Intelligent online detection and review system and method for RF tag production

Abstract

The present invention relates to an intelligent online detection and review system and method for RF tag production, and relates to the field of intelligent detection. The system calibrates the standard installation position of the RF tag by combining a predefined RF tag standard position library with target product models and positioning information, and constructs a list of radio frequency interference medium related information based on the position. Then, the system optimizes the RF tag production parameters to obtain a constraint space, calculates a deviation vector by receiving monitoring parameters, calculates the proportion of abnormal RF tags to predict the abnormality probability, and decides whether to perform a full inspection or a random inspection based on the abnormality probability threshold. This makes the detection strategy flexible and adaptable, solves the problems of low efficiency and high misjudgment rate of RF tag production detection, and the inability of fixed parameter settings to adapt to the diverse requirements of different product models and positioning information, thereby affecting the quality and efficiency of RF tag production. The system improves the detection efficiency and quality control of defective products produced by RF tags, and optimizes the allocation of quality inspection resources.

Description

Technical Field

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

[0002] With the rapid development of the Internet of Things (IoT) and smart tag technology, RF (Radio Frequency) tags, wireless communication devices capable of storing and transmitting information, have been widely used in fields such as logistics tracking, asset management, and anti-counterfeiting and traceability. As RF tag applications continue to expand, their production quality and efficiency have become key factors restricting the industry's development.

[0003] The existing RF tag production process often relies on manual inspection and fixed parameter settings, which presents numerous drawbacks. Manual inspection is not only inefficient but also susceptible to human factors, leading to inconsistent test results and high rates of false positives. Furthermore, fixed parameter settings cannot adapt to the diverse requirements of different product models and positioning information, resulting in inconsistent performance of RF tags in different application scenarios. Furthermore, as the scale of RF tag production expands, existing methods are no longer able to meet the demands of large-scale, high-efficiency production. Summary of the Invention

[0004] The present invention addresses the technical problems in the prior art where RF tag production and inspection rely on manual labor, resulting in low efficiency and a high misjudgment rate, and where fixed parameter settings cannot adapt to the diverse requirements of different product models and positioning information, thereby affecting the quality and efficiency of RF tag production. An intelligent online inspection and review system and method for RF tag production are provided to solve these problems.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides an intelligent online detection and review system for RF tag production, the system comprising: a position calibration module for 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; a structure acquisition module for obtaining target product structure information, and constructing 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 for optimizing RF tag production based on 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. , obtain the installation parameter constraint space and the radio frequency parameter constraint space; a deviation calculation module, used to receive the RF tag production monitoring parameters, calculate the installation parameter deviation vector with the installation parameter constraint space, and the radio frequency parameter deviation vector with the radio frequency parameter constraint space; a statistical analysis module, used to 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, and obtain the RF tag abnormality probability; a quality inspection execution module, used to send the target product to the full inspection warehouse for quality inspection when the RF tag abnormality probability is greater than or equal to the abnormality probability threshold, otherwise, send the target product to the random inspection warehouse for quality inspection.

[0007] In a second aspect, the present invention provides an intelligent online detection and review method for RF tag production, the method comprising: 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; obtaining the target product structure information, and constructing a radio frequency interference medium type list, a medium distribution distance list, and a medium structure list based on the RF tag standard installation position; performing 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, and obtaining an installation parameter constraint space and a radio frequency parameter constraint space; receiving RF tag production monitoring parameters, calculating an installation parameter deviation vector from the installation parameter constraint space, and a radio frequency parameter deviation vector from the radio frequency parameter constraint space; counting 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 obtaining the RF tag abnormality probability; when the RF tag abnormality probability is greater than or equal to the abnormality probability threshold, sending the target product to the full inspection library for quality inspection, otherwise, sending the target product to the spot inspection library for quality inspection.

[0008] The beneficial effects of the present invention are as follows: through a predefined RF tag standard position library, the standard installation position of the RF tag is calibrated in combination with the target product model and positioning information, and a list of radio frequency interference medium related information is constructed based on the position, and then the RF tag production parameter optimization is performed to obtain the constraint space, the deviation vector is calculated by receiving monitoring parameters, the proportion of abnormal RF tags is counted to predict the abnormal probability, and the execution of full inspection or random inspection is decided according to the abnormal probability threshold, so that the detection strategy has flexibility and adaptability, improves the detection efficiency and quality control of defective products produced by RF tags, and optimizes the allocation of quality inspection resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is a structural diagram of the intelligent online detection and review system for RF tag production provided by the present invention.

[0010] Figure 2 This is a flow chart of the intelligent online detection and review method for RF tag production provided by the present invention.

[0011] Explanation 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 DESCRIPTION

[0012] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0013] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0014] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, 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 is consistent with the widest scope consistent with the principles and features disclosed herein.

[0015] Embodiment one:

[0016] like Figure 1 As shown, an embodiment of the present invention provides an intelligent online detection and review system for RF tag production, the system comprising:

[0017] The position calibration module 11 is used to process the target product model and target product location through a predefined RF tag standard position library, and calibrate the RF tag standard installation position.

[0018] For example, RF tag is short for Radio Frequency Tag, also known as electronic tag or radio frequency identification tag. RF tags use radio signals to achieve contactless information identification and data exchange, and are widely used in logistics, retail, transportation, medical care, anti-counterfeiting and other fields. During specific use, the reader emits radio waves of a specific frequency through the antenna, and the tag responds to the signal. Active tags have their own batteries and actively send stored signals; passive tags do not have batteries and activate the chip by receiving radio frequency energy from the reader, reflecting the signal and transmitting data. Data exchange then occurs, that is, the reader analyzes the signal returned by the tag, obtains the data, and transmits it to the system for processing. RF tags operate contactlessly and do not require human intervention. They can read multiple tags in batches, improving efficiency. At the same time, they have strong anti-interference capabilities and can operate in harsh environments (such as dust, humidity, and high temperature).

[0019] To ensure accurate and effective tag installation, this solution relies on a predefined RF tag standard location library. This library serves as a core reference framework, integrating standard installation location data for various product models. The target product's model information is first processed and matched to the corresponding preset product model data in the standard location library. This information is then combined with the target product's positioning information, which details its specific position and orientation in three-dimensional space, providing a spatial reference for subsequent precise installation.

[0020] By comparing and analyzing the target product's model and location information with the preset data in the standard location library, the system can accurately determine the standard installation position of the RF tag on the target product. For example, when producing a specific model of smart packaging box, by identifying the box model (such as "Model X") and combining it with the preset RF tag installation area location information within the box, the system retrieves the standard RF tag installation coordinates corresponding to that model of box from the standard location library. This ensures that the tag is accurately installed in the designated location, effectively avoiding problems such as signal interference or recognition failure caused by installation deviations.

[0021] The structure acquisition module 12 is used to obtain 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.

[0022] Optionally, to ensure stable tag performance and accurate reading, further analysis of the target product's internal structure is required. By acquiring detailed structural information about the target product, including the layout, material properties, and spatial relationships of its components, the system gains a comprehensive understanding of the product's internal environment. Subsequently, using the previously calibrated standard RF tag installation location as a reference point, the system begins identifying and analyzing potential RF interference media surrounding that location. These media, such as metal components and liquid containers, can absorb, reflect, or scatter RF signals, potentially adversely affecting the proper functioning of the RF tag. Based on this analysis, the system constructs three key lists: a RF interference media type list, which details all identified interfering media types, such as stainless steel, aluminum components, and conductive liquids; a media distribution distance list, which precisely identifies the spatial distance between each interfering media and the standard RF tag installation location. This data is crucial for assessing interference intensity; and a media structure list, which describes the specific form and layout characteristics of the interfering media, such as the size and shape of metal plates and their installation angle within the product. For example, when producing an industrial equipment casing with integrated electronic components, the system analyzed its structural information and identified the presence of a large aluminum heat sink inside the casing as a radio frequency interference medium. This heat sink is only a few centimeters away from the predetermined RF tag installation location and has a flat surface and a large area. This information is accurately recorded in the corresponding list, providing an important basis for the subsequent optimization and adjustment of RF tag production parameters.

[0023] The production optimization module 13 is used to optimize RF tag production according to the RF tag standard installation position, the RF interference medium type list, the medium distribution distance list and the medium structure list, and obtain the installation parameter constraint space and the RF parameter constraint space.

[0024] To further ensure optimal tag performance, the system performs an RF tag production optimization process based on the established standard RF tag installation location, a list of RF interference media types, a list of media distribution distances, and a list of media structures. Specifically, the system first uses the standard RF tag installation location as a reference point. It then combines the various media identified in the RF interference media type list (such as metal components and liquid containers), the specific distance data provided in the media distribution distance list, and the media morphology and layout characteristics described in the media structure list to comprehensively analyze the potential impact of these factors on RF tag performance. Based on this, an algorithmic model fine-tunes and optimizes RF tag installation parameters (such as placement pressure and temperature, which directly affect the adhesion and stability of the tag to the product) and RF parameters (such as transmit power and frequency tuning value, which affects the read / write distance of the tag and adjusts the matching between the tag antenna and the reader's transmission frequency). For example, if a large metal component is detected near the standard installation location, the system may reduce the RF tag's transmit power to reduce the metal's reflection interference on the RF signal and adjust the frequency tuning value to ensure the tag antenna operates efficiently at the specified frequency. After this series of optimization calculations, a set of installation parameter constraint spaces and RF parameter constraint spaces were finally determined. These spaces define the parameter ranges that should be followed to ensure stable RF tag performance under different production conditions, thus providing a scientific basis for subsequent production control.

[0025] The deviation calculation module 14 is configured to receive RF tag production monitoring parameters and calculate an installation parameter deviation vector relative to the installation parameter constraint space and a radio frequency parameter deviation vector relative to the radio frequency parameter constraint space.

[0026] Furthermore, to ensure that the production process meets preset standards and 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 in the RF tag production process (such as mounting pressure, temperature, etc., which are directly related to the bonding strength and stability between the tag and the product) and RF parameters (such as transmission power, frequency tuning value, etc., which affect the tag's read and write performance and communication quality). After receiving these parameters, the system will immediately compare and analyze them with the installation parameter constraint space and RF parameter constraint space previously determined through the optimization process. Specifically, the deviation between the actual monitored installation parameters and the boundary values ​​of the installation parameter constraint space is calculated 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, the deviation between the actual monitored RF parameters and the boundary values ​​of the RF parameter constraint space is calculated to form an RF parameter deviation vector to evaluate the deviation of the RF parameters. For example, if the actual monitored placement pressure for a batch of RF tags is 4.5N, while the installation parameter constraint specifies a placement pressure range of 4.0N to 5.0N, the system will calculate a deviation of +0.5N and incorporate this into the installation parameter deviation vector. This calculation and analysis enables real-time monitoring of parameter deviations during RF tag production, providing data support for subsequent anomaly probability prediction and quality inspection decisions.

[0027] The statistical analysis module 15 is configured to calculate the percentage 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 an abnormal probability of the RF tag.

[0028] Preferably, during the quality monitoring phase of RF tag production, the system collects and organizes the corresponding installation parameter deviation vectors and RF parameter deviation vectors for each batch or continuous production run of RF tag samples. The installation parameter deviation vector records the degree of deviation between parameters such as mounting pressure and temperature in actual production and the preset installation parameter constraint space, while the RF parameter deviation vector reflects the difference between RF parameters such as transmit power and frequency tuning value and the RF parameter constraint space. The system then performs a comprehensive analysis of these deviation vectors and calculates the proportion of abnormal RF tags by counting the ratio of the number of abnormal RF tags in the sample set to the total number of samples. The criteria for determining abnormal RF tags include, but are not limited to, parameter deviations exceeding preset thresholds and tag performance test failures. For example, if the system detects that 10% of the RF tag samples in a production batch have installation parameter or RF parameter deviations outside the allowable range, and these tags perform abnormally in subsequent performance tests, the system will determine that the proportion of abnormal RF tags in that batch is 10%. Based on this, the system derives the probability of abnormality for the RF tags, providing a basis for subsequent quality control measures.

[0029] The quality inspection execution module 16 is configured to send the target product to the full inspection warehouse for quality inspection when the RF tag abnormality probability is greater than or equal to the abnormality probability threshold; otherwise, send the target product to the random inspection warehouse for quality inspection.

[0030] Specifically, during the quality control process for RF tag production, the previously calculated RF tag abnormality probability is compared with a preset abnormality probability threshold. This threshold, a key indicator for determining the quality of a production batch, is determined based on historical data analysis and accumulated production experience. If the system determines that the RF tag abnormality probability is greater than or equal to the threshold, it indicates that a high proportion of abnormal RF tags in the current production batch exists, potentially impacting overall product quality and performance. To this end, the system automatically triggers a full inspection mechanism, sending the target products to a full inspection warehouse for comprehensive quality inspection to ensure that all RF tags meet pre-set standards. Conversely, if the RF tag abnormality probability is lower than the threshold, it indicates that the quality of the production batch is relatively stable, with a low proportion of abnormal RF tags. In this case, the system sends the target products to a spot inspection warehouse for sampling quality inspection, effectively and economically monitoring production quality. For example, if the abnormality probability threshold is set at 5%, if the RF tag abnormality probability in a batch reaches or exceeds this value, the system transfers all products in the batch to the full inspection warehouse for detailed inspection. If the abnormality probability is only 2%, the batch is transferred to a spot inspection warehouse for random quality inspection.

[0031] In a preferred embodiment, a predefined RF tag standard position library is used to process the target product model and target product positioning, and calibrate the standard installation position of the RF tag, including: 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 RF interference medium three-dimensional coordinates from the preset product three-dimensional coordinates based on a preset RF interference medium type set and in combination with the preset product structure information; filtering the preset product three-dimensional coordinates through the user terminal to obtain an RF tag installable area; in the RF tag installable area, optimizing the RF interference minimum position for the RF interference medium three-dimensional coordinates to obtain the RF tag standard installation position.

[0032] Specifically, a predefined RF tag standard location library integrates various preset product models and their corresponding detailed product structure information. This information covers the material and structure of the components at different locations within the product. To calibrate the standard installation position of the RF tag on the target product, the system first obtains the target product model information and retrieves the preset product structure information matching this model from the standard location library. The system then maps this preset product structure information into a three-dimensional coordinate system, constructing a precise 3D model of the product, thereby obtaining a 3D coordinate representation of the preset product.

[0033] Taking into account 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 metals, liquids, and other media types 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 based on the preset product structure information. These coordinate information provides an important reference for subsequently determining the installation location of the RF tag.

[0034] To ensure that RF tags are installed in locations with minimal or no RF interference, the system allows operators to visually filter the 3D coordinates of pre-set products through a user interface to determine the appropriate installation area for the RF tag. Within this area, an optimization algorithm is used to comprehensively analyze the 3D coordinates of any RF interference medium, identifying the location with minimal RF interference as the standard installation location for the RF tag. For example, when manufacturing a smart home appliance, the system retrieves the product's pre-set structural information, including the material and location of components such as the internal circuit board and metal casing, by searching a standard location database. After mapping this information to a 3D coordinate system, it identifies the locations of metal components near the circuit board that could potentially generate RF interference. Subsequently, through user-side filtering, an open area away from these metal components is identified as the appropriate installation area for the RF tag. Ultimately, the system identifies the location within this area with minimal RF interference, which is used as the standard installation location for the RF tag, ensuring stable and accurate operation of the tag throughout its subsequent use.

[0035] In a preferred embodiment, in the RF tag installable area, the three-dimensional coordinates of the RF interference medium are optimized for the position with minimum RF interference to obtain the standard installation position of the RF tag, including: obtaining the RF tag pattern outline, enumerating the installation deployment in the RF tag installable area, and obtaining several RF tag initial installation positions; based on the three-dimensional coordinates of the RF interference medium, combined with the preset product structure information, obtaining the type of RF interference medium; based on the RF interference medium type and the three-dimensional coordinates of the RF interference medium, performing medium interference intensity analysis to obtain the medium RF interference intensity; sorting the three-dimensional coordinates of the RF interference medium from small to large according to the RF interference intensity of the medium to obtain the sorting result of the three-dimensional coordinates of the RF interference medium; 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, optimizing the position with minimum RF interference for the initial installation positions of the several RF tags to obtain the standard installation position of the RF tag.

[0036] Preferably, after determining the area where the RF tag can be installed, in order to further optimize the installation position of the RF tag to reduce radio frequency interference, the pattern outline information of the RF tag is first obtained. The outline defines the physical size and shape of the tag. Subsequently, enumerated installation deployment is performed within the installable area, that is, the installation conditions of the RF tags in different positions are simulated, thereby obtaining the initial installation positions of several RF tags. Next, the types of these interference media, such as metals, liquids, etc., are identified using the known three-dimensional coordinates of the radio frequency interference medium, combined with the preset product structure information. These media types have different interference characteristics for radio frequency signals. Then, based on the identified radio frequency interference medium type and its three-dimensional coordinates, a medium interference intensity analysis is performed. By means of calculation or simulation, the degree of interference that each interference medium may cause to the RF tag is quantified, thereby obtaining the medium radio frequency interference intensity.

[0037] To efficiently find the installation location with the least RF interference, the system sorts the three-dimensional coordinates of the RF interference medium in ascending order of the medium's RF interference intensity, forming a sorting result for the three-dimensional coordinates of the RF interference medium. Finally, based on this sorting result and combined with the three-dimensional coordinates of the RF interference medium, the system optimizes the initial installation positions of the RF tags previously enumerated for the location with the least RF interference. During the optimization process, the total RF interference received by each initial installation position is evaluated, and the location with the least interference is selected as the standard installation position for the RF tag. For example, in the production of smart packaging boxes, the system identifies a metal plate near the edge of the box as an RF interference medium. Calculations show that the metal plate has a high interference intensity on the RF tag. The system then enumerates multiple initial installation positions within the installable area, and combined with the three-dimensional coordinates of the metal plate and the interference intensity sorting results, it ultimately determines a location away from the metal plate with the least RF interference as the standard installation position for the RF tag.

[0038] In a preferred embodiment, based on the RF interference medium three-dimensional coordinate sorting result and the RF interference medium three-dimensional coordinate, the RF interference minimum position of the several RF tag initial installation positions is optimized to obtain the RF tag standard installation position, including: obtaining the first RF tag initial installation position of the several RF tag initial installation positions; traversing the RF interference medium three-dimensional coordinate, performing distance evaluation with the first RF tag initial installation position, and obtaining a RF interference medium three-dimensional coordinate distance set; based on the three-dimensional coordinate sequence number of the RF interference medium three-dimensional coordinate sorting result, multiplying the RF interference medium three-dimensional coordinate distance set to obtain a RF interference medium three-dimensional coordinate corrected distance set; adding the RF interference medium three-dimensional coordinate corrected distance set to obtain the first RF tag initial installation position fitness, and adding it to the initial installation position fitness of several RF tags; performing minimum value sorting on the initial installation position fitness of the several RF tags to obtain the RF tag standard installation position.

[0039] Furthermore, during the RF tag installation location optimization process, the enumerated initial RF tag installation locations are evaluated one by one. First, focusing on the first RF tag initial installation location, the Euclidean distance (or other appropriate distance metric) between this initial installation location and the 3D coordinates of each interfering medium is calculated by traversing the 3D coordinate set of RF interference media, which is sorted by interference strength. This generates a 3D distance set of RF interference medium coordinates, where each element corresponds to the spatial distance between an interfering medium and the initial installation location.

[0040] Subsequently, based on the three-dimensional coordinate sequence number in the three-dimensional coordinate sorting result of the radio frequency interference medium (the sequence number is positively correlated with the interference intensity, and the smaller the sequence number, the weaker the interference), each distance value in the distance set is multiplied and corrected, that is, each distance value is multiplied by a weight factor related to the sequence number of the corresponding interference medium (for example, the smaller the sequence number of the interference medium, the smaller its weight factor, thereby reducing its impact on the overall interference assessment), to form a three-dimensional coordinate corrected distance set of the radio frequency interference medium.

[0041] By summing all elements in the corrected distance set, the system obtains the fitness value for the first RF tag's initial installation location. This value comprehensively reflects the degree of RF interference experienced at that location. The smaller the fitness value, the less interference experienced. This fitness value is then added to the fitness sets for several RF tag initial installation locations.

[0042] Repeat the above steps until the fitness values ​​of all RF tag initial installation positions are calculated and added to the fitness set. Finally, perform a minimum value 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 is least subject to RF interference under the current evaluation conditions. For example, in the production of smart 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 interfering media is the smallest, indicating that this position is subject to the least RF 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.

[0043] In a preferred embodiment, a medium interference intensity analysis is performed based on the RF interference medium type and the three-dimensional coordinates of the RF interference medium to obtain the medium RF interference intensity, including: setting a preset medium distribution distance and preset RF parameters through a user terminal; collecting multiple groups of data of a preset RF interference medium type according to the preset medium distribution distance and the preset RF parameters, wherein any group of the multiple groups of data includes medium length record data, medium width record data and medium height record data, and a set of RF signal attenuation record data; clustering analysis is performed on the set of RF signal attenuation record data according to a RF signal attenuation deviation threshold to obtain multiple clusters of RF signal attenuation record data; extracting RF signal attenuation record data whose amount is less than or equal to the record data amount threshold A cluster of values ​​is deleted from the multiple clusters of RF signal attenuation record data to obtain the remaining RF signal attenuation record data, and the maximum value is extracted and set 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, the multiple groups of data are retrieved, and a medium RF interference intensity prediction model is trained through machine learning, and is bound to the preset RF interference medium type to build a medium RF interference intensity analysis library; according to the RF interference medium type, a target medium RF interference intensity prediction model is matched from the medium RF interference intensity analysis library, and the medium length, medium width and medium height extracted from the three-dimensional coordinates of the RF interference medium are processed to obtain the medium RF interference intensity.

[0044] For example, during the medium interference intensity analysis process, the user first sets a preset medium distribution distance (e.g., the expected distance between the interfering medium and the RF tag) and preset RF parameters (e.g., transmission frequency, power, etc.). These parameters provide baseline conditions for subsequent data collection and analysis. Based on these preset conditions, multiple sets of data are collected for the preset RF interference medium types. Each set of data includes recorded data on the medium's length, width, and height, as well as a corresponding set of recorded data on RF signal attenuation. These data reflect the degree of interference of different sized media on the RF signal.

[0045] The system then performs cluster analysis on the collected RF signal attenuation data using a threshold for RF signal attenuation deviation, dividing the data into multiple clusters with similar attenuation characteristics. To eliminate abnormal or noisy data, clusters with RF signal attenuation data less than or equal to the threshold are extracted and removed from the clusters, resulting in the remaining, more representative RF signal attenuation data. The maximum RF signal attenuation value is then extracted from the remaining data as the medium's RF interference strength indicator, which directly reflects the medium's maximum interference capability to RF signals.

[0046] Next, using the media's RFI strength identification data, combined with recorded data on the media's length, width, and height, and retrieving multiple sets of previously collected data, a machine learning algorithm is used to train a media RFI strength prediction model. This RFI strength prediction model predicts the RFI strength of a medium based on its dimensional parameters and is associated with preset RFI media types to construct a media RFI strength analysis library. In practical applications, when analyzing the interference strength of a specific RFI media type, the system matches the corresponding target RFI strength prediction model from the RFI strength analysis library. Using the media length, width, and height extracted from the RFI media's three-dimensional coordinates as input parameters, the model processes the RFI strength. For example, in a smart warehousing system, to analyze the interference strength of metal shelf panels on a shelf to an RF tag, the system can match the corresponding prediction model from the analysis library and input the shelf panels' actual dimensional parameters to quickly determine their RFI strength, providing a basis for optimal RF tag installation.

[0047] In a preferred embodiment, RF tag production optimization is performed based on 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 to obtain the installation parameter constraint space and the radio frequency parameter constraint space, including: retrieving the historical RF tag production sample set of 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; performing a central trend analysis on the installation parameter attributes based on the historical RF tag production sample set to construct the installation parameter constraint space; performing a central trend analysis on the radio frequency parameter attributes based on the historical RF tag production sample set to construct the radio frequency parameter constraint space.

[0048] Specifically, during the RF tag production optimization process, historical RF tag production sample sets containing standard RF tag installation positions, a list of RF interference media types, a list of media distribution distances, and a list of media structures are retrieved. These sample sets record installation and parameter information under different conditions in past production, providing a data basis for subsequent analysis.

[0049] For installation parameter attributes, the system extracts multidimensional parameters related to installation from historical sample sets, such as placement pressure, temperature, and speed, to construct a multidimensional coordinate system. Using central tendency analysis methods (such as mean and median statistics), the system extracts a concentrated set of coordinates for these parameters within the multidimensional coordinate system. This set reflects the range of installation parameters that ensured stable operation of RF tags and minimal RF interference in historical production. Based on this concentrated set of coordinates, the system constructs an installation parameter constraint space, which defines the boundary conditions that installation parameters should adhere to in future production to ensure RF tag installation quality.

[0050] Similarly, the system analyzes RF parameter attributes, extracting RF-related parameters such as transmit power and frequency tuning values ​​from historical sample sets. Similarly, it constructs a multidimensional coordinate system and performs centralized trend analysis, resulting in a centralized set of RF parameter coordinates. This set represents the RF parameter range that achieved optimal communication performance and minimized interference during historical production. Based on this centralized set of coordinates, the system constructs an RF parameter constraint space, providing clear guidance for setting RF parameters in subsequent production. For example, in one RF tag production batch, analyzing the historical sample set revealed that optimal RF tag installation quality was achieved when the placement pressure was controlled at 10-15N and the temperature was maintained at 20-25°C. Furthermore, optimal tag communication performance was achieved when the transmit power was set at 1-2W and the frequency was tuned to around 915MHz. Based on these findings, the system constructed corresponding installation and RF parameter constraint spaces, providing strong support for subsequent production.

[0051] 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 RF parameter deviation vector is counted to obtain the probability of RF tag abnormality, including: configuring abnormal RF tag triggering rules: constructing a first triggering condition for abnormal RF tags: when the RF tag falls off within a preset service period, it is regarded as an abnormal RF tag; constructing a second triggering condition for abnormal RF tags: when the RF tag is a dead sign or an empty sign, 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 rules; based on the abnormal RF tag triggering rules, the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the RF parameter deviation vector is counted to obtain the probability of RF tag abnormality.

[0052] Specifically, during the RF tag production quality assessment process, trigger rules for abnormal RF tags are configured. These rules consist of two core conditions: The first condition establishes the first trigger condition for abnormal RF tags. This condition states that if an RF tag falls off within a preset service life (e.g., 30 days), the system identifies it as an abnormal RF tag. This condition is intended to capture tag shedding caused by improper installation parameters or material quality issues. The second condition establishes the second trigger condition for abnormal RF tags. This condition states that if an RF tag is identified as dead or empty (i.e., a tag that cannot read or write data normally), it is also considered an abnormal RF tag. This condition is used to identify communication failures caused by incorrect RF parameter settings or defects in the tag itself.

[0053] Furthermore, to combine the two aforementioned conditions, the system configures a logical OR condition, meaning that an RF tag is considered abnormal if any of the above conditions is met. Based on this abnormal RF tag trigger rule, the system counts a set of RF tag production samples, including both the installation parameter deviation vector and the RF parameter deviation vector. Specifically, the system iterates over each RF tag in the sample set, checks whether it meets the abnormal RF tag trigger condition, and records the number of abnormal RF tags. It then calculates the proportion of abnormal RF tags in the total number of samples, which serves as the RF tag abnormality probability. For example, in a batch of RF tags produced, the system counted 1,000 samples, of which 10 fell off within 30 days of service, and another 5 were identified as dead tags. According to the abnormal RF tag trigger rule, all 15 tags were deemed abnormal, resulting in a 1.5% (15 / 1,000) probability of abnormal RF tags in this batch. This probability helps manufacturers assess the stability of their current production processes and make targeted adjustments to installation or RF parameters to reduce the incidence of abnormal tags.

[0054] The intelligent online detection and review system for RF tag production provided by the embodiment of the present invention has at least the following technical effects:

[0055] 1. Through the predefined RF tag standard position library, combined with the target product model and positioning information, the standard installation position of the RF tag can be accurately calibrated. Furthermore, by constructing a list of RF interference media types, a list of media distribution distances, and a list of media structures, and optimizing the position with minimum RF interference within the RF tag installation area, the stability and reliability of the RF tag in complex electromagnetic environments is ensured, thereby effectively reducing the problem of tag shedding or communication failure caused by improper installation position, and improving the production quality and application effect of RF tags.

[0056] 2. Using historical RF tag production sample sets, we conducted a central trend analysis of installation parameters and RF parameters, and constructed an installation parameter constraint space and a RF parameter constraint space. This provided clear parameter guidance for RF tag production, ensuring that parameter settings during the production process were always within the optimal range. Through intelligent parameter constraints and production optimization, we not only improved the production efficiency of RF tags, but also significantly reduced production anomalies and cost waste caused by improper parameter settings.

[0057] 3. By configuring abnormal RF tag triggering rules, the system can quickly identify and count the proportion of abnormal RF tags, thereby obtaining the probability of RF tag abnormality. When the abnormal probability exceeds the preset threshold, the system automatically sends the target product to the full inspection warehouse for quality inspection; otherwise, it is sent to the random inspection warehouse. This enables rapid identification and accurate quality inspection of abnormal tags, effectively preventing unqualified products from entering the market. At the same time, by optimizing quality inspection strategies, quality inspection efficiency and accuracy are improved, and quality inspection costs and time costs are reduced.

[0058] Example 2:

[0059] like Figure 2 As shown, based on the same inventive concept as the intelligent online detection and review system for RF tag production provided in Example 1, an embodiment of the present invention also provides an intelligent online detection and review method for RF tag production, the method comprising:

[0060] Through the predefined RF tag standard position library, the target product model and target product positioning are processed, and the standard installation position of the RF tag is calibrated.

[0061] The target product structure information is obtained, and based on the standard installation position of the RF tag, a radio frequency interference medium type list, a medium distribution distance list, and a medium structure list are constructed.

[0062] RF tag production optimization is performed according to the RF tag standard installation position, the RF interference medium type list, the medium distribution distance list and the medium structure list to obtain the installation parameter constraint space and the RF parameter constraint space.

[0063] Receive RF tag production monitoring parameters, calculate installation parameter deviation vectors from the installation parameter constraint space, and calculate radio frequency parameter deviation vectors from the radio frequency parameter constraint space.

[0064] 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 counted to obtain the RF tag abnormality probability.

[0065] When the abnormal probability of the RF tag is greater than or equal to the abnormal probability threshold, the target product is sent to the full inspection warehouse for quality inspection; otherwise, the target product is sent to the random inspection warehouse for quality inspection.

[0066] Furthermore, through the predefined RF tag standard position library, the target product model and target product positioning are processed, and the standard installation position of the RF tag is calibrated, including: obtaining the 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 the preset RF interference medium type set, combined with the preset product structure information, extracting the RF interference medium three-dimensional coordinates from the preset product three-dimensional coordinates; through the user terminal, screening the preset product three-dimensional coordinates to obtain the RF tag installable area; in the RF tag installable area, optimizing the RF interference minimum position of the RF interference medium three-dimensional coordinates to obtain the RF tag standard installation position.

[0067] Furthermore, in the RF tag installable area, the three-dimensional coordinates of the RF interference medium are optimized for the position with minimum RF interference to obtain the standard installation position of the RF tag, including: obtaining the RF tag pattern outline, enumerating the installation deployment in the RF tag installable area, and obtaining several initial installation positions of the RF tag; based on the three-dimensional coordinates of the RF interference medium, combined with the preset product structure information, obtaining the type of the RF interference medium; based on the RF interference medium type and the three-dimensional coordinates of the RF interference medium, performing medium interference intensity analysis to obtain the medium RF interference intensity; sorting the three-dimensional coordinates of the RF interference medium from small to large according to the RF interference intensity of the medium to obtain the sorting result of the three-dimensional coordinates of the RF interference medium; 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, optimizing the position with minimum RF interference for the initial installation positions of the several RF tags to obtain the standard installation position of the RF tag.

[0068] Furthermore, 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, the initial installation positions of the several RF tags are optimized for the position with the minimum radio frequency interference to obtain the standard installation position of the RF tag, including: obtaining the first RF tag initial installation position of the several RF tag initial installation positions; traversing the three-dimensional coordinates of the radio frequency interference medium, performing distance evaluation with the first RF tag initial installation position, and obtaining a set of three-dimensional coordinate distances of the radio frequency interference medium; performing multiplication calculation on the three-dimensional coordinate distance set of the radio frequency interference medium based on the three-dimensional coordinate sequence number of the sorting result of the three-dimensional coordinates of the radio frequency interference medium, and obtaining a set of corrected three-dimensional coordinate distances of the radio frequency interference medium; adding the corrected three-dimensional coordinate distance set of the radio frequency interference medium to obtain the fitness of the first RF tag initial installation position, and adding it to the fitness of the initial installation positions of several RF tags; performing minimum value sorting on the fitness of the initial installation positions of the several RF tags to obtain the standard installation position of the RF tag.

[0069] Furthermore, a medium interference intensity analysis is performed based on the RF interference medium type and the three-dimensional coordinates of the RF interference medium to obtain the medium RF interference intensity, including: setting a preset medium distribution distance and preset RF parameters through a user terminal; collecting multiple groups of data of a preset RF interference medium type according to the preset medium distribution distance and the preset RF parameters, wherein any group of the multiple groups of data includes medium length record data, medium width record data and medium height record data, as well as a set of RF signal attenuation record data; performing cluster analysis on the set of RF signal attenuation record data according to a RF signal attenuation deviation threshold to obtain multiple clusters of RF signal attenuation record data; extracting clusters whose RF signal attenuation record data volume is less than or equal to the record data volume threshold , delete the multiple clusters of RF signal attenuation record data to obtain the remaining RF signal attenuation record data, extract the maximum value, and set 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, retrieve the multiple groups of data, and train the medium RF interference intensity prediction model through machine learning, and bind it with the preset RF interference medium type to build a medium RF interference intensity analysis library; according to the RF interference medium type, match the target medium RF interference intensity prediction model from the medium RF interference intensity analysis library, process the medium length, medium width and medium height extracted from the three-dimensional coordinates of the RF interference medium, and obtain the medium RF interference intensity.

[0070] Furthermore, RF tag production optimization is performed based on 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 to obtain the installation parameter constraint space and the radio frequency parameter constraint space, including: retrieving the historical RF tag production sample set of 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; performing a central trend analysis on the installation parameter attributes based on the historical RF tag production sample set to construct the installation parameter constraint space; performing a central trend analysis on the radio frequency parameter attributes based on the historical RF tag production sample set to construct the radio frequency parameter constraint space.

[0071] Furthermore, the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the RF parameter deviation vector is counted to obtain the probability of RF tag abnormality, including: configuring abnormal RF tag triggering rules: constructing a first triggering condition for abnormal RF tags: when the RF tag falls off within a preset service period, it is regarded as an abnormal RF tag; constructing a second triggering condition for abnormal RF tags: when the RF tag is a dead sign or an empty sign, it is regarded as an abnormal RF tag; configuring logical OR conditions 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 rules; based on the abnormal RF tag triggering rules, the proportion of abnormal RF tags in the RF tag production sample set of the installation parameter deviation vector and the RF parameter deviation vector is counted to obtain the probability of RF tag abnormality.

[0072] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0074] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. Intelligent online detection and review system for RF tag production, characterized by: The system comprises: The position calibration module is used to process the target product model and target product location through the predefined RF tag standard position library, and calibrate the standard installation position of the RF tag; A structure acquisition module is used to obtain 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 is used to optimize RF tag production based on the standard installation position of the RF tag, the list of RF interference media types, the list of media distribution distances, and the list of media structures, to obtain an installation parameter constraint space and a RF parameter constraint space; a deviation calculation module, configured to receive RF tag production monitoring parameters, calculate an installation parameter deviation vector from the installation parameter constraint space, and a radio frequency parameter deviation vector from the radio frequency parameter constraint space; A statistical analysis module is used to 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 abnormality probability; A quality inspection execution module is configured to send the target product to a full inspection warehouse for quality inspection when the abnormal probability of the RF tag is greater than or equal to the abnormal probability threshold; otherwise, the target product is sent to a random inspection warehouse for quality inspection; Through the predefined RF tag standard location library, the target product model and target product location are processed, and the standard installation location of the RF tag is calibrated, including: Obtain 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 three-dimensional coordinates of the preset product according to the preset radio frequency interference medium type set and in combination with the preset product structure information; Through the user terminal, the three-dimensional coordinates of the preset product are screened to obtain the RF tag installation area; In the RF tag installable area, optimizing the RF interference minimum position of the RF interference medium's three-dimensional coordinates to obtain the RF tag's standard installation position; In the RF tag installable area, optimizing the RF interference minimum position of the RF interference medium's three-dimensional coordinates to obtain the RF tag's standard installation position includes: Obtaining an RF tag pattern outline, performing enumeration installation deployment in the RF tag installable area, and obtaining initial installation positions of several RF tags; Obtaining the type of the radio frequency interference medium based on the three-dimensional coordinates of the radio frequency interference medium and the preset product structure information; 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; Sort the three-dimensional coordinates of the radio frequency interference media according to the radio frequency interference intensity of the media from small to large, to obtain a sorting result of the three-dimensional coordinates of the radio frequency interference media; 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 the initial installation positions of the plurality of RF tags for positions with minimum radio frequency interference to obtain the standard installation position of the RF tag; 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 the initial installation positions of the plurality of RF tags for positions with minimum radio frequency interference to obtain the standard installation position of the RF tag includes: Obtaining a first RF tag initial installation position of the plurality of RF tag initial installation positions; Traversing the three-dimensional coordinates of the radio frequency interference medium, performing distance evaluation with the initial installation position of the first RF tag, and obtaining a three-dimensional coordinate distance set of the radio frequency interference medium; Based on the three-dimensional coordinate sequence number of the three-dimensional coordinate sorting result of the radio frequency interference medium, the three-dimensional coordinate distance set of the radio frequency interference medium is multiplied to obtain a corrected three-dimensional coordinate distance set of the radio frequency interference medium; Add the three-dimensional coordinate corrected distance set of the radio frequency interference medium to obtain the initial installation position fitness of the first RF tag, and add it to the initial installation position fitness of several RF tags; performing minimum value sorting on the initial installation position adaptability of the plurality of RF tags to obtain the standard installation position of the RF tag; 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 includes: Through the user terminal, set the preset distance of medium distribution and preset radio frequency parameters; Collecting multiple sets of data on a preset radio frequency interference medium type based on the preset medium distribution distance and the preset radio frequency parameters, wherein any one of the multiple sets of data includes a medium length record data, a medium width record data, a medium height record data, and a radio frequency signal attenuation record data set; Performing cluster analysis on the radio frequency signal attenuation record data set according to the radio frequency signal attenuation deviation threshold, Obtaining multi-cluster radio frequency signal attenuation record data; Extracting clusters whose radio frequency signal attenuation record data volume is less than or equal to a record data volume threshold, deleting the clusters from the multiple clusters of radio frequency signal attenuation record data, obtaining remaining radio frequency signal attenuation record data, extracting the maximum value, and setting it as medium radio frequency interference intensity identification data; Retrieving the plurality of sets of data based on the medium radio frequency interference intensity identification data, the medium length record data, the medium width record data, and the medium height record data, training a medium radio frequency interference intensity prediction model through machine learning, and binding the model with the preset radio frequency interference medium type to construct a medium radio frequency interference intensity analysis library; According to the type of the radio frequency interference medium, matching the 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 radio frequency interference intensity of the medium; Counting 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 includes: Configure the triggering rules for abnormal RF tags: Construct the first trigger condition of abnormal RF tags: when an RF tag falls off within the preset service time, it is considered an abnormal RF tag; Construct the second trigger condition for abnormal RF tags: When the RF tag is dead or empty, it is considered an abnormal RF tag; configuring a logic OR condition for the abnormal RF tag first trigger condition and the abnormal RF tag second trigger condition to obtain the abnormal RF tag trigger rule; Based on the abnormal RF tag triggering rule, 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 counted to obtain the RF tag abnormality probability.

2. The intelligent online detection and review system for RF tag production according to claim 1, characterized in that: RF tag production optimization is performed based on the standard RF tag installation position, the RF interference medium type list, the medium distribution distance list, and the medium structure list to obtain an installation parameter constraint space and a RF parameter constraint space, including: Retrieving a historical RF tag production sample set of the RF tag standard installation position, the RF interference medium type list, the medium distribution distance list, and the medium structure list; Performing a central tendency analysis on installation parameter attributes based on the historical RF tag production sample set to construct the installation parameter constraint space; Based on the historical RF tag production sample set, a central tendency analysis is performed on radio frequency parameter attributes to construct the radio frequency parameter constraint space.

3. An intelligent online detection and verification method for RF tag production, characterized in that: The method is applied to the intelligent online detection and review system for RF tag production according to any one of claims 1-2, and the method comprises: Through the predefined RF tag standard position library, the target product model and target product location are processed and the standard installation position of the RF tag is calibrated; Obtain 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; Optimize RF tag production based on the standard RF tag installation position, the RF interference medium type list, the medium distribution distance list, and the medium structure list to obtain an installation parameter constraint space and a RF parameter constraint space; Receiving RF tag production monitoring parameters, calculating an installation parameter deviation vector from the installation parameter constraint space, and a radio frequency parameter deviation vector from the radio frequency parameter constraint space; Counting 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 abnormal probability of the RF tag is greater than or equal to the abnormal probability threshold, the target product is sent to the full inspection warehouse for quality inspection; otherwise, the target product is sent to the random inspection warehouse for quality inspection.

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