RFID Wristband Production Monitoring Method and System Based on Data Analysis

Abstract

The present invention discloses an RFID wristband production monitoring method and system based on data analysis, which relates to the technical field of product production monitoring. An expansion effect factor of the base material is generated according to the environmental parameters of the processing workshop, and the working state and motion characteristics of the mounting equipment are monitored in real time. After dynamically modeling the mounting equipment by using a Markov model, the change of the working parameters of the mounting equipment is predicted, and a change anomaly factor is output for the mounting equipment. By combining and analyzing the expansion effect factor and the change anomaly factor, it is judged whether it is necessary to dynamically adjust the correction mounting pressure in advance, and a corresponding control strategy is generated for the mounting equipment according to the judgment result. The monitoring system combines the expansion effect factor and the change anomaly factor, can actively adjust the mounting pressure, effectively reduce the feed point error when the influencing factors change, and generate a corresponding control strategy for the mounting equipment according to the multi-factor decision result, so that it can maintain the best mounting accuracy under complex environmental conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of product production monitoring, and particularly to an RFID wristband production monitoring method and system based on data analysis. Background Art

[0002] An RFID wristband is an intelligent wearable device integrating radio frequency identification (RFID) technology, which is usually used in scenarios such as identity identification, information tracking, and management control. It communicates with a reading device by embedding an RFID tag to achieve fast and contactless data transmission. An RFID wristband usually consists of two parts: an embedded chip (tag) and a supporting reading device (such as an RFID reader).

[0003] The prior art has the following defects:

[0004] During the chip mounting process of an RFID wristband, when applied to the ultra-high frequency (UHF) or microwave frequency band (such as 2.4 GHz), the size of the antenna is extremely small (for example, the side length of a microstrip antenna is only at the 10 mm level), and the position of the feeding point directly affects the resonant frequency and impedance matching. The feeding point error is essentially the deviation of the antenna physical structure from the design parameters, manifested as the offset between the actual contact position of the chip and the antenna feeding point and the theoretical optimal position. This offset will change the antenna resonant frequency and impedance matching characteristics. The wristband antenna usually adopts a microstrip or flexible antenna design, and a feeding point offset of 0.1 mm can cause a frequency offset of more than 50 MHz. This offset will cause the tag resonant frequency to deviate from the reader working frequency band (such as 860 - 960 MHz for UHF), resulting in signal attenuation or even failure.

[0005] Based on this, the present invention proposes an RFID wristband production monitoring method and system based on data analysis, which can actively adjust the mounting pressure according to the multi-factor decision result, effectively reduce the feeding point error when the influencing factors change, generate corresponding control strategies for the mounting equipment, and enable it to maintain the best mounting accuracy under complex environmental conditions. Summary of the Invention

[0006] The purpose of the present invention is to provide an RFID wristband production monitoring method and system based on data analysis to solve the deficiencies in the background art.

[0007] To achieve the above purpose, the present invention provides the following technical solution: An RFID wristband production monitoring method based on data analysis, the monitoring method includes the following steps:

[0008] The monitoring system obtains the label substrate and chip information of the currently produced RFID wristband, and substitutes the substrate and chip information into the database to match the initial mounting pressure;

[0009] In each mounting process, after analyzing the substrate thickness deviation and chip size deviation, the initial mounting pressure is corrected based on an optimization algorithm to obtain the corrected mounting pressure;

[0010] Generate an expansion effect factor of the substrate according to the environmental parameters of the processing workshop;

[0011] Monitor the working state and motion characteristics of the mounting equipment in real time. After dynamically modeling the mounting equipment using a Markov model, predict the changes in the working parameters of the mounting equipment and output a change anomaly factor for the mounting equipment;

[0012] Combine the analysis of the expansion effect factor and the change anomaly factor to determine whether it is necessary to dynamically adjust the corrected mounting pressure in advance, and generate corresponding control strategies for the mounting equipment according to the judgment results.

[0013] In a preferred embodiment, combining the analysis of the expansion effect factor and the change anomaly factor to dynamically adjust the corrected mounting pressure includes the following steps:

[0014] After obtaining the expansion effect factor and the change anomaly factor, calculate and obtain a dynamic adjustment index, and the expression is: , where is the dynamic adjustment index, is the expansion effect factor, is the change anomaly factor, , are weight coefficients, and ;

[0015] Adjust the corrected mounting pressure through the dynamic adjustment index, and the adjustment algorithm expression is:

[0016] , where is the adjusted mounting pressure, is the corrected mounting pressure, is the dynamic adjustment index.

[0017] In a preferred embodiment, outputting a change anomaly factor for the mounting equipment includes the following steps:

[0018] Normalize the nozzle clogging degree and the vibration amplitude of the mounting head so that the value ranges of the nozzle clogging degree and the vibration amplitude of the mounting head are mapped to between [0, 1], and sum the nozzle clogging degree and the vibration amplitude of the mounting head after the normalization process to obtain a proportionality coefficient;

[0019] Combine the proportionality coefficient with the probability of transitioning to state to generate a change anomaly factor, and the expression is: , where is the change anomaly factor, is the proportionality coefficient, is the probability of transferring to state .

[0020] In a preferred embodiment, an expansion effect factor of the substrate is generated based on the environmental parameters of the processing workshop;

[0021] During the process of transporting the substrate to the mounting device through the transport device, the workshop environmental temperature is recorded in real time by a temperature sensor, and the workshop environmental humidity is recorded in real time by a humidity sensor;

[0022] The workshop environmental temperature recorded in real time is compared with a preset temperature threshold, and the time period when the workshop environmental temperature exceeds the temperature threshold is recorded as the temperature warning time period. The workshop environmental humidity recorded in real time is compared with a preset humidity threshold, and the time period when the workshop environmental humidity exceeds the humidity threshold is recorded as the humidity warning time period;

[0023] The temperature warning time period and the humidity warning time period are accumulated, and an integral operation is performed to generate the expansion effect factor of the substrate. The expression is: ; In the formula, is the expansion effect factor, is the volume change amount of the substrate at time , is the temperature warning time period, is the humidity warning time period.

[0024] In a preferred embodiment, after analyzing the substrate thickness deviation and the chip size deviation, the initial mounting pressure is corrected based on an optimization algorithm, including the following steps;

[0025] The initial mounting pressure is corrected according to the thickness deviation and the size deviation, and the initial mounting pressure is corrected based on the optimization algorithm. The expression of the optimization algorithm is: , in the formula, is the corrected mounting pressure, is the initial mounting pressure, is the thickness deviation, is the size deviation, , are adjustment coefficients, and the adjustment coefficients are greater than 0.

[0026] In a preferred embodiment, a laser thickness gauge or an inductive thickness sensor is used to measure the current substrate thickness, the thickness deviation is obtained by subtracting the standard substrate thickness from the current substrate thickness, an industrial camera is used to obtain the size of the current chip, and the size deviation is obtained by subtracting the standard chip size from the current chip size.

[0027] In a preferred embodiment, the monitoring system substitutes the substrate and chip information into the database to match the initial mounting pressure, including the following steps:

[0028] Obtain the substrate type, read the substrate thickness, record the substrate flexibility characteristics, and record the chip size;

[0029] According to the substrate type, chip size, and packaging type, retrieve the historical production data to find the matching pressure range constraint conditions;

[0030] If there is historical mounting data with the same material, size, and process, extract its pressure setting value as the initial mounting pressure;

[0031] If there is no completely matching data, use a recommendation algorithm based on similarity calculation to estimate the initial mounting pressure, and the initial mounting pressure needs to meet the pressure range constraint conditions.

[0032] In a preferred embodiment, a recommendation algorithm based on similarity calculation is used to estimate the initial mounting pressure, including the following steps:

[0033] Extract the set of feature vectors {V_1, V_2,..., V_N} of all historical production batches in the database, where N represents the number of historical production batches, and V_i represents the production feature vector of the i-th batch;

[0034] Use cosine similarity to calculate the similarity score between the current batch feature vector V_cur and the historical data {V_1,..., V_N};

[0035] Take the M groups of data with similarity scores greater than the score threshold as the reference data set, sum the similarity scores of all batch production feature vectors in the reference data set to obtain the total similarity score value, and obtain the weight of each batch production by dividing the similarity score by the total similarity score value;

[0036] Extract the historical mounting pressures of each batch production in the reference data set, marked as: {P_1, P_2,..., P_M}, and use the weighted average method to calculate the initial mounting pressure.

[0037] In a preferred embodiment, the cosine similarity is used to calculate the similarity between the current batch feature vector V_cur and the historical data {V_1,..., V_N}, and the expression is: , where is the similarity score between the current batch production feature vector and the i-th batch production feature vector, represents the dot product between the current batch production feature vector and the i-th batch production feature vector, is the norm of the current batch production feature vector, is the norm of the i-th batch production feature vector;

[0038] Use the weighted average method to calculate the initial mounting pressure, and the expression is: , where is the initial mounting pressure, is the number of batches in the reference dataset, is the weight of the production of the i-th batch, is the historical mounting pressure of the production of the i-th batch.

[0039] The present invention also provides an RFID wristband production monitoring system based on data analysis, including an initial matching module, a correction module, an analysis module, and a control module;

[0040] Initial matching module: Obtain the label substrate and chip information of the currently produced RFID wristband, and substitute the substrate and chip information into the database to match the initial mounting pressure;

[0041] Correction module: During each mounting process, after analyzing the substrate thickness deviation and chip size deviation, correct the initial mounting pressure based on an optimization algorithm to obtain the corrected mounting pressure;

[0042] Analysis module: Generate an expansion effect factor of the substrate based on the environmental parameters of the processing workshop, real-time monitor the working state and motion characteristics of the mounting equipment, dynamically model the mounting equipment using a Markov model, predict the changes in the working parameters of the mounting equipment, and output a change anomaly factor for the mounting equipment;

[0043] Control module: Combine the analysis of the expansion effect factor and the change anomaly factor to determine whether it is necessary to dynamically adjust the corrected mounting pressure in advance, and generate corresponding control strategies for the mounting equipment based on the judgment result.

[0044] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0045] 1. The present invention generates an expansion effect factor of the substrate based on the environmental parameters of the processing workshop, real-time monitors the working state and motion characteristics of the mounting equipment, dynamically models the mounting equipment using a Markov model, predicts the changes in the working parameters of the mounting equipment, and outputs a change anomaly factor for the mounting equipment. By combining the analysis of the expansion effect factor and the change anomaly factor, it is determined whether it is necessary to dynamically adjust the corrected mounting pressure in advance, and corresponding control strategies are generated for the mounting equipment based on the judgment result. The monitoring system combines the expansion effect factor and the change anomaly factor, enabling the system to actively adjust the mounting pressure, effectively reducing the offset of the feeding point when the influencing factors change. Based on the multi-factor decision result, corresponding control strategies are generated for the mounting equipment, enabling it to maintain the best mounting accuracy under complex environmental conditions.

[0046] 2. The present invention obtains the substrate and chip information of the currently produced RFID wristband tags, substitutes the substrate and chip information into the database to match the initial mounting pressure. During each mounting process, after analyzing the substrate thickness deviation and chip size deviation, the initial mounting pressure is corrected based on an optimization algorithm to obtain the corrected mounting pressure. Since in actual applications, the substrates and chips of the same batch may have thickness and size variations, the monitoring can adjust the mounting equipment to the optimal mounting pressure according to the substrate thickness and chips during each mounting process, improving the mounting accuracy and ensuring the quality of RFID wristband tags.

[0047] 3. The generation of the expansion effect factor in the present invention helps production enterprises deeply understand the relationship between environmental factors and substrate performance. Based on this factor, enterprises can further optimize the production process, such as adjusting parameters such as mounting pressure according to different environmental conditions, making the production process more adaptable to environmental changes, and improving the stability of production efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0051] Embodiment 1: Please refer to Figure 1 As shown, the monitoring method for RFID wristband production based on data analysis in this embodiment includes the following steps:

[0052] The monitoring system obtains the substrate and chip information of the currently produced RFID wristband tags, and substitutes the substrate and chip information into the database to match the initial mounting pressure;

[0053] Obtain the substrate type (such as PI, PET, FR4, etc.). Read the substrate thickness (such as a tolerance range of ±0.02 mm). Record the flexible characteristics of the substrate (rigid or flexible substrate, which determines the mounting method). Record the chip size (length, width, thickness), which affects the calculation of the mounting pressure. Collect the size and alignment requirements of the pad area (such as a mounting accuracy requirement of ±0.1 mm). Obtain the characteristics of the chip packaging material (such as silicon, ceramic, polymer, which affects the stress-bearing capacity).

[0054] According to the substrate type, chip size, and packaging type, retrieve the historical production data to find the most matching pressure range constraint conditions. If there is historical mounting data with the same material, size, and process, extract its pressure setting value as the initial mounting pressure. If there is no completely matching data, use a recommendation algorithm based on similarity calculation to estimate the initial mounting pressure, and the initial mounting pressure needs to meet the pressure range constraint conditions (P min ~P max） , ensuring both firm mounting and no damage to the chip or substrate.

[0055] In this application, a recommendation algorithm based on similarity calculation is used to estimate the initial mounting pressure, including the following steps:

[0056] Extract the set of feature vectors of all historical production batches in the database {V_1, V_2,..., V_N}, where N represents the number of historical production batches, and V_i represents the production feature vector of the i-th batch;

[0057] Use cosine similarity to calculate the similarity between the current batch feature vector V_cur and the historical data {V_1,..., V_N}, and the expression is: , where is the similarity score between the current batch production feature vector and the i-th batch production feature vector, represents the dot product between the current batch production feature vector and the i-th batch production feature vector, is the norm of the current batch production feature vector, is the norm of the i-th batch production feature vector, and the higher the similarity score, the higher the similarity;

[0058] Take the M groups of data with similarity scores greater than the score threshold as the reference data set, sum the similarity scores of all batch production feature vectors in the reference data set to obtain the total similarity score value, and obtain the weight of each batch production by dividing the similarity score by the total similarity score value;

[0059] Extract the historical mounting pressures of each batch production in the reference data set, marked as: {P_1, P_2,..., P_M}, and use the weighted average method to calculate the initial mounting pressure, and the expression is:

[0060] , where is the initial placement pressure, is the number of batches in the reference dataset, is the weight of the production in the i-th batch, is the historical placement pressure of the production in the i-th batch.

[0061] During the production process of RFID wristbands, factors such as process parameters, substrate characteristics, chip specifications, and equipment status may vary for each production batch. Therefore, we need to structurally process the data of each production batch to generate corresponding batch production feature vectors;

[0062] The batch production feature vector is a multi-dimensional vector that contains feature data of multiple categories:

[0063] (1) Substrate characteristics (Material_Features)

[0064] Substrate type (such as PI, PET, FR4);

[0065] Substrate thickness (such as 0.1mm, 0.2mm);

[0066] Coefficient of thermal expansion (CTE) (such as 20ppm / ℃);

[0067] Surface roughness (such as Ra0.01μm~0.05μm);

[0068] Substrate cutting tolerance (such as 0.03mm~0.05mm);

[0069] (2) Chip characteristics (ChipFeatures)

[0070] Chip size (length, width, thickness, such as 2mm×2mm×0.3mm);

[0071] Pad area (such as 0.8mm²);

[0072] Package type (die, ceramic package, polymer package);

[0073] Placement point structure (single point vs. multi-point placement);

[0074] (3) Equipment parameters (Equipment_Features)

[0075] Equipment model (such as JUKIRX-7, ASMSiplaceX);

[0076] Nozzle diameter (such as 0.3mm, 0.5mm);

[0077] Placement accuracy requirements (such as ±0.05mm, ±0.1mm);

[0078] Vacuum adsorption capacity of the nozzle (e.g., 30 kPa, 50 kPa);

[0079] Range of placement pressure (e.g., 1 N to 10 N);

[0080] (4) Workshop environmental parameters (Environment_Features)

[0081] Current temperature (e.g., 25 °C);

[0082] Current humidity (e.g., 50%);

[0083] Vibration amplitude of the equipment (e.g., 5 μm, 10 μm);

[0084] Workshop air flow disturbance index (e.g., low, medium, high);

[0085] (5) Production process parameters (Process_Features)

[0086] Placement speed (e.g., 3000 CPH, 6000 CPH);

[0087] Pressure control mode (constant pressure mode / adaptive mode);

[0088] Type of vision system (2D vs. 3D);

[0089] Calibration accuracy of the vision system (e.g., ±0.05 mm);

[0090] (6) Placement historical data (Historical_Features)

[0091] Placement pressure for the last time with the same substrate + chip;

[0092] Yield rate for the last time with the same substrate + chip;

[0093] Average pressure value for the last 10 placements;

[0094] Defect rate for the last 10 placements (e.g., chip offset, poor soldering);

[0095] Collect production data for batches and construct feature vectors

[0096] (1) Collect data, and the data sources include:

[0097] MES (Manufacturing Execution System): data such as substrate, chip, equipment model, placement process, etc.

[0098] Equipment sensors: data such as placement pressure, nozzle pressure, equipment vibration, temperature and humidity, etc.

[0099] Vision inspection system: data such as placement accuracy, pad alignment deviation, etc.

[0100] Historical production database: Historical placement data under similar process parameters.

[0101] During each placement process, after analyzing the substrate thickness deviation and chip size deviation, the initial placement pressure is corrected based on an optimization algorithm to obtain the corrected placement pressure.

[0102] During the placement process of RFID wristbands, small deviations in substrate thickness and chip size will affect the placement quality. Therefore, we need to dynamically adjust the placement pressure before each placement to ensure that the chip adheres stably to the substrate, while avoiding damaging the chip due to excessive pressure or deforming the substrate.

[0103] The core objective of optimizing and correcting the placement pressure is to adapt to the fluctuations in substrate thickness and chip size and ensure placement reliability. Dynamically adjust the pressure through the optimization algorithm to improve placement accuracy and reduce the defect rate.

[0104] Use a laser thickness gauge or an inductive thickness sensor to measure the current substrate thickness. Subtract the standard substrate thickness from the current substrate thickness to obtain the thickness deviation. When the thickness deviation is greater than 0, it indicates that the current substrate thickness has increased and the placement pressure needs to be increased. When the thickness deviation is less than 0, it indicates that the current substrate thickness has decreased and the placement pressure needs to be reduced. When the thickness deviation is equal to 0, it indicates that the current substrate thickness is equal to the standard substrate thickness, and the initial placement pressure is maintained.

[0105] Use a high-precision industrial camera (such as 3D structured light scanning) to obtain the size of the current chip. The size includes the height. Subtract the standard chip size from the current chip size to obtain the size deviation. When the size deviation is greater than 0, it indicates that the current chip size has increased and the placement pressure needs to be increased. When the size deviation is less than 0, it indicates that the current chip size has decreased and the placement pressure needs to be reduced. When the size deviation is equal to 0, it indicates that the current chip size remains unchanged, and the initial placement pressure is maintained.

[0106] Correct the initial placement pressure according to the thickness deviation and size deviation. Correct the initial placement pressure based on the optimization algorithm. The expression of the optimization algorithm is: , where is the corrected placement pressure, is the initial placement pressure, is the thickness deviation, is the size deviation, , are adjustment coefficients, and the adjustment coefficients , are greater than 0. In this application, the adjustment coefficients are determined by fitting experimental data.

[0107] Generate the expansion effect factor of the substrate according to the environmental parameters of the processing workshop.

[0108] During the process of transporting the substrate to the mounting device through the conveying equipment, the ambient temperature of the workshop is recorded in real time by a temperature sensor, and the ambient humidity of the workshop is recorded in real time by a humidity sensor;

[0109] The ambient temperature of the workshop recorded in real time is compared with a preset temperature threshold, and the time period when the ambient temperature of the workshop exceeds the temperature threshold is recorded as the temperature warning period. The ambient humidity of the workshop recorded in real time is compared with a preset humidity threshold, and the time period when the ambient humidity of the workshop exceeds the humidity threshold is recorded as the humidity warning period;

[0110] The temperature warning period and the humidity warning period are accumulated and integrated to generate the expansion effect factor of the substrate. The expression is: ; In the formula, is the expansion effect factor, is the volume change of the substrate at time moment, is the temperature warning period, is the humidity warning period.

[0111] During the production process, the temperature and humidity environment in the workshop will affect the substrate, especially may cause the expansion of the substrate. By recording the ambient temperature and humidity of the workshop in real time, comparing with the preset threshold to determine the warning period, and then using integral operation to generate the expansion effect factor, the comprehensive influence of temperature and humidity changes on the substrate expansion at different time periods can be accurately quantified. This enables the effect of environmental factors on the substrate during the production process to be represented by specific values, providing accurate data support for subsequent production control and quality analysis.

[0112] This application means that by recording the temperature warning period and the humidity warning period, it is possible to promptly detect when the environmental parameters exceed the normal range. When these warning periods occur, production personnel can take measures in advance, such as adjusting the air conditioning system in the workshop to control the temperature and humidity, or taking special treatment methods for the substrate, to avoid quality problems such as excessive expansion of the substrate caused by environmental factors, thus playing a role of early warning and prevention and reducing the production of defective products.

[0113] The generation of the expansion effect factor helps production enterprises deeply understand the relationship between environmental factors and substrate performance. Based on this factor, enterprises can further optimize the production process, for example, adjust parameters such as mounting pressure according to different environmental conditions, making the production process more adaptable to environmental changes and improving the stability of production efficiency and product quality.

[0114] During the mounting process of RFID wristband production, an increase in environmental temperature and humidity will cause the substrate to expand, mainly due to the thermal expansion and hygroscopic expansion effects of the material.

[0115] When the base material (such as FR4 glass fiber board, polyimide PI film, PET plastic) is heated, the intermolecular distance inside it will increase, resulting in an overall increase in the size of the material.

[0116] The coefficients of thermal expansion (CTE) of different base materials are shown in Table 1:

[0117] Table 1 Coefficients of thermal expansion of different base materials

[0118] Material Linear Thermal Expansion Coefficient (ppm / °C) FR4 (Glass Fiber Board) 10~16 ppm / °C PI (Polyimide) 20~40 ppm / °C PET (Polyethylene Terephthalate) 50~70 ppm / °C

[0119] FR4 substrate: Due to the presence of glass fibers, the thermal expansion is small, but it still expands by a micron level with temperature changes.

[0120] PI (polyimide) and PET (plastic film): The thermal expansion is more obvious. A temperature rise may cause an expansion of the order of 0.01 mm, affecting the mounting accuracy.

[0121] The substrate of the RFID wristband is usually a multi-layer structure (such as a PI / FR4 laminate).

[0122] Due to the different CTEs of the materials in different layers, when the temperature rises, the expansion is uneven, resulting in local deformation, warping or micro-displacement.

[0123] High-frequency RFID antennas require extremely high precision, but the expansion of the substrate may cause the position of the feeding point to shift (such as ±0.05 mm), affecting the wireless performance.

[0124] Base materials such as FR4, PI, PET, etc. are mostly polymer materials, which will absorb moisture in the air, causing the volume to expand. After water molecules enter the material, the intermolecular distance will increase, resulting in an increase in the material size, which is called hygroscopic expansion. Humidity changes will affect the dielectric constant and dimensional stability of the material, especially significantly in high-precision RFID production. The hygroscopic expansion coefficients (typical values) of different base materials are shown in Table 2:

[0125] Table 2 Hygroscopic expansion coefficients of different base materials

[0126] Material Moisture Expansion Coefficient (ppm / %RH) FR4 (Glass Fiber Board) 10~20 ppm / %RH PI (Polyimide) 50~100 ppm / %RH PET (Polyethylene Terephthalate) 150~250 ppm / %RH

[0127] The FR4 board has a low moisture absorption rate and a small hygroscopic expansion effect, but long-term high humidity will still cause dimensional drift. The PI and PET substrates have a higher moisture absorption rate. When the humidity increases by 10%RH, it may cause an expansion of the order of 0.01 mm, affecting the mounting accuracy. In a multi-layer RFID structure, the hygroscopic expansion degrees of each layer are different, resulting in interlayer delamination or warping of the substrate. In extreme cases (humidity > 85%RH), it will cause micro-displacement of the antenna circuit, affecting the RFID reading and writing distance and frequency matching.

[0128] Monitor the working status and motion characteristics of the placement equipment in real time. After dynamically modeling the placement equipment using the Markov model, predict the changes in the working parameters of the placement equipment and output the abnormal change factors for the placement equipment.

[0129] Collect the degree of nozzle blockage and the vibration amplitude of the placement head of the placement equipment. The calculation logic for the degree of nozzle blockage is as follows: Obtain the vacuum degree drop value by subtracting the current vacuum degree of the nozzle from the normal vacuum degree of the nozzle, and divide the vacuum degree drop value by the normal vacuum degree of the nozzle to obtain the nozzle blockage degree value. The vibration amplitude of the placement head is directly obtained through the vibration sensor set on the placement head. During the placement process, if the vibration amplitude of the placement head is too large, it may cause the placement head to fail to firmly press the chip, resulting in insufficient pressure. The greater the degree of nozzle blockage, the lower the pressure under the nozzle, reducing the placement force and thus the adsorption force. If the adsorption force is insufficient, the chip may slide slightly during the placement process, resulting in a smaller placement pressure.

[0130] Collect the degree of nozzle blockage and the vibration amplitude of the placement head of the placement equipment. When the obtained degree of nozzle blockage and the vibration amplitude of the placement head change, use the Markov model to dynamically model the placement equipment and define the following state variables for the placement equipment:

[0131] Let be the state of the placement equipment at time : , represents the degree of nozzle blockage, represents the vibration amplitude of the placement head. State space division:

[0132] S1: Normal state (stable placement pressure, low blockage rate, low vibration amplitude);

[0133] S2: Slightly abnormal (predicted that the placement pressure is slightly reduced and affected, need to increase the placement pressure in advance to ensure placement stability, blockage rate or vibration amplitude is slightly high);

[0134] S3: Moderately abnormal (predicted that the placement pressure is reduced and affected, the placement equipment is not supported for use, blockage rate and vibration amplitude are medium);

[0135] S4: Seriously abnormal (predicted that the placement pressure is severely reduced and affected, the placement equipment is not supported for use, high blockage rate, high vibration amplitude).

[0136] Statistically analyze the historical state change situation of the placement equipment. Calculate the probability that the placement equipment transfers from state to state when the degree of nozzle blockage and the vibration amplitude of the placement head change, and estimate the state transition matrix P through historical data: , where, represents the current state Probability of transferring to the next moment state .

[0137] Calculate the state probability distribution at the next moment. Let the current state probability be: , then the state probability at the next moment is: , if , ([[]] is the threshold, usually set to 50%), analyze that when the nozzle clogging degree and the vibration amplitude of the placement head increase, it is predicted that the placement pressure will decrease. If , it is judged that the placement equipment does not support operation and needs to be managed in advance. The management includes increasing the placement pressure in advance, ensuring the stability of the placement pressure, and controlling the placement equipment to stop running;

[0138] Predict that the placement pressure will decrease and output the change abnormal factor for the placement equipment, including the following steps:

[0139] Normalize the nozzle clogging degree and the vibration amplitude of the placement head, map the value ranges of the nozzle clogging degree and the vibration amplitude of the placement head to between [0, 1], sum the normalized nozzle clogging degree and the vibration amplitude of the placement head to obtain the proportionality coefficient. The larger the proportionality coefficient, usually the greater the nozzle clogging degree and the vibration amplitude of the placement head;

[0140] Combine the proportionality coefficient with the probability of transferring to state to generate the change abnormal factor. The expression is: , where in the formula, is the change abnormal factor, is the proportionality coefficient, is the probability of transferring to state . The larger the change abnormal factor, it indicates that under comprehensive analysis, when the nozzle clogging degree and the vibration amplitude of the placement head increase, the probability of transferring to state is greater.

[0141] Combine the analysis of the expansion effect factor and the change abnormal factor to judge whether it is necessary to dynamically adjust the corrected placement pressure in advance, , ([[]] is the threshold, usually set to 50%), analyze that when the nozzle clogging degree and the vibration amplitude of the placement head increase, it is predicted that the placement pressure will decrease, and judge that it is necessary to dynamically adjust the corrected placement pressure in advance, then no adjustment is required when , and generate the corresponding control strategy for the placement equipment according to the judgment result;

[0142] When the expansion effect factor is relatively large, it indicates that the volume change of the substrate during the humidity warning period is relatively large, that is, the substrate has expanded significantly due to the change in environmental humidity. Therefore, when the expansion effect factor of the substrate is relatively large, the mounting pressure should be appropriately reduced to avoid damage to the chip caused by excessive mounting pressure, adapt to the volume change of the substrate, and reduce the risk of mounting misalignment.

[0143] When the change anomaly factor is relatively large, it indicates that the probability of transfer to the state increases due to the nozzle blockage degree and the increase in the vibration amplitude of the mounting head. At this time, it is predicted that the mounting pressure will decrease. In order to ensure stable mounting, it is necessary to increase the mounting pressure in advance.

[0144] To sum up, after obtaining the expansion effect factor and the change anomaly factor, calculate the dynamic adjustment index. The expression is: , where is the dynamic adjustment index, is the expansion effect factor, is the change anomaly factor, , are the weight coefficients, and ;

[0145] Adjust the corrected mounting pressure through the dynamic adjustment index. The adjustment algorithm expression is:

[0146] , where is the adjusted mounting pressure, is the corrected mounting pressure, is the dynamic adjustment index. Assuming that in the dynamic adjustment index, when the value of the expansion effect factor is 0, if the change anomaly factor increases at this time, the dynamic adjustment index is negative and becomes smaller and smaller, indicating that the predicted mounting pressure will decrease. Substituting it into the adjustment algorithm, the adjusted mounting pressure will increase. When the expansion effect factor increases and the mounting pressure needs to be reduced, if the change anomaly factor increases and the predicted mounting pressure will decrease at this time, the value of the dynamic adjustment index may be 0, that is, no further adjustment is required, improving the control efficiency.

[0147] Generate corresponding control strategies for the mounting equipment according to the judgment results. The control strategy is: generate control instructions according to the adjusted mounting pressure to control the operation of the mounting equipment.

[0148] It should be noted that when the output adjusted mounting pressure is not within the constraint conditions of the mounting pressure range, continuous processing will lead to an increase in the defective product rate. At this time, the monitoring system sends a warning signal to the management personnel and controls the mounting equipment to stop running.

[0149] This application generates an expansion effect factor for the substrate based on the environmental parameters of the processing workshop, monitors the working state and motion characteristics of the mounter in real time. After dynamically modeling the mounter using the Markov model, it predicts the changes in the working parameters of the mounter and outputs a change anomaly factor for the mounter. By combining and analyzing the expansion effect factor and the change anomaly factor, it determines whether it is necessary to dynamically adjust the corrected mounting pressure in advance, and generates corresponding control strategies for the mounter according to the judgment result. The monitoring system combines the expansion effect factor and the change anomaly factor, enabling the system to actively adjust the mounting pressure, effectively reducing the feeder point error when the influencing factors change. According to the multi-factor decision result, it generates corresponding control strategies for the mounter, enabling it to maintain the best mounting accuracy under complex environmental conditions.

[0150] This application obtains the substrate and chip information of the currently produced RFID wristband tags, substitutes the substrate and chip information into the database to match the initial mounting pressure. During each mounting process, after analyzing the substrate thickness deviation and chip size deviation, it corrects the initial mounting pressure based on an optimization algorithm to obtain the corrected mounting pressure. Since in actual applications, the thickness and size of the substrates and chips in the same batch may vary, the monitoring can adjust the mounter to the optimal mounting pressure according to the substrate thickness and chips during each mounting process, improving the mounting accuracy and ensuring the quality of the RFID wristband tags.

[0151] Embodiment 2: The RFID wristband production monitoring system based on data analysis described in this embodiment includes an initial matching module, a correction module, an analysis module, and a control module;

[0152] Initial matching module: Obtains the substrate and chip information of the currently produced RFID wristband tags, substitutes the substrate and chip information into the database to match the initial mounting pressure, and sends the initial mounting pressure to the correction module;

[0153] Correction module: During each mounting process, after analyzing the substrate thickness deviation and chip size deviation, it corrects the initial mounting pressure based on an optimization algorithm to obtain the corrected mounting pressure, and sends the corrected mounting pressure to the analysis module;

[0154] Analysis module: Generates an expansion effect factor for the substrate based on the environmental parameters of the processing workshop, monitors the working state and motion characteristics of the mounter in real time. After dynamically modeling the mounter using the Markov model, it predicts the changes in the working parameters of the mounter and outputs a change anomaly factor for the mounter, and sends the change anomaly factor and the expansion effect factor to the control module;

[0155] Control module: By combining the analysis of the expansion effect factor and the anomaly change factor, it determines whether it is necessary to dynamically adjust the correction mounting pressure in advance, and generates corresponding control strategies for the mounting equipment according to the judgment result.

[0156] All the above formulas are dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0157] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0158] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific embodiments. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the relevant technical fields can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. The RFID wristband production monitoring method based on data analysis is characterized by: The monitoring method comprises the following steps: The monitoring system obtains the substrate and chip information of the currently produced RFID wristband tags, and substitutes the substrate and chip information into the database to match the initial mounting pressure; In each placement process, after analyzing the substrate thickness deviation and chip size deviation, the initial placement pressure is corrected based on the optimization algorithm to obtain the corrected placement pressure; Generate the expansion effect factor of the substrate according to the environmental parameters of the processing workshop; Real-time monitoring of the working status and motion characteristics of the placement equipment. After dynamically modeling the placement equipment using the Markov model, the changes in the working parameters of the placement equipment are predicted, and the abnormal change factor is output for the placement equipment. The expression is: , where is the abnormal change factor, is the proportionality coefficient, To transfer to the state The probability of Indicates a mild abnormal state; Combined with the analysis of the expansion effect factor and the abnormal change factor, it is determined whether it is necessary to dynamically adjust the corrected placement pressure in advance, and a corresponding control strategy is generated for the placement equipment based on the judgment result.

2. The RFID wristband production monitoring method based on data analysis according to claim 1 is characterized in that: Combined with the analysis of the expansion effect factor and the abnormal change factor, the correction mounting pressure is dynamically adjusted, including the following steps: After obtaining the expansion effect factor and the abnormal change factor, the dynamic adjustment index is calculated and the expression is: , where is the dynamic adjustment index, is the expansion effect factor, is the abnormal change factor, , is the weight coefficient, and ; The modified placement pressure is adjusted by dynamically adjusting the index, and the adjustment algorithm expression is: , where To adjust the mounting pressure, To correct the mounting pressure, It is a dynamic adjustment index.

3. The RFID wristband production monitoring method based on data analysis according to claim 2 is characterized in that: The calculation logic of the proportional coefficient is as follows: normalize the nozzle blockage degree and the vibration amplitude of the placement head so that the value range of the nozzle blockage degree and the vibration amplitude of the placement head is mapped to [0,1], and sum the nozzle blockage degree and the vibration amplitude of the placement head after the normalization process to obtain the proportional coefficient.

4. The RFID wristband production monitoring method based on data analysis according to claim 3 is characterized in that: Generate the expansion effect factor of the substrate according to the environmental parameters of the processing workshop; When the substrate is conveyed to the mounting equipment through the conveying equipment, the workshop environment temperature is recorded in real time through the temperature sensor, and the workshop environment humidity is recorded in real time through the humidity sensor; The real-time recorded workshop ambient temperature is compared with the preset temperature threshold, and the period when the workshop ambient temperature exceeds the temperature threshold is recorded as the temperature warning period; the real-time recorded workshop ambient humidity is compared with the preset humidity threshold, and the period when the workshop ambient humidity exceeds the humidity threshold is recorded as the humidity warning period; The temperature warning period and the humidity warning period are accumulated and integrated to generate the expansion effect factor of the substrate. The expression is: ; In the formula, is the expansion effect factor, For the substrate at time The volume change at a given moment, It is the temperature warning period. This is the humidity warning period.

5. The RFID wristband production monitoring method based on data analysis according to claim 4 is characterized in that: After analyzing the substrate thickness deviation and chip size deviation, the initial mounting pressure is corrected based on the optimization algorithm, including the following steps; The initial mounting pressure is corrected according to the thickness deviation and size deviation, and the initial mounting pressure is corrected based on the optimization algorithm. The optimization algorithm expression is: , where To correct the mounting pressure, is the initial mounting pressure, is the thickness deviation, is the size deviation, , is the adjustment coefficient, and the adjustment coefficient is greater than 0.

6. The RFID wristband production monitoring method based on data analysis according to claim 5 is characterized in that: A laser thickness gauge or an inductive thickness sensor is used to measure the current substrate thickness, and the thickness deviation is obtained by subtracting the standard substrate thickness from the current substrate thickness. An industrial camera is used to obtain the size of the current chip, and the size deviation is obtained by subtracting the standard chip size from the current chip size.

7. The RFID wristband production monitoring method based on data analysis according to claim 6 is characterized in that: The monitoring system enters the substrate and chip information into the database to match the initial placement pressure, including the following steps: Obtain substrate type, read substrate thickness, record substrate flexibility characteristics, and record chip size; Search historical production data to find matching pressure range constraints based on substrate type, die size, and package type; If there is historical mounting data with the same material, size, and process, extract its pressure setting value as the initial mounting pressure; If there is no completely matching data, a recommendation algorithm based on similarity calculation is used to estimate the initial mounting pressure, and the initial mounting pressure needs to meet the pressure range constraint.

8. The RFID wristband production monitoring method based on data analysis according to claim 7 is characterized in that: The initial placement pressure is estimated using a recommended algorithm based on similarity calculation, including the following steps: Extract the feature vector set {V_1, V_2, ..., V_N} of all historical production batches in the database, where N represents the number of historical production batches and V_i represents the production feature vector of the i-th batch; The cosine similarity is used to calculate the similarity score between the current batch feature vector V_cur and the historical data {V_1,...,V_N}; The M groups of data with similarity scores greater than the score threshold are used as reference data sets, and the similarity scores of all batch production feature vectors in the reference data sets are summed to obtain the total similarity score value, and the weight of each batch production is obtained by dividing the similarity score by the total similarity score value; The historical mounting pressure of each batch of production in the reference data set is extracted and marked as: {P_1, P_2, ..., P_M}, and the weighted average method is used to calculate the initial mounting pressure.

9. The RFID wristband production monitoring method based on data analysis according to claim 8 is characterized in that: The cosine similarity is used to calculate the similarity between the current batch feature vector V_cur and the historical data {V_1,...,V_N}. The expression is: , where Score the similarity between the current batch production feature vector and the i-th batch production feature vector, represents the dot product of the current batch production feature vector and the i-th batch production feature vector, Produce the feature vector norm for the current batch, Generate the feature vector norm for the i-th batch; The weighted average method is used to calculate the initial mounting pressure, and the expression is: , where is the initial mounting pressure, is the number of batches in the reference dataset, is the weight of the i-th batch production, is the historical mounting pressure of the i-th batch of production.

10. An RFID wristband production monitoring system based on data analysis, used to implement the monitoring method according to any one of claims 1 to 9, characterized in that: It includes an initial matching module, a correction module, an analysis module, and a control module; Initial matching module: obtain the substrate and chip information of the currently produced RFID wristband label, and substitute the substrate and chip information into the database to match the initial mounting pressure; Correction module: In each placement process, after analyzing the substrate thickness deviation and chip size deviation, the initial placement pressure is corrected based on the optimization algorithm to obtain the corrected placement pressure; Analysis module: Generates the expansion effect factor of the substrate according to the environmental parameters of the processing workshop, monitors the working status and motion characteristics of the placement equipment in real time, uses the Markov model to dynamically model the placement equipment, predicts the changes in the working parameters of the placement equipment, and outputs the abnormal change factor for the placement equipment; Control module: Combined with the analysis of the expansion effect factor and the abnormal change factor, it is determined whether it is necessary to dynamically adjust the corrected placement pressure in advance, and a corresponding control strategy is generated for the placement equipment based on the judgment result.

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