna sensitive domain, determine the deformation feature points of the antenna according to the contour measurement data and thermal diffusion data, and obtain the label with real deformation risk; The radio frequency performance evaluation module 103 is used to perform a partitioned microwave side-beam test and an impedance mismatch analysis on the tag with a real deformation risk, obtain the scattered signal, reflected signal, resonance peak position and impedance matching data of the antenna, evaluate the radio frequency performance of the antenna according to the scattered signal, reflected signal, resonance peak position and impedance matching data, and obtain the tag that needs to be repaired; The intelligent repair module 104 is used to perform intelligent section repair processing on the label to be repaired, obtain deformation degree data of the repaired label, determine whether the repair is successful according to the deformation degree data, and obtain a repair success label and a repair failure label; The optimization and adjustment module 105 is used to obtain the detection data, evaluation results and repair records of each section, perform multi-dimensional fusion analysis on the acquired data, obtain the optimization parameters of the manufacturing process, and perform real-time adaptive adjustment on each section of the RFID tag manufacturing line according to the optimization parameters.

[0064] above Figure 2 The detection device in the RFID tag manufacturing process in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The detection device in the RFID tag manufacturing process in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0065] Figure 3 1 is a schematic diagram of the structure of a detection device in the manufacturing process of an RFID tag provided by an embodiment of the present invention. The detection device 200 in the manufacturing process of the RFID tag may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 210 (for example, one or more processors) and a memory 220, and one or more storage media 230 (for example, one or more mass storage device terminals) storing application programs 233 or data 232. Among them, the memory 220 and the storage medium 230 can be short-term storage or permanent storage. The program stored in the storage medium 230 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the detection device 200 in the manufacturing process of the RFID tag. Furthermore, the processor 210 can be configured to communicate with the storage medium 230, and execute a series of instruction operations in the storage medium 230 on the detection device 200 in the manufacturing process of the RFID tag to implement the steps of the detection method in the manufacturing process of the RFID tag described above.

[0066] The detection device 200 in the RFID tag manufacturing process may also include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input and output interfaces 260, and / or one or more operating systems 231, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 3 The structure of the detection device in the RFID tag manufacturing process shown does not constitute a limitation on the detection device in the RFID tag manufacturing process provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0067] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the detection method in the RFID tag manufacturing process.

[0068] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device or unit described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0069] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0070] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A detection method in the manufacturing process of RFID tags, characterized in that: include: Obtain stress monitoring data of multiple sections of the RFID tag manufacturing line, and perform preliminary screening of RFID tags based on the stress monitoring data to obtain suspected abnormal tags; Perform multi-dimensional optical and thermal inspections on the high-risk areas of the suspected abnormal labels to obtain contour measurement data and thermal diffusion data of the antenna sensitive domain, determine the deformation feature points of the antenna based on the contour measurement data and thermal diffusion data, and obtain labels with real deformation risks; Performing a partitioned microwave side-beam test and an impedance mismatch analysis on the tag with a real risk of deformation, obtaining the scattered signal, reflected signal, resonance peak position and impedance matching data of the antenna, evaluating the RF performance of the antenna based on the scattered signal, reflected signal, resonance peak position and impedance matching data, and obtaining the tag that needs to be repaired; Perform intelligent section repair processing on the label to be repaired, obtain deformation degree data of the repaired label, judge whether the repair is successful according to the deformation degree data, and obtain a repair success label and a repair failure label; The detection data, evaluation results and repair records of each section are obtained, and a multi-dimensional fusion analysis is performed on the obtained data to obtain the optimization parameters of the manufacturing process. According to the optimization parameters, each section of the RFID tag manufacturing line is adaptively adjusted in real time.

2. The detection method in the RFID tag manufacturing process according to claim 1, characterized in that: The step of obtaining stress monitoring data of multiple sections of the RFID tag manufacturing line and preliminarily screening the RFID tags according to the stress monitoring data to obtain suspected abnormal tags includes: Obtaining the surface unevenness data and material property data of the packaging boxes in the preparatory area of ​​the RFID tag manufacturing line, and performing a fitting risk assessment on the packaging boxes based on the surface unevenness data and material property data of the packaging boxes to obtain high-risk boxes; Obtaining the robot arm mounting pressure data, mounting angle data and label characteristic data of the mounting execution area of ​​the RFID label manufacturing assembly line, analyzing the label bonding condition according to the robot arm mounting pressure data, mounting angle data and label characteristic data, and obtaining the label with abnormal bonding; Obtain radio frequency read / write test data of the preliminary detection area of ​​the RFID tag manufacturing line, and quickly screen the tags according to the radio frequency read / write test data to obtain the tags that failed to be read; A comprehensive analysis is performed on the labels on the high-risk boxes, the labels with abnormal fitting, and the labels with failed reading to obtain suspected abnormal labels.

3. The detection method in the RFID tag manufacturing process according to claim 2, characterized in that: The step of obtaining the robot arm mounting pressure data, mounting angle data and label characteristic data of the mounting execution area of ​​the RFID label manufacturing assembly line, analyzing the label fitting status according to the robot arm mounting pressure data, mounting angle data and label characteristic data, and obtaining the abnormally fitted label includes: Obtaining pressure change data, angle change data, and position change data of the robot arm during the label attaching process, and calculating the stress distribution during the label attaching process according to the pressure change data, angle change data, and position change data to obtain stress distribution data; Acquire antenna structure parameters and chip position data of the RFID tag, analyze antenna deformation degree and chip displacement risk according to the stress distribution data, and obtain tag structure risk data; Acquire instantaneous acceleration data of the robot arm, calculate the impact degree of the tag according to the instantaneous acceleration data, and obtain impact risk data; Acquire flexibility parameters of the label substrate, analyze the possibility of label performance degradation according to the label structure risk data, impact risk data and flexibility parameters, and obtain performance degradation risk data; A performance degradation threshold is set, the performance degradation risk data is evaluated, labels exceeding the performance degradation threshold are obtained, labels exceeding the performance degradation threshold are determined as performance abnormality labels, and fitted abnormality labels are obtained.

4. The detection method in the RFID tag manufacturing process according to claim 1, characterized in that: The multi-dimensional optical detection and thermal detection are performed on the high-risk area of ​​the suspected abnormal label to obtain contour measurement data and thermal diffusion data of the antenna sensitive domain, and the deformation feature points of the antenna are determined according to the contour measurement data and thermal diffusion data to obtain a label with a real deformation risk, including: Acquire antenna design parameters and operating frequency data of the RFID tag, calculate the antenna current distribution theoretical value according to the antenna design parameters and operating frequency data, and obtain the antenna sensitive area map; Gridding the antenna sensitive area map to obtain high-precision optical contour data, and comparing the high-precision optical contour data with a standard contour template to obtain micro-deformation area coordinates and contour measurement data; Acquiring thermal characteristic parameters of the label substrate, designing a pulse thermal excitation scheme according to the thermal characteristic parameters, performing directional thermal excitation on the coordinates of the micro-deformation region, and obtaining a thermal response curve and thermal diffusion data; Analyze the peak value, decay rate and steady-state temperature of the thermal response curve to obtain a thermal anomaly index, and obtain suspected internal structural abnormal points according to the matching degree between the thermal anomaly index and the coordinates of the micro-deformation area; Obtaining the nominal resonant frequency of the tag antenna, performing local high-frequency electromagnetic field excitation on the suspected internal structural abnormal point, and obtaining local impedance change data; The contour measurement data, heat diffusion data and local impedance change data are comprehensively analyzed to obtain a three-dimensional distribution map of deformation feature points, and the degree of antenna performance degradation is evaluated according to the three-dimensional distribution map to obtain a label with a real deformation risk.

5. The detection method in the RFID tag manufacturing process according to claim 4, characterized in that: The step of obtaining the nominal resonant frequency of the tag antenna and performing local high-frequency electromagnetic field excitation on the suspected internal structural abnormal point to obtain local impedance change data includes: Acquire the spatial distribution data of suspected internal structural abnormal points, design the adaptive electromagnetic field excitation probe array layout according to the spatial distribution data, and obtain multi-point precise excitation coordinates; Obtain the nominal resonant frequency of the tag antenna, generate a frequency scanning sequence including the frequency bands adjacent to the resonant frequency based on the nominal resonant frequency data, perform local high-frequency electromagnetic field excitation on the multi-point precise excitation coordinates, obtain the reflection coefficient spectrum of each excitation point, and obtain local electromagnetic response data; Analyzing the local electromagnetic response data, extracting the actual resonant frequency and bandwidth parameters of each excitation point, and comparing them with the nominal resonant frequency data to obtain the frequency offset and bandwidth change; According to the frequency offset and bandwidth variation, combined with the equivalent circuit model of the RFID tag antenna, the equivalent impedance variation of each excitation point is calculated to obtain a local impedance variation spectrum; The dynamic input impedance characteristics of the RFID chip are obtained, and based on the local impedance change spectrum and the chip dynamic input impedance characteristics, the change of impedance matching with frequency is calculated to obtain local impedance change data.

6. The detection method in the RFID tag manufacturing process according to claim 1, characterized in that: The method includes performing a partitioned microwave side-beam test and an impedance mismatch analysis on the tag with a real deformation risk, obtaining a scattered signal, a reflected signal, a resonance peak position and impedance matching data of the antenna, evaluating the RF performance of the antenna according to the scattered signal, the reflected signal, the resonance peak position and the impedance matching data, and obtaining a tag to be repaired, including: Obtain theoretical radiation pattern data of the RFID tag antenna, design a multi-angle microwave side-firing test scheme based on the theoretical radiation pattern data, and obtain a side-firing angle sequence; Performing microwave scanning on the tag with real deformation risk according to the side-firing angle sequence to obtain scattered signal intensity and phase data at different angles, reconstructing the actual radiation pattern according to the scattered signal intensity and phase data, and obtaining antenna scattering characteristic data; Obtaining the working frequency range of the RFID tag, performing frequency scanning within the working frequency range, obtaining the reflection coefficient spectrum of the tag antenna, extracting the resonance peak position and bandwidth data according to the reflection coefficient spectrum, and obtaining antenna reflection characteristic data; Based on the antenna scattering characteristic data and the antenna reflection characteristic data, the actual gain and directivity of the antenna are calculated, and compared with the standard antenna parameters to obtain antenna performance deviation data; Obtaining the nominal input impedance data of the RFID chip, calculating the input impedance of the antenna according to the antenna reflection characteristic data, performing matching analysis with the nominal input impedance data of the chip, and obtaining impedance matching data; The antenna performance deviation data and impedance matching data are comprehensively analyzed to establish a radio frequency performance evaluation index for RFID tags, and the tags are graded according to the radio frequency performance evaluation index to obtain tags that need to be repaired.

7. The detection method in the RFID tag manufacturing process according to claim 1, characterized in that: The intelligent section repair process is performed on the label to be repaired, deformation degree data of the repaired label is obtained, and whether the repair is successful is determined according to the deformation degree data to obtain a repair success label and a repair failure label, including: Determine the repair priority order according to the antenna performance deviation data and impedance matching data of the tag to be repaired, and obtain a hierarchical repair list; Perform antenna structure analysis on each tag in the hierarchical repair list, determine the area to be repaired according to the radio frequency performance evaluation index of the tag, and obtain a section repair plan; According to the section repair plan, the conductive material is precisely sprayed on the label to be repaired, real-time impedance change data is obtained during the spraying process, and the spraying parameters are dynamically adjusted according to the real-time impedance change data to obtain a preliminary repaired label; Performing local laser annealing on the preliminary repair label, obtaining temperature distribution data during the annealing process, and controlling the annealing intensity and time according to the temperature distribution data to obtain a reinforced repair label; Performing high-frequency electromagnetic field scanning on the enhanced repair tag to obtain local current distribution data after repair, calculating the current distribution uniformity according to the local current distribution data, and obtaining the deformation degree data after repair; A repair success threshold is set, and the repaired deformation degree data is compared with the repair success threshold. Labels with current distribution uniformity higher than the threshold are classified as repair success labels, and labels with current distribution uniformity lower than the threshold are classified as repair failure labels, thereby obtaining repair success labels and repair failure labels.

8. The detection method in the RFID tag manufacturing process according to claim 1, characterized in that: The detection data, evaluation results and repair records of each section are obtained, and the obtained data are analyzed in a multi-dimensional fusion manner to obtain the optimization parameters of the manufacturing process, and each section of the RFID tag manufacturing line is adaptively adjusted in real time according to the optimization parameters, including: Acquire the detection data of the placement preparation area, the placement execution area and the preliminary detection area, perform multi-parameter correlation analysis on the detection data, and obtain a manufacturing front-end abnormality correlation map; Acquire contour measurement data, thermal diffusion data, and deformation feature point data of the depth detection area, perform cluster analysis on the data, and obtain a label deformation type distribution map; Acquire scattered signals, reflected signals, resonance peak positions and impedance matching data in the radio frequency performance evaluation area, perform multi-parameter regression analysis on the data, and obtain performance evaluation model parameters; Acquire the repair plan data, the deformation degree data after repair and the repair success rate data of the repair treatment area, perform association rule mining on the data, and obtain the optimal repair strategy set; A multi-dimensional fusion analysis is performed on the manufacturing front-end abnormal association map, label deformation type distribution map, performance evaluation model parameters and optimal repair strategy set to obtain the optimization parameters of the manufacturing process, and real-time adaptive adjustment instructions for the RFID label manufacturing line are generated according to the optimization parameters, and real-time adaptive adjustment is performed on the mounting preparation area, mounting execution area, preliminary detection area, deep detection area, radio frequency performance evaluation area and repair processing area of ​​the RFID label manufacturing line.

9. A detection device in the RFID tag manufacturing process, characterized in that: The detection device in the RFID tag manufacturing process adopts the detection method in the RFID tag manufacturing process according to any one of claims 1 to 8, and the detection device in the RFID tag manufacturing process comprises: A preliminary screening module is used to obtain stress monitoring data of multiple sections of the RFID tag manufacturing line, and perform preliminary screening of RFID tags based on the stress monitoring data to obtain suspected abnormal tags; A multi-dimensional detection module is used to perform multi-dimensional optical detection and thermal detection on the high-risk area of ​​the suspected abnormal label, obtain contour measurement data and thermal diffusion data of the antenna sensitive domain, determine the deformation feature points of the antenna according to the contour measurement data and thermal diffusion data, and obtain a label with a real deformation risk; A radio frequency performance evaluation module is used to perform a partitioned microwave side-beam test and an impedance mismatch analysis on the tag with a real deformation risk, obtain the scattered signal, reflected signal, resonance peak position and impedance matching data of the antenna, evaluate the radio frequency performance of the antenna according to the scattered signal, reflected signal, resonance peak position and impedance matching data, and obtain the tag that needs to be repaired; An intelligent repair module is used to perform intelligent section repair processing on the label to be repaired, obtain deformation degree data of the repaired label, determine whether the repair is successful according to the deformation degree data, and obtain a repair success label and a repair failure label; The optimization and adjustment module is used to obtain the detection data, evaluation results and repair records of each section, perform multi-dimensional fusion analysis on the acquired data, obtain the optimization parameters of the manufacturing process, and perform real-time adaptive adjustment on each section of the RFID tag manufacturing line according to the optimization parameters.

10. A detection device in the RFID tag manufacturing process, characterized in that: The detection device in the RFID tag manufacturing process includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the detection device in the RFID tag manufacturing process to perform the steps of the detection method in the RFID tag manufacturing process as described in any one of claims 1-8.

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