Dynamic management method of logistics supply chain based on Internet of Things

Abstract

The present invention discloses a dynamic management method for a logistics supply chain based on the Internet of Things, which relates to the field of logistics management technology and includes the following steps: constructing a radio frequency interference spatial fingerprint library, identifying interference areas and generating a radio frequency interference distribution map by collecting the transmission frequency, signal strength and spatial distribution of multiple radio frequency identification reading and writing devices. The present invention accurately identifies interference hotspots by constructing a radio frequency interference spatial fingerprint library and a distribution map, and combines dynamic tag gain and timing management to avoid signal conflicts and response failures. Real-time monitoring of communication stability and implementation of local adjustment and self-checking can achieve rapid abnormal response and self-repair. Optimize equipment deployment and transmission power, dynamically eliminate interference blind spots, improve identification stability and data accuracy, reduce logistics errors and risks, enhance supply chain transparency and responsiveness, promote intelligent and reliable logistics management, and improve the system's anti-interference ability and overall operational efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a dynamic management method for a logistics supply chain based on the Internet of Things. Background Art

[0002] IoT-based dynamic logistics supply chain management leverages IoT technologies, using sensors, RFID tags, GPS positioning, and other devices to achieve real-time monitoring and data collection across the entire logistics supply chain. These technologies enable the system to capture real-time data on various aspects of the process, such as inventory status, shipping locations, and cargo status, and dynamically adjust and optimize management through data analysis. This approach improves supply chain transparency and responsiveness, making logistics operations more intelligent and efficient. It can flexibly adjust to market demand, logistics conditions, and emergencies, optimize resource allocation, reduce delays and costs, and improve overall supply chain operational efficiency and customer satisfaction.

[0003] The existing technology has the following shortcomings: In existing IoT-based warehouse management systems, multiple RFID reader / writer devices are typically deployed in different operating areas to automatically identify and track the status of materials entering and leaving the warehouse. However, when multiple RFID reader / writer systems with different operating frequency bands or overlapping functional coverage areas exist in the same warehouse environment, the problem of mutual interference between RF signals is particularly prominent. In particular, when multiple devices are operating in parallel, electromagnetic interference, signal coverage overlap, or response conflicts may occur between readers / writers, affecting the stable communication status of the tags. As a result, some RFID tags in the interference coupling area are unable to continuously respond to reading requests, which in turn causes recognition anomalies such as tag data reading failure or periodic loss, resulting in the phenomenon of "instantaneous invisibility" of cargo information. This problem is highly hidden and discontinuous, making it difficult for the system to immediately detect it during normal operation, and it is very easy to cause misidentification of key data and incorrect decisions in the logistics management process. For example, the system may mistakenly judge materials actually in stock as having been shipped out or missing, thereby triggering a series of chain reactions such as abnormal replenishment instructions, delivery failures, and order mismatches. In serious cases, it may also lead to the loss of control over the logistics status of regulated materials, bringing safety risks and compliance issues.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a dynamic management method for the logistics supply chain based on the Internet of Things. By constructing a radio frequency interference spatial fingerprint library and distribution map, interference hotspots can be accurately identified, and dynamic tag gain and timing management can be combined to avoid signal conflicts and response failures. Real-time monitoring of communication stability and implementation of local adjustment and self-checking can achieve rapid abnormal response and self-repair. Optimize equipment deployment and transmission power, dynamically eliminate interference blind spots, improve identification stability and data accuracy, reduce logistics errors and risks, enhance supply chain transparency and responsiveness, promote intelligent and reliable logistics management, and improve the system's anti-interference ability and overall operational efficiency to solve the problems in the above-mentioned background technology.

[0006] In order to achieve the above objectives, the present invention provides the following technical solution: a dynamic management method for logistics supply chain based on the Internet of Things, comprising the following steps:

[0007] Build a RF interference spatial fingerprint library to identify interference areas and generate RF interference distribution maps by collecting the transmission frequency, signal strength and spatial distribution of multiple RFID readers and writers;

[0008] Divide interference sensitivity level areas based on the RF interference distribution map and set the reading gain threshold of RFID tags in the corresponding areas;

[0009] Dynamically allocate RFID reader / writer identification timing according to interference sensitivity level, coordinate working order through non-overlapping identification time slots, and reduce tag response failure rate;

[0010] Real-time statistics of tag response frequency are generated to generate a stability index. When the stability index is lower than the threshold, it is marked as a potential interference object, and the reading gain and recognition time slot are locally adjusted;

[0011] Periodically poll the communication status of potential interference targets, update the RF interference distribution map and correct the fingerprint database data based on the communication recovery results;

[0012] Based on the interference feature update results, the deployment location and transmission power configuration of RFID reader / writers are optimized to dynamically eliminate interference blind spots and improve recognition stability.

[0013] Preferably, constructing a radio frequency interference spatial fingerprint library comprises the following steps:

[0014] Deploy multiple RFID readers and writers and collect emission frequency, signal strength, and spatial distribution characteristics;

[0015] The storage environment is divided into multiple three-dimensional space cells, and the signals in each cell are superimposed and calculated;

[0016] Construct an interference intensity matrix to identify spatial areas of frequency overlap, signal coverage overlap, and response conflicts;

[0017] A radio frequency interference distribution map is established based on spatial coordinates and time indexes, and a radio frequency interference spatial fingerprint library is generated for dynamic management.

[0018] Preferably, setting the read gain threshold of the RFID tag includes the following steps:

[0019] Calculate the interference sensitivity score of each spatial unit based on the RF interference distribution map and divide it into multiple sensitivity level areas;

[0020] Set the read gain threshold parameter according to the sensitivity level corresponding to the current position of the RFID tag;

[0021] Adjust the signal receiving gain of the RFID tag through control instructions to enhance the tag's signal receiving capability in high-sensitivity areas;

[0022] Continuously monitor tag communication quality and automatically revise gain threshold setting strategies based on recognition performance feedback.

[0023] Preferably, allocating the identification timing of the radio frequency identification reader / writer device includes the following steps:

[0024] Calculate the interference sensitivity level of the area where each RFID reader / writer is located based on the interference distribution map and generate an identification priority index;

[0025] Construct a non-overlapping recognition time slot arrangement table according to the recognition priority index and allocate the recognition time window to each read / write device;

[0026] Send identification time slot scheduling instructions to each RFID reader / writer to start the identification task within the specified time slot;

[0027] Based on the real-time feedback of the tag response rate and interference index, the identification time slot arrangement table is dynamically adjusted to achieve local refresh and timing redistribution.

[0028] Preferably, generating the tag stability index and performing local adjustments comprises the following steps:

[0029] Real-time collection of RFID tag response frequency, average response delay and recognition interval data;

[0030] Calculate the label stability index based on the collected data and compare it with the set threshold;

[0031] When the stability index is lower than the threshold, the corresponding label is marked as a potential interference object;

[0032] The reading gain parameters and identification time slot configuration of the RFID reader / writer in the spatial area where the RFID tag is currently located are locally adjusted to improve the communication stability of the RFID tag.

[0033] Preferably, periodically polling the communication status of the potential interference object includes the following steps:

[0034] Periodically poll the communication status of RFID tags marked as potential interference targets and record the response delay, signal strength, and data integrity;

[0035] Calculate the communication recovery index based on the polling results and determine whether it is higher than the recovery threshold;

[0036] When the communication recovery index of the RFID tag is higher than the recovery threshold, the interference characteristics of the spatial area where the RFID tag is located are updated, and the interference level score of the current spatial area is adjusted;

[0037] The adjusted interference characteristics are synchronously corrected to the RF interference spatial fingerprint library for subsequent identification parameter optimization.

[0038] Preferably, optimizing the deployment location and transmission power configuration of the RFID reader / writer includes the following steps:

[0039] Identify interference overlap areas based on the updated interference distribution map and analyze the interference contribution of RFID readers and writers;

[0040] Generate device position adjustment recommendations and determine target deployment coordinates to minimize interference zone overlap;

[0041] Adjust the transmit power level based on the interference level of the spatial unit where the device is located, limiting the power in high-interference areas and enhancing the signal strength in marginal areas.

[0042] After deployment and power adjustment are completed, an identification performance evaluation is performed and the optimized configuration is written to the database.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] The present invention constructs a radio frequency interference spatial fingerprint library and an interference distribution map, enabling the system to clearly identify interference hotspots and sensitive areas, and combines dynamic tag gain adjustment with timing management to effectively avoid signal conflicts and tag response failures. Real-time monitoring of tag communication stability and implementation of local adjustments and periodic self-checks for abnormal tags ensures the system's rapid response and self-repair to communication anomalies. Ultimately, by optimizing equipment deployment and transmission power, interference blind spots are dynamically eliminated, significantly improving tag recognition stability and data accuracy, reducing logistics data errors and management risks, enhancing the transparency and responsiveness of the supply chain, and achieving intelligent and reliable logistics operations management. This closed-loop dynamic control mechanism not only enhances the system's anti-interference capability, but also optimizes resource allocation, improving overall supply chain operating efficiency and customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0046] Figure 1 This is a flow chart of the method for dynamic management of logistics supply chain based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0047] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0048] The present invention provides Figure 1 The dynamic management method of logistics supply chain based on the Internet of Things includes the following steps:

[0049] Build a RF interference spatial fingerprint library. By collecting the transmission frequency, signal strength and spatial distribution characteristics of multiple RFID readers and writers, identify the interference areas between devices, generate and store RF interference distribution maps;

[0050] The process of building a RF interference spatial fingerprint library can be carried out as follows:

[0051] Deploy multiple RFID reader / writer devices in the target warehouse environment to simulate the parallel working state of multiple devices under actual operating conditions. Each RFID reader / writer device adopts a different installation height, transmission power, antenna direction and communication frequency band layout method to form a complex spatial structure with multiple signal coverage. During the system initialization phase, by activating each reader / writer and controlling them to perform transmission operations in turn or in combination, the signal transmission characteristic parameters at different time slices and different spatial points are collected, including but not limited to basic wireless physical parameters such as transmission frequency, power intensity, modulation mode, carrier frequency deviation, and channel utilization. At the same time, a spatial positioning module or a three-dimensional coordinate system is introduced to accurately locate the data collection point to ensure a one-to-one correspondence between signal characteristics and spatial positions. The main goal of this stage is to establish a preliminary spatial RF signal strength map and obtain the original RF distribution data of the warehouse environment when multiple readers / writers coexist.

[0052] Based on the data collected above, the storage space is grid-modeled, and the entire storage area is divided into several three-dimensional spatial cells with a fixed coordinate range. The signal strength information from different RFID readers and writers is summarized in each cell. On this basis, a multi-source signal superposition algorithm based on Gaussian mixture modeling is adopted, combined with the signal propagation path model and the actual reflection interference model, to perform numerical calculation and fitting of the RF field strength in each cell, and obtain the interference overlap probability distribution map generated by multiple device signals in space. After identifying the signal overlap area, spectrum analysis technology is further used to detect whether there are cross-interference, harmonic interference, co-frequency interference and other phenomena, so as to identify the potential interference relationship between different RFID readers and writers in this spatial unit. The technical key of this stage is to introduce multi-source data fusion algorithms and spatial frequency correlation models to establish refined interference identification capabilities.

[0053] Combined with the working frequency scheduling mode of each RFID reader / writer in the actual application scenario, the signal strength superposition between different devices in adjacent spatial cells is dynamically analyzed. By constructing a timing conflict matrix, the possibility of RF signal conflict between devices with similar working frequencies and in the same or similar working cycles is identified. The interference index evaluation mechanism is further adopted to quantify the comprehensive interference level of each spatial unit in any time period, and then screen out high-risk interference areas. This mechanism takes into account multi-dimensional factors such as signal amplitude overlap rate, spectrum overlap, and response delay impact weight to generate a spatial interference intensity matrix that is ultimately used for interference judgment. Through this process, areas in the storage space that may cause unstable tag communication can be quantitatively identified, providing a decision-making basis for subsequent tag gain adjustment and device identification scheduling.

[0054] The above processing results are unified and integrated to generate a radio frequency interference distribution map, and a radio frequency interference spatial fingerprint library is constructed with time tags and spatial coordinates as index parameters. This fingerprint library records the radio frequency interference characteristic parameters of each three-dimensional coordinate unit in different time periods in the form of a data structure, including interference level, main interference source device number, frequency band overlap information, signal attenuation model parameters, etc. This database will serve as the core interference information perception module, providing a data foundation and real-time reference basis for subsequent functional modules such as the adaptive adjustment of the gain threshold of the radio frequency identification tag, dynamic coordination of the identification time slot, tag stability index monitoring, and local identification and reconstruction of the interference area. In addition, the radio frequency interference spatial fingerprint library also has a dynamic update mechanism. During the operation of the system, it can continuously receive equipment operation data and tag response feedback results, and modify and iterate the original fingerprint characteristics, thereby realizing long-term adaptive modeling and precise maintenance of the warehouse radio frequency interference environment.

[0055] Based on the RF interference distribution map, the warehouse environment is divided into multiple interference sensitivity levels. The reading gain threshold of the RFID tag is set for each level area to enhance the tag's signal reception capability in high-interference areas.

[0056] To improve the communication stability of RFID tags in complex interference environments, the warehouse environment can be divided into interference sensitivity levels based on the constructed RF interference distribution map, and a partition gain control strategy can be set to enhance the tag's signal reception capability in high-interference areas. The specific steps include:

[0057] Based on the three-dimensional spatial interference feature information recorded in the radio frequency interference distribution map, a sensitivity level analysis is performed on the entire storage environment. This analysis uses spatial units as the basic granularity, and generates interference sensitivity scores by quantifying parameters such as interference intensity, signal overlap, interference source density, and spectrum conflict probability within the unit space. On this basis, a grading strategy is adopted to divide the score range into multiple intervals, corresponding to grade types such as "high sensitivity area", "medium sensitivity area", and "low sensitivity area". In order to improve the scientificity and accuracy of the grading, this implementation method introduces a support vector machine classification model to train and classify historical tag response stability data under different interference modes, so that the division of sensitivity levels not only depends on the static characteristics of spatial interference intensity, but also combines the dynamic feedback characteristics of the actual performance of tag communication to achieve data-driven partition modeling.

[0058] Based on the aforementioned sensitivity level classification results, the three-dimensional coordinate space of the warehouse environment is mapped into multiple independent interference zones. Each zone is identified by its corresponding interference sensitivity level, and a mapping relationship between zone number and spatial index is established. The boundaries of each zone are generated using a spatial clustering algorithm to ensure that each interference level area is continuous and deployable in physical space. Furthermore, a wireless positioning system or mobile trajectory sensing module is used to determine the current spatial location of each RFID tag in real time. This location information is mapped to the corresponding interference level zone, achieving dynamic binding between tags and spatial interference levels.

[0059] According to the interference sensitivity level of the tag, the reading gain threshold in its communication parameters is dynamically set. In the high-sensitivity area, the tag receiving end sets a higher signal gain threshold and optimizes the bandwidth of its matching carrier identification filter to enhance its reception capability of useful identification signals and reduce sensitivity to interference noise; in the medium-sensitivity area, a medium-level gain threshold is set to maintain a balance between identification efficiency and energy consumption; in the low-sensitivity area, the gain threshold is appropriately lowered to reduce over-response and improve overall system resource utilization. This implementation method specifically introduces a gain control module, in which the central identification control system uniformly manages the gain setting strategy of each tag, and issues a gain update command to the tag through wireless instructions, so that the tag can adaptively switch the signal reception parameters.

[0060] In order to ensure that the setting of the read gain threshold is continuously adaptable, this implementation introduces a feedback adjustment mechanism. The system continuously monitors the communication quality indicators of each tag, including data such as average response time, recognition success rate, and number of repeated requests, and correlates them with the interference level of the area where the tag is currently located. When it is detected that the tag has an abnormal trend in recognition performance in a certain sensitivity level area, the system will automatically correct the gain threshold setting strategy for the area and update the partition parameter model at the same time to form an adaptive adjustment closed loop based on the actual recognition performance. Through this mechanism, the signal reception capability of the tag can be continuously optimized according to changes in the interference environment and the operating status of the device, thereby effectively improving the stability and robustness of tag communication in a multi-device parallel environment.

[0061] Dynamically allocate the recognition timing of RFID readers based on the interference sensitivity level. By setting non-overlapping recognition time slots, the working order of the devices in high-sensitivity areas is coordinated to reduce the probability of tag response failure.

[0062] To improve the RFID system's multi-device parallel recognition performance in complex interference environments, a dynamic recognition timing allocation mechanism is used, combining the previously constructed RFID interference distribution map and interference sensitivity level classification results. This mechanism coordinates the working order of RFID readers and writers in different areas, effectively reducing the probability of RFID tag response failures. This process may include the following steps:

[0063] Based on the RF interference spatial fingerprint library and interference distribution map, the system maps the physical coverage of all RFID readers and writers to the corresponding interference sensitivity level areas, and establishes a device-area mapping relationship. By real-time monitoring of the current activity status and spatial signal occupancy of each device, combined with the tag response efficiency recorded in the historical identification data, the system assigns a dynamic identification priority index to each RFID reader and writer. The priority index is calculated by weighting multiple factors, including the interference level value of the area where the device is located, the current task load of the device, the response stability of the connected tags, and other factors. The higher the priority index value, the lower the device's tolerance for interference in the identification environment, and its independent communication window must be prioritized to obtain identification priority in subsequent timing allocation.

[0064] All RFID reading and writing devices are sorted according to the above-mentioned priority index, and an identification time slot arrangement table is constructed in units of time slices. The arrangement table assigns the start and end time of the identification task to each device in the form of non-overlapping time windows to ensure that two or more devices will not activate the identification process at the same time in the same interference sensitivity level area. In order to further improve the flexibility and stability of time allocation, this implementation method introduces a conflict avoidance algorithm based on the graph coloring model, which divides devices with high mutual interference in space into different conflict sets, and preferentially allocates identification time gaps with larger intervals to devices in high-conflict sets, thereby minimizing the potential risk of signal overlap. The above-mentioned arrangement table runs in a periodic update manner, and automatically recalculates the priority index and conflict set distribution after each cycle to adapt to the dynamic changes in the device operating status and spatial interference environment.

[0065] After the arrangement table is completed, the central control system sends specific identification time slot scheduling instructions to each RFID reader / writer device, including parameters such as start time, duration, and delayed activation strategy. After receiving the instruction, the device will only start the identification function within the allocated time period, and will be in a listening or standby state for the rest of the time to avoid the amplification effect of disordered communication on the interference environment. In order to enhance the real-time response capability of the system, this implementation method introduces an emergency adjustment channel in each identification cycle. When it is detected that the tag identification response rate in the area where a certain device is located suddenly drops, the device signal quality is abnormal, or the interference index increases sharply, it can immediately trigger a partial refresh of the arrangement table, and reallocate more suitable identification time slots to high-priority devices, thereby improving the system's ability to resist sudden interference and recognition efficiency.

[0066] To evaluate the effectiveness of the dynamic identification time slot scheduling mechanism, the system continuously records the key performance indicators of each RFID reader / writer during the execution of its assigned tasks, including recognition success rate, average tag response time, tag miss rate, and interference index change trends. These indicators are centrally analyzed by the central control module after each identification cycle and a cycle identification efficiency report is generated. Based on this report, the system dynamically adjusts priority calculation weights, arrangement algorithm parameters, and conflict thresholds, enabling the identification time slot allocation mechanism to adapt and evolve. Through this entire process, the system continuously senses environmental changes, proactively coordinates identification resource scheduling, and effectively improves RFID stability and tag communication success rates in high-interference areas, providing strong technical support for logistics supply chains operating in parallel with multiple devices.

[0067] Real-time statistics of RFID tag response frequencies are generated to generate a tag stability index. The stability index is then tested to see if it is below a set threshold. If the test result is below the threshold, the tag is marked as a potential interference target and the read gain parameters and identification time slots in the corresponding area are locally adjusted.

[0068] To dynamically monitor RFID tag recognition stability and adjust interference response, the system constructs a tag stability index model to quantitatively evaluate the tag communication status, identify potential interference impacts, and adjust the local recognition strategy accordingly. This process includes the following steps:

[0069] During each identification cycle, the system records the tag response frequency data collected by the RFID reader in real time. Specifically, for each registered tag, the system counts the number of times it is successfully identified within a unit time window, the average response delay, the interval between repeated identifications, and whether there is any identification interruption. In order to eliminate the fluctuation misjudgment caused by the movement of the tag itself, environmental changes or other incidental factors, this implementation method introduces a sliding window statistical mechanism to perform weighted smoothing on the response data of the tag for multiple consecutive cycles to ensure the stability and representativeness of the data. At the same time, the system also jointly models the response frequency with the power parameters of the identification device, the interference level of the space where the tag is located, and the historical communication performance to provide multi-dimensional data support for subsequent indicator calculations.

[0070] Based on the collected data, a tag stability index model is constructed. This model uses metrics such as the tag's average response frequency, recognition interval fluctuation rate, number of abnormal responses, and signal strength variation as inputs. Through weighted scoring, it calculates a numerical index that represents communication stability. The index typically ranges from 0 to 1, with values ​​closer to 1 indicating more stable communication and values ​​closer to 0 indicating severe instability or frequent disconnections. To improve the model's adaptability and generalization capabilities, the system utilizes machine learning models trained on historical recognition data, such as random forest classifiers or support vector regressors, to optimize the weighting of each index metric, dynamically adapting to recognition differences in different usage environments and device configurations. The calculated results are compared with a pre-set stability threshold to serve as the basis for interference detection.

[0071] When it is detected that the tag stability index is lower than the set threshold, the system immediately marks the tag as a potential interference object. This marking process not only generates a status identification in the tag management database, but also synchronously pushes the tag's spatial coordinate position, the last identified device number, the current reading power and time slot information to the identification control center. Based on this information, the identification control center starts the local optimization mechanism and dynamically adjusts the reading gain parameters and identification time slot configuration of the spatial cell where the tag is located. In terms of gain parameters, the system increases the transmission power and tag receiving sensitivity of the relevant RFID reading and writing devices in the area to expand the communication coverage; in terms of identification time slots, the identification order and time interval of the reading and writing devices in the area are rearranged to reduce the impact of concurrent interference. The above adjustments will be synchronously updated to the identification scheduling module and will take effect in the next identification cycle.

[0072] In order to prevent the waste of resources or system fluctuations caused by mis-triggering of optimization behaviors, this implementation method introduces a feedback verification mechanism. After the local optimization adjustment is executed, the system continuously monitors the potential interference tag in several consecutive identification cycles, recalculates its stability index, and observes whether its response frequency and signal quality have been significantly improved. When it is found that the communication status of the tag has returned to the stable range and the index is higher than the recovery threshold, the system automatically cancels the interference mark and restores the relevant gain parameters and identification time slot configuration to the default settings to release scheduling resources. At the same time, the system uses the previous and subsequent communication status data of the tag for iterative optimization of the training stability index model to continuously improve the accuracy of anomaly recognition and the overall adaptability of the recognition system. Through the above mechanism, the system can realize timely detection, local repair and long-term self-learning evolution of RFID communication anomalies, significantly improving the recognition robustness and data reliability in a multi-device interference environment.

[0073] Perform periodic tag signal stability self-checking operations, poll the communication status of the marked tags, update the RF interference distribution map based on the communication recovery situation, and synchronously correct the interference feature data in the RF interference spatial fingerprint library;

[0074] To ensure that RFID tags marked as potential interference targets remain effectively monitored and dynamically iteratively optimize the spatial characteristics of RF interference, the system introduces a periodic tag signal stability self-checking mechanism. This mechanism forms a real-time feedback control loop for the RF interference environment through polling communication, status assessment, feature update, and model correction. Specifically, it includes the following steps:

[0075] The system automatically initiates a tag signal stability self-check at a preset interval. This operation is performed by the identification and scheduling control module, which periodically retrieves a list of RFID tags currently in the "potential interference object" state from the tag status database. For each tag in the list, the system generates an active identification task and dispatches RFID readers and writers that match its communication parameters to initiate a one-to-one polling identification instruction during a low-interference period. To improve the recognition success rate and reduce the risk of introducing new interference, the system uses a minimum power interference routing mechanism when performing polling, selecting the reader / writer closest to the tag's physical location and with the lowest interference index as the execution terminal to ensure the accuracy of the polling operation and the validity of the data.

[0076] During the polling identification process, the system records the response behavior of each tag in multiple dimensions, including whether the response is successful, response delay, signal strength, return data integrity, and communication continuity. All collected data will be associated with the polling task and stored in the communication status temporary pool, and serve as the core basis for judging whether the tag has "returned to normal". In order to improve the accuracy of the judgment results, this implementation method introduces a weighted discriminant model to conduct trend analysis based on the polling results of multiple identification cycles to avoid erroneous judgment that the tag communication status has been restored due to occasional successful identification. The model outputs a tag communication recovery index. When the index is stably higher than the set recovery threshold, the system adjusts the tag status from "potential interference object" to "normal tag" and cancels its priority protection strategy.

[0077] When the system determines that the communication status of a tag has returned to normal, it will immediately trigger the interference data update process of the spatial area associated with it. The system recalibrates the changes in the interference characteristics of the spatial unit in which it is located based on the historical identification trajectory of the tag and the current communication recovery area. If the area was originally determined to be a high interference sensitivity area, and the communication status of multiple tags has returned to normal, the system will lower the interference level score of the spatial unit and simultaneously adjust the interference parameter value of the corresponding unit in the RF interference distribution map. In addition, the system also includes the read-write device number, communication parameters and signal path records involved in the tag recovery process into the analysis scope to explore potential system factors that cause interference fluctuations, such as sudden changes in frequency band overlap, fluctuations in device transmission power, and movement of spatial obstacles.

[0078] After completing the update of the interference distribution map, the system will synchronize the newly generated interference features to the RF interference spatial fingerprint library. As the core data reference structure of the RFID system, the fingerprint library needs to be continuously synchronized with the physical environment. To this end, the system has designed a differentiated data injection mechanism to rewrite the feature parameters only for the spatial units within the scope of this update to avoid unnecessary interference to the entire library data. During the correction process, the system retains the version history of the original interference features and establishes a data snapshot index to facilitate rollback or comparison when judgment deviations occur. At the same time, the corrected fingerprint data will be immediately available for the identification and scheduling module to call, and be used to update the tag gain configuration, identification time slot arrangement and priority sorting in subsequent cycles. Through the above mechanism, the system realizes a closed-loop linkage between the tag communication status and the RF interference environment, which not only improves the self-repair ability of the identification system to communication anomalies, but also significantly enhances the long-term robustness and adaptability of interference modeling and scheduling control.

[0079] Based on the updated interference signature results, the deployment location and transmission power configuration of RFID readers and writers are re-optimized to dynamically eliminate interference blind spots during the parallel identification process of multiple devices, thereby improving overall identification stability and data reliability.

[0080] To effectively respond to the dynamically updated interference signature data in the RF interference spatial fingerprint library and continuously improve the recognition stability and data transmission reliability of the RFID system under the parallel operation of multiple devices, the system has established a device deployment and transmit power adaptive optimization mechanism driven by interference feedback. This mechanism is centered on spatial reconstruction and parameter tuning, combined with the dynamic changes in actual interference distribution, and is implemented in the following steps:

[0081] After each interference assessment cycle is completed, the system automatically triggers the interference signature analysis program. This program extracts all spatial units marked as high interference levels in the RF interference distribution map, and performs spatial correlation analysis with the current deployment location, transmission power, and coverage of the RFID reader / writer to form an interference overlap area map. A spatial conflict matrix model is introduced during the analysis process to quantify the contribution of each device to the interference hotspots in the area it covers. To enhance the physical authenticity of the analysis, the system also introduces actual tag response feedback data to reversely model the interference impact generated by a single device, thereby distinguishing the impact boundary between the device's own emission behavior and external interference coupling, providing a more targeted optimization basis for subsequent adjustments.

[0082] Based on the interference overlap area map, the system prioritizes the generation of position adjustment recommendations for RFID readers and writers deployed at the edge of the interference blind area. This recommendation is established based on the optimization objective function, whose goal is to minimize the cross-coverage area of ​​devices in high-interference areas while maximizing the uniform signal coverage within the entire identification area. At the algorithm level, a multi-objective genetic optimization model is introduced to search for the optimal solution among multiple alternative deployment coordinates, and recommend device location migration paths under the premise of meeting physical installation restrictions and communication path accessibility. The system provides a coordinate-level device displacement guidance plan, indicating the device number to be moved, the target location point, the moving distance, and the expected reduction in interference contribution. In order to avoid affecting the real-time operation of the identification system, this implementation method adopts an offline deployment simulation strategy, that is, first completing the deployment change simulation calculation and evaluating the expected improvement effect in the system background, and then notifying the maintenance personnel or robot to perform the physical displacement operation after reaching the threshold.

[0083] After optimizing the device deployment locations, the system further activates the transmit power configuration optimization module. This module automatically adjusts the transmit power level of each RFID reader / writer based on the interference level and tag density distribution characteristics of the device's new spatial unit. Power adjustment follows the principle of "limiting in high-interference areas and compensating in low-interference areas." Transmit power is lowered for devices in high-interference areas to reduce the risk of signal overlap; power is increased for devices in edge areas, where signal attenuation is severe, to improve tag recognition success rates. Power settings are performed using step control to avoid introducing new interference mutations. Each adjustment is combined with the predicted signal field strength distribution corresponding to the simulation model output to ensure the continuity and stability of the system's overall recognition coverage.

[0084] After the deployment location and transmit power configuration are adjusted, the system will write the updated results to the configuration database and perform a performance evaluation of the optimized identification cycle through the identification control platform. Evaluation indicators include average tag response rate, number of identification repetitions, interference bit error rate, and communication packet loss rate. If the evaluation result reaches the system's preset performance improvement threshold, the system will automatically record the current configuration as the optimal state and lock it as the baseline for the next cycle scheduling; if the target is not achieved, the next round of fine-tuning strategy will be triggered, and the optimized parameter combination will be retained as an alternative for subsequent comparison. Through this closed-loop self-optimization mechanism, the system can continuously eliminate interference blind spots in the RFID environment, realize dynamic reconstruction and intelligent power control at the device level, and effectively improve the overall communication stability, the system's anti-interference ability, and the reliability of multi-tag concurrent processing while ensuring parallel identification efficiency.

[0085] This IoT-based dynamic logistics supply chain management method enables precise perception and proactive control of signal interference issues caused by the concurrent operation of multiple devices in complex RF environments. By building a spatial RF interference fingerprint database and interference distribution map, the system can clearly identify interference hotspots and sensitive areas. Combined with dynamic tag gain adjustment and timing management, this effectively avoids signal conflicts and tag response failures. Real-time monitoring of tag communication stability and localized adjustments and periodic self-calibration for abnormal tags ensure rapid system response and self-healing to communication anomalies. Ultimately, by optimizing device deployment and transmit power, interference blind spots are dynamically eliminated, significantly improving tag recognition stability and data accuracy. This reduces logistics data errors and management risks, enhances supply chain transparency and responsiveness, and enables intelligent and reliable logistics operations management. This closed-loop dynamic control mechanism not only improves the system's anti-interference capabilities but also optimizes resource allocation, improving overall supply chain efficiency and customer satisfaction.

[0086] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0087] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0088] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0089] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0090] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0091] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0092] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0093] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0094] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A dynamic management method for logistics supply chain based on the Internet of Things, characterized by: The following steps are involved: Build a RF interference spatial fingerprint library to identify interference areas and generate RF interference distribution maps by collecting the transmission frequency, signal strength and spatial distribution of multiple RFID readers and writers; Divide interference sensitivity level areas based on the RF interference distribution map and set the reading gain threshold of RFID tags in the corresponding areas; Dynamically allocate RFID reader / writer identification timing according to interference sensitivity level, and coordinate working order through non-overlapping identification time slots; Real-time statistics of tag response frequency are generated to generate a stability index. When the stability index is lower than the threshold, it is marked as a potential interference object, and the reading gain and recognition time slot are locally adjusted; Periodically poll the communication status of potential interference targets, update the RF interference distribution map and correct the fingerprint database data based on the communication recovery results; Based on the interference signature update results, the deployment location and transmission power configuration of RFID readers and writers are optimized to dynamically eliminate interference blind spots. Generating a label stability index and performing local adjustments involves the following steps: Real-time collection of RFID tag response frequency, average response delay and recognition interval data; Calculate the label stability index based on the collected data and compare it with the set threshold; When the stability index is lower than the threshold, the corresponding label is marked as a potential interference object; Locally adjust the reading gain parameters and identification time slot configuration of the RFID reader / writer in the spatial area where the RFID tag is currently located.

2. The method for dynamic management of logistics supply chain based on Internet of Things according to claim 1, characterized in that: Building a RF interference spatial fingerprint library includes the following steps: Deploy multiple RFID readers and writers and collect emission frequency, signal strength, and spatial distribution characteristics; The storage environment is divided into multiple three-dimensional space cells, and the signals in each cell are superimposed and calculated; Construct an interference intensity matrix to identify spatial areas of frequency overlap, signal coverage overlap, and response conflicts; A radio frequency interference distribution map is established based on spatial coordinates and time indexes, and a radio frequency interference spatial fingerprint library is generated for dynamic management.

3. The method for dynamic management of logistics supply chain based on Internet of Things according to claim 1, characterized in that: Setting the read gain threshold for RFID tags involves the following steps: Calculate the interference sensitivity score of each spatial unit based on the RF interference distribution map and divide it into multiple sensitivity level areas; Set the read gain threshold parameter according to the sensitivity level corresponding to the current position of the RFID tag; Adjust the signal receiving gain of the RFID tag through control instructions to enhance the tag's signal receiving capability in high-sensitivity areas; Continuously monitor tag communication quality and automatically revise gain threshold setting strategies based on recognition performance feedback.

4. The method for dynamic management of logistics supply chain based on Internet of Things according to claim 1, characterized in that: Assigning an RFID reader / writer an identification sequence includes the following steps: Calculate the interference sensitivity level of the area where each RFID reader / writer is located based on the interference distribution map and generate an identification priority index; Construct a non-overlapping recognition time slot arrangement table according to the recognition priority index and allocate the recognition time window to each read / write device; Send identification time slot scheduling instructions to each RFID reader / writer to start the identification task within the specified time slot; Based on the real-time feedback of the tag response rate and interference index, the identification time slot arrangement table is dynamically adjusted to achieve local refresh and timing redistribution.

5. The method for dynamic management of logistics supply chain based on Internet of Things according to claim 1, characterized in that: Periodically polling the communication status of a potential interfering object includes the following steps: Periodically poll the communication status of RFID tags marked as potential interference targets and record the response delay, signal strength, and data integrity; Calculate the communication recovery index based on the polling results and determine whether it is higher than the recovery threshold; When the communication recovery index of the RFID tag is higher than the recovery threshold, the interference characteristics of the spatial area where the RFID tag is located are updated, and the interference level score of the current spatial area is adjusted; The adjusted interference characteristics are synchronously corrected to the RF interference spatial fingerprint library for subsequent identification parameter optimization.

6. The method for dynamic management of logistics supply chain based on Internet of Things according to claim 1, characterized in that: Optimizing the deployment location and transmit power configuration of RFID readers and writers includes the following steps: Identify interference overlap areas based on the updated interference distribution map and analyze the interference contribution of RFID readers and writers; Generate device position adjustment recommendations and determine target deployment coordinates to minimize interference zone overlap; Adjust the transmit power level based on the interference level of the spatial unit where the device is located, limiting the power in high-interference areas and enhancing the signal strength in marginal areas. After deployment and power adjustment are completed, an identification performance evaluation is performed and the optimized configuration is written to the database.

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