Material table operation control method and device based on RFID tag verification

Abstract

The application relates to the technical field of label verification, and provides a material table operation control method and device based on RFID label verification. A label group signal of materials on a material table is acquired by an RFID reader, the signal strength, spatial position and time stamp of each material label in the label group signal are acquired, linear processing is performed to determine the priority parameters of the material label, the label group signal is encoded based on the priority parameters to generate a label data stream, a target material is determined from the material table based on the label sequence in the label data stream, verification is performed between the characteristic information of the target material and the label data through a decision tree, if the verification is passed, the next material is detected, and if the verification is not passed, an alarm is given. The label signal is ordered through the linear processing and encoding steps, the material detection is performed based on the label data stream, the accuracy and efficiency of the operation are improved, and the smooth progress of the production process is ensured.

Description

Technical Field

[0001] This application relates to the field of tag verification technology, and more specifically, to a material platform operation control method and device based on RFID tag verification. Background Technology

[0002] In today's era of rapid development in manufacturing, industrial automation has become crucial for improving production efficiency and competitiveness. Modern production environments are increasingly complex, with a wide variety of materials in diverse forms, and frequent dynamic changes during the production process. Traditional material handling and control methods often rely on manual intervention, which is not only inefficient but also prone to human error, making it difficult to meet the demands of large-scale, high-precision production.

[0003] Especially in applications of intelligent scanning stations, traditional Radio Frequency Identification (RFID) scanners suffer from high misread rates due to their difficulty in handling dense tag conflicts. Furthermore, the existence of verification blind spots severely impacts the accuracy of material information collection. In today's increasingly demanding environment of precision production and logistics management, such a high misread rate can lead to serious problems such as material mismatch and production process chaos. Summary of the Invention

[0004] This application provides a material platform operation control method and device based on RFID tag verification, which can at least partially solve the problem of high misread rate in the traditional RFID scanning process.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of this application, a material station operation control method based on RFID tag verification is provided, comprising: acquiring a group signal of tags of densely arranged materials on the material station through an RFID reader / writer; acquiring the signal strength, spatial position, and timestamp of each material tag in the group signal; performing linear processing on the signal strength, spatial position, and timestamp of the material tags to determine the priority parameters of the material tags; encoding the group signal of the tags based on the priority parameters to generate a tag data stream; detecting the materials on the material station based on the tag order in the tag data stream to sequentially determine target materials; acquiring the feature information of the target materials and the tag data corresponding to the material tags of the target materials; verifying the feature information and the tag data through a decision tree; if the verification is successful, detecting the next material; if the verification fails, issuing an alarm.

[0007] In this application, based on the aforementioned scheme, the step of acquiring the tag group signal of densely arranged materials from the material platform using an RFID reader, and acquiring the signal strength, spatial position, and timestamp of each material tag in the tag group signal, includes: initializing and dividing the reading environment of the material platform using time segmentation and spatial segmentation techniques to generate segmented areas; driving the RFID reader to scan the densely arranged materials in each segmented area to acquire the tag group signal, and the signal strength, spatial position, and timestamp of each material tag in the tag group signal.

[0008] In this application, based on the aforementioned scheme, the step of linearly processing the signal strength, spatial location, and timestamp of the material tag to determine the priority parameters of the material tag, and encoding the tag group signal based on the priority parameters to generate a tag data stream includes: linearly processing the signal strength, spatial location, and timestamp of the material tags of adjacent materials to determine the priority parameters of the material tag; and sorting and encoding the tag group signal based on the priority parameters to generate a tag data stream.

[0009] In this application, based on the aforementioned scheme, the step of linearly processing the signal strength, spatial location, and timestamp of the material tags of adjacent materials to determine the priority parameters of the material tags includes: assuming the tag set corresponding to the tag group signal is... Each material label It includes three attributes: signal strength timestamp and spatial location Linear processing is performed on the signal strength, spatial location, and timestamp of the material tags of adjacent materials to determine the material tag. i Corresponding priority parameters for:

[0010]

[0011] in, These represent the signal factor, time factor, and location factor, respectively, and are obtained through simulation using historical information. i , j as well as n These represent material identifiers. j express i Adjacent materials.

[0012] In this application, based on the aforementioned scheme, the step of detecting materials on the material platform based on the tag order in the tag data stream and sequentially determining target materials includes: parsing the tag data stream to determine the tag order; detecting materials on the material platform according to the tag order to obtain target materials and their location information; and performing path planning based on the location information of the target materials to sequentially determine the target materials.

[0013] In this application, based on the aforementioned scheme, the step of obtaining the feature information of the target material and the tag data corresponding to the material label of the target material, and verifying the feature information and the tag data through a decision tree, includes: obtaining the feature information of the target material and the tag data corresponding to the material label of the target material; performing multimodal fusion on the feature information and the tag data to generate fused data; inputting the fused data into a pre-trained decision tree, and outputting a verification result on whether the feature information and the tag data are consistent.

[0014] In this application, based on the aforementioned scheme, the step of issuing an alarm if the verification fails includes: issuing an alarm if the verification fails, adjusting the detection position, and performing verification again.

[0015] According to one aspect of this application, a material handling station operation control device based on RFID tag verification is provided, comprising:

[0016] The acquisition unit is used to acquire the tag group signal of densely arranged materials from the material platform through an RFID reader / writer, and to acquire the signal strength, spatial position and timestamp of each material tag in the tag group signal;

[0017] The encoding unit is used to perform linear processing on the signal strength, spatial location and timestamp of the material tag, determine the priority parameters of the material tag, encode the tag group signal based on the priority parameters, and generate a tag data stream.

[0018] The detection unit is used to detect the materials on the material platform based on the tag order in the tag data stream, and sequentially determine the target materials;

[0019] The verification unit is used to acquire the feature information of the target material and the label data corresponding to the material label of the target material, and to perform verification between the feature information and the label data through a decision tree;

[0020] The feedback unit is used to detect the next material if the verification passes, and to issue an alarm if the verification fails.

[0021] According to one aspect of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the material station operation control method based on RFID tag verification as described in the above embodiments.

[0022] According to one aspect of this application, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the material platform operation control method based on RFID tag verification as described in the above embodiments.

[0023] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the RFID tag-based material handling control method provided in the various optional implementations described above.

[0024] The main differences and technical effects of the technical solution of this application compared with the prior art are as follows:

[0025] On the one hand, traditional RFID scanners cannot handle dense tag conflicts, resulting in a high false read rate and verification blind spots. In production scenarios with a wide variety of materials and dense arrangement, this high false read rate severely affects the accurate acquisition of material information, potentially leading to material mismatches in subsequent production processes. This application's technical solution initializes and divides the reading environment of the material station using time-segmentation and spatial-segmentation technologies, generating segmented areas, and then drives the RFID reader to scan the densely arranged materials within each segmented area. This effectively avoids interference between tag signals, improves the identification capability of dense tags, and reduces the false read rate. Simultaneously, it acquires information such as the signal strength, spatial location, and timestamp of each material tag, providing comprehensive data for subsequent accurate processing. Compared to traditional RFID scanning methods, it can more accurately acquire material information and eliminate verification blind spots.

[0026] Secondly, in scenarios such as automated material handling, existing inertial navigation technologies suffer from cumulative errors. These positioning errors can lead to inaccurate placement or retrieval of materials, impacting production system efficiency and product quality. The technical solution of this application, based on acquired information such as the spatial location of material tags, linearly processes the tag group signals to determine priority parameters and generate a tag data stream. Then, materials are detected according to the tag order to obtain the target material and its location information, and path planning is performed to detect and identify the target material. This approach enables more accurate determination of material positions, reduces positioning errors, and improves the accuracy of material handling by using accurate location information for subsequent detection and identification. Compared to methods relying on inertial navigation, this effectively avoids the impact of cumulative errors.

[0027] Thirdly, manual clamping relies on visual inspection, which is prone to errors and inefficient. Especially with the current expansion of production scale and increased demands for efficiency, manual inspection is insufficient. The proposed solution acquires the characteristic information of the target material and the corresponding label data, performs multimodal fusion, and inputs it into a pre-trained decision tree. The output verifies whether the characteristic information and label data are consistent. If the verification fails, an alarm is triggered, and the clamping position is adjusted for re-verification. This achieves automated material verification, reducing manual intervention and lowering the human error rate. Furthermore, the decision tree verification method offers high accuracy and reliability, enabling timely detection of issues such as material clamping, significantly improving verification efficiency and accuracy compared to manual visual inspection.

[0028] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0030] Figure 1 The flowchart illustrating a material platform operation control method based on RFID tag verification in one embodiment of this application is shown.

[0031] Figure 2 The flowchart illustrating the determination of a target material from a material table is shown in one embodiment of this application.

[0032] Figure 3The illustration shows a schematic diagram of a material platform operation control device based on RFID tag verification in one embodiment of this application.

[0033] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0034] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0035] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0036] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or application-specific integrated circuits (ASICs), or in different network and / or processor devices and / or microcontroller devices.

[0037] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0038] The implementation details of the technical solution of this application are described below:

[0039] Figure 1 A flowchart illustrating a material station operation control method based on RFID tag verification according to an embodiment of this application is shown. (Refer to...) Figure 1 As shown, the material station operation control method based on RFID tag verification includes at least steps S110 to S150, which are described in detail below:

[0040] S110: Obtain the tag group signal of the densely arranged materials from the material platform through the RFID reader / writer, and obtain the signal strength, spatial position and timestamp of each material tag in the tag group signal.

[0041] In this embodiment, the RFID reader activates by transmitting a specific frequency radio signal to the area where the material station is located, thereby activating the RFID tags densely arranged on the materials. Upon receiving the signal, these activated material tags reflect their stored information and related status signals back to the RFID reader. After receiving the reflected signals, the reader accurately separates the signal corresponding to each material tag from the signal cluster. Subsequently, it further analyzes and extracts information such as signal strength, spatial location, and signal reception timestamp from the signals corresponding to the material tags, providing basic data support for subsequent accurate identification, positioning, and status tracking of the materials.

[0042] In one embodiment of this application, an RFID reader acquires the tag group signal of densely arranged materials from a material platform, and acquires the signal strength, spatial location, and timestamp of each material tag in the tag group signal, including:

[0043] The reading environment of the material station is initialized and divided using time-segmentation and spatial-segmentation techniques to generate segmented regions;

[0044] The RFID reader is driven to scan the densely arranged materials in each segmented area to obtain the tag group signal, as well as the signal strength, spatial position and timestamp of each material tag in the tag group signal.

[0045] In one embodiment of this application, the RFID reader acquires the signal corresponding to the material tag. It can emit radio signals of a specific frequency, such as the common 13.56MHz. As a type of IoT chip, the RFID chip inside the material tag is activated when the RFID tag enters the effective working range of the reader. Each material tag contains unique identification information and has a certain signal reflection or transmission capability. In densely packed scenarios, numerous tags are simultaneously within the reader's operating area.

[0046] For example, firstly, a command is sent to the RFID reader to activate it. The reader then emits radio signals at a set frequency, creating an electromagnetic field within a specific range. When densely packed RFID tags are within this electromagnetic field, the chip inside the tag is activated. Depending on the tag type—active or passive—the response differs slightly. Passive tags obtain energy from the electromagnetic field emitted by the reader through electromagnetic induction, then use this energy to modulate their stored information and reflect it back to the reader; active tags, on the other hand, use their own power source to actively transmit the tag information.

[0047] Optionally, in this embodiment, time slicing and spatial slicing techniques can be used to initialize and divide the reading environment of the material station. Time slicing involves dividing a continuous time axis into numerous discrete time segments of equal or unequal length, preparing for the subsequent orderly reading of tag signals at different time periods. Spatial slicing, on the other hand, divides the entire space into multiple sub-regions based on the physical characteristics of the reading area. These two slicing methods enable segmented identification of dense RFID tags, establishing a basic framework for segmented identification of dense RFID tags. This disperses the dense tag group into different time and spatial units, reducing the number of tags read simultaneously and thus lowering the possibility of signal collisions.

[0048] RFID readers have signal receiving capabilities, enabling them to receive reflected or actively transmitted signals from densely packed tags. This drives the RFID reader to begin scanning material tags within each segmented area. During scanning, the reader acquires relevant attribute information for each material tag. For each tag, its signal strength is recorded; this strength reflects the strength of the tag's signal, and tags with stronger signals may have a higher reading priority in subsequent processing. Simultaneously, a timestamp is recorded for each tag, indicating the moment the tag signal arrived at the reader. This timestamp information helps analyze the temporal density of tag distribution. Furthermore, through the reader's positioning function or in conjunction with an external positioning system, the specific spatial location of each material tag is determined. This spatial location information is used to analyze the spatial distribution of the material tags. This attribute information is stored in memory, providing data support for subsequent material tag identification order decisions.

[0049] Optionally, these material label signals can be pre-processed using edge computing nodes deployed near the control panel, such as through amplification and filtering, to improve signal quality and identifiability. The pre-processed signals are then transmitted to a connected storage medium. This signal data is received via an interface (such as USB or serial port) and stored in memory or hard drives for subsequent segmentation and conflict resolution, thereby enabling the collection, pre-processing, and storage of industrial big data.

[0050] The above process, through time-segmentation and spatial-segmentation techniques, initializes and divides the material station reading environment, effectively avoiding signal interference and enabling RFID readers to more accurately scan densely packed materials within each segmented area. This improves the accuracy of acquiring tag group signals and the signal strength, spatial location, and timestamp of each material tag, providing a reliable data foundation for subsequent processing and ensuring more precise identification and positioning of materials.

[0051] S120, perform linear processing on the signal strength, spatial location and timestamp of the material tag to determine the priority parameters of the material tag, encode the tag group signal based on the priority parameters, and generate a tag data stream.

[0052] In this embodiment, after acquiring the signal strength, spatial location, and timestamp information of the material tags, a linear processing flow is initiated. Following a predetermined logical order, these different types of information are analyzed and processed sequentially, comprehensively considering their correlations and impacts. Through this linear processing, priority parameters for each material tag are determined, reflecting the importance of the material tag among numerous materials or the order of processing. Then, based on the determined priority parameters, the tag group signals are sequentially encoded, transforming the originally chaotic tag signals into a tag data stream with specific rules and order, enabling more efficient subsequent data transmission and processing.

[0053] In one embodiment of this application, the signal strength, spatial location, and timestamp of the material tag are linearly processed to determine the priority parameters of the material tag. Based on the priority parameters, the tag group signal is encoded to generate a tag data stream, including:

[0054] Linear processing is performed on the signal strength, spatial location, and timestamp of the material tags of adjacent materials to determine the priority parameters of the material tags;

[0055] The tag group signals are sorted and encoded based on the priority parameters to generate a tag data stream.

[0056] Based on the collected tag attribute information, the system comprehensively considers the tag's signal strength, temporal distribution, and spatial distribution. Regarding signal strength, tags with stronger signals are prioritized, as strong signals generally indicate more reliable readings. For temporal distribution, the system focuses on the density of tag timestamps; tags with densely packed timestamps are prioritized for reading to avoid signal conflicts caused by time overlap. In terms of spatial distribution, tags with similar spatial locations are prioritized to prevent spatial signal interference. Through complex internal logic and comparison mechanisms, a recognition priority is dynamically determined for each tag. This priority determines the tag reading order, ensuring that multi-tag conflicts are effectively avoided throughout the reading process, thus improving reading accuracy.

[0057] Specifically, let the set of tags corresponding to the tag group signal be . Each material label It has three attributes: signal strength. The timestamp is Spatial location is Among them, signal strength Reflects the signal strength of the material tag; timestamp It recorded the signal arrival time information; spatial location. The spatial location of the material tags was determined. Linear processing was performed on the signal strength, spatial location, and timestamp of the material tags of adjacent materials to pinpoint the material tags. i Corresponding priority parameters for:

[0058]

[0059] in, These represent the signal factor, time factor, and location factor, respectively, and are obtained through simulation using historical information. i , j as well as n These represent material identifiers. j express i Adjacent materials.

[0060] Next, the material tags are sorted and encoded based on the calculated priority parameters, generating a tag data stream. According to the tag identification order in the tag data stream, the RFID reader is controlled to read the detailed information of each tag sequentially. During the reading process, the reading status is monitored in real time to ensure that the information of each tag is accurately acquired. Once a tag is successfully read, its information is added to the output data stream. After all tags have been read sequentially, the integrated, conflict-free, and highly accurate RFID tag identification data stream is output. Through precise control of the reader and intelligent processing of tag information, efficient and accurate identification of dense RFID tags is achieved.

[0061] The above process linearly processes the signal strength, spatial location, and timestamp of adjacent material tags to determine priority parameters. Based on these parameters, the tag group signals are sorted and encoded to generate a tag data stream, transforming the originally chaotic tag signals into an ordered sequence. This ordered tag data stream facilitates the processing of materials according to preset rules, improving the system's efficiency in processing material information and ensuring that subsequent material detection operations can proceed smoothly.

[0062] S130, the materials on the material platform are detected based on the tag order in the tag data stream, and the target materials are determined sequentially.

[0063] In this embodiment, the materials on the material platform are inspected systematically according to the label order determined by the generated label data stream. The material status and related information corresponding to each label are identified one by one according to the label order in the label data stream. During the inspection process, it is determined in real time whether a target material meeting the requirements has been found. Once the target material is locked, a precise command is sent to the inspection module to control the module to obtain the relevant information of the target material.

[0064] like Figure 2 As shown, the materials on the material platform are detected based on the tag order in the tag data stream to sequentially determine the target materials, including:

[0065] S210, parse the tag data stream to determine the tag order;

[0066] S220, the materials on the material platform are detected according to the label sequence to obtain the target material and its position information;

[0067] S230, Path planning is performed based on the location information of the target materials, and the target materials are determined sequentially.

[0068] Upon receiving the tag data stream, the system first parses the data, reading the tag information one by one according to the transmission order. Each tag corresponds to a material on the material platform. Using a pre-stored tag-material mapping, the specific material represented by each tag is identified, determining the tag order of the materials to be inspected. Then, based on a pre-defined target material list, the system searches for the corresponding target material tag in the parsed tag information. Once the target material tag is found, the association information of the target material on the material platform is established, providing the foundational data for subsequent inspection operations.

[0069] After identifying the target material, the system combines location-related information that may be present in the label data stream, such as the spatial location data of the label during reading, with the layout model of the material platform. The layout model, stored in memory, describes the coordinates of each position on the material platform and the material placement rules. A location matching algorithm is then used to map the target material label to its actual position on the material platform. If the label data stream contains direct location coordinate information, that coordinate can be used directly; otherwise, if only relative location information is available, coordinate transformation is performed based on the material platform layout model to ultimately determine the accurate coordinates of the target material on the material platform.

[0070] After identifying the target material's location, and considering the surrounding environment of the material platform, including potential obstacles, the robotic arm's range of motion, and limitations, path planning is used to determine the optimal path from the detection module to the target material, ensuring no collisions with obstacles. By comprehensively considering factors such as path length, movement time, and the detection module's flexibility, the detection operation is ensured to be completed efficiently and smoothly. The planned path is broken down into a series of specific motion commands, providing detailed guidance for subsequent detection operations.

[0071] The above solution, by parsing the tag data stream to generate a tag sequence, then detecting materials on the material platform according to this sequence, can quickly and accurately locate the target material and its location information. Path planning based on the target material's location information makes the detection operation more targeted and efficient, reducing unnecessary movement and search time, and improving the efficiency and accuracy of material detection.

[0072] S140, acquire the feature information of the target material and the label data corresponding to the material label of the target material, and verify the feature information and the label data through a decision tree.

[0073] In one embodiment of this application, after the target material is determined, an information acquisition program is initiated to comprehensively collect the characteristic information of the target material, while simultaneously retrieving the label data of the corresponding material label. Then, a pre-built decision tree verification model is invoked, inputting the acquired characteristic information and label data together. The decision tree, based on its internal preset rules and judgment logic, performs a detailed comparison and analysis of these two types of information, verifying the consistency and matching degree between the characteristic information and the label data, thereby determining whether the loading and other related conditions of the target material meet the requirements.

[0074] In one embodiment of this application, the method of obtaining feature information of the target material and tag data corresponding to the material tag of the target material, and verifying the feature information and the tag data through a decision tree, includes:

[0075] Obtain the feature information of the target material and the tag data corresponding to the material tag of the target material;

[0076] The feature information and the label data are fused based on multimodal characteristics to generate fused data;

[0077] The fused data is input into a pre-trained decision tree, and a verification result is outputting whether the feature information and the label data are consistent.

[0078] In this embodiment, the tag data corresponding to the material tag of the target material is read by an RFID reader, which stores key information such as the unique identifier of the material; the feature information of the target material is collected by multiple sensors integrated in the clamping station, such as a vision sensor to obtain material shape and color information, and a force sensor to measure clamping force and related dimensional information, and is transmitted to the storage device after preliminary processing inside the sensor.

[0079] Optionally, the two types of received data can be preprocessed. For example, for RFID tag data, decoding and verification can be performed to ensure the accuracy and integrity of the data; for physical feature data, filtering, normalization and other operations can be performed to eliminate noise interference and make the data within a uniform dimension range, which is convenient for subsequent fusion and verification.

[0080] In this embodiment, the acquired RFID tag data and physical feature data are in their raw state and need to be fused to maximize their value. For these two types of data, they are mapped to a high-dimensional feature space using preset basis functions. In this high-dimensional space, the covariance matrix is ​​calculated by analyzing the correlation between the data. Then, a preset number of principal components are extracted from the covariance matrix. These principal components can summarize the key information in the original data. These principal components are combined to construct a projection matrix U. The projection matrix is ​​used to transform the data mapped to the high-dimensional space to obtain preliminary fused data Y. Next, the information factors corresponding to the features of these principal components are calculated. The information factors reflect the degree of uncertainty of the information contained in the features. Based on the information factors, the feature factors of each feature are determined. The larger the feature factor, the more effective information the feature contains. Finally, the feature factors are weighted and summed with the preliminary fused data Y to achieve the fusion of multimodal data, generating fused data that is more comprehensive and representative.

[0081] For example, with the first l Taking the first feature as an example, calculate the first feature. l The information factors for each feature are:

[0082]

[0083] in, s This indicates the number of possible values ​​for the feature. Indicates the first i The information factor measures the uncertainty of a feature's values. The smaller the information factor, the more concentrated the feature's values ​​are, and the more effective information it contains.

[0084] In this embodiment, the feature factors for each feature are determined based on the calculated information factors. Let the feature factor vector be: Among them, characteristic factors It was calculated in the following way:

[0085]

[0086] in, l, j, k These represent the identifiers of the characteristic factors.

[0087] The feature factor reflects the importance of a feature in the fused data. The larger the feature factor, the more effective information the feature contains and the greater its contribution to the final fusion result.

[0088] Finally, data fusion is achieved using F=W⊙Y, where ⊙ represents the Hadamard product (element-wise multiplication) and Y represents the initially fused data. The fused data F integrates information from various features after multidimensional transformation and is weighted according to feature importance, improving data quality and usability. Through this processing, key features are effectively extracted from the data, reducing data dimensionality, computational complexity, and storage requirements, while retaining the main information. Determining weights based on feature information content allows the fused data to more rationally integrate information from different features, improving data representativeness and reliability, and providing a more accurate foundation for subsequent data analysis and decision-making.

[0089] In this embodiment, the construction of the decision tree relies on a large amount of historical data and expert knowledge. The historical data includes a large amount of multimodal fusion data of materials that were correctly clamped in the past, as well as the corresponding correct labels. This data provides the basic samples for the learning of the decision tree. Expert knowledge is used to clarify the importance of data features and the division rules, such as determining which features are more critical for judging whether the clamping is correct.

[0090] A decision tree construction algorithm is employed, starting from the root node and traversing all data features and possible split points. The Gini index is calculated to evaluate the effectiveness of each feature and split point in classifying the data. The feature and split point with the largest decrease in Gini index are selected to split the data, generating branches of the decision tree. This process is recursively repeated, continuously splitting the data until preset stopping conditions are met, such as insufficient sample size in a node or a sufficiently small Gini index. Ultimately, a complete decision tree is formed for subsequent clamping and validation.

[0091] The fused data is input into the pre-constructed decision tree model. Starting from the root node, the data moves downwards along the corresponding branches according to the specific values ​​of the input data for each feature and the partitioning rules recorded in the decision tree. At each node, the data is evaluated based on the partitioning rules of that node to determine the next step. This process is repeated until the data reaches the leaf node of the decision tree. The category corresponding to the leaf node is the final result of the binning verification. If the category corresponding to the leaf node matches the feature information and the label data, the binning is correct; if they do not match, there is a problem with the binning.

[0092] The above solution acquires the characteristic information of the target material and the tag data corresponding to the material label, and performs multimodal fusion to generate fused data. This fully utilizes different types of information, making the data more comprehensive and richer. The fused data is then input into a pre-trained decision tree, which outputs a verification result regarding the consistency between the characteristic information and the tag data. Leveraging the decision tree's judgment capabilities, the consistency of material information can be accurately verified, ensuring that the material meets requirements and improving the quality and reliability of the operation.

[0093] S150: If the verification passes, proceed to the next material; if the verification fails, issue an alarm.

[0094] In this embodiment, if the verification results are consistent, the verification is considered successful, and this positive result is fed back to the control module. Based on the pre-planned task sequence, the control module immediately sends instructions to the detection module, guiding it to move to the location of the next target material. During this process, the movement status of the detection module and its surrounding environment are continuously monitored to ensure that it can accurately and smoothly collect information about the target material, thereby smoothly initiating the processing flow for the next target material.

[0095] If the verification results are inconsistent, it indicates an abnormality in the clamping process. In this case, an error prevention mechanism is triggered, such as activating an audible and visual alarm to alert the operator to the loading error. Simultaneously, the system automatically attempts to adjust the position of the detection module, using precise control to fine-tune the material's position before re-verifying. If the verification passes, normal production resumes, for example, by detecting the next target material. If multiple adjustments fail to pass verification, manual intervention may be necessary to further investigate the problem, replace the target material, or trigger an alarm.

[0096] The above-described solution outputs the clamping accuracy verification results and corresponding operation instructions. If the verification fails, an alarm is triggered, promptly alerting operators to the problem. Simultaneously, the detection position is adjusted and verification is repeated, providing a proactive approach to resolving the issue. This mechanism can prevent production accidents caused by inconsistent material information or clamping errors, providing a basis for subsequent production process control, ensuring the continuity and stability of the entire production process, and reducing production losses.

[0097] This application's technical solution acquires the tag group signal of densely arranged materials from a material platform using an RFID reader / writer. It obtains the signal strength, spatial position, and timestamp of each material tag within the tag group signal. Linear processing is performed on the signal strength, spatial position, and timestamp of the material tags to determine their priority parameters. Based on these priority parameters, the tag group signal is encoded to generate a tag data stream. Materials on the material platform are detected based on the tag order in the tag data stream, sequentially identifying target materials. The feature information of the target material and the tag data corresponding to the target material's material tag are acquired. A decision tree is used to verify the feature information and the tag data. If the verification passes, the next material is detected; if the verification fails, an alarm is triggered. Accurate acquisition of material tag information using RFID technology provides foundational data for subsequent operations, ensuring precise material identification. The linear processing and encoding steps order the tag signals, and material detection based on the tag data stream improves operational accuracy and efficiency. The use of a decision tree for verification ensures the consistency between material feature information and tag data, enhancing operational reliability and guaranteeing the smooth operation of the production process.

[0098] The following describes embodiments of the RFID tag-based material platform operation control device of this application, which can be used to execute the RFID tag-based material platform operation control method in the above embodiments of this application. It is understood that the RFID tag-based material platform operation control device can be a computer program (including program code) running on a computer device. For example, the RFID tag-based material platform operation control device can be equipped with industrial application software or industrial control software (industrial control software); the RFID tag-based material platform operation control device can be used to execute the corresponding steps in the method provided in the embodiments of this application. For details not disclosed in the embodiments of the RFID tag-based material platform operation control device of this application, please refer to the embodiments of the RFID tag-based material platform operation control method described above in this application.

[0099] Figure 3 A block diagram of a material station operation control device based on RFID tag verification according to an embodiment of this application is shown.

[0100] Reference Figure 3 As shown, a material station operation control device based on RFID tag verification according to an embodiment of this application includes:

[0101] The acquisition unit 310 is used to acquire the tag group signal of densely arranged materials from the material platform through an RFID reader, and to acquire the signal strength, spatial position and timestamp of each material tag in the tag group signal;

[0102] The encoding unit 320 is used to perform linear processing on the signal strength, spatial location and timestamp of the material tag, determine the priority parameters of the material tag, encode the tag group signal based on the priority parameters, and generate a tag data stream.

[0103] The detection unit 330 is used to detect the materials on the material platform based on the tag order in the tag data stream, and sequentially determine the target materials;

[0104] The verification unit 340 is used to acquire the feature information of the target material and the label data corresponding to the material label of the target material, and to perform verification between the feature information and the label data through a decision tree;

[0105] Feedback unit 350 is used to detect the next material if the verification passes; and to issue an alarm if the verification fails.

[0106] In this application, based on the aforementioned scheme, the step of acquiring the tag group signal of densely arranged materials from the material platform using an RFID reader, and acquiring the signal strength, spatial position, and timestamp of each material tag in the tag group signal, includes: initializing and dividing the reading environment of the material platform using time segmentation and spatial segmentation techniques to generate segmented areas; driving the RFID reader to scan the densely arranged materials in each segmented area to acquire the tag group signal, and the signal strength, spatial position, and timestamp of each material tag in the tag group signal.

[0107] In this application, based on the aforementioned scheme, the step of linearly processing the signal strength, spatial location, and timestamp of the material tag to determine the priority parameters of the material tag, and encoding the tag group signal based on the priority parameters to generate a tag data stream includes: linearly processing the signal strength, spatial location, and timestamp of the material tags of adjacent materials to determine the priority parameters of the material tag; and sorting and encoding the tag group signal based on the priority parameters to generate a tag data stream.

[0108] In this application, based on the aforementioned scheme, the step of linearly processing the signal strength, spatial location, and timestamp of the material tags of adjacent materials to determine the priority parameters of the material tags includes: assuming the tag set corresponding to the tag group signal is... Each material label It includes three attributes: signal strength timestamp and spatial location Linear processing is performed on the signal strength, spatial location, and timestamp of the material tags of adjacent materials to determine the material tag. i Corresponding priority parameters for:

[0109]

[0110] in, These represent the signal factor, time factor, and location factor, respectively, and are obtained through simulation using historical information. i , j as well as n These represent material identifiers. j express i Adjacent materials.

[0111] In this application, based on the aforementioned scheme, the step of detecting materials on the material platform based on the tag order in the tag data stream and sequentially determining target materials includes: parsing the tag data stream to determine the tag order; detecting materials on the material platform according to the tag order to obtain target materials and their location information; and performing path planning based on the location information of the target materials to sequentially determine the target materials.

[0112] In this application, based on the aforementioned scheme, the step of obtaining the feature information of the target material and the tag data corresponding to the material label of the target material, and verifying the feature information and the tag data through a decision tree, includes: obtaining the feature information of the target material and the tag data corresponding to the material label of the target material; performing multimodal fusion on the feature information and the tag data to generate fused data; inputting the fused data into a pre-trained decision tree, and outputting a verification result on whether the feature information and the tag data are consistent.

[0113] In this application, based on the aforementioned scheme, the step of issuing an alarm if the verification fails includes: issuing an alarm if the verification fails, adjusting the detection position, and performing verification again.

[0114] This application's technical solution acquires the tag group signal of densely arranged materials from a material platform using an RFID reader / writer. It obtains the signal strength, spatial position, and timestamp of each material tag within the tag group signal. Linear processing is performed on the signal strength, spatial position, and timestamp of the material tags to determine their priority parameters. Based on these priority parameters, the tag group signal is encoded to generate a tag data stream. Materials on the material platform are detected based on the tag order in the tag data stream, sequentially identifying target materials. The feature information of the target material and the tag data corresponding to the target material's material tag are acquired. A decision tree is used to verify the feature information and the tag data. If the verification passes, the next material is detected; if the verification fails, an alarm is triggered. Accurate acquisition of material tag information using RFID technology provides foundational data for subsequent operations, ensuring precise material identification. The linear processing and encoding steps order the tag signals, and material detection based on the tag data stream improves operational accuracy and efficiency. The use of a decision tree for verification ensures the consistency between material feature information and tag data, enhancing operational reliability and guaranteeing the smooth operation of the production process.

[0115] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0116] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not impose any limitations on the function and scope of use of the embodiments of this application.

[0117] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes based on programs stored in read-only memory 402 or programs loaded from storage section 408 into random access memory 403, such as executing the material platform operation control method based on RFID tag verification described in the above embodiment. The random access memory 403 also stores various programs and data required for system operation, thereby realizing big data storage and big data management. The central processing unit 401, read-only memory 402, and random access memory 403 are interconnected via bus 404. Input / output interface 405 is also connected to bus 404.

[0118] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0119] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit 401, it performs various functions defined in the system of this application.

[0120] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0122] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0123] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0124] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the material platform operation control method based on RFID tag verification as described in the above embodiments.

[0125] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0126] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0127] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0128] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A material table operation control method based on RFID tag verification, characterized by, The method comprises the following steps: acquiring, by an RFID reader, a tag group signal of densely arranged materials on a material table, acquiring a signal strength, a spatial position and a time stamp of each material tag in the tag group signal; linearly processing the signal strength, the spatial position and the time stamp of the material tag, determining a priority parameter of the material tag, encoding the tag group signal based on the priority parameter, and generating a tag data stream; detecting the materials on the material table based on the tag sequence in the tag data stream, and sequentially determining a target material; acquiring feature information of the target material and tag data corresponding to the material tag of the target material, and verifying between the feature information and the tag data through a decision tree; if the verification is passed, detecting a next material; if the verification is not passed, alarming; wherein the acquiring, by an RFID reader, a tag group signal of densely arranged materials on a material table, acquiring a signal strength, a spatial position and a time stamp of each material tag in the tag group signal, comprises: initially dividing the reading environment of the material table through time slicing and space slicing technology, and generating a slicing area; driving the RFID reader to scan the densely arranged materials in each slicing area, and acquiring a tag group signal, a signal strength, a spatial position and a time stamp of each material tag in the tag group signal; wherein the linearly processing the signal strength, the spatial position and the time stamp of the material tag, determining a priority parameter of the material tag, encoding the tag group signal based on the priority parameter, and generating a tag data stream, comprises: linearly processing the signal strength, the spatial position and the time stamp of the material tag of the adjacent material, and determining the priority parameter of the material tag; sequentially sorting and encoding the tag group signal based on the priority parameter, and generating a tag data stream.

2. The RFID tag verification-based material bay operation control method according to claim 1, characterized by, The linearly processing the signal strength, the spatial position and the time stamp of the material tag of the adjacent material, and determining the priority parameter of the material tag, comprises: The tag set corresponding to the tag group signal is Each material tag Contains three attributes: signal strength , timestamp And spatial position ; The signal strength, spatial position and time stamp of the material tag of the adjacent material are linearly processed to determine the material tag i The corresponding priority parameter is: wherein, respectively represent a signal factor, a time factor and a position factor, which are simulated by historical information; i , j and n respectively represent a material identification, j represent i adjacent material identifications.

3. The RFID tag-based verification based magazine operation control method according to claim 1, characterized by, The detecting the materials on the material table based on the tag sequence in the tag data stream, and sequentially determining a target material, comprises: analyzing the tag data stream to determine a tag sequence; detecting the materials on the material table according to the tag sequence, and detecting a target material and position information thereof; sequentially determining the target material based on the position information of the target material.

4. The RFID tag-based verification-based magazine operation control method according to claim 1, characterized by, The acquiring feature information of the target material and tag data corresponding to the material tag of the target material, and verifying between the feature information and the tag data through a decision tree, comprises: acquiring feature information of the target material and tag data corresponding to the material tag of the target material; performing multi-modal fusion on the feature information and the tag data to generate fusion data; inputting the fusion data into a pre-trained decision tree, and outputting a verification result of whether the feature information and the tag data are consistent.

5. The RFID tag-based validation of a dock operation control method according to claim 1, characterized by, if the verification is not passed, alarming, comprising: if the verification is not passed, alarming, adjusting a detection position, and verifying again.

6. A material table operation control device based on RFID tag verification, characterized by, The method comprises the following steps: An acquisition unit is configured to acquire, by an RFID reader, a tag group signal of densely arranged materials on a material table, and acquire a signal strength, a spatial position, and a time stamp of each material tag in the tag group signal; An encoding unit is configured to linearly process the signal strength, the spatial position, and the time stamp of the material tag, determine a priority parameter of the material tag, encode the tag group signal based on the priority parameter, and generate a tag data stream; A detection unit is configured to detect materials on the material table based on a tag sequence in the tag data stream, and determine target materials in sequence; A verification unit is configured to acquire feature information of the target materials and tag data corresponding to material tags of the target materials, and verify between the feature information and the tag data by a decision tree; A feedback unit is configured to detect a next material if the verification is passed; If the verification is not passed, an alarm is given; The acquisition of the tag group signal of the densely arranged materials on the material table by the RFID reader, the acquisition of the signal strength, the spatial position, and the time stamp of each material tag in the tag group signal, includes: initializing and dividing a reading environment of the material table by time slicing and space slicing technology to generate a slicing area; driving the RFID reader to scan the densely arranged materials in each slicing area to acquire the tag group signal, and the signal strength, the spatial position, and the time stamp of each material tag in the tag group signal; The linear processing of the signal strength, the spatial position, and the time stamp of the material tag, the determination of the priority parameter of the material tag, the encoding of the tag group signal based on the priority parameter, and the generation of the tag data stream, include: linearly processing the signal strength, the spatial position, and the time stamp of the material tags of adjacent materials to determine the priority parameter of the material tags; sorting and encoding the tag group signal based on the priority parameter to generate the tag data stream.

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