A Real-Time Status Assessment Method and Related System for UHF RFID Robots Based on Dynamic Scenarios

Abstract

This invention belongs to the field of RFID robot technology and discloses a real-time status assessment method and related system for UHF RFID robots based on dynamic scenarios. This invention utilizes MAC layer and physical layer performance indicators to construct a MAC layer system status assessment model and a physical layer link quality assessment model. By integrating the MAC layer system status and physical layer link quality assessments, a real-time status assessment model for the UHF RFID robot system is constructed to obtain the real-time status level of the RFID system. This invention, by acquiring multiple feature values ​​and time slot information per unit time, can monitor the status of the UHF RFID robot in real time, promptly identify problems, and make adjustments. By simultaneously evaluating the performance indicators of the MAC layer and physical layer, a comprehensive system status assessment is provided, which can more accurately reflect the working status of the entire RFID system.

Description

Technical Field

[0001] This invention belongs to the field of RFID robot technology, specifically relating to a real-time status assessment method and related system for UHF RFID robots based on dynamic scenarios. Background Technology

[0002] Real-time status assessment of UHF RFID robots in dynamic scenarios involves using an RFID robot-mounted reader to emit electromagnetic signals to activate electronic tags in a mobile environment. The system collects multiple feature values ​​returned by the activated tags to the reader within a unit of time, as well as time slot information acquired by a spectrum analyzer within the same unit of time. In-depth analysis of the system status is performed at both the MAC (Media Access Control Layer) and physical layers, and MAC layer system status assessment models and physical layer link quality assessment models are constructed. Based on the evaluation results of these two models, a real-time status assessment method and system for UHF RFID robots in dynamic scenarios is proposed.

[0003] In recent years, RFID applications have expanded beyond fixed, static locations. Tags can be placed on mobile devices such as conveyor belts and production lines, while readers can be mounted on robots, drones, and AGVs, forming mobile RFID reading systems based on dynamic scenarios. Because readers and tags are in relative motion in dynamic scenarios, this mobility can lead to reduced system recognition rates and missed tag reads, thus degrading the reading performance of mobile RFID systems. In-depth analysis of the system state at the MAC and physical layers allows for real-time observation of the RFID system state in dynamic scenarios, enabling the development of targeted adaptive adjustment strategies for the RFID system.

[0004] In the prior art, the invention patent application "A UHF RFID Tag Performance Grading Evaluation Method and Evaluation System" (application number: 202111223856.1) evaluates the performance of RFID tags from four aspects in the claims: static performance, environmental adaptability, sensitivity degradation, and multi-tag performance. It only evaluates the tag performance in static scenarios, lacks an evaluation of the overall reading status of the RFID system, and the method cannot be applied to dynamic scenarios.

[0005] The invention patent application "A Self-Test Method for Transceiver Link of a Passive RFID Reader Based on UHF Band" (application number: 201610950974.5) evaluates the connectivity and functionality of the reader's transmitting and receiving links by analyzing and processing the signals of the radio frequency transmitting and receiving links. However, it only utilizes the physical characteristics of the reader in the RFID system hardware for system evaluation, failing to utilize the signal characteristics returned by tag backscattering, and lacks an overall evaluation of the RFID system's reading status.

[0006] The invention patent application "A Wireless Communication System and Method Between Direct Devices" (Application No.: 202410417410.X) evaluates the RFID wireless communication system by utilizing the electromagnetic interference level in the area where the RFID reader is located, the signal attenuation between the reader and the tag, and the stability information of the tag after activation, thereby achieving comprehensive monitoring and early warning of wireless communication. However, this method requires the use of specialized electromagnetic field strength measuring instruments to measure the electromagnetic field strength in the area where the RFID reader is located, increasing the system's complexity and cost. Furthermore, different application scenarios require re-measuring the electromagnetic field strength, resulting in poor adaptability to various application scenarios. The electromagnetic influence threshold is set by technicians themselves, which can affect the reliability of the system evaluation results.

[0007] The invention patent application "A Performance Evaluation Method for Anti-collision Protocols of RFID Readers" (application number: 201910757057.9) studies anti-collision protocols at the MAC layer in its claims. Based on graph theory principles and the characteristics of RFID systems, it evaluates the performance of various anti-collision protocols for RFID readers. However, it only evaluates the performance of anti-collision protocols at the MAC layer of the RFID system, without considering the performance indicators of the physical layer. The evaluation method is relatively simple and cannot assess the status of the RFID system in real time.

[0008] In conclusion, many existing methods are limited to evaluation in static scenarios and lack the ability to assess the performance of RFID systems in dynamic application scenarios (such as reader movement, tag movement, or environmental changes), making them difficult to adapt to the complex and ever-changing environments in real-world applications. Some technologies only evaluate the performance of a single layer (such as the physical layer or MAC layer), neglecting other important layers (such as the backscattered signal characteristics of RFID tags or the performance of the physical layer), resulting in a simplistic evaluation method. Summary of the Invention

[0009] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a real-time status assessment method and related system for UHF RFID robots based on dynamic scenarios.

[0010] To achieve the above objectives, the present invention adopts the following technical solution:

[0011] In a first aspect, the present invention provides a real-time status assessment method for UHF RFID robots based on dynamic scenarios, comprising the following steps:

[0012] In dynamic scenarios, the fixed-frequency electromagnetic waves transmitted and received by the UHF RFID robot are acquired, and the multiple feature values ​​of the RFID tag returned per unit time are obtained.

[0013] Based on the fixed-frequency electromagnetic waves transmitted and received by the UHF RFID robot, the RFID tag time slot information per unit time is obtained, and the MAC layer performance index is calculated based on the RFID tag time slot information per unit time.

[0014] The physical layer performance index is calculated based on the multiple feature values ​​of the RFID tags returned per unit time and the RFID tag time slot information per unit time.

[0015] Based on MAC layer performance metrics and physical layer performance metrics, evaluate the system status of the MAC layer and the link quality of the physical layer per unit time.

[0016] The real-time status of a UHF RFID robot in a dynamic scenario is evaluated based on the system status of the MAC layer and the link quality of the physical layer at all times.

[0017] A further improvement of this invention lies in the following method for obtaining multiple feature values ​​of RFID tags returned per unit time from the fixed-frequency electromagnetic waves transmitted and received by the UHF RFID robot in dynamic scenarios:

[0018] The reader mounted on the UHF RFID robot sends electromagnetic waves to the tag at a fixed frequency through a single antenna. After receiving the fixed frequency electromagnetic waves, the tag uses the reader to obtain multiple feature values ​​of the tag per unit time based on the relative distance and relative angle between the tag and the reader antenna, as well as the physical characteristics of the tag itself.

[0019] A further improvement of this invention is that the specific method for calculating the MAC layer performance index based on the RFID tag time slot information per unit time is as follows:

[0020] The transmission waveform between the reader and the tag is captured using a spectrum analyzer. The transmission waveform is analyzed based on the command interaction process between the reader and the tag to determine the type of time slot in each frame per unit time, as well as the number and duration of successful time slots, collision time slots, and idle time slots.

[0021] Based on the type of time slots in each frame per unit time and the number and duration of successful time slots, collision time slots, and idle time slots, time efficiency, recognition efficiency, and throughput are calculated as MAC layer performance indicators.

[0022] A further improvement of this invention is that the time efficiency is the statistical average of the ratio of the time occupied by a successful time slot in each frame to the total time per unit time.

[0023] Recognition efficiency is the total number of tags successfully recognized by the reader per unit time.

[0024] Throughput is the ratio of the number of successful time slots per unit time to the total number of time slots.

[0025] A further improvement of this invention lies in the following method for calculating the physical layer performance indicators based on the multiple feature values ​​of the RFID tags returned per unit time and the RFID tag time slot information per unit time:

[0026] Based on the multiple feature values ​​of the RFID tags returned per unit time, the number of inventory counts and the tag recognition rate per unit time are calculated.

[0027] Based on the multiple feature values ​​of the RFID tags returned within a unit time and the RFID tag time slot information within a unit time, the average signal strength, link quality fluctuation indication, and bit error rate within a unit time are calculated.

[0028] The number of inventory counts, tag recognition rate, average signal strength, link quality fluctuation indication, and bit error rate obtained per unit time are used as physical layer performance indicators.

[0029] A further improvement of this invention lies in the following specific method for evaluating the system state of the MAC layer and the link quality of the physical layer per unit time based on MAC layer performance indicators and physical layer performance indicators:

[0030] The MAC layer performance indicators are assigned weight values, and the MAC layer system state score per unit time is calculated using the approximation ideal solution sorting method as the system state of the MAC layer.

[0031] The dataset composed of physical layer performance indicators is subjected to data dimensionality reduction and clustering to obtain the physical layer link quality classification results per unit time.

[0032] A further improvement of this invention lies in the following method for evaluating the real-time status of a UHF RFID robot based on dynamic scenarios, according to the system state of the MAC layer and the link quality of the physical layer at all times:

[0033] The proximity analysis method is used to map the physical layer link quality to the physical layer link quality score;

[0034] By combining the physical layer link quality score with the MAC layer system status, the real-time status of the UHF RFID robot system is evaluated, and a classification result of the real-time status of the UHF RFID robot system is obtained.

[0035] Secondly, the present invention provides a real-time status assessment system for UHF RFID robots based on dynamic scenarios, comprising:

[0036] The multi-feature value acquisition module is used to acquire the fixed-frequency electromagnetic waves transmitted and received by the UHF RFID robot and obtain the multi-feature values ​​of the RFID tag returned per unit time.

[0037] The MAC layer performance index acquisition module is used to obtain the RFID tag time slot information per unit time based on the fixed frequency electromagnetic waves transmitted and received by the UHF RFID robot, and to calculate the MAC layer performance index based on the RFID tag time slot information per unit time.

[0038] The physical layer performance index acquisition module is used to calculate the physical layer performance index based on the multiple feature values ​​of the RFID tag returned per unit time and the RFID tag time slot information per unit time.

[0039] The state quality assessment module is used to assess the system state of the MAC layer and the link quality of the physical layer per unit time based on MAC layer performance indicators and physical layer performance indicators.

[0040] The real-time status assessment module is used to assess the real-time status level of the UHF RFID robot based on dynamic scenarios, according to the system status of the MAC layer and the link quality of the physical layer at all times.

[0041] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a real-time status assessment method for a UHF RFID robot based on a dynamic scene.

[0042] Fourthly, the present invention provides a storage medium storing a computer program thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a real-time status assessment method for a UHF RFID robot based on a dynamic scene.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] This invention utilizes MAC layer and physical layer performance indicators to construct a MAC layer system state evaluation model and a physical layer link quality evaluation model. By integrating these assessments, a real-time state evaluation model for a UHF RFID robot system is built, obtaining the real-time state level of the RFID robot system. By acquiring multiple feature values ​​and time slot information per unit time, this invention can monitor the state of the UHF RFID robot in real time, promptly identifying and adjusting problems. Simultaneously evaluating the performance indicators of the MAC and physical layers provides a comprehensive system state assessment, more accurately reflecting the overall operating status of the RFID system. It focuses on performance evaluation in dynamic scenarios, adapting to reader movement, tag movement, and environmental changes, thus improving the system's application flexibility. Utilizing multiple feature values ​​of RFID tags for analysis allows for a deeper understanding of tag performance under different conditions, thereby optimizing system performance. By evaluating the link quality of the physical layer, this invention can effectively identify signal attenuation and interference issues, ensuring the reliability of data transmission. This method can be adjusted and extended according to different application needs and environments, and has good flexibility and adaptability. In summary, this method not only improves the real-time performance and accuracy of UHF RFID robot status assessment, but also enhances its applicability in dynamic environments, providing a good foundation for the further development of related technologies. Attached Figure Description

[0045] Figure 1 This is a flowchart of the present invention;

[0046] Figure 2 This is a system diagram of the present invention;

[0047] Figure 3 A schematic diagram illustrating the command interaction process between the reader and the tag provided in this embodiment;

[0048] Figure 4 A diagram showing the relationship between received signal strength and the relative distance between the tag and the antenna is provided for an embodiment.

[0049] Figure 5 A diagram showing the relationship between the received signal strength and the relative angle between the tag and the antenna is provided for an embodiment.

[0050] Figure 6 A schematic diagram of the relative angle between the tag and the antenna provided for an embodiment;

[0051] Figure 7 This is a schematic diagram of a real-time status assessment scenario for a UHF RFID robot system provided in this embodiment.

[0052] Figure 8 A schematic diagram of the MAC layer system state assessment results provided for this embodiment;

[0053] Figure 9 A schematic diagram of physical layer link quality assessment results provided for an embodiment;

[0054] Figure 10 This is a schematic diagram illustrating the real-time status level classification of a UHF RFID robot system provided in this embodiment.

[0055] Figure 11 This is a schematic diagram illustrating the real-time status assessment results of the UHF RFID robot system provided in this embodiment.

[0056] Figure 12 A system diagram provided in real time. Detailed Implementation

[0057] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0058] See Figure 1 A real-time status assessment method for UHF RFID robots based on dynamic scenarios includes the following steps:

[0059] S101, in dynamic scenarios, uses a reader mounted on a UHF RFID robot to transmit fixed-frequency electromagnetic waves to the tag, and obtains multiple feature values ​​of the RFID tag returned per unit time.

[0060] S102 uses a spectrum analyzer to obtain RFID tag time slot information per unit time, and calculates MAC layer performance indicators based on the RFID tag time slot information per unit time.

[0061] S103, calculate the physical layer performance index based on the multiple feature values ​​of the RFID tag returned per unit time and the RFID tag time slot information per unit time.

[0062] S104 evaluates the system status of the MAC layer and the link quality of the physical layer per unit time based on MAC layer performance indicators and physical layer performance indicators.

[0063] S105 evaluates the real-time status of the UHF RFID robot based on dynamic scenarios, according to the system status of the MAC layer and the link quality of the physical layer at all times.

[0064] See Figure 2 A real-time status assessment system for UHF RFID robots based on dynamic scenarios, comprising:

[0065] The multi-feature value acquisition module is used to obtain the multi-feature values ​​of RFID tags returned per unit time based on the tag return signal received by the single antenna of the reader in dynamic scenarios.

[0066] The MAC layer performance indicator acquisition module is used to obtain RFID tag timeslot information per unit time using a spectrum analyzer, and to calculate MAC layer performance indicators based on the RFID tag timeslot information per unit time.

[0067] The physical layer performance index acquisition module is used to calculate the physical layer performance index based on the multiple feature values ​​of the RFID tags returned per unit time and the RFID tag time slot information per unit time.

[0068] The State Quality Assessment Module is used to assess the system state of the MAC layer and the link quality of the physical layer per unit time based on MAC layer performance indicators and physical layer performance indicators.

[0069] The real-time status assessment module is used to assess the real-time status level of the UHF RFID robot based on dynamic scenarios, according to the system status of the MAC layer and the link quality of the physical layer at all times.

[0070] Example 1:

[0071] This embodiment further defines step S101. In a dynamic scenario, the specific method for obtaining multiple feature values ​​of the RFID tag returned per unit time by using a reader mounted on a UHF RFID robot to emit fixed-frequency electromagnetic waves to the tag is as follows:

[0072] S1011, the reader mounted on the UHF RFID robot sends electromagnetic waves to the tag at a fixed frequency through a single antenna;

[0073] S1012 After the tag receives the fixed-frequency electromagnetic waves emitted by the reader, it uses the reader to obtain multiple feature values ​​of the tag per unit time based on the relative distance and relative angle between the tag and the reader antenna, as well as the physical characteristics of the tag itself.

[0074] The movement of an RFID robot equipped with a reader and a single antenna causes changes in the relative distance and angle between the tag and the reader antenna, resulting in the tag reflecting different characteristic values. The reader can then acquire multiple characteristic values ​​of the tag per unit time.

[0075] Example 2:

[0076] This embodiment further defines step S102. The specific method for calculating the MAC layer performance index based on the RFID tag time slot information obtained by the spectrum analyzer per unit time is as follows:

[0077] S1021, use a spectrum analyzer to capture the transmission waveform between the reader and the tag, analyze the command interaction process between the reader and the tag, and determine the type of time slot in each frame per unit time and the number and duration of successful time slots, collision time slots, and idle time slots.

[0078] S1022, based on the type of time slots in each frame per unit time and the number and duration of successful time slots, collision time slots, and idle time slots, calculates time efficiency, recognition efficiency, and throughput as MAC layer performance indicators.

[0079] Assumption , and They represent the first i frame The number of idle time slots, success time slots, and collision time slots. , and They represent the first i frame The duration of idle time slots, success time slots, and collision time slots. k This represents the number of frames per unit of time.

[0080] Time efficiency is the statistical average of the ratio of the time occupied by successful time slots in each frame to the total time per unit time, and its formula is:

[0081]

[0082] in, This indicates that The time efficiency within a frame is expressed by the following formula:

[0083]

[0084] The recognition efficiency is the total number of tags successfully recognized by the reader per unit time, and its formula is:

[0085]

[0086] Throughput is the ratio of successful time slots to the total number of time slots per unit time, and its formula is:

[0087] .

[0088] Example 3:

[0089] This embodiment further defines step S103. The specific method for calculating the physical layer performance index based on the multiple feature values ​​of the RFID tag returned per unit time and the RFID tag time slot information per unit time is as follows:

[0090] S1031, by counting the number of tags that return feature value information obtained by the reader within a unit of time, the number of inventory counts and the tag recognition rate within a unit of time are calculated. The tag recognition rate is defined as the ratio of the number of tags successfully recognized by the reader within a unit of time to the total number of tags within the reader's recognition range.

[0091] S1032, based on the multiple feature values ​​of the RFID tags returned per unit time and the RFID tag time slot information per unit time, the mean signal strength, link quality fluctuation indication, and bit error rate are calculated per unit time. The mean signal strength is the average value of the tag signal strength per unit time; LQVI is the weighted value of the variance, skewness, and kurtosis of the tag signal strength per unit time. The weights of the variance, skewness, and kurtosis of the signal strength are calculated using the entropy weight method; the bit error rate is the ratio of the number of bit error time slots per unit time to the total number of time slots. Sending an ACK command followed by a QueryRepeat command is considered a successful read; otherwise, the time slot is considered a bit error time slot.

[0092] S1033 uses the number of inventory counts, tag recognition rate, average signal strength, link quality fluctuation indication, and bit error rate obtained per unit time as physical layer performance indicators.

[0093] Example 4:

[0094] This embodiment further defines step S104. The specific method for evaluating the system state of the MAC layer and the link quality of the physical layer per unit time based on MAC layer performance indicators and physical layer performance indicators is as follows:

[0095] S1041 uses a combined weighting method to assign weight values ​​to time efficiency, recognition efficiency, and throughput in the MAC layer performance indicators. The system state score of the MAC layer per unit time is calculated using the approximation ideal solution ranking method as the system state of the MAC layer. Specifically, the Analytic Hierarchy Process (AHP) and the Entropy Weighting Method (EW) are used to assign weights to time efficiency, recognition efficiency, and throughput. The two weights are combined using Kendall's Coefficient of Performance (W) and the Critical Weighting Method (CRITIC). The TOPSIS method is then used to calculate the system state score of the MAC layer per unit time.

[0096] S1042, the dataset consisting of physical layer performance metrics such as inventory count, tag recognition rate, mean signal strength, LQVI, and bit error rate is subjected to dimensionality reduction and clustering to obtain the physical layer link quality classification result per unit time. Specifically, the Unified Manifold Approximation and Projection Method (UMAP) is used to reduce the dimensionality of the dataset, and the K-Means clustering algorithm is used to cluster the first three principal components obtained from the dimensionality reduction to obtain the physical layer link quality classification result per unit time.

[0097] Example 5:

[0098] This embodiment further defines step S105. The specific method for evaluating the real-time status of a UHF RFID robot based on a dynamic scenario, based on the system state of the MAC layer and the link quality of the physical layer at all times, is as follows:

[0099] S1051 uses proximity analysis to map the physical layer link quality to a physical layer link quality score. Specifically, K-Means clustering algorithm is used to obtain the range of each performance indicator under each link quality level, and combined with the mapping relationship between link level and score, proximity analysis is used to accurately map the physical layer link quality level to the physical layer link quality score.

[0100] S1052, combining the physical layer link quality score with the MAC layer system status, a classification algorithm is used to evaluate the real-time status of the UHF RFID robot system, obtaining a classification result for the real-time status of the UHF RFID robot system. Specifically, by comprehensively utilizing the MAC layer system status score and the physical layer link quality score, a classification regression tree (CART) is used to evaluate the real-time status of the UHF RFID robot system, obtaining a classification result for the real-time status of the UHF RFID robot system.

[0101] Example 6:

[0102] This invention utilizes a reader / writer mounted on a UHF RFID robot to transmit fixed-frequency electromagnetic waves to the tag via a single antenna. The reader / writer and a spectrum analyzer are used to acquire multiple feature values ​​and time slot information from the tag, respectively. For example, let the reader / writer frequency be... The reader receives multiple feature values ​​from the tags, including signal strength, number of counts, and number of tags identified per unit time. The spectrum analyzer obtains tag time slot information, including the time slot type, number and duration of successful time slots, collision time slots, and idle time slots per unit time.

[0103] According to step S102, the MAC layer performance metrics are calculated. For example, the command interaction process between the reader and the tag is as follows: Figure 3As shown, the interaction commands between the reader and the tag differ depending on the type of time slot. The time slot type is analyzed, and the number and duration of successful time slots, collision time slots, and idle time slots per frame per unit time are counted. Specifically, a successful time slot contains one tag response and successfully transmits EPC information; a collision time slot contains multiple tag responses; and an idle time slot contains no tag responses. MAC layer performance metrics are calculated using the time slot information, including time efficiency, recognition efficiency, and throughput.

[0104] For example, suppose , and They represent the first i frame The number of idle time slots, success time slots, and collision time slots. , and They represent the first i frame The duration of idle time slots, success time slots, and collision time slots. k This represents the number of frames per unit of time.

[0105] Time efficiency Defined as the statistical average of the ratio of the time occupied by successful time slots in each frame to the total time per unit time, its calculation formula is:

[0106]

[0107] in, This indicates that The time efficiency within a frame is calculated using the following formula:

[0108]

[0109] Recognition efficiency Defined as the total number of tags successfully recognized by the reader per unit time, its calculation formula is:

[0110]

[0111] Throughput Defined as the ratio of the number of successful time slots per unit time to the total number of time slots, its calculation formula is:

[0112]

[0113] In summary, the MAC layer performance metrics are calculated based on the tag time slot information.

[0114] According to step S103, the physical layer performance metrics are calculated. For example, the relationship between signal strength and the relative distance between the tag and the antenna is as follows: Figure 4As shown, the signal strength and the relative angle between the tag and the antenna are as follows: Figure 5 As shown in the diagram, the relative angle between the tag and the antenna is as follows: Figure 6 As shown in the figure, it can be seen that the signal strength value changes as the relative distance and relative angle between the tag and the antenna change.

[0115] For example, such as Figure 7 The image shows a schematic diagram of a real-time status assessment scenario for a UHF RFID robot system. The labels are evenly distributed over a length of [number]. l The number of layers is c The floor height is h On the file shelf, a robot equipped with a reader and a single antenna... v A device moves at a constant speed of (m / s) in front of the file rack. The reader / writer is used to obtain the... t Multiple features of the tag within a second include signal strength. Number of inventory checks n Number of successfully identified tags Use a spectrum analyzer to count the number of time slots with bit errors per unit time. .

[0116] Tag recognition rate RoI Defined as the ratio of the number of tags successfully recognized by the reader per unit time to the total number of tags within the reader's recognition range, its calculation formula is:

[0117]

[0118] in, Indicates the first t The total number of tags that the reader can recognize within a second is calculated using the following formula:

[0119]

[0120] Mean signal strength Defined as the average value of the tag signal strength per unit time, its calculation formula is:

[0121]

[0122] LQVI is defined as the weighted average of the variance, skewness, and kurtosis of the tag signal strength per unit time, and its calculation formula is as follows:

[0123]

[0124] Among them, variance skewness Skewness and kurtosis KurtosisDefined as the variance of the tag signal strength per unit time, the standard third-order central moment, and the standard fourth-order central moment, their calculation formulas are as follows:

[0125]

[0126]

[0127]

[0128] The weights for the variance, kurtosis, and skewness of the signal strength are calculated using the entropy weight method, and the weight allocation is shown in Table 1:

[0129] Table 1 Weight Allocation Results

[0130]

[0131] Bit error rate BER Defined as the ratio of the number of error time slots per unit time to the total number of time slots, its calculation formula is:

[0132]

[0133] Specifically, the reader sends the ACK command before sending... QueryRepeat The command is considered a successful read; otherwise, the time slot is considered an error time slot.

[0134] In summary, physical layer performance indicators are calculated based on multiple feature values ​​of the tags and time slot information.

[0135] Based on the evaluation of MAC layer system status and physical layer link quality in step S104, for example, based on the MAC layer performance indicators obtained in step S102, the indicator weights are calculated using the AHP method and the EW method respectively. The weight allocation results are shown in Table 2.

[0136] Table 2 Weight Allocation Results

[0137]

[0138] The Kendall synergy coefficient is used to test the consistency of the index weights calculated by the AHP method and the EW method. If the test passes, it means that the weights calculated by the AHP method and the EW method are not significantly different, and the combined weights are then used. The calculation formula is:

[0139]

[0140] in, The index weights are calculated using the AHP and EW methods.

[0141] If the test fails, it indicates a significant difference between the weights calculated by the AHP method and the EW method. The CRITIC method should be used to calculate a combined weight from the weights calculated by the AHP and EW methods. The calculation formula is:

[0142]

[0143] in, , , Indicates weight i With weight j The correlation coefficient between them Indicates weight j The standard deviation.

[0144] In summary, the combined weight allocation results of the AHP method and the EW method are shown in Table 3:

[0145] Table 3. Results of Combined Weight Allocation

[0146]

[0147] Based on combined weights Construct a weighted decision matrix ,in , The optimal solution matrix in the TOPSIS method is obtained from the standardized data. worst solution matrix They are respectively:

[0148]

[0149]

[0150] The comprehensive score of the evaluation object is calculated based on the best solution matrix and the worst solution matrix. The calculation formula is as follows:

[0151]

[0152] in, and They represent the first i The evaluation object and the optimal solution matrix and the first evaluation object i The distance between each evaluation object and the worst solution matrix is ​​calculated using the following formula:

[0153]

[0154]

[0155] In summary, the combined weighting and TOPSIS methods are used to evaluate the MAC layer system state. Figure 8 The figure shown is a diagram of the MAC layer system status evaluation results during the 60-second horizontal movement of the RFID robot.

[0156] For example, based on the physical layer performance indicators obtained in step S103, UMAP is used for data dimensionality reduction analysis to obtain three link characteristic parameters. The K-Means algorithm is used to cluster the link characteristic parameters with a cluster size of K=5, and the entropy weight method is used to calculate the weight of each indicator. The clusters are then weighted and ranked to classify the link quality into levels I to V, with level I being the best and level V the worst. The weights of each indicator are shown in Table 4, and the clustering results are as follows: Figure 9 As shown, the average silhouette coefficient of the cluster is 0.70184.

[0157] Table 4 Indicator Weights

[0158]

[0159] The real-time status of the RFID robot system is comprehensively evaluated in step S105. Table 5 shows the range of performance indicators for each link quality level obtained using the K-Means clustering algorithm, and Table 6 shows the mapping relationship between link quality level and score.

[0160] Table 5 Link Class and Parameter Range

[0161]

[0162] Table 6 Relationship between Link Level and Scoring

[0163]

[0164] The proximity analysis method is used to accurately map the physical layer link quality level to the physical layer link quality score. The calculation formula is as follows:

[0165]

[0166] C To calculate the overall similarity, the formula is as follows: , n For the total number of indicators, This represents the minimum overall similarity, typically 0. This represents the maximum overall similarity score, typically 1. This represents the current rating range.

[0167] For link quality i The formula for calculating the closeness of a performance indicator is as follows:

[0168]

[0169] in, The performance metrics for link quality include the number of inventory checks, tag recognition rate, mean signal strength, LQVI, and bit error rate. and The respective indicators at the 1st j The lower and upper limits of the indicator range under each level.

[0170] Based on the combined MAC layer system status score and physical layer link quality score, Figure 10 The real-time status level classification of the UHF RFID robot system uses CART decision tree to evaluate the real-time status of the UHF RFID robot system. The evaluation results are as follows: Figure 11 As shown in Table 7, the performance of the classification algorithm is as follows:

[0171] Table 7 Classification Algorithm Evaluation

[0172]

[0173] In summary, by combining the MAC layer system status and physical layer link quality to comprehensively evaluate the real-time status of the UHF RFID robot system, a classification result of the real-time status of the RFID system is obtained.

[0174] This invention evaluates the real-time status of a UHF RFID robot system using multiple feature values ​​of the tags. First, it calculates the MAC layer performance index using tag time slot information acquired by a spectrum analyzer per unit time. Then, it calculates the physical layer performance index using multiple tag feature values ​​acquired by the reader and time slot information acquired by the spectrum analyzer per unit time. Next, it designs algorithms based on the MAC layer and physical layer performance indices to evaluate the MAC layer system status and physical layer link quality respectively. Finally, it comprehensively utilizes the MAC layer system status and physical layer link quality to use a classification algorithm to evaluate the real-time status of the UHF RFID robot system and obtain a classification result of the RFID system's real-time status.

[0175] This invention enables a UHF RFID robot system to perform real-time evaluation of the overall reading status of the RFID system in dynamic scenarios. To improve the accuracy of the system's reading status evaluation, this invention utilizes MAC layer performance indicators—identification efficiency, time efficiency, and throughput—and physical layer performance indicators—mean signal strength, number of inventory counts, recognition rate, link quality fluctuation indicator (LQVI), and bit error rate—to construct a MAC layer system status evaluation model and a physical layer link quality evaluation model. This further yields the real-time status evaluation classification results of the UHF RFID robot system and also allows for the development of targeted adaptive adjustment strategies for the RFID system.

[0176] Example 7:

[0177] Please see Figure 12As shown, the present invention also provides an electronic device 100 for a real-time status assessment method for UHF RFID robots based on dynamic scenes; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0178] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the real-time status assessment method for a UHF RFID robot based on a dynamic scene as described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0179] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0180] The memory 101 in the electronic device 100 stores multiple instructions to implement a real-time status assessment method for a UHF RFID robot based on dynamic scenarios, and the processor 102 can execute the multiple instructions to achieve the following:

[0181] In dynamic scenarios, a reader mounted on a UHF RFID robot transmits electromagnetic waves of a fixed frequency to the tag, and obtains multiple feature values ​​of the RFID tag returned per unit time.

[0182] The RFID tag time slot information per unit time is obtained using a spectrum analyzer, and the MAC layer performance index is calculated based on the RFID tag time slot information per unit time.

[0183] The physical layer performance index is calculated based on the multiple feature values ​​of the RFID tags returned per unit time and the RFID tag time slot information per unit time.

[0184] Based on MAC layer performance metrics and physical layer performance metrics, evaluate the system status of the MAC layer and the link quality of the physical layer per unit time.

[0185] The real-time status of a UHF RFID robot in a dynamic scenario is evaluated based on the system status of the MAC layer and the link quality of the physical layer at all times.

[0186] Example 8:

[0187] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0188] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0189] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0190] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0191] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A real-time status assessment method for UHF RFID robots based on dynamic scenarios, characterized in that, Includes the following steps: In dynamic scenarios, the fixed-frequency electromagnetic waves transmitted and received by the UHF RFID robot are acquired, and the multiple feature values ​​of the RFID tag returned per unit time are obtained. Based on the fixed-frequency electromagnetic waves transmitted and received by the UHF RFID robot, the RFID tag time slot information per unit time is obtained. The MAC layer performance indicators are then calculated based on this information. The specific method is as follows: The transmission waveform between the reader and the tag is captured using a spectrum analyzer. The transmission waveform is analyzed based on the command interaction process between the reader and the tag to determine the type of time slot in each frame per unit time, as well as the number and duration of successful time slots, collision time slots, and idle time slots. Based on the type of time slots in each frame per unit time and the number and duration of successful time slots, collision time slots, and idle time slots, time efficiency, recognition efficiency, and throughput are calculated as MAC layer performance indicators. Based on the multiple feature values ​​of the RFID tags returned per unit time and the RFID tag time slot information per unit time, the physical layer performance index is calculated. The specific method is as follows: Based on the multiple feature values ​​of the RFID tags returned per unit time, the number of inventory counts and the tag recognition rate per unit time are calculated. Based on the multiple feature values ​​of the RFID tags returned within a unit time and the RFID tag time slot information within a unit time, the average signal strength, link quality fluctuation indication, and bit error rate within a unit time are calculated. The number of inventory counts, tag recognition rate, average signal strength, link quality fluctuation indication, and bit error rate obtained per unit time are used as physical layer performance indicators. Based on MAC layer performance metrics and physical layer performance metrics, the system status of the MAC layer and the link quality of the physical layer are evaluated per unit time. The specific method is as follows: The MAC layer performance indicators are assigned weight values, and the MAC layer system state score per unit time is calculated using the approximation ideal solution sorting method as the system state of the MAC layer. The dataset composed of physical layer performance indicators is subjected to data dimensionality reduction and clustering to obtain the physical layer link quality classification results per unit time. Based on the system state at the MAC layer and the link quality at the physical layer at all times, the real-time status of the UHF RFID robot in a dynamic scenario is evaluated. The specific method is as follows: The proximity analysis method is used to map the physical layer link quality to the physical layer link quality score; By combining the physical layer link quality score with the MAC layer system status, the real-time status of the UHF RFID robot system is evaluated, and a classification result of the real-time status of the UHF RFID robot system is obtained.

2. The real-time status assessment method for UHF RFID robots based on dynamic scenarios according to claim 1, characterized in that, In dynamic scenarios, the specific method for acquiring fixed-frequency electromagnetic waves transmitted and received by a UHF RFID robot and obtaining multiple feature values ​​of the RFID tag returned per unit time is as follows: The reader mounted on the UHF RFID robot sends electromagnetic waves to the tag at a fixed frequency through a single antenna. After receiving the fixed frequency electromagnetic waves, the tag uses the reader to obtain multiple feature values ​​of the tag per unit time based on the relative distance and relative angle between the tag and the reader antenna, as well as the physical characteristics of the tag itself.

3. The real-time status assessment method for UHF RFID robots based on dynamic scenarios according to claim 1, characterized in that, Time efficiency is the statistical average of the ratio of the time occupied by successful time slots in each frame to the total time per unit time; Recognition efficiency is the total number of tags successfully recognized by the reader per unit time. Throughput is the ratio of the number of successful time slots per unit time to the total number of time slots.

4. A real-time status assessment system for UHF RFID robots based on dynamic scenarios, characterized in that, The real-time status assessment method for UHF RFID robots based on dynamic scenarios as described in claim 1 includes: The multi-feature value acquisition module is used to acquire the fixed-frequency electromagnetic waves transmitted and received by the UHF RFID robot and obtain the multi-feature values ​​of the RFID tag returned per unit time. The MAC layer performance index acquisition module is used to obtain the RFID tag time slot information per unit time based on the fixed frequency electromagnetic waves transmitted and received by the UHF RFID robot, and to calculate the MAC layer performance index based on the RFID tag time slot information per unit time. The physical layer performance index acquisition module is used to calculate the physical layer performance index based on the multiple feature values ​​of the RFID tag returned per unit time and the RFID tag time slot information per unit time. The state quality assessment module is used to assess the system state of the MAC layer and the link quality of the physical layer per unit time based on MAC layer performance indicators and physical layer performance indicators. The real-time status assessment module is used to assess the real-time status of the UHF RFID robot based on dynamic scenarios, according to the system status of the MAC layer and the link quality of the physical layer at all times.

5. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the real-time status assessment method for UHF RFID robots based on dynamic scenes as described in any one of claims 1 to 3.

6. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the real-time status assessment method for UHF RFID robots based on dynamic scenes as described in any one of claims 1 to 3.

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