Intelligent online detection rechecking system and method for RF tag production

Abstract

The invention relates to an intelligent on-line detection rechecking system and method for RF tag production, and relates to the field of intelligent detection.A standard mounting position of an RF tag is calibrated through a predefined RF tag standard position library in combination with a target product model and positioning information, and a radio frequency interference medium related information list is constructed based on the position; and then RF tag production parameter optimization is carried out to obtain a constraint space, a deviation vector is calculated by receiving monitoring parameters, an abnormal RF tag proportion is counted to predict an abnormal probability, and full inspection or sampling inspection is determined to be executed according to an abnormal probability threshold, so that the detection strategy has flexibility and adaptability, and the problems of low RF tag production detection efficiency, high misjudgment rate and low detection efficiency are solved. The problem that the RF tag production quality and efficiency are affected due to the fact that fixed parameter setting cannot adapt to different product models and positioning information diversification requirements is solved, the detection efficiency and quality control of RF tag production defective products are improved, and meanwhile distribution of quality inspection resources is optimized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent detection, and particularly to an intelligent online detection and verification system and method for RF tag production. Background Art

[0002] At present, with the rapid development of the Internet of Things (IoT) and intelligent tag technology, RF (Radio Frequency) tags, as a wireless communication device capable of storing and transmitting information, have been widely used in many fields such as logistics tracking, asset management, anti-counterfeiting and traceability. With the continuous expansion of the application scenarios of RF tags, their production quality and efficiency have become one of the key factors restricting the development of the industry.

[0003] The existing RF tag production process often relies on manual detection and fixed parameter settings, and this method has many drawbacks. Manual detection is not only inefficient, but also easily affected by human factors, resulting in inconsistent detection results and a high false positive rate. Secondly, the fixed parameter settings cannot meet the diverse requirements of different product models and positioning information, making the performance of RF tags vary in different application scenarios. Moreover, with the expansion of the RF tag production scale, the existing methods are difficult to meet the large-scale and high-efficiency production requirements. Summary of the Invention

[0004] In view of the technical problems in the prior art that the production detection of RF tags relies on manual labor, resulting in low efficiency and high false positive rate, and the fixed parameter settings cannot meet the diverse requirements of different product models and positioning information, thus affecting the production quality and efficiency of RF tags, the present invention provides an intelligent online detection and verification system and method for RF tag production to solve these problems.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides an intelligent online detection and verification system for RF tag production. The system includes: a position calibration module for calibrating the standard installation position of the RF tag by processing the target product model and target product positioning through a predefined RF tag standard position library; a structure acquisition module for obtaining the target product structure information and constructing a list of radio frequency interference medium types, a list of medium distribution distances, and a list of medium structures based on the standard installation position of the RF tag; a production optimization module for optimizing RF tag production according to the standard installation position of the RF tag, the list of radio frequency interference medium types, the list of medium distribution distances, and the list of medium structures to obtain an installation parameter constraint space and a radio frequency parameter constraint space; a deviation calculation module for receiving RF tag production monitoring parameters and calculating an installation parameter deviation vector with respect to the installation parameter constraint space and a radio frequency parameter deviation vector with respect to the radio frequency parameter constraint space; a statistical analysis module for statistically analyzing the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector to obtain an RF tag abnormal probability; and a quality inspection execution module for sending the target product to the full inspection library for quality inspection when the RF tag abnormal probability is greater than or equal to the abnormal probability threshold, otherwise, sending the target product to the sampling inspection library for quality inspection.

[0006] In a second aspect, the present invention provides an intelligent online detection and verification method for RF tag production. The method includes: calibrating the standard installation position of the RF tag by processing the target product model and target product positioning through a predefined RF tag standard position library; obtaining the target product structure information and constructing a list of radio frequency interference medium types, a list of medium distribution distances, and a list of medium structures based on the standard installation position of the RF tag; optimizing RF tag production according to the standard installation position of the RF tag, the list of radio frequency interference medium types, the list of medium distribution distances, and the list of medium structures to obtain an installation parameter constraint space and a radio frequency parameter constraint space; receiving RF tag production monitoring parameters and calculating an installation parameter deviation vector with respect to the installation parameter constraint space and a radio frequency parameter deviation vector with respect to the radio frequency parameter constraint space; statistically analyzing the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector to obtain an RF tag abnormal probability; and sending the target product to the full inspection library for quality inspection when the RF tag abnormal probability is greater than or equal to the abnormal probability threshold, otherwise, sending the target product to the sampling inspection library for quality inspection.

[0007] The beneficial effects of the present invention are as follows: By means of a predefined standard position library of RF tags, in combination with the target product model and positioning information, the standard installation positions of RF tags are calibrated, and a list of information related to radio frequency interference media is constructed based on these positions. Furthermore, the production parameters of RF tags are optimized to obtain a constraint space. The deviation vector is calculated by receiving monitoring parameters, the proportion of abnormal RF tags is statistically analyzed to predict the abnormal probability, and full inspection or sampling inspection is determined according to the abnormal probability threshold, making the detection strategy flexible and adaptable, improving the detection efficiency of defective products in RF tag production and quality control, and at the same time optimizing the allocation of quality inspection resources. Brief Description of the Drawings

[0008] Figure 1 It is a schematic structural diagram of an intelligent online detection and verification system for RF tag production provided by the present invention.

[0009] Figure 2 It is a schematic flowchart of an intelligent online detection and verification method for RF tag production provided by the present invention.

[0010] Description of the reference numerals: Position calibration module 11, structure acquisition module 12, production optimization module 13, deviation calculation module 14, statistical analysis module 15, quality inspection execution module 16. Detailed Embodiments

[0011] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0012] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0013] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but rather to be in line with the broadest scope consistent with the principles and features disclosed in the present invention.

[0014] Embodiment 1:

[0015] As Figure 1 shown, the embodiment of the present invention provides an intelligent online detection and verification system for RF tag production, and the system includes: A position calibration module 11, configured to process a target product model and a target product positioning through a predefined RF tag standard position library, and calibrate the standard installation position of the RF tag.

[0016] Exemplarily, an RF tag is short for a Radio Frequency Tag, also known as an electronic tag or a radio frequency identification tag. RF tags achieve contactless information identification and data exchange through radio signals, and are widely used in fields such as logistics, retail, transportation, healthcare, and anti-counterfeiting. In specific use, a reader emits radio waves of a specific frequency through an antenna, and the tag responds to the signal. Among them, an active tag has its own battery and actively sends the stored signal; a passive tag has no battery, activates the chip by receiving the RF energy of the reader, reflects the signal and transmits data, and then conducts data interaction, that is, the reader analyzes the signal returned by the tag, obtains the data and transmits it to the system for processing. RF tags can read multiple tags in batches through non-contact operation without manual intervention, improving efficiency; at the same time, they have strong anti-interference ability and can work in harsh environments (such as dust, humidity, high temperature).

[0017] In this solution, to ensure the accuracy and effectiveness of tag installation, first rely on a predefined RF tag standard position library, which serves as a core reference framework and integrates the standard installation position data corresponding to various product models. In specific operation, first process the model information of the target product, and the model information is used to match the corresponding preset product model data in the standard position library; at the same time, combine the positioning information of the target product, which details the specific position and orientation of the product in three-dimensional space, providing a spatial reference for subsequent precise installation.

[0018] By comparing and analyzing the model number and positioning information of the target product with the preset data in the standard location library, the system can accurately calibrate the standard installation position of the RF tag on the target product. For example, when manufacturing a specific model of intelligent packaging box, by identifying the model number of the packaging box (such as "Model X") and combining the positioning information of the preset RF tag installation area inside it, the standard installation coordinates of the RF tag corresponding to this model of packaging box are retrieved from the standard location library, so as to ensure that the tag can be accurately installed at the predetermined position, effectively avoiding problems such as signal interference or recognition failure caused by installation deviation.

[0019] The structure acquisition module 12 is used to obtain the target product structure information and construct a radio frequency interference medium type list, a medium distribution distance list, and a medium structure list based on the standard installation position of the RF tag.

[0020] Optionally, to ensure the stable performance and accurate reading of the tag, it is necessary to further analyze the internal structure of the target product. By obtaining the detailed structure information of the target product, which covers the layout, material properties, and spatial relationships of various components inside the product, the system can comprehensively understand the internal environment of the product. Subsequently, taking the previously calibrated standard installation position of the RF tag as a reference point, the system starts to identify and analyze the possible radio frequency interference media around this position. These media, such as metal components, liquid containers, etc., may have an adverse impact on the normal operation of the RF tag because they absorb, reflect, or scatter radio frequency signals. Based on this analysis, the system constructs three key lists, namely the radio frequency interference medium type list, which details all the identified interference medium types, such as stainless steel, aluminum components, conductive liquids, etc.; the medium distribution distance list, which accurately marks the spatial distance between each interference medium and the standard installation position of the RF tag, and this data is crucial for evaluating the interference intensity; and the medium structure list, which describes the specific form and layout characteristics of the interference medium, such as the size, shape of the metal plate and its installation angle inside the product. For example, when manufacturing the shell of an industrial device integrated with electronic components, the system analyzes its structure information and identifies a large aluminum heat sink inside the shell as a radio frequency interference medium. The heat sink is only a few centimeters away from the predetermined RF tag installation position, and its surface is flat and the area is large. All these information are accurately recorded in the corresponding lists, providing an important basis for the subsequent optimization and adjustment of the RF tag production parameters.

[0021] The production optimization module 13 is used to perform RF tag production optimization according to the standard installation position of the RF tag, the radio frequency interference medium type list, the medium distribution distance list, and the medium structure list, and obtain the installation parameter constraint space and the radio frequency parameter constraint space.

[0022] Specifically, to further ensure the optimization of label performance, the system executes an RF label production optimization process based on the determined standard installation positions of RF labels, the list of types of RF interference media, the list of media distribution distances, and the list of media structures. Specifically, the system first uses the standard installation positions of RF labels as reference points, combines various types of media identified in the list of types of RF interference media (such as metal components, liquid containers, etc.), the specific distance data provided by the list of media distribution distances, and the media form and layout characteristics described in the list of media structures, and comprehensively analyzes the possible impacts of these factors on the working performance of RF labels. On this basis, the installation parameters (such as mounting pressure, temperature, which directly affect the adhesion effect and stability between the label and the product) and RF parameters (such as transmit power, frequency tuning value, the transmit power affects the read / write distance of the label, and the frequency tuning value is used to adjust the matching degree between the label antenna and the transmit frequency of the reader / writer) of the RF label are finely adjusted and optimized through an algorithm model. For example, when a large metal component is detected near the standard installation position, the system may reduce the transmit power of the RF label to reduce the reflection interference of the metal on the RF signal and adjust the frequency tuning value to ensure the efficient operation of the label antenna at a specific frequency. After this series of optimization calculations, a set of installation parameter constraint spaces and RF parameter constraint spaces are finally determined. These spaces define the parameter ranges that should be followed to ensure the stable performance of RF labels under different production conditions, thus providing a scientific basis for subsequent production control.

[0023] The deviation calculation module 14 is configured to receive the RF label production monitoring parameters, calculate the installation parameter deviation vector from the installation parameter constraint space, and the RF parameter deviation vector from the RF parameter constraint space.

[0024] Furthermore, to ensure that the production process complies with the preset standards and to monitor potential problems in real time, the system continuously receives RF tag production monitoring parameters from the production line. These monitoring parameters cover key installation parameters (such as mounting pressure, temperature, etc., which are directly related to the bonding strength and stability between the tag and the product) and radio frequency parameters (such as transmit power, frequency tuning value, etc., which affect the reading and writing performance and communication quality of the tag) during the RF tag production process. After receiving these parameters, the system immediately compares and analyzes them with the installation parameter constraint space and radio frequency parameter constraint space determined through the optimization process. Specifically, it calculates the deviation between the actually monitored installation parameters and the boundary values of the installation parameter constraint space to form an installation parameter deviation vector, which intuitively reflects the degree of deviation of the installation parameters in the current production process. At the same time, it calculates the deviation between the actually monitored radio frequency parameters and the boundary values of the radio frequency parameter constraint space to form a radio frequency parameter deviation vector, which is used to evaluate the deviation of the radio frequency parameters. For example, if the actually monitored mounting pressure of a batch of RF tags is 4.5N, and the mounting pressure range specified by the installation parameter constraint space is 4.0N to 5.0N, the system will calculate the deviation of this parameter as +0.5N and include it in the installation parameter deviation vector. Through such calculations and analyses, it is possible to grasp the parameter deviation situation in the RF tag production process in real time, providing data support for subsequent abnormal probability prediction and quality inspection decisions.

[0025] The statistical analysis module 15 is used to statistically analyze the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector, and obtain the RF tag abnormal probability.

[0026] Preferably, in the quality monitoring link of RF tag production, the system collects and organizes the corresponding installation parameter deviation vector and radio frequency parameter deviation vector for each batch or the RF tag sample set in the continuous production process. The installation parameter deviation vector records the degree of deviation of parameters such as mounting pressure and temperature in actual production from the preset installation parameter constraint space, while the radio frequency parameter deviation vector reflects the difference between radio frequency parameters such as transmit power and frequency tuning value and the radio frequency parameter constraint space. The system then comprehensively analyzes these deviation vectors, and calculates the proportion of abnormal RF tags by statistically analyzing the ratio of the number of abnormal RF tags to the total number of samples in the sample set. The determination basis for abnormal RF tags includes but is not limited to parameter deviation exceeding the preset threshold, unqualified tag performance test, etc. For example, if in a certain production batch, the system detects that 10% of the RF tag samples have installation parameter or radio frequency parameter deviations exceeding the allowable range, and these tags show abnormalities in subsequent performance tests, the system will determine that the proportion of abnormal RF tags in this batch is 10%, and accordingly obtain the abnormal probability of RF tags, providing a basis for subsequent quality control measures.

[0027] The quality inspection execution module 16 is used to send the target product to the full-inspection library for quality inspection when the abnormal probability of the RF tag is greater than or equal to the abnormal probability threshold; otherwise, send the target product to the sampling-inspection library for quality inspection.

[0028] Specifically, in the quality control process of RF tag production, the previously calculated abnormal probability of the RF tag is compared with the preset abnormal probability threshold. The abnormal probability threshold, as a key indicator for judging the quality status of the production batch, is set based on historical data analysis and production experience accumulation. When the system determines that the abnormal probability of the RF tag is greater than or equal to this threshold, it indicates that there is a relatively high proportion of abnormal RF tags in the current production batch, which may affect the overall quality and performance of the product. Therefore, the system will automatically trigger the full-inspection mechanism and send the target product to the full-inspection library for comprehensive quality inspection to ensure that all RF tags meet the preset standards. On the contrary, if the abnormal probability of the RF tag is lower than the threshold, it indicates that the quality of the production batch is relatively stable and the proportion of abnormal RF tags is relatively low. In this case, the system sends the target product to the sampling-inspection library to execute the sampling quality inspection process to monitor the production quality in an efficient and economical manner. For example, if the abnormal probability threshold is set at 5%, when the abnormal probability of a certain batch of RF tags reaches or exceeds this value, the system transfers all products in this batch to the full-inspection library for detailed inspection; if the abnormal probability is only 2%, then the products in this batch will enter the sampling-inspection library to undergo random sampling quality inspection.

[0029] In a preferred embodiment, through a predefined standard position library of RF tags, the target product model and the target product positioning are processed to calibrate the standard installation position of the RF tag, including: obtaining the preset product model and the preset product structure information; positioning the preset product structure information in a three-dimensional coordinate system to obtain the three-dimensional coordinates of the preset product; according to the preset set of radio frequency interference medium types, combining the preset product structure information, extracting the three-dimensional coordinates of the radio frequency interference medium from the three-dimensional coordinates of the preset product; screening the three-dimensional coordinates of the preset product through the user terminal to obtain the installable area of the RF tag; and optimizing the position with the minimum radio frequency interference for the three-dimensional coordinates of the radio frequency interference medium in the installable area of the RF tag to obtain the standard installation position of the RF tag.

[0030] Specifically, the constructed predefined standard position library of RF tags integrates various preset product models and their corresponding detailed product structure information, which covers the materials, structures, etc. of the component elements at different positions inside the product. To calibrate the standard installation position of the RF tag on the target product, the system first obtains the model information of the target product and retrieves the preset product structure information matching this model from the standard position library. Subsequently, the system maps this preset product structure information to the three-dimensional coordinate system to construct an accurate three-dimensional model of the product, thereby obtaining the three-dimensional coordinate representation of the preset product.

[0031] Considering the impact of radio frequency interference on the performance of RF tags, the system further combines a preset set of radio frequency interference medium types (such as medium types like metal and liquid that may interfere with radio frequency signals), and extracts the three-dimensional coordinates of the radio frequency interference medium from the three-dimensional coordinates of the preset product according to the preset product structure information. This coordinate information provides an important reference for determining the installation position of the RF tag in the subsequent process.

[0032] To ensure that the RF tag can be installed at a position with no or minimal radio frequency interference, the system allows the operator to visually screen the three-dimensional coordinates of the preset product through the user interface, thereby determining the installable area of the RF tag. Within this area, an optimization algorithm is used to comprehensively analyze the three-dimensional coordinates of the radio frequency interference medium to find the position with the minimum radio frequency interference as the standard installation position of the RF tag. For example, when manufacturing an intelligent home appliance product, the system retrieves the preset structure information of the product through the standard position library, including the materials and positions of components such as the internal circuit board and metal shell. After mapping this information to the three-dimensional coordinate system, the positions of metal components close to the circuit board and likely to cause radio frequency interference are identified. Subsequently, through user-side screening, an area far from these metal components and with an open space is determined as the installable area of the RF tag. Finally, the system finds the position with the minimum radio frequency interference within this area as the standard installation position of the RF tag to ensure that the tag can work stably and accurately in subsequent use.

[0033] In a preferred embodiment, in the installable area of the RF tag, optimizing to find the position with the minimum radio frequency interference for the three-dimensional coordinates of the radio frequency interference medium to obtain the standard installation position of the RF tag includes: obtaining the pattern contour of the RF tag, performing enumerative installation deployment in the installable area of the RF tag to obtain several initial installation positions of the RF tag; based on the three-dimensional coordinates of the radio frequency interference medium, combining the preset product structure information to obtain the type of the radio frequency interference medium; performing medium interference intensity analysis based on the type of the radio frequency interference medium and the three-dimensional coordinates of the radio frequency interference medium to obtain the medium radio frequency interference intensity; sorting the three-dimensional coordinates of the radio frequency interference medium in ascending order according to the medium radio frequency interference intensity to obtain the sorting result of the three-dimensional coordinates of the radio frequency interference medium; based on the sorting result of the three-dimensional coordinates of the radio frequency interference medium and the three-dimensional coordinates of the radio frequency interference medium, optimizing to find the position with the minimum radio frequency interference for the several initial installation positions of the RF tag to obtain the standard installation position of the RF tag.

[0034] Preferably, after determining the installable area for the RF tag, to further optimize the installation position of the RF tag to reduce radio frequency interference, first obtain the pattern contour information of the RF tag, which defines the physical size and shape of the tag. Subsequently, perform an enumerative installation deployment within the installable area, that is, simulate the installation situation of the RF tag at different positions, so as to obtain several initial installation positions of the RF tag. Then, using the known three-dimensional coordinates of the radio frequency interference media and combining with the preset product structure information, identify the types of these interference media, such as metal, liquid, etc. These media types have different interference characteristics on radio frequency signals. Furthermore, based on the identified radio frequency interference media types and their three-dimensional coordinates, conduct a media interference intensity analysis, through calculation or simulation means, to quantify the possible interference degree of each interference medium on the RF tag, and then obtain the media radio frequency interference intensity.

[0035] To efficiently find the installation position with the least radio frequency interference, the system sorts the three-dimensional coordinates of the radio frequency interference media in ascending order of the media radio frequency interference intensity, forming a sorted result of the three-dimensional coordinates of the radio frequency interference media. Finally, based on this sorted result and combining with the three-dimensional coordinates of the radio frequency interference media, perform an optimization for the least radio frequency interference position among the several initial installation positions of the RF tag obtained by enumeration before. During the optimization process, evaluate the total radio frequency interference received by each initial installation position, and select the position with the least interference as the standard installation position of the RF tag. For example, in the production of an intelligent packaging box, the system identifies a metal plate near the edge inside the packaging box as a radio frequency interference medium, and calculates that the interference intensity of this metal plate on the RF tag is relatively large. The system then enumerates multiple initial installation positions within the installable area, and combines with the three-dimensional coordinates and the sorted result of the interference intensity of the metal plate, and finally determines a position far from the metal plate and with the least radio frequency interference as the standard installation position of the RF tag.

[0036] In a preferred embodiment, based on the sorted result of the three-dimensional coordinates of the radio frequency interference media and the three-dimensional coordinates of the radio frequency interference media, perform an optimization for the least radio frequency interference position among the several initial installation positions of the RF tag to obtain the standard installation position of the RF tag, including: obtaining the first initial installation position of the RF tag among the several initial installation positions of the RF tag; traversing the three-dimensional coordinates of the radio frequency interference media, performing a distance evaluation with the first initial installation position of the RF tag to obtain a set of distances of the three-dimensional coordinates of the radio frequency interference media; based on the three-dimensional coordinate sequence number of the sorted result of the three-dimensional coordinates of the radio frequency interference media, perform a product calculation on the set of distances of the three-dimensional coordinates of the radio frequency interference media to obtain a corrected set of distances of the three-dimensional coordinates of the radio frequency interference media; sum up the corrected set of distances of the three-dimensional coordinates of the radio frequency interference media to obtain the fitness of the first initial installation position of the RF tag, and add it to the fitness of the several initial installation positions of the RF tag; perform a minimum value sorting on the fitness of the several initial installation positions of the RF tag to obtain the standard installation position of the RF tag.

[0037] Further, during the optimization process of the RF tag installation position, a series of evaluations are carried out for each of the several initial RF tag installation positions obtained by enumeration. First, focus on the first initial RF tag installation position. By traversing the three-dimensional coordinate set of radio frequency interference media sorted by interference intensity, calculate the Euclidean distance (or other appropriate distance metric indicators) between this initial installation position and the three-dimensional coordinates of each interference medium, and generate a three-dimensional coordinate distance set of radio frequency interference media. Each element in this set corresponds to the spatial distance between an interference medium and the initial installation position.

[0038] Subsequently, according to the three-dimensional coordinate serial numbers in the sorting result of the three-dimensional coordinates of radio frequency interference media (this serial number is positively correlated with the interference intensity, and the smaller the serial number, the weaker the interference), perform product correction on each distance value in the distance set, that is, multiply each distance value by a weight factor related to the serial number of the corresponding interference medium (for example, for interference media with smaller serial numbers, their weight factors are smaller, thereby reducing their impact on the total interference evaluation), and form a corrected three-dimensional coordinate distance set of radio frequency interference media.

[0039] By summing all the elements in the corrected distance set, the system obtains the fitness value of the first initial RF tag installation position. This value comprehensively reflects the degree of radio frequency interference received at this position. The smaller the fitness value, the less interference this position receives. Then add this fitness value to the fitness set of several initial RF tag installation positions.

[0040] Repeat the above steps until the fitness values of all initial RF tag installation positions are calculated and added to the fitness set. Finally, perform a minimum sorting operation on the fitness set, that is, select the initial installation position with the smallest fitness value from the set. This position is the standard installation position of the RF tag that receives the least radio frequency interference under the current evaluation conditions. For example, in the production of intelligent logistics pallets, when the system evaluates multiple initial installation positions, it is found that the sum of the corrected distances between a certain position and all interference media is the smallest, indicating that this position receives the least radio frequency interference. Therefore, it is selected as the standard installation position of the RF tag to ensure the communication stability of the tag in subsequent use.

[0041] In a preferred embodiment, based on the radio frequency interference medium type and the three-dimensional coordinates of the radio frequency interference medium, medium interference intensity analysis is performed to obtain the medium radio frequency interference intensity, including: through the user terminal, setting a preset distance for medium distribution and preset radio frequency parameters; according to the preset distance for medium distribution and the preset radio frequency parameters, collecting multiple groups of data of the preset radio frequency interference medium type, where any one of the multiple groups of data includes medium length record data, medium width record data, medium height record data, and a set of radio frequency signal attenuation amount record data; performing clustering analysis on the set of radio frequency signal attenuation amount record data according to the radio frequency signal attenuation amount deviation threshold to obtain multiple clusters of radio frequency signal attenuation amount record data; extracting the clusters with the radio frequency signal attenuation amount record data volume less than or equal to the record data volume threshold, deleting them from the multiple clusters of radio frequency signal attenuation amount record data, obtaining the remaining radio frequency signal attenuation amount record data, extracting the maximum value, and setting it as the medium radio frequency interference intensity identification data; according to the medium radio frequency interference intensity identification data, the medium length record data, the medium width record data, and the medium height record data, retrieving the multiple groups of data, training a medium radio frequency interference intensity prediction model through machine learning, and binding it to the preset radio frequency interference medium type to construct a medium radio frequency interference intensity analysis library; according to the radio frequency interference medium type, matching a target medium radio frequency interference intensity prediction model from the medium radio frequency interference intensity analysis library, processing the medium length, medium width, and medium height extracted from the three-dimensional coordinates of the radio frequency interference medium, and obtaining the medium radio frequency interference intensity.

[0042] Exemplarily, during the medium interference intensity analysis process, first, the user terminal sets a preset distance for medium distribution (such as the expected distance between the interference medium and the RF tag) and preset radio frequency parameters (such as transmission frequency, power, etc.), which provide benchmark conditions for subsequent data collection and analysis. Then, based on these preset conditions, multiple groups of data are collected for the preset radio frequency interference medium type. Each group of data includes record data of the medium length, width, and height, as well as a corresponding set of radio frequency signal attenuation amount record data, which reflect the interference degree of media with different sizes on the radio frequency signal.

[0043] Subsequently, the system performs clustering analysis on the set of radio frequency signal attenuation amount record data collected using the radio frequency signal attenuation amount deviation threshold, dividing the data into multiple clusters, and the data within each cluster has similar attenuation characteristics. To eliminate abnormal or noise data, the clusters with the radio frequency signal attenuation amount record data volume less than or equal to the record data volume threshold are extracted and deleted from the multiple clusters of data, thereby obtaining the remaining, more representative radio frequency signal attenuation amount record data. Then, the maximum value of the radio frequency signal attenuation amount is extracted from the remaining data as the medium radio frequency interference intensity identification data, which directly reflects the maximum interference ability of the medium on the radio frequency signal.

[0044] Next, using the medium radio frequency interference intensity identification data, combined with the recorded data of the medium's length, width, and height, multiple groups of previously collected data are retrieved, and a medium radio frequency interference intensity prediction model is trained through a machine learning algorithm. The medium radio frequency interference intensity prediction model can predict the radio frequency interference intensity of the medium based on its size parameters and is bound to the preset radio frequency interference medium type to construct a medium radio frequency interference intensity analysis library. In practical applications, when it is necessary to analyze the interference intensity of a specific radio frequency interference medium type, the system matches the corresponding target medium radio frequency interference intensity prediction model from the medium radio frequency interference intensity analysis library, and uses the length, width, and height of the medium extracted from the three-dimensional coordinates of the radio frequency interference medium as input parameters to obtain the medium radio frequency interference intensity through model processing. For example, in an intelligent warehousing system, if it is necessary to analyze the interference intensity of a metal shelf board on an RF tag on a shelf, the system can match the corresponding prediction model of the metal shelf board from the analysis library and input the actual size parameters of the shelf board to quickly obtain its radio frequency interference intensity, providing a basis for the reasonable installation of the RF tag.

[0045] In a preferred embodiment, RF tag production optimization is performed according to the standard installation position of the RF tag, the list of radio frequency interference medium types, the list of medium distribution distances, and the list of medium structures to obtain an installation parameter constraint space and a radio frequency parameter constraint space, including: retrieving the historical RF tag production sample set of the standard installation position of the RF tag, the list of radio frequency interference medium types, the list of medium distribution distances, and the list of medium structures; performing a central tendency analysis on the installation parameter attributes according to the historical RF tag production sample set to construct the installation parameter constraint space; and performing a central tendency analysis on the radio frequency parameter attributes according to the historical RF tag production sample set to construct the radio frequency parameter constraint space.

[0046] Specifically, during the RF tag production optimization process, a historical RF tag production sample set including the standard installation position of the RF tag, the list of radio frequency interference medium types, the list of medium distribution distances, and the list of medium structures is retrieved. These sample sets record the installation and parameter information under different conditions in past production, providing a data basis for subsequent analysis.

[0047] For the installation parameter attributes, the system extracts multi-dimensional parameters related to installation in the historical sample set, such as mounting pressure, temperature, speed, etc., constructs a multi-dimensional coordinate system, and uses a central tendency analysis method (such as statistics like mean, median, etc.) to extract the set of central coordinates of these parameters in the multi-dimensional coordinates. This set reflects the range of installation parameters that ensure the stable operation of the RF tag and are less affected by radio frequency interference in historical production. Based on this set of central coordinates, an installation parameter constraint space is constructed. This space defines the boundary conditions that the installation parameters should follow in future production to ensure the installation quality of the RF tag.

[0048] Similarly, the system analyzes the radio frequency parameter attributes, extracts radio frequency related parameters such as transmit power and frequency tuning values from the historical sample set, constructs a multi-dimensional coordinate system and conducts a central tendency analysis in the same way, and obtains the central coordinate set of the radio frequency parameters. This set reflects the radio frequency parameter range that optimizes the communication performance of the RF tag and minimizes interference during historical production. Based on this central coordinate set, the system constructs a radio frequency parameter constraint space, providing clear guidance for the setting of radio frequency parameters in subsequent production. For example, in a certain production batch of RF tags, the system analyzes the historical sample set and finds that when the mounting pressure is controlled between 10 - 15 N and the temperature is maintained between 20 - 25 °C, the installation quality of the RF tag is the best; at the same time, when the transmit power is set between 1 - 2 W and the frequency is tuned to around 915 MHz, the communication performance of the tag is the best. Based on these findings, the system constructs the corresponding installation parameter constraint space and radio frequency parameter constraint space, providing strong support for subsequent production.

[0049] In a preferred embodiment, the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector is statistically analyzed to obtain the RF tag abnormal probability, including: configuring an abnormal RF tag trigger rule: constructing a first trigger condition for abnormal RF tags: when an RF tag falls off within a preset service duration, it is regarded as an abnormal RF tag; constructing a second trigger condition for abnormal RF tags: when an RF tag belongs to a dead tag or an empty tag, it is regarded as an abnormal RF tag; configuring a logical OR condition for the first trigger condition for abnormal RF tags and the second trigger condition for abnormal RF tags to obtain the abnormal RF tag trigger rule; based on the abnormal RF tag trigger rule, statistically analyzing the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector to obtain the RF tag abnormal probability.

[0050] Specifically, during the process of evaluating the production quality of RF tags, a trigger rule for abnormal RF tags is configured. This rule consists of two core conditions: the first condition is to construct a first trigger condition for abnormal RF tags, that is, when an RF tag falls off within a preset service duration (such as 30 days), the system determines it as an abnormal RF tag. This condition aims to capture the tag falling off caused by improper installation parameters or material quality problems. The second condition is to construct a second trigger condition for abnormal RF tags, that is, when an RF tag is identified as a dead tag or an empty tag (i.e., a tag that cannot be read or written to normally), it is also regarded as an abnormal RF tag. This condition is used to identify communication failures caused by incorrect radio frequency parameter settings or tag defects.

[0051] Further, to combine the above two conditions, the system configures a logical OR condition, that is, as long as any of the above conditions is met, the RF tag is determined to be abnormal. Based on this abnormal RF tag triggering rule, the system statistically analyzes the RF tag production sample set containing the installation parameter deviation vector and the radio frequency parameter deviation vector. Specifically, the system traverses each RF tag in the sample set, checks whether it meets the triggering conditions of the abnormal RF tag, and records the number of abnormal RF tags. Subsequently, the proportion of abnormal RF tags in the total number of samples is calculated, and this proportion is the abnormal probability of the RF tag. For example, in the production of a certain batch of RF tags, the system statistically analyzed a total of 1000 samples, among which 10 tags fell off within 30 days of service, and another 5 tags were identified as dead tags. According to the abnormal RF tag triggering rule, these 15 tags were all determined to be abnormal. Therefore, the abnormal probability of the RF tags in this batch is 1.5% (15 / 1000). This probability value helps the manufacturer evaluate the stability of the current production process and adjust the installation parameters or radio frequency parameters accordingly to reduce the incidence of abnormal tags.

[0052] The intelligent online detection and verification system for RF tag production provided by the embodiments of the present invention has at least the following technical effects: 1. Through the predefined standard position library of RF tags, combined with the target product model and positioning information, the standard installation position of the RF tag can be accurately calibrated. Further, by constructing a list of radio frequency interference medium types, a list of medium distribution distances, and a list of medium structures, and optimizing the position with the minimum radio frequency interference within the installable area of the RF tag, the stability and reliability of the RF tag in a complex electromagnetic environment are ensured, thereby effectively reducing the problems of tag detachment or communication failure caused by improper installation positions, and improving the production quality and application effect of the RF tag.

[0053] 2. Using the historical RF tag production sample set, the central tendency analysis of the installation parameters and radio frequency parameters is carried out, and the installation parameter constraint space and the radio frequency parameter constraint space are constructed, providing clear parameter guidance for the production of RF tags, ensuring that the parameter settings in the production process are always within the optimal range. Through intelligent parameter constraint and production optimization, not only the production efficiency of the RF tag is improved, but also the production abnormalities and cost waste caused by improper parameter settings are significantly reduced.

[0054] 3. By configuring the abnormal RF tag triggering rule, the proportion of abnormal RF tags can be quickly identified and statistically analyzed, so as to obtain the abnormal probability of the RF tag. When the abnormal probability exceeds the preset threshold, the system automatically sends the target product to the full inspection library for quality inspection, otherwise it sends it to the sampling inspection library, realizing the rapid identification and accurate quality inspection of abnormal tags, effectively preventing unqualified products from flowing into the market. At the same time, by optimizing the quality inspection strategy, the quality inspection efficiency and accuracy are improved, and the quality inspection cost and time cost are reduced.

[0055] Example 2:

[0056] As Figure 2 shown, based on the same inventive concept of the intelligent online detection and verification system for RF tag production provided in Example 1, the embodiment of the present invention also provides an intelligent online detection and verification method for RF tag production, and the method includes: Process the target product model and target product positioning through a predefined RF tag standard position library to calibrate the standard installation position of the RF tag.

[0057] Obtain the target product structure information, and based on the standard installation position of the RF tag, construct a list of radio frequency interference medium types, a list of medium distribution distances, and a list of medium structures.

[0058] Perform optimization for RF tag production according to the standard installation position of the RF tag, the list of radio frequency interference medium types, the list of medium distribution distances, and the list of medium structures to obtain an installation parameter constraint space and a radio frequency parameter constraint space.

[0059] Receive the RF tag production monitoring parameters, calculate the installation parameter deviation vector from the installation parameter constraint space, and the radio frequency parameter deviation vector from the radio frequency parameter constraint space.

[0060] Count the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector to obtain the RF tag abnormal probability.

[0061] When the RF tag abnormal probability is greater than or equal to the abnormal probability threshold, send the target product to the full inspection library for quality inspection, otherwise, send the target product to the sampling inspection library for quality inspection.

[0062] Further, processing the target product model and target product positioning through a predefined RF tag standard position library to calibrate the standard installation position of the RF tag includes: obtaining a preset product model and preset product structure information; positioning the preset product structure information in a three-dimensional coordinate system to obtain the preset product three-dimensional coordinates; according to a preset set of radio frequency interference medium types, combining with the preset product structure information, extracting the three-dimensional coordinates of the radio frequency interference medium from the preset product three-dimensional coordinates; screening the preset product three-dimensional coordinates through the user terminal to obtain the RF tag installable area; in the RF tag installable area, perform optimization for the position with the minimum radio frequency interference on the three-dimensional coordinates of the radio frequency interference medium to obtain the standard installation position of the RF tag.

[0063] Further, in the RF tag installable area, perform optimization for the position with the minimum RF interference on the three-dimensional coordinates of the RF interference medium to obtain the standard installation position of the RF tag, including: obtaining the RF tag pattern outline, performing enumerative installation and deployment in the RF tag installable area to obtain several initial installation positions of the RF tag; based on the three-dimensional coordinates of the RF interference medium and combining the preset product structure information, obtaining the type of the RF interference medium; performing medium interference intensity analysis based on the type of the RF interference medium and the three-dimensional coordinates of the RF interference medium to obtain the medium RF interference intensity; sorting the three-dimensional coordinates of the RF interference medium from smallest to largest according to the medium RF interference intensity to obtain the sorting result of the three-dimensional coordinates of the RF interference medium; performing optimization for the position with the minimum RF interference on the several initial installation positions of the RF tag based on the sorting result of the three-dimensional coordinates of the RF interference medium and the three-dimensional coordinates of the RF interference medium to obtain the standard installation position of the RF tag.

[0064] Further, performing optimization for the position with the minimum RF interference on the several initial installation positions of the RF tag based on the sorting result of the three-dimensional coordinates of the RF interference medium and the three-dimensional coordinates of the RF interference medium to obtain the standard installation position of the RF tag, including: obtaining the first initial installation position of the several initial installation positions of the RF tag; traversing the three-dimensional coordinates of the RF interference medium and performing distance evaluation with the first initial installation position of the RF tag to obtain a set of distances of the three-dimensional coordinates of the RF interference medium; performing product calculation on the set of distances of the three-dimensional coordinates of the RF interference medium based on the three-dimensional coordinate serial numbers of the sorting result of the three-dimensional coordinates of the RF interference medium to obtain a set of corrected distances of the three-dimensional coordinates of the RF interference medium; summing up the set of corrected distances of the three-dimensional coordinates of the RF interference medium to obtain the fitness of the first initial installation position of the RF tag and adding it to the fitness of the several initial installation positions of the RF tag; performing minimum value sorting on the fitness of the several initial installation positions of the RF tag to obtain the standard installation position of the RF tag.

[0065] Further, based on the RF interference medium type and the three-dimensional coordinates of the RF interference medium, perform medium interference intensity analysis to obtain the medium RF interference intensity, including: setting a preset distance for medium distribution and preset RF parameters through the user terminal; collecting multiple groups of data of the preset RF interference medium type according to the preset distance for medium distribution and the preset RF parameters, where any one of the multiple groups of data includes medium length record data, medium width record data, medium height record data, and a set of RF signal attenuation amount record data; performing cluster analysis on the set of RF signal attenuation amount record data according to the RF signal attenuation amount deviation threshold to obtain multiple clusters of RF signal attenuation amount record data; extracting the clusters with the RF signal attenuation amount record data volume less than or equal to the record data volume threshold, deleting them from the multiple clusters of RF signal attenuation amount record data, obtaining the remaining RF signal attenuation amount record data, extracting the maximum value, and setting it as the medium RF interference intensity identification data; according to the medium RF interference intensity identification data, the medium length record data, the medium width record data, and the medium height record data, retrieving the multiple groups of data, training a medium RF interference intensity prediction model through machine learning, and binding it to the preset RF interference medium type to construct a medium RF interference intensity analysis library; according to the RF interference medium type, matching a target medium RF interference intensity prediction model from the medium RF interference intensity analysis library, processing the medium length, medium width, and medium height extracted from the three-dimensional coordinates of the RF interference medium, and obtaining the medium RF interference intensity.

[0066] Further, perform RF tag production optimization according to the standard installation position of the RF tag, the list of RF interference medium types, the list of medium distribution distances, and the list of medium structures to obtain an installation parameter constraint space and a radio frequency parameter constraint space, including: retrieving the historical RF tag production sample set of the standard installation position of the RF tag, the list of RF interference medium types, the list of medium distribution distances, and the list of medium structures; performing central tendency analysis on the installation parameter attributes according to the historical RF tag production sample set to construct the installation parameter constraint space; performing central tendency analysis on the radio frequency parameter attributes according to the historical RF tag production sample set to construct the radio frequency parameter constraint space.

[0067] Further, count the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector to obtain the RF tag abnormal probability, including: configuring an abnormal RF tag triggering rule: constructing a first triggering condition for abnormal RF tags: when an RF tag falls off within a preset service duration, it is regarded as an abnormal RF tag; constructing a second triggering condition for abnormal RF tags: when an RF tag belongs to a dead tag or an empty tag, it is regarded as an abnormal RF tag; configuring a logical OR condition for the first triggering condition for abnormal RF tags and the second triggering condition for abnormal RF tags to obtain the abnormal RF tag triggering rule; based on the abnormal RF tag triggering rule, count the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector to obtain the RF tag abnormal probability.

[0068] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0070] This specification and the drawings are only exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. An intelligent on-line detection and verification system for RF tag production, characterized in that, The system includes: A position calibration module, which is used to process the target product model and target product positioning through a predefined RF tag standard position library, and calibrate the standard installation position of the RF tag; A structure acquisition module, which is used to obtain the target product structure information, and construct a radio frequency interference medium type list, a medium distribution distance list, and a medium structure list based on the standard installation position of the RF tag; A production optimization module, which is used to perform RF tag production optimization according to the standard installation position of the RF tag, the radio frequency interference medium type list, the medium distribution distance list, and the medium structure list, and obtain an installation parameter constraint space and a radio frequency parameter constraint space; A deviation calculation module, which is used to receive RF tag production monitoring parameters, and calculate the installation parameter deviation vector from the installation parameter constraint space, and the radio frequency parameter deviation vector from the radio frequency parameter constraint space; A statistical analysis module, which is used to statistically analyze the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector, and obtain the RF tag abnormal probability; A quality inspection execution module, which is used to send the target product to the full inspection library for quality inspection when the RF tag abnormal probability is greater than or equal to the abnormal probability threshold, otherwise, send the target product to the sampling inspection library for quality inspection.

2. The intelligent online detection and verification system for RF tag production according to claim 1, wherein Processing the target product model and target product positioning through a predefined RF tag standard position library, and calibrating the standard installation position of the RF tag includes: Obtaining a preset product model and preset product structure information; Positioning the preset product structure information in a three-dimensional coordinate system to obtain the preset product three-dimensional coordinates; Extracting the three-dimensional coordinates of the radio frequency interference medium from the preset product three-dimensional coordinates according to a preset radio frequency interference medium type set and combining the preset product structure information; Filtering the preset product three-dimensional coordinates through a user terminal to obtain the RF tag installable area; In the RF tag installable area, perform optimization for the position with the minimum radio frequency interference on the three-dimensional coordinates of the radio frequency interference medium to obtain the standard installation position of the RF tag.

3. The intelligent on-line detection and verification system for RF tag production according to claim 2, wherein In the RF tag installable area, performing optimization for the position with the minimum radio frequency interference on the three-dimensional coordinates of the radio frequency interference medium to obtain the standard installation position of the RF tag includes: Obtaining the RF tag pattern outline, and performing enumerative installation deployment in the RF tag installable area to obtain several initial RF tag installation positions; Based on the three-dimensional coordinates of the radio frequency interference medium and combining the preset product structure information, obtaining the radio frequency interference medium type; Performing medium interference intensity analysis based on the radio frequency interference medium type and the three-dimensional coordinates of the radio frequency interference medium to obtain the medium radio frequency interference intensity; Sorting the three-dimensional coordinates of the radio frequency interference medium in ascending order according to the medium radio frequency interference intensity to obtain the sorting result of the three-dimensional coordinates of the radio frequency interference medium; Based on the sorting result of the three-dimensional coordinates of the radio frequency interference medium and the three-dimensional coordinates of the radio frequency interference medium, perform optimization for the position with the minimum radio frequency interference on the several initial RF tag installation positions to obtain the standard installation position of the RF tag.

4. The intelligent on-line detection and verification system for RF tag production according to claim 3, wherein Based on the three-dimensional coordinate sorting result of the radio frequency interference medium and the three-dimensional coordinates of the radio frequency interference medium, optimize the minimum radio frequency interference position for the initial installation positions of the several RF tags to obtain the standard installation positions of the RF tags, including: Obtain the initial installation position of the first RF tag among the initial installation positions of the several RF tags; Traverse the three-dimensional coordinates of the radio frequency interference medium, conduct distance evaluation with the initial installation position of the first RF tag, and obtain a set of three-dimensional coordinate distances of the radio frequency interference medium; Based on the three-dimensional coordinate serial numbers of the three-dimensional coordinate sorting result of the radio frequency interference medium, perform product calculation on the set of three-dimensional coordinate distances of the radio frequency interference medium to obtain a set of corrected three-dimensional coordinate distances of the radio frequency interference medium; Sum the set of corrected three-dimensional coordinate distances of the radio frequency interference medium to obtain the fitness of the initial installation position of the first RF tag, and add it to the fitness of the initial installation positions of the several RF tags; Perform minimum value sorting on the fitness of the initial installation positions of the several RF tags to obtain the standard installation positions of the RF tags.

5. The intelligent online detection and verification system for RF tag production according to claim 3, wherein, Conduct medium interference intensity analysis based on the radio frequency interference medium type and the three-dimensional coordinates of the radio frequency interference medium to obtain the medium radio frequency interference intensity, including: Set the preset distance of medium distribution and preset radio frequency parameters through the user terminal; According to the preset distance of medium distribution and the preset radio frequency parameters, collect multiple groups of data of the preset radio frequency interference medium type, where any one of the multiple groups of data includes medium length recording data, medium width recording data, medium height recording data, and a set of radio frequency signal attenuation amount recording data; Conduct clustering analysis on the set of radio frequency signal attenuation amount recording data according to the radio frequency signal attenuation amount deviation threshold to obtain multiple clusters of radio frequency signal attenuation amount recording data; Extract the clusters with the amount of radio frequency signal attenuation amount recording data less than or equal to the recording data amount threshold, delete them from the multiple clusters of radio frequency signal attenuation amount recording data, obtain the remaining radio frequency signal attenuation amount recording data, extract the maximum value, and set it as the medium radio frequency interference intensity identification data; According to the medium radio frequency interference intensity identification data, the medium length recording data, the medium width recording data, and the medium height recording data, retrieve the multiple groups of data, and through machine learning, train a medium radio frequency interference intensity prediction model and bind it to the preset radio frequency interference medium type to construct a medium radio frequency interference intensity analysis library; According to the radio frequency interference medium type, match the target medium radio frequency interference intensity prediction model from the medium radio frequency interference intensity analysis library, process the medium length, medium width, and medium height extracted from the three-dimensional coordinates of the radio frequency interference medium, and obtain the medium radio frequency interference intensity.

6. The intelligent online detection and verification system for RF tag production according to claim 1, characterized in that, Conduct RF tag production optimization according to the standard installation positions of the RF tags, the list of radio frequency interference medium types, the list of medium distribution distances, and the list of medium structures to obtain the installation parameter constraint space and the radio frequency parameter constraint space, including: Retrieve the historical RF tag production sample set of the standard installation positions of the RF tags, the list of radio frequency interference medium types, the list of medium distribution distances, and the list of medium structures; Produce a sample set according to the historical RF tags, conduct a central tendency analysis on the installation parameter attributes, and construct the installation parameter constraint space; Produce a sample set according to the historical RF tags, conduct a central tendency analysis on the radio frequency parameter attributes, and construct the radio frequency parameter constraint space.

7. The intelligent online detection and verification system for RF tag production according to claim 1, wherein Statistically analyze the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector to obtain the RF tag abnormality probability, including: Configure the abnormal RF tag trigger rule: Construct the first trigger condition for abnormal RF tags: When an RF tag falls off within a preset service duration, it is regarded as an abnormal RF tag; Construct the second trigger condition for abnormal RF tags: When an RF tag belongs to a dead tag or an empty tag, it is regarded as an abnormal RF tag; Configure a logical OR condition for the first trigger condition for abnormal RF tags and the second trigger condition for abnormal RF tags to obtain the abnormal RF tag trigger rule; Based on the abnormal RF tag trigger rule, statistically analyze the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector to obtain the RF tag abnormality probability.

8. An intelligent online detection and verification method for RF tag production, characterized in that The method is applied to the intelligent online detection and verification system for RF tag production according to any one of claims 1-7, and the method includes: Process the target product model and target product positioning through a predefined RF tag standard position library to calibrate the RF tag standard installation position; Obtain the target product structure information, and based on the RF tag standard installation position, construct a radio frequency interference medium type list, a medium distribution distance list, and a medium structure list; Conduct RF tag production optimization according to the RF tag standard installation position, the radio frequency interference medium type list, the medium distribution distance list, and the medium structure list to obtain the installation parameter constraint space and the radio frequency parameter constraint space; Receive the RF tag production monitoring parameters, calculate the installation parameter deviation vector with respect to the installation parameter constraint space, and the radio frequency parameter deviation vector with respect to the radio frequency parameter constraint space; Statistically analyze the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the radio frequency parameter deviation vector to obtain the RF tag abnormality probability; When the RF tag abnormality probability is greater than or equal to the abnormality probability threshold, send the target product to the full inspection library for quality inspection, otherwise, send the target product to the sampling inspection library for quality inspection.

Images