Radio frequency adaptive collection and multi-dimensional de-duplication reading method and system of drug information

Abstract

The application relates to the fields of radio frequency identification and digital medical technologies, and discloses a radio frequency adaptive collection and multi-dimensional deduplication reading method and system for medicine information, wherein the method is applied to a radio frequency adaptive collection and multi-dimensional deduplication reading system for medicine information, the system comprises a radio frequency identification reader-writer, a backend platform and a radio frequency identification label arranged on medicine, the backend platform is in communication connection with the radio frequency identification reader-writer, the method comprises the following steps: based on a dynamic environment adaptive radio frequency signal collection strategy, the radio frequency identification reader-writer is controlled to collect signals of the radio frequency identification label; based on a dynamic time slot allocation strategy, a mixed anti-collision strategy and a grouping reading strategy, the collected signals are read to obtain initial results; and based on a multi-dimensional dynamic deduplication strategy, the initial results are subjected to deduplication processing and fusion processing to obtain deduplication event records. Therefore, high reliability, high fidelity and data uniqueness of medicine information reading are realized.

Description

Technical Field

[0001] This application relates to the fields of radio frequency identification and digital medical technology, and in particular to a method and system for adaptive radio frequency acquisition and multi-dimensional deduplication of drug information. Background Technology

[0002] Radio frequency identification (RFID) technology is widely used in pharmaceutical management due to its advantages such as non-contact and batch reading capabilities. However, current RFID readers typically operate at fixed frequencies and transmission powers. In contrast, medical environments like hospitals are subject to complex electromagnetic interference from Wi-Fi, Bluetooth, and various medical devices, leading to unstable signals and low read success rates. Furthermore, pharmaceuticals pass through multiple business nodes throughout their lifecycle, with readers at each node generating massive amounts of redundant data through high-frequency readings. This results in tag conflicts within the same radio frequency field, and the inability to identify and merge duplicate data across nodes and time periods, causing data redundancy and distorted inventory status. Summary of the Invention

[0003] Based on this, it is necessary to address the technical problems of existing technologies, such as the difficulty in stably acquiring radio frequency signals in complex electromagnetic environments and the inability to effectively deduplicate massive amounts of duplicate data generated by multiple nodes and processes, resulting in low reading reliability and data redundancy. Therefore, a radio frequency adaptive acquisition and multi-dimensional deduplication reading method and system for drug information is proposed.

[0004] In a first aspect, a method for radio frequency adaptive acquisition and multi-dimensional deduplication reading of drug information is provided. The method is applied to a system for radio frequency adaptive acquisition and multi-dimensional deduplication reading of drug information. The system includes: a radio frequency identification reader, a back-end platform, and a radio frequency identification tag set on the drug. The back-end platform is communicatively connected to the radio frequency identification reader. The method includes: Based on a dynamic environment adaptive radio frequency signal acquisition strategy, the radio frequency identification reader is controlled to acquire the signal of the radio frequency identification tag. Based on the dynamic time slot allocation strategy, the hybrid anti-collision strategy and the group reading strategy, the acquired signals are read to obtain the initial results; Based on a multi-dimensional dynamic deduplication strategy, the initial results are deduplicated and fused to obtain deduplicated event records.

[0005] Secondly, a radio frequency adaptive acquisition and multi-dimensional deduplication reading system for drug information is provided, the system comprising: Radio frequency identification (RFID) tags are attached to drug packaging to store drug information. An RFID reader / writer is communicatively connected to the RFID tag; The backend platform is connected to the radio frequency identification reader.

[0006] The system is configured to implement the steps of the radio frequency adaptive acquisition and multi-dimensional deduplication reading method for drug information described in the first aspect.

[0007] Thirdly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-mentioned radio frequency adaptive acquisition and multi-dimensional deduplication reading method for drug information.

[0008] The radio frequency adaptive acquisition and multi-dimensional deduplication reading method and system for drug information in this application adopts a dynamic environment adaptive radio frequency signal acquisition strategy, which enables the radio frequency identification reader to sense changes in the current radio frequency field environment in real time and dynamically adjust the signal acquisition parameters accordingly. This maintains a stable and reliable signal link in a complex and ever-changing electromagnetic environment, effectively avoiding signal attenuation or interruption caused by environmental interference, thus improving the success rate and stability of drug label signal acquisition.

[0009] Meanwhile, by comprehensively utilizing dynamic time slot allocation strategy, hybrid anti-collision strategy and group reading strategy during the reading process, the system can dynamically optimize the reading method according to the actual number and distribution characteristics of tags. It can efficiently complete tag recognition while taking into account both throughput in high-concurrency scenarios and accuracy in low-concurrency scenarios, thus providing complete and accurate initial results for subsequent data processing.

[0010] Furthermore, by employing a multi-dimensional dynamic deduplication strategy, the initial results undergo cross-dimensional deduplication and fusion processing, which enables intelligent identification and merging of redundant data from multiple dimensions, effectively eliminating duplicate records and thus improving the uniqueness and accuracy of the final deduplicated event records.

[0011] The technical solution of this application enables the system to stably acquire signals from RFID tags set on drugs in complex environments through end-to-end collaborative optimization from signal acquisition to data processing. It also performs efficient and accurate deduplication and fusion of the original reading results, ultimately achieving high reliability, high fidelity and data uniqueness in drug information reading, thus providing a solid data foundation for accurate traceability and management of drugs throughout their entire life cycle. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] in: Figure 1 This is a flowchart of radio frequency adaptive acquisition and multi-dimensional deduplication reading of drug information in one embodiment; Figure 2 This is a flowchart of radio frequency adaptive acquisition and multi-dimensional deduplication reading of drug information in one embodiment; Figure 3 This is a flowchart of radio frequency adaptive acquisition and multi-dimensional deduplication reading of drug information in one embodiment; Figure 4 This is a flowchart of radio frequency adaptive acquisition and multi-dimensional deduplication reading of drug information in one embodiment; Figure 5 This is a block diagram of a radio frequency adaptive acquisition and multi-dimensional deduplication reading system for drug information in one embodiment. Detailed Implementation

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] The radio frequency adaptive acquisition and multi-dimensional deduplication reading system for drug information disclosed in this application includes a backend platform, an RFID reader (hereinafter referred to as the reader), and RFID tags (hereinafter referred to as tags) attached to the drugs. The backend platform is communicatively connected to the RFID reader and is used to receive and process the read data reported by the RFID reader, and to issue control commands and configuration parameters. In actual deployment, the backend platform can be a local server deployed in a hospital pharmacy or central drug warehouse, or a virtualized service platform deployed in the cloud or hospital information center; this application does not impose specific limitations on this.

[0016] In the drug management scenario applicable to the embodiments of this application, the RFID reader / writer can be deployed at key nodes in the entire lifecycle of the drug. To achieve accurate traceability of the entire process of drug entry, exit, storage, dispensing, and use, at least one RFID reader / writer needs to be configured, and an RFID tag attached to the smallest sales package or individual drug unit is required. The RFID tag stores a unique identifier for the drug, which includes, but is not limited to, a combination of the drug's generic name, specifications, production batch number, and individual unit serial number.

[0017] Depending on the drug management scenario and deployment location, the radio frequency adaptive acquisition and multi-dimensional deduplication reading system for drug information in this application can be further subdivided into several typical deployment modes: Mode 1 (Drug Storage Unit Deployment Mode): The RFID reader / writer is integrated inside the drug storage unit. The drug storage unit employs a metal shielding structure to form a Faraday cage shielding structure, confining the RFID signal within the cabinet to prevent signal leakage and misreading. The drug storage unit includes a cabinet and a door that covers the cabinet. Conductive material (such as conductive foam or metal springs) is installed at the contact point between the door and the cabinet to eliminate signal leakage at the gap when the door is closed. The inner wall of the cabinet is lined with electromagnetic wave absorbing material (such as ferrite or absorbing sponge) to suppress signal reflection and standing wave formation inside the cabinet, improving reading stability. The drug storage unit also includes a door status sensor to detect the door's open / closed status in real time. The RFID reader / writer is communicatively connected to the door status sensor to dynamically adjust its transmission power based on the open / closed status signal collected by the sensor. In this mode, the radio frequency identification reader is mainly used to perform real-time inventory and location monitoring of medicines stored in the cabinet, as well as to record the outbound and usage information of medicines when they are opened to retrieve them.

[0018] Mode Two (Deployment Mode for Critical Business Nodes): The RFID readers are deployed in a fixed or mobile manner at critical business nodes in the drug circulation process, such as drug receiving and inspection stations, outpatient pharmacy dispensing windows, inpatient pharmacy dispensing stations, operating room pharmacy rooms, or ward ambulances. In this mode, the RFID readers are mainly used to read drugs passing through the node in batches, recording business operations such as drug receipt, dispensing, dispensing, verification, or replenishment. The RFID readers can be desktop, handheld, or channel-type to adapt to different operating scenarios and throughput requirements.

[0019] In any of the above modes, the system is configured to: control the RFID reader to acquire signals from the RFID tag based on a dynamic environment adaptive RFID signal acquisition strategy; read the acquired signals to obtain initial results based on a dynamic time slot allocation strategy, a hybrid anti-collision strategy, and a group reading strategy; and perform deduplication and fusion processing on the initial results based on a multi-dimensional dynamic deduplication strategy to obtain deduplication event records.

[0020] By adopting a dynamic environment adaptive radio frequency signal acquisition strategy, the RFID reader can sense changes in the current radio frequency field environment in real time (such as electromagnetic interference generated by WiFi, Bluetooth, microwave ovens, or medical devices), and dynamically adjust the operating frequency and transmission power accordingly. This ensures a stable and reliable signal link in complex and ever-changing electromagnetic environments, effectively avoiding signal attenuation or interruption caused by environmental interference, thus improving the success rate and stability of drug label signal acquisition.

[0021] Meanwhile, by comprehensively utilizing dynamic time-slot allocation strategies, hybrid anti-collision strategies, and group reading strategies during the reading process, the system can dynamically optimize the reading method based on the actual number and distribution characteristics of tags. For example, when the cabinet door of the medicine storage unit is closed, the number of tags inside is large and dense, and the system can automatically switch to the dynamic time-slot ALOHA algorithm to improve throughput; when the cabinet door is open for single-item medicine retrieval, the number of active tags inside is small, and the system can switch to the binary search tree algorithm to achieve accurate reading. Furthermore, by reading the pre-written grouping identifiers in the tags based on medicine category or usage frequency attributes, the system can prioritize reading tags for emergency medicines or high-frequency medicines, thus efficiently completing tag identification while balancing throughput in high-concurrency scenarios and accuracy in low-concurrency scenarios, providing complete and accurate initial results for subsequent data processing.

[0022] Furthermore, by employing a multi-dimensional dynamic deduplication strategy, the initial results undergo cross-dimensional deduplication and fusion processing. For example, the system first performs initial deduplication using the unique identifier of the drug as the primary key, eliminating data repeatedly reported due to the same tag during a single read. Subsequently, based on the geographical location node where the read event occurred (e.g., "inpatient pharmacy dispensing station") and business logic (e.g., "drugs transitioning from the warehousing state to the dispensing state"), the system merges read records of the same drug generated at different nodes, forming a complete state transition trajectory. Finally, by combining the operator's identity information, read events that do not conform to the double-check or authorized operation process are filtered out. This intelligently identifies and merges redundant data from multiple dimensions, effectively eliminating duplicate records and improving the uniqueness and accuracy of the final deduplicated event records.

[0023] The technical solution of this application enables the system to stably acquire signals from RFID tags placed on drugs in complex electromagnetic environments through end-to-end collaborative optimization from signal acquisition to data processing. It also performs efficient and accurate deduplication and fusion of the original reading results, ultimately achieving high reliability, high fidelity and data uniqueness in drug information reading, thus providing a solid data foundation for accurate traceability and management of drugs throughout their entire life cycle.

[0024] This application is particularly suitable for medical application scenarios with stringent requirements for drug traceability accuracy, data accuracy, and operational compliance, such as the "five-specialized" management of anesthetic drugs and Class I psychotropic drugs, the secondary warehouse management of high-value consumables, real-time temperature monitoring and accurate inventory counting of cold chain drugs, and the management of drug base and expiration date warning in special areas such as operating rooms and ICUs.

[0025] Please see Figure 1This application provides a method for radio frequency adaptive acquisition and multi-dimensional deduplication reading of drug information. The method is applied to a system for radio frequency adaptive acquisition and multi-dimensional deduplication reading of drug information. The system includes: a radio frequency identification reader, a back-end platform, and a radio frequency identification tag set on the drug. The back-end platform is communicatively connected to the radio frequency identification reader. The method includes: S1: Based on a dynamic environment adaptive radio frequency signal acquisition strategy, control the radio frequency identification reader to acquire the signal of the radio frequency identification tag; S2: Based on the dynamic time slot allocation strategy, hybrid anti-collision strategy and group reading strategy, the acquired signal is read to obtain the initial result; S3: Based on a multi-dimensional dynamic deduplication strategy, the initial results are deduplicated and fused to obtain deduplicated event records.

[0026] Dynamic environment adaptive RF signal acquisition strategy: This refers to the control mechanism by which the RFID reader dynamically adjusts its operating frequency and transmission power based on the real-time perceived characteristics of the current environmental spectrum. For example, when the reader detects interference signals such as WiFi (Wireless Fidelity) or Bluetooth in the current frequency band, it automatically switches to the backup frequency with the least interference and adjusts the RFID reader's transmission power in real time based on the strength indication value and signal-to-noise ratio of the RFID tag's returned signal to ensure the stability of the signal link.

[0027] The signal from the RFID tag refers to the radio frequency (RF) echo signal returned to the RFID reader via backscatter coupling after the RFID tag responds to an instruction from the reader. For example, when the RFID reader sends an interrogation command, the passive tag attached to the medicine packaging (i.e., the RFID tag) uses the received RF energy to activate its own circuitry and modulates the unique identification information of the medicine stored in the RFID tag's chip onto the reflected wave for transmission back. The data source for this signal is the baseband data output after demodulation by the RFID reader, and its quality is quantified by the signal strength indicator and signal-to-noise ratio.

[0028] Dynamic time slot allocation strategy: This refers to the time slot allocation mechanism in which the RFID reader dynamically adjusts the frame length of each reading round based on the estimated number of tags to be read. For example, when the RFID reader estimates the number of tags to be read in the current radio frequency field to be N using short command statistics, the algorithm's built-in mathematical model calculates the theoretically optimal number of time slots M, minimizing the tag collision probability. M is then used as the frame length for the next reading round for tag identification, while the subsequent frame length is dynamically fine-tuned based on the collision rate during the actual reading process.

[0029] Hybrid anti-collision strategy: This refers to an anti-collision mechanism in which the RFID reader intelligently switches between deterministic and probabilistic algorithms based on the number of tags to be read in the current radio frequency field. For example, when the number of tags to be read is less than a preset threshold, the system uses a binary search tree algorithm for deterministic tag identification to ensure accuracy. When the number of tags to be read is greater than or equal to the preset threshold, the system switches to the dynamic time-slot ALOHA (Additive Links On-line Hawaii Area) algorithm for probabilistic identification to improve read throughput in high-concurrency scenarios.

[0030] Grouped reading strategy: This refers to the mechanism by which an RFID reader acquires the group identifier stored in the RFID tag during the reading process and reads the tags in batches according to a preset group priority. For example, when medicines are put into storage, the system writes a group identifier to a specific storage area of ​​the RFID tag based on the medicine's category or usage frequency. In a medication retrieval scenario in an operating room, the RFID reader carries the group identifier as a filtering condition when sending instructions, only waking up and prioritizing the reading of RFID tags in high-frequency medication groups, while other RFID tags remain dormant.

[0031] Initial result: This refers to the set of tag data obtained by the RFID reader after completing the identification and data reading of all tags to be read within the current radio frequency field, without deduplication. For example, in a batch reading scenario for drug warehousing, the RFID reader sequentially reads multiple RFID tags using dynamic time slot allocation and a hybrid anti-collision strategy, summarizing the unique drug identification and associated information of each successfully read tag to form the initial result.

[0032] Multi-dimensional dynamic deduplication strategy: This refers to a data processing method where the backend platform uses the unique identifier of the drug, geographical location node, business logic, and operator identity information as dimensions to perform layer-by-layer deduplication, fusion, and filtering of the initial results. For example, it first uses the combination of the drug's generic name, specification, production batch number, and single-unit serial number as the primary key for precise deduplication and status verification. Then, it merges duplicate records based on the geographical location node where the reading event occurred and business logic such as prescriptions. Finally, it filters events that do not meet compliance requirements by combining preset operation procedures such as dual-person operation.

[0033] Deduplication event record: This refers to the drug information record generated by the backend platform after sequentially performing deduplication of the drug's unique identifier, fusion of geographical location nodes and business logic, and filtering of operator identity compliance for the initial results. It uniquely corresponds to a single, genuine business operation. For example, when an anesthetic drug is issued from a "secondary pharmacy," it may generate several initial results from multiple reads by multiple readers within a short period. After processing by a multi-dimensional dynamic deduplication strategy, these results are merged into a single unique record containing the drug's identifier, issuance time, issuance node, and operator information. This deduplication event record will serve as the final basis for drug lifecycle traceability and inventory status updates.

[0034] In step S1, the radio frequency identification reader is controlled to scan the signal strength within the preset working frequency band to generate an environmental noise spectrum diagram. The interference signal is analyzed to obtain the interference source analysis result. Based on the result, the frequency point with the least interference is dynamically selected as the target frequency point. The signal strength indicator value and signal-to-noise ratio of the acquired signal are monitored in real time. The signal quality is evaluated based on the signal strength indicator value and signal-to-noise ratio. When the signal quality is lower than the preset threshold, the transmission power is increased. When the signal strength is too high, the transmission power is reduced, thereby completing the signal acquisition.

[0035] In step S2, a short command is sent to count the number of responses to estimate the number of tags to be read in the current radio field. When the number of tags to be read is less than a preset threshold, a binary search tree algorithm is used for deterministic reading. When the number is greater than or equal to the preset threshold, a dynamic time slot ALOHA algorithm is used for probabilistic reading. During the reading process, the group identifier stored in the tag is obtained. The tags are grouped and compared according to the preset group priority. The tag data that is successfully read is used as the initial result.

[0036] In step S3, the unique identification identifier of the drug stored in the RFID tag is extracted from the initial result. The drug details and current status are retrieved from the database using the identifier as the primary key. Anomaly filtering is performed to obtain the first intermediate result. Then, deduplication and fusion processing is performed based on the geographical location node and business logic corresponding to the first intermediate result to obtain the second intermediate result. Finally, the operator's identity information in the second intermediate result is bound to the drug reading data. Compliance verification is performed according to the preset operation process, and reading events that do not conform to the process are filtered to obtain deduplicated event records.

[0037] This embodiment employs a dynamic environment adaptive radio frequency signal acquisition strategy, enabling the RFID reader to sense changes in the current radio frequency field environment in real time and dynamically adjust the signal acquisition parameters accordingly. This maintains a stable and reliable signal link in complex and ever-changing electromagnetic environments, effectively avoiding signal attenuation or interruption caused by environmental interference, thus improving the success rate and stability of drug label signal acquisition.

[0038] Meanwhile, by comprehensively utilizing dynamic time slot allocation strategy, hybrid anti-collision strategy and group reading strategy during the reading process, the system can dynamically optimize the reading method according to the actual number and distribution characteristics of tags. It can efficiently complete tag recognition while taking into account both throughput in high-concurrency scenarios and accuracy in low-concurrency scenarios, thus providing complete and accurate initial results for subsequent data processing.

[0039] Furthermore, by employing a multi-dimensional dynamic deduplication strategy, the initial results undergo cross-dimensional deduplication and fusion processing, which enables intelligent identification and merging of redundant data from multiple dimensions, effectively eliminating duplicate records and thus improving the uniqueness and accuracy of the final deduplicated event records.

[0040] The technical solution of this embodiment achieves high reliability, high fidelity and data uniqueness in drug information reading through end-to-end collaborative optimization from the signal acquisition end to the data processing end. This enables the system to stably acquire signals from RFID tags placed on drugs in complex environments and efficiently and accurately deduplicate and fuse the original reading results. As a result, it provides a solid data foundation for accurate traceability and management of drugs throughout their entire life cycle.

[0041] Please see Figure 2 In one embodiment, the step of controlling the RFID reader to acquire signals from the RFID tag based on a dynamic environment adaptive RFID signal acquisition strategy includes: S11: Control the radio frequency identification reader to scan the signal strength within the preset working frequency band, generate an environmental noise spectrum diagram, analyze the interference signal based on the environmental noise spectrum diagram, obtain the interference source analysis result, and dynamically select the frequency point with the least interference as the target frequency point based on the interference source analysis result; S12: Based on the target frequency, control the RFID reader to collect the signal of the RFID tag, and monitor the signal strength indicator value and signal-to-noise ratio of the signal collected by the RFID tag in real time; S13: When the signal strength indication value is lower than the first threshold, or the signal-to-noise ratio is lower than the second threshold, the transmit power of the radio frequency identification reader is increased according to the preset power increase configuration until the signal strength indication value or the signal-to-noise ratio meets the preset quality conditions. S14: When the signal strength indication value is higher than the third threshold, the transmit power of the radio frequency identification reader is reduced according to the preset power reduction configuration.

[0042] Preset operating frequency band: This refers to the frequency range pre-configured for scanning and communication by the RFID reader before performing signal acquisition tasks. For example, in an UHF RFID system, the preset operating frequency band is 860MHz to 960MHz, and the reader scans the signal strength of each frequency point within this band in fixed steps. The data source for this preset operating frequency band is the operating frequency range pre-configured by the system according to RFID technology standards and reader hardware specifications.

[0043] An environmental noise spectrum diagram is a spectral energy distribution map generated by an RFID reader after scanning the signal strength at various frequency points within a preset operating frequency band in fixed steps. For example, the reader measures the received signal strength at each frequency point in the 860-960MHz band in 200kHz steps, and plots the frequency and corresponding signal strength values ​​as a continuous spectrum curve, forming a visual map reflecting the current distribution of electromagnetic interference in the environment.

[0044] Interference signals refer to non-target radio frequency signals emitted by external wireless devices that exist within the preset operating frequency band of the RFID reader and interfere with the RFID tag signals. For example, in a hospital environment, the radio frequency signals emitted by WiFi devices, Bluetooth devices, microwave ovens, and high-frequency electrosurgical units, when their strength exceeds a preset threshold, can overwhelm or interfere with the weak echo signals returned by the tags.

[0045] Interference source analysis results refer to the identification of the main interference source type and its attribute information based on the frequency band characteristics where the signal strength in the environmental noise spectrum continuously exceeds a preset threshold, combined with a preset interference source feature library. For example, when the signal strength of a certain frequency band in the environmental noise spectrum is continuously higher than -80dBm and its center frequency matches the center frequency of the WiFi channel, the system determines that the interference source type is WiFi interference and further analyzes its channel information.

[0046] Target frequency: This refers to the current operating frequency that the RFID reader locks onto after analyzing interference sources from a candidate frequency set and conducting actual communication quality tests. For example, the system generates a candidate frequency set by excluding heavily occupied frequency bands based on the identified interference source types, then evaluates the measured quality indicators such as the bit error rate of each candidate frequency point by sending test commands, and finally selects the frequency point with the highest comprehensive score as the target frequency point.

[0047] Signal strength indication value: This refers to the measured strength of the signal returned by the tag to the RFID reader. It characterizes the communication link quality and distance between the tag and the reader. For example, each time the reader reads a tag, it simultaneously records the signal strength indication value. When this value is below -65dBm, the tag is considered to be in a weak signal area; when the value is above -30dBm, the signal is considered too strong. The data source for this signal strength indication value is the quantized signal strength output from the automatic gain control (AGC) stage in the RFID reader's receiving link.

[0048] Signal-to-noise ratio (SNR): This refers to the ratio of the effective signal power received by an RFID reader to the noise power, used to characterize the degree of signal interference from the environment. For example, when reading a tag, the reader extracts the tag echo signal power using digital signal processing technology, while simultaneously measuring the noise floor power within the receiving frequency band, calculating the ratio as the SNR. When this ratio is below 15dB, the signal quality is considered insufficient for stable demodulation. The SNR data source is the signal-to-noise power ratio output by the signal processing module in the RFID reader's receiving channel.

[0049] The first threshold is a critical value used to determine whether the signal strength indication value returned by the RFID tag is below the lower limit of the acceptable range. For example, the system may pre-set -65dBm as the first threshold. When the signal strength indication value collected by the reader is lower than this value, the tag is determined to be in a weak signal area, and stable reading cannot be guaranteed. The data source for this first threshold is a parameter pre-configured by the system based on the reader's receiving sensitivity and the signal stability requirements of the application scenario.

[0050] The second threshold is a critical value used to determine whether the signal-to-noise ratio (SNR) of the RFID tag's returned signal is below the acceptable range. For example, the system may pre-set 15 dB as the second threshold. When the SNR of the signal demodulated by the reader is lower than this value, it is determined that the ambient noise is too high, and the tag signal is difficult to demodulate accurately. The data for this second threshold comes from parameters pre-configured by the system based on the minimum SNR requirements of the reader's demodulation algorithm and the characteristics of the on-site electromagnetic environment.

[0051] The third threshold is the upper limit used to determine whether the signal strength indication value returned by the RFID tag is too high. For example, the system may pre-set -30dBm as the third threshold. When the signal strength indication value collected by the reader exceeds this value, it is determined that the tag is too close or the signal is too strong. In this case, it is unnecessary to maintain high-power transmission, and the transmission power can be appropriately reduced to avoid receiver link saturation, reduce unnecessary energy consumption, and minimize radiation impact on operators. The data source for this third threshold is a parameter pre-configured by the system based on the maximum linear input range of the reader's receiver link, electromagnetic radiation safety standards, and energy-saving requirements.

[0052] Preset quality conditions refer to the combination of signal strength indicator value and signal-to-noise ratio (SNR) threshold value set by the RFID reader to determine that the current signal quality meets the requirements for stable reading. For example, the system may pre-configure a signal strength indicator value of no less than -65dBm and an SNR of no less than 15dB as quality compliance conditions. When the actual acquired signal meets both conditions simultaneously, the current transmit power is deemed sufficient to ensure stable reading. The data source for these preset quality conditions is the threshold parameters pre-configured by the system based on the reader's hardware performance and application scenario requirements.

[0053] Power increase configuration: This refers to a set of control parameters that allow the RFID reader to gradually increase its transmission power in preset increments when the signal quality is deemed insufficient. For example, the system may pre-set an increase of 0.5 dBm as the power increase step and set a maximum transmission power limit. When the signal quality does not meet the preset quality conditions, the reader will gradually increase its transmission power according to this step value. The data source for this power increase configuration is the power adjustment parameters pre-configured by the system based on the reader's power amplifier characteristics and electromagnetic compatibility requirements.

[0054] Power reduction configuration refers to a set of control parameters that cause the RFID reader to gradually reduce its transmission power in preset increments when the signal strength is deemed too strong. For example, the system may pre-set a power reduction step of 0.5 dBm and a minimum transmission power limit. When the signal strength indication value exceeds a third threshold, the reader will gradually reduce its transmission power according to this step value. The data source for this power reduction configuration is the power adjustment parameters pre-configured by the system based on the reader's power amplifier characteristics and energy-saving requirements.

[0055] In step S11, the radio frequency identification reader is controlled to scan the signal strength within the preset working frequency band, and the collected signal strength data of each frequency point is used to generate an environmental noise spectrum. The interference signal is analyzed based on the frequency band characteristics where the signal strength in the spectrum continuously exceeds the preset threshold, and the interference source analysis result is obtained. Based on the analysis result, the frequency point with the least interference is dynamically selected as the target frequency point.

[0056] In step S12, the operating frequency of the reader is configured according to the determined target frequency point, the radio frequency identification reader is controlled to collect the signal of the radio frequency identification tag, and the signal strength indication value and signal-to-noise ratio of the tag return signal are monitored in real time during the collection process, which serve as the basis for subsequent power adjustment.

[0057] In step S13, when the detected signal strength indicator value is lower than the first threshold or the detected signal-to-noise ratio is lower than the second threshold, it is determined that the current signal quality is insufficient. The transmit power of the radio frequency identification reader is gradually increased according to the preset power increase configuration until the signal strength indicator value or signal-to-noise ratio meets the preset quality conditions, so as to ensure that the signal can be stably demodulated.

[0058] In step S14, when the detected signal strength indicator value is higher than the third threshold, it is determined that the current signal is too strong. The transmit power of the radio frequency identification reader is reduced according to the preset power reduction configuration to avoid receiving link saturation and unnecessary energy consumption and radiation caused by strong signals.

[0059] This embodiment controls the RFID reader to scan a preset operating frequency band to generate an environmental noise spectrum map. This allows the system to perceive the interference distribution characteristics of the current radio frequency field in real time and dynamically select the target frequency point with the least interference, thereby actively avoiding external interference sources such as WiFi and Bluetooth, and establishing a stable communication link for subsequent signal acquisition. Based on this, the system monitors the signal strength indication value and signal-to-noise ratio of the tag's returned signal in real time. When the signal quality does not meet the preset quality conditions, the transmission power is dynamically increased to ensure the success rate of reading weak signal tags; when the signal is too strong, the transmission power is appropriately reduced to reduce energy consumption and radiation, achieving refined power control with on-demand power supply. Therefore, through the closed-loop adjustment of "spectrum sensing - frequency point selection - power adaptation," this application enables the RFID reader to maintain a stable and reliable signal link in complex and changing electromagnetic environments, significantly improving the success rate and stability of drug tag signal acquisition.

[0060] In one embodiment, the step of dynamically selecting the frequency point with the least interference as the target frequency point based on the interference source analysis results includes: S111: Generate a candidate frequency point set based on the interference source analysis results and the preset interference source and frequency band mapping library; S112: Based on the analysis results of the interference sources, determine the interference mode of the current environment; S113: If the interference mode includes a single mode, then the frequency hopping strategy is determined based on the time characteristics of the interference source analysis results. If the time characteristics are continuous interference, then the frequency hopping strategy is determined to be active frequency hopping. If the time characteristics are pulse interference, then the interval of the pulse interference is obtained, and the communication quality of the current frequency point is evaluated within the interval. If the communication quality meets the preset conditions, then the current frequency point of the RFID reader is taken as the target frequency point. If the communication quality does not meet the preset conditions, then the frequency hopping strategy is determined to be passive frequency hopping. S114: If the interference mode includes a mixed mode, then predict the interference source combination mode at the next moment according to the interference mode, and determine the frequency hopping strategy according to the predicted interference source combination mode. S115: When the frequency hopping strategy is active frequency hopping or passive frequency hopping, the communication quality of the candidate frequency points in the candidate frequency point set is measured to obtain the measured quality index. Based on the measured quality index and the preset comprehensive scoring model, the optimal frequency point is selected from the candidate frequency point set as the target frequency point.

[0061] The pre-built interference source and frequency band mapping library refers to a pre-constructed dataset that stores the correspondence between various interference source types and their characteristic frequency bands. For example, this library records information such as the 2.4GHz frequency band and specific channel center frequency corresponding to WiFi interference, the 2.4GHz ISM (Industrial, Scientific and Medical) frequency band corresponding to Bluetooth interference, the 2450MHz center frequency and harmonic frequency band corresponding to microwave oven interference, and the operating frequency band corresponding to medical equipment interference. The data source for this pre-built interference source and frequency band mapping library is a parameter library pre-configured by the system based on wireless communication standards and the spectral characteristics of medical equipment.

[0062] Candidate frequency set: This refers to a collection of multiple alternative operating frequencies selected from preset operating frequency bands based on interference source analysis results, which can be used by RFID readers. For example, based on the identified WiFi interference source type, the system selects 3 to 5 frequencies with relatively low interference from the idle frequency bands between its channels as the candidate frequency set. The candidate frequency set includes the following data: the frequency value of each candidate frequency; the ambient noise floor corresponding to each candidate frequency; the interference source type associated with each candidate frequency and the weight adjustment coefficient determined based on the interference source analysis results. Specifically, when the interference source analysis results include WiFi interference, the offset after avoiding the center frequency is recorded; when Bluetooth interference or microwave oven interference is included, the weight value after harmonic frequency band downweighting is recorded; when medical device interference is included, the weight value after excluding conflicting frequency bands and upweighting is recorded; and the historical success rate of each candidate frequency.

[0063] Interference mode: This refers to the classification of interference status based on the types and combinations of interference sources present in the current environment. For example, when only a single type of interference source, such as WiFi interference, exists in the environment, the interference mode is a single mode; when multiple interference sources, such as WiFi interference and microwave oven interference, exist simultaneously in the environment, the interference mode is a mixed mode.

[0064] Single mode: This refers to an interference state in which only one type of interference source exists in the current radio frequency environment. For example, when only the WiFi signal strength is continuously detected exceeding a preset threshold in the ambient noise spectrum, and no other types of interference sources are identified, the system determines that the current interference mode is single mode.

[0065] Mixed mode: This refers to the interference state in the current radio frequency environment where multiple types of interference sources coexist. For example, when both continuous WiFi interference signals and periodic microwave oven pulse interference signals are detected simultaneously in the ambient noise spectrum, the system determines the current interference mode to be a mixed mode. The data source for this mixed mode is the interference source analysis results that include two or more types of interference sources.

[0066] Temporal characteristics refer to the sustained pattern of interference signals in the time domain, used to distinguish between persistent and pulsed interference. For example, WiFi signals typically exhibit a characteristic of continuously occupying the frequency band, while high-frequency electrosurgical signals exhibit a pulsed characteristic that appears periodically or suddenly. The data source for this temporal characteristic is the duration and interval pattern of the interference signal obtained by the RFID reader after performing time-series analysis on the ambient noise spectrum.

[0067] Persistent interference refers to interference signals that persist over time without significant intervals. For example, signal interference generated by a WiFi access point continuously transmitting beacon frames, with the signal strength remaining stable over a long period. The data source for this persistent interference is the temporal characteristic of the signal strength consistently exceeding a preset threshold in the environmental noise spectrum without significant periodic interruptions.

[0068] Pulse-type interference refers to interference signals that appear periodically or suddenly with distinct intervals. For example, the interference signal generated when a microwave oven is operating appears periodically, while a high-frequency electrosurgical unit generates sudden pulse interference during surgical procedures. The data source for this pulse-type interference is the temporal characteristics of the signal intensity in the environmental noise spectrum, which shows periodic or sudden peaks with distinct intervals.

[0069] Interval period: This refers to the interference-free time window between two consecutive interference pulses. For example, in a microwave oven's operating cycle, the interference signal lasts for several seconds followed by an interval of several seconds, during which the radio frequency field environment is relatively clean. The data for this interval period comes from the interference pulse interval duration calculated by the system after timing sampling of the pulse interference signal.

[0070] The system captures the start and end times of interference pulses through hardware interrupts and uses a preset timer function to accurately calculate the interval period to ensure that all frequency switching operations are completed within the interval period.

[0071] Current frequency: This refers to the operating frequency that the RFID reader is currently using. For example, during frequency hopping decisions, the system needs to assess whether the current frequency can still maintain stable communication. If the communication quality of the current frequency meets the requirements during the pulse interference interval, the current frequency will be maintained without switching. The data source for this current frequency is the configuration value of the RFID reader's current operating register.

[0072] Communication quality refers to the comprehensive performance indicators of RFID communication at a specific frequency, including parameters such as bit error rate (BER) and read success rate. For example, the system evaluates the BER of candidate frequencies by sending test commands; a lower BER indicates better communication quality. The data source for this communication quality is the measured quality indicators obtained from actual communication quality measurements of candidate frequencies.

[0073] Frequency hopping strategy: refers to the decision rules determined by the RFID reader based on the interference mode and time characteristics, used to guide frequency selection and switching. For example, an active frequency hopping strategy is used for persistent interference, a passive frequency hopping strategy is used for pulse interference, and a predictive frequency hopping strategy is used for mixed modes.

[0074] Active frequency hopping refers to a frequency hopping strategy where, when an RFID reader detects persistent interference, it actively switches from its current frequency to a frequency with a higher overall score from a pool of candidate frequencies. For example, when persistent WiFi interference is detected on the current frequency, the system immediately triggers a frequency switch to quickly escape the continuously occupied frequency band.

[0075] Passive frequency hopping refers to a frequency hopping strategy where, when an RFID reader detects pulse interference and the communication quality of the current frequency does not meet requirements, it switches frequencies after the interference pulse has passed. For example, if pulse interference causes the bit error rate of the current frequency to be too high, and the system determines that switching is necessary after evaluating the communication quality within the interval, it will wait for the current pulse to end before switching to a candidate frequency. The data source for this passive frequency hopping is the frequency switching decision triggered by the system based on the communication quality evaluation results within the pulse interference interval.

[0076] Communication quality testing refers to the process by which an RFID reader performs actual communication tests on each frequency point in a candidate frequency set before frequency hopping to obtain true quality indicators. For example, before switching to a candidate frequency point, the system does not perform a formal read operation but instead sends a set of test commands to a reference tag and calculates the bit error rate of the returned signal. The data source for this communication quality testing includes the bit error rate, read success rate, and other indicator data collected after conducting actual communication tests on the candidate frequency points.

[0077] Measured quality metrics refer to quantified performance parameters obtained through actual communication quality measurements of candidate frequency points. For example, after the system sends a test command on a candidate frequency point, the bit error rate of the signal returned by the reference tag is used as a measured quality metric. The data source for this measured quality metric is the actual communication performance parameters recorded during the communication quality measurement process.

[0078] Preset conditions: These refer to a pre-defined set of threshold conditions used to determine whether the communication quality of the current frequency point is acceptable. For example, the system pre-sets a bit error rate below 0.1% and a read success rate above 95% as acceptable communication quality conditions. When the measured bit error rate and read success rate of the current frequency point simultaneously meet these conditions during the interval of pulse interference, the communication quality is determined to meet the preset conditions, and the current frequency point is maintained without switching. The data source for these preset conditions is the quality threshold parameters pre-configured by the system based on the reader / writer hardware performance and the application scenario's requirements for communication stability.

[0079] The pre-built comprehensive scoring model refers to a mathematical model pre-constructed for multi-dimensional weighted scoring of candidate frequency points. For example, this model integrates multiple indicators such as ambient noise floor, interference source type weight, measured bit error rate, and historical success rate to calculate a comprehensive score for each candidate frequency point. The data source for this pre-built comprehensive scoring model is the scoring algorithm and weight parameters pre-configured by the system based on historical communication data and frequency point quality assessment requirements. The comprehensive scoring model adopts a weighted linear regression model structure. Input data includes ambient noise floor, interference source type weight, measured bit error rate, and historical success rate; output data is the comprehensive score for each candidate frequency point. The comprehensive scoring model is trained using supervised learning, using historical frequency point quality data as training samples and actual communication success rate as the label. The least squares method is used to fit the weight coefficients of each input indicator, minimizing the error between the model output and the actual communication quality. The weight coefficients of the comprehensive scoring model can be obtained through offline experimental data, trained using the least squares method or other machine learning methods. Frequency points with high historical success rates are given higher weights in the comprehensive scoring model; frequency points with high error rates are given lower weights in the comprehensive scoring model.

[0080] For example: Overall score = w1 (1 / Environmental noise floor noise) + w2 (1 - weight of interference source type) + w3 (1 - measured bit error rate) + w4 Historical success rate, where the ambient noise floor in the formula is the normalized ambient noise floor (dBm); the model weight coefficients w1, w2, w3, and w4 are determined through offline supervised learning, for example, by collecting 500 sets of measured data of each candidate frequency point under different electromagnetic environments as training samples, using the actual communication success rate as the label, and fitting the weight coefficients using the least squares method.

[0081] In the pre-defined comprehensive scoring model, w1, w2, w3, and w4 are the weight coefficients corresponding to the ambient noise floor, interference source type weight, measured bit error rate, and historical success rate, respectively. The ambient noise floor reflects the static background interference level of the frequency point, the interference source type weight reflects the degree of influence of the frequency point on interference sources such as WiFi, Bluetooth, microwave ovens, or medical devices, the measured bit error rate characterizes the real-time communication quality of the current frequency point, and the historical success rate reflects the long-term stability of the frequency point in past use. The determination of the above weights can be pre-set based on electromagnetic compatibility theory and radio frequency communication principles (such as emphasizing interference avoidance and real-time quality in medical scenarios), or the optimal combination can be obtained by performing multiple linear regression or machine learning training (such as least squares method) on measured data under different electromagnetic environments. It can also be adaptively adjusted according to the operating scenario to ensure that the comprehensive scoring model can accurately evaluate the comprehensive quality of each candidate frequency point.

[0082] Optimal frequency point: refers to the frequency point with the highest score after comprehensive scoring of each candidate frequency point by a preset comprehensive scoring model. For example, the system calculates the comprehensive score for each of the three frequency points in the candidate frequency point set, selects the frequency point with the highest score as the optimal frequency point, and locks it as the new working frequency point.

[0083] In step S111, based on the interference source type identified in the interference source analysis results and the preset interference source and frequency band mapping library, differentiated processing is performed on the preset working frequency band: when the interference source analysis results include WiFi interference, the channel information of WiFi interference is parsed to obtain the center frequency, and frequency points are selected from the preset working frequency band by adopting a strategy of avoiding the center frequency; when the interference source analysis results include Bluetooth interference or microwave oven interference, its harmonic frequency band is calculated based on the power intensity of the interference source, and frequency points falling into the harmonic frequency band in the preset working frequency band are filtered after weight reduction processing; when the interference source analysis results include medical device interference, frequency points that conflict with the working frequency band of medical device interference are excluded from the preset working frequency band, and frequency points far away from the working frequency band are filtered after weight increase processing, thereby generating a candidate frequency point set.

[0084] Downweighting: When a frequency is adversely affected by an interference source, its weight is reduced. For example, when microwave oven interference is detected, frequencies within its harmonic band are downweighted, causing the system to tend to avoid these frequencies during frequency hopping.

[0085] Weighting: When a frequency point is far from the interference source or can avoid interference, its weight is increased. For example, when interference from medical equipment is detected, frequencies far from its operating frequency band are weighted, so that the system prioritizes these safer and cleaner frequencies when hopping frequencies.

[0086] When the interference source analysis results include Bluetooth interference or microwave oven interference, harmonic frequency bands are calculated based on their power intensity. The weights of frequency points falling into these harmonic frequency bands within the candidate frequency set are multiplied by a weighting factor of the first data to reduce their probability of selection. The first data is greater than 0 and less than 1. The first data can be determined based on limited experiments and is not limited here.

[0087] When the interference source analysis results include interference from medical devices, frequency points that conflict with the operating frequency band of the medical device interference are excluded from the candidate frequency point set. Furthermore, the weights of frequency points in the candidate frequency point set that are far from the operating frequency band of the medical device interference are increased by a weighting factor of a second data point to enhance their selection priority. The second data point is greater than 0 and less than 100%. The second data point can be determined based on limited experiments and is not limited here.

[0088] In step S112, the interference mode of the current environment is determined based on the number and combination of interference source types identified in the interference source analysis results: if the interference source analysis results contain only one type of interference source, the interference mode is determined to be a single mode; if the interference source analysis results contain two or more types of interference sources, the interference mode is determined to be a mixed mode.

[0089] In step S113, when the interference mode is a single mode, the frequency hopping strategy is determined based on the time characteristics of the interference source analysis results: if the time characteristics of the interference source analysis results are continuous interference, the frequency hopping strategy is determined to be active frequency hopping, which instructs subsequent steps to immediately perform frequency switching; if the time characteristics of the interference source analysis results are pulse interference, the interval of pulse interference is obtained from the interference source analysis results, and the communication quality of the current frequency point is evaluated by sending test commands within the interval period. If the communication quality meets the preset conditions, the frequency hopping strategy is determined to maintain the current frequency point; if the communication quality does not meet the preset conditions, the frequency hopping strategy is determined to be passive frequency hopping.

[0090] In step S114, when the interference mode is a mixed mode, based on the time characteristics and historical occurrence patterns of multiple interference sources in the interference source analysis results, a hidden Markov model or recurrent neural network is used to perform time-series pattern learning on the historical interference source sequence. The interference source type, signal strength, and timestamp at the current moment are used as inputs to predict the probability distribution of the interference source combination mode at the next moment. Based on the interference source combination mode with the highest probability in the prediction results, the corresponding frequency hopping strategy is matched from the preset frequency hopping strategy mapping table.

[0091] For example, when the interference mode is a mixed mode, a first-order discrete hidden Markov model (HMM) is used to predict the combination mode of interference sources at the next time step. The observation sequence is defined as 0 = {o1, o2, ..., ot}, where ot is the observation feature vector at time t (i.e., the current time step), and the observation feature vector includes the signal strength and type encoding of each interference source. The hidden state sequence is defined as S = {s1, s2, ..., st}, where st represents the combination mode of interference sources at time t (i.e., the current time step) (e.g., "WiFi only", "WiFi + Bluetooth", "WiFi + microwave oven", etc.). Using a forward-backward algorithm (e.g., the Baum-Welch algorithm), the model parameters λ = (A, B, π) are trained with 2000 historical samples to obtain the state transition probability matrix A and the observation probability matrix B. During real-time prediction, the observed feature vector ot at the current moment is input into the trained HMM model, decoded by the Viterbi Algorithm, and outputs the hidden state s{t+1} with the highest probability at the next moment, which is the predicted combination mode of interference sources. Based on this mode, the corresponding frequency hopping strategy is matched from the preset frequency hopping strategy mapping table (for example, when the prediction is "WiFi + microwave oven" mode, a hybrid strategy of passive frequency hopping + active frequency hopping is matched).

[0092] The Baum-Welch algorithm is a parameter training method for Hidden Markov Models (HMMs). It learns state transitions and observation probabilities from the observation sequence through expectation maximization (EM) iteration.

[0093] In step S115, the frequency switching decision is executed in response to the frequency hopping strategy: when the frequency hopping strategy is to maintain the current frequency, the current frequency is taken as the target frequency; when the frequency hopping strategy is active frequency hopping or passive frequency hopping, test commands are sent to each candidate frequency in the candidate frequency set and the bit error rate and read success rate of the returned signal are calculated as measured quality indicators. The measured quality indicators, along with the environmental noise floor, interference source type weight, and historical success rate of the candidate frequency, are input into a preset comprehensive scoring model. The model outputs a comprehensive score for each candidate frequency, and the candidate frequency with the highest comprehensive score is selected as the target frequency.

[0094] This embodiment generates a candidate frequency set based on interference source analysis results and a pre-defined interference source-frequency band mapping library. This enables the system to quickly eliminate heavily occupied frequency bands, providing a high-quality candidate set for subsequent frequency selection. Based on this, a frequency hopping strategy is intelligently determined according to the interference mode and time characteristics: for persistent interference, active frequency hopping is used to quickly escape the persistently occupied frequency band; for pulse interference, communication quality is evaluated during the interval before deciding whether to hop frequency to avoid unnecessary frequent switching; for mixed modes, the next interference source combination is predicted to switch to the optimal frequency in advance. Furthermore, by conducting actual communication quality measurements on the candidate frequencies and combining this with a comprehensive scoring model to select the optimal frequency, the frequency selection is ensured to be based on measured quality rather than solely relying on historical data. Therefore, through a closed-loop mechanism of "interference mode identification - frequency hopping strategy decision - measured quality verification," this application achieves a leap from passive fixed frequency points to active intelligent frequency hopping, enabling the RFID reader to always operate at the optimal frequency in complex dynamic electromagnetic environments, significantly improving the stability of signal acquisition and anti-interference capabilities.

[0095] In one embodiment, the step of generating a candidate frequency point set based on the interference source analysis results and a preset interference source and frequency band mapping library includes: S1111: Generate the candidate frequency point set based on the interference source analysis results and the preset interference source and frequency band mapping library; S1112: When the interference source analysis result includes WiFi interference, the channel information of the WiFi interference is parsed according to the interference source analysis result to obtain the center frequency, and the candidate frequency point set is updated by adopting a strategy to avoid the center frequency. S1113: When the interference source analysis result includes Bluetooth interference or microwave oven interference, the harmonic frequency band is calculated based on the power intensity of the Bluetooth interference or the power intensity of the microwave oven interference in the interference source analysis result, and the weight of the frequency points falling into the harmonic frequency band is reduced in the candidate frequency point set. S1114: When the interference source analysis result includes medical device interference, according to the interference source analysis result, frequency points that conflict with the working frequency band of the medical device interference are excluded from the candidate frequency point set, and the weights of frequency points far away from the working frequency band of the medical device interference are increased in the candidate frequency point set.

[0096] WiFi interference refers to radio frequency signals emitted by wireless local area network (WLAN) devices that interfere with the signals of RFID tags. For example, in a hospital environment, wireless access points continuously transmit beacon frames, and if the signal strength consistently exceeds a preset threshold near a specific channel center frequency, it can overwhelm or interfere with the weak echo signals returned by the tags. The data source for this WiFi interference is the type of interference and its channel information identified by the RFID reader after analyzing the environmental noise spectrum.

[0097] Center frequency: This refers to the frequency value at the center of the frequency band occupied by the interfering signal, used to characterize the main operating frequency location of the interference source. For example, when WiFi interference occurs in the 2.4GHz band, the center frequency of channel 1 is 2412MHz, the center frequency of channel 6 is 2437MHz, and the center frequency of channel 11 is 2462MHz. This center frequency data comes from the frequency value obtained by the RFID reader after analyzing the frequency points corresponding to the signal intensity peaks in the environmental noise spectrum.

[0098] Bluetooth interference refers to radio frequency signals emitted by Bluetooth devices during operation that interfere with RFID tag signals. For example, in a hospital environment, devices such as Bluetooth beacons and Bluetooth headsets operate in the 2.4GHz ISM band, and their frequency-hopping communication mode generates wideband interference signals. The data source for this Bluetooth interference is the interference type and its power intensity information identified by the RFID reader after analyzing the environmental noise spectrum.

[0099] Microwave oven interference refers to radio frequency signals generated by microwave ovens during operation that interfere with RFID tag signals. For example, when a microwave oven in a hospital rest area or cafeteria is operating, the center frequency of the radiation generated by its magnetron is approximately 2450MHz, accompanied by multiple harmonic components. The data source for this microwave oven interference is the interference type and its power intensity information identified by the RFID reader after analyzing the environmental noise spectrum.

[0100] Harmonic frequency band: This refers to the frequency range in which integer multiples of the fundamental frequency signal of the interfering source fall, resulting from nonlinear distortion. For example, a microwave oven operates at approximately 2450MHz; its second harmonic frequency is approximately 4900MHz, and its third harmonic frequency is approximately 7350MHz. These harmonic components may fall within the operating frequency band of RFID readers, causing latent interference. The data for this harmonic frequency band is dynamically calculated by the system based on the power intensity of the interfering source in the interference source analysis results and a preset harmonic calculation model.

[0101] Medical equipment interference refers to radio frequency signals emitted by specialized medical equipment during operation that interfere with the signals of RFID tags. For example, high-frequency electrosurgical units used in operating rooms generate strong electromagnetic radiation over a wide frequency band, while X-ray machines and MRI machines also produce electromagnetic interference in specific frequency bands during operation. The data source for this medical equipment interference is the RFID reader analyzing the environmental noise spectrum for interference sources, combined with a pre-set medical equipment interference feature database to identify the interference type and its operating frequency band.

[0102] A medical device interference feature library can be built through offline learning or online updates. For example, the system can pre-collect electromagnetic interference spectra generated by various medical devices during operation as training samples, and build an interference source classification model based on support vector machines or neural networks to achieve online identification of medical device interference types.

[0103] In step S1111, the system queries the corresponding characteristic frequency band information from the preset interference source and frequency band mapping library according to the interference source type identified in the interference source analysis results, performs preliminary screening on the preset working frequency band, excludes the frequency points occupied by the interference source, and forms an initial candidate frequency point set.

[0104] In step S1112, the system analyzes the channel center frequency of the identified WiFi interference based on the data corresponding to the WiFi interference in the interference source analysis results. It prioritizes retaining frequency points far from the center frequency in the candidate frequency point set and adjusts the weight ranking of the frequency points in the candidate frequency point set so that the frequency point with the least interference gets higher priority.

[0105] In step S1113, the system calculates the frequency range in which the harmonic components may fall based on the power intensity of the interference source in the interference source analysis results and the preset harmonic attenuation model. The system reduces the comprehensive score weight of the candidate frequency points that fall into the harmonic frequency band, thereby reducing their priority in subsequent frequency point selection.

[0106] In step S1114, based on the operating frequency band information of medical equipment interference in the interference source analysis results, frequency points that conflict with the operating frequency band are directly removed from the candidate frequency point set, and the weight of frequency points that are far away from the operating frequency band is increased to ensure that the operating frequency of the RFID reader and the operating frequency band of the key medical equipment maintain a safe distance.

[0107] This embodiment generates a candidate frequency set based on interference source analysis results and a pre-defined interference source-frequency band mapping library. Differentiated frequency filtering and weight adjustment strategies are applied to WiFi interference, Bluetooth interference, microwave oven interference, and medical device interference, enabling the system to address each type of interference source based on its spectral characteristics, harmonic properties, and safety priorities. The center frequency avoidance strategy for WiFi interference accurately bypasses continuously occupied communication channels; the harmonic band weighting strategy for Bluetooth or microwave oven interference proactively prevents latent harmonic interference; and the conflicting frequency band exclusion and safe frequency band weighting strategy for medical device interference ensures that the RFID system does not interfere with the normal operation of critical medical equipment. Therefore, through these differentiated processing steps, the generated candidate frequency set not only eliminates directly conflicting frequency bands but also predicts and avoids potential interference, providing a higher-quality and more reliable candidate set for subsequent frequency selection. This significantly improves the signal acquisition stability and electromagnetic compatibility of the RFID reader in complex medical environments.

[0108] In one embodiment, the method further includes: When the interference source analysis results include medical device interference, the interference intensity of the medical device interference is detected. When the interference intensity exceeds a preset interference threshold, the transmission power of the RFID reader is reduced until the transmission power is lower than the target power. The target power is an upper limit of the transmission power that the RFID reader can use to prevent the medical device from interfering with its normal operation, which is dynamically determined based on the operating frequency band and interference intensity of the medical device interference.

[0109] Interference intensity refers to the power of the interference source signal measured at the receiver of the RFID reader, used to quantify the impact of the interference source on the RFID communication link. For example, the electromagnetic radiation intensity generated by a high-frequency electrosurgical unit during operation can reach tens of milliwatts, and when its signal strength continuously exceeds -70 dBm, it will significantly drown out the weak echo signal returned by the tag. The data source for this interference intensity is the signal strength value measured in the corresponding frequency band when the RFID reader performs interference source analysis on the environmental noise spectrum.

[0110] Preset interference threshold: This refers to a pre-set threshold used to determine whether interference with medical equipment has reached a level that may affect the operation of the RFID reader or interfere with the normal operation of the medical equipment. For example, based on the electromagnetic compatibility standards of the medical equipment and the hardware specifications of the RFID reader, the system pre-sets -65dBm as the interference intensity threshold. When the interference intensity exceeds this value, a power adjustment process is triggered. The data source for this preset interference threshold is a parameter pre-configured by the system according to the electromagnetic radiation safety regulations of the medical equipment and the performance parameters of the reader.

[0111] Transmit power: This refers to the power of the radio frequency signal radiated outward by the RFID reader through its antenna. It is used to control the activation distance of the tag and the signal coverage area. For example, in normal inventory mode, the reader uses a transmit power of 30dBm to cover the entire interior of the cabinet, and reduces it to 20dBm when the cabinet door is open to cover only the open area. This transmit power data comes from the output value of the power amplifier in the RFID reader's transmit link and is dynamically configured by the system according to a power adjustment algorithm.

[0112] Target power: This refers to the upper limit of the transmit power of the RFID reader / writer, dynamically determined based on the operating frequency band and interference intensity of the medical equipment, to ensure that the RFID reader / writer does not interfere with the normal operation of the medical equipment. For example, when a high-frequency electrosurgical unit is detected operating in the operating room, the system calculates that the transmit power of the RFID reader / writer must not exceed 25dBm based on its operating frequency band (approximately 200kHz to 5MHz) and the interference intensity (-60dBm) to ensure sufficient frequency separation between the two devices and that the RFID signal will not cause electromagnetic interference to the medical equipment. The data source for this target power is the upper limit of power dynamically calculated by the system based on the operating frequency band and interference intensity of the medical equipment interference and a preset electromagnetic compatibility model.

[0113] The target power P_target is calculated using the model: P_target = min(P_base, P_limit). Here, min is the minimum calculated value, and P_base is the upper limit of the base power. It is obtained from a pre-defined frequency band-power mapping table based on the frequency interval Δf = |f_dev - f_RFID| between the operating frequency band of the medical device (f_dev) and the current operating frequency band of the RFID reader (f_RFID). This frequency band-power mapping table is constructed based on electromagnetic compatibility standards: if Δf ≤ 5MHz, then P_base = 20dBm; if 5 < Δf ≤ 10MHz, then P_base = 25dBm; if Δf > 10MHz, then P_base = 30dBm.

[0114] P_limit is the upper limit of the correction based on the interference intensity, calculated as: P_limit = P_base - (I_dev - I_th) / 3. Where I_dev is the detected interference intensity of the medical device (dBm), and I_th is the preset interference threshold (dBm). (I_dev - I_th) / 3 represents a linear inverse correction where the power limit decreases by 1dB for every 3dB the interference intensity exceeds the threshold.

[0115] After identifying interference from medical devices, the system continuously monitors the signal strength of the interference source measured at the receiver of the RFID reader. When the signal strength exceeds the preset interference threshold, the system determines that the transmission power of the RFID reader may cause electromagnetic interference to the medical devices. The system dynamically calculates the upper limit of the transmission power that will not interfere with the normal operation of the medical devices based on the operating frequency band and interference intensity of the medical devices, and uses it as the target power. The system then gradually reduces the transmission power of the RFID reader until the current transmission power is lower than the target power.

[0116] First, based on the frequency interval between the operating frequency band of the medical device and the current operating frequency band of the RFID reader, the baseline power limit is looked up from a preset frequency-power mapping table—the larger the frequency interval, the higher the allowed transmission power. Second, the baseline power limit is adjusted according to the interference intensity; for example, for every 3dBm increase in interference intensity, the power limit is reduced by 1-2dBm, forming an inverse correction relationship. The final target power is the smaller of the baseline power limit and the correction value, ensuring that the radiation intensity of the RFID signal in the medical device's frequency band is always below its electromagnetic immunity threshold.

[0117] For example, based on the frequency band-power mapping table, if the frequency interval is greater than 10MHz, the upper limit of the basic power is 30dBm; if the interval is between 5-10MHz, the upper limit is 25dBm; and if the interval is less than 5MHz, the upper limit is 20dBm.

[0118] Optionally, the target power is calculated as min(base power upper limit, base power upper limit - (interference intensity - threshold) / 3). min: a minimum value function, meaning the target power is the smaller of the "base power upper limit" and "base power upper limit - (interference intensity - target threshold) / 3" to ensure the transmission power does not exceed a safe range. This formula uses a linear inverse proportional correction mechanism to dynamically reduce the target power as the interference intensity increases, thereby ensuring that the RFID reader's transmission power always remains within a range that allows for safe coexistence with medical devices.

[0119] Basic power limit: The initial power limit value is obtained from the preset frequency band-power mapping table based on the frequency interval between the operating frequency band of the medical equipment interference and the current operating frequency band of the RFID reader. The larger the frequency interval, the higher the basic power limit. The unit is dBm.

[0120] Interference intensity: The power value of the interference signal from the medical device detected in real time by the RFID reader, measured in dBm, reflecting the radiation intensity of the interference source to the current environment.

[0121] Target threshold: A preset interference intensity trigger threshold in dBm. When the interference intensity exceeds this value, the system determines that a power reduction operation needs to be performed. This threshold is preset according to the electromagnetic immunity standards of medical devices and the electromagnetic compatibility requirements of RFID readers.

[0122] This embodiment dynamically reduces the transmit power of the RFID reader / writer until it falls below a target power when the interference intensity from medical equipment exceeds a preset interference threshold. This allows the system to proactively reduce its own radiation intensity after identifying interference from critical medical equipment. Since an interference intensity exceeding the preset threshold indicates the presence of a strong electromagnetic interference source in the RFID reader / writer's operating environment and that the RFID system's own transmit power may affect the medical equipment, dynamically determining the target power upper limit based on the operating frequency band and interference intensity of the medical equipment interference ensures that the RFID reader / writer's transmit power remains within a safe range for coexistence with the medical equipment. This avoids electromagnetic interference from the RFID system to the normal operation of critical medical equipment such as high-frequency electrosurgical units and X-ray machines. Therefore, through this interference intensity-driven dynamic transmit power suppression mechanism, this application ensures both the stability of RFID signal acquisition and the safety of medical equipment use and the reliability of clinical operations.

[0123] Please see Figure 3 In one embodiment, the step of reading the acquired signal based on a dynamic time slot allocation strategy, a hybrid anti-collision strategy, and a group reading strategy to obtain an initial result includes: S21: Send a short command, count the number of random response signals received corresponding to the short command, use the number of responses as the response count, and estimate the number of tags to be read in the current radio frequency field based on the response count; S22: When the number of tags to be read is less than a preset threshold, a binary search tree algorithm is used to deterministically read the RFID tags; S23: When the number of tags to be read is greater than or equal to the preset number threshold, the dynamic time-slot ALOHA algorithm is used to read the radio frequency identification tags probabilistically; S24: When using the dynamic time-slot ALOHA algorithm to probabilistically read the RFID tag, the optimal number of time slots is calculated based on the number of tags to be read, and the RFID tag is read using the optimal number of time slots as the frame length. The proportion of collision time slots to total time slots during the reading process is counted as the collision rate, and the frame length of the next round of reading is dynamically adjusted based on the collision rate. S25: During the reading process, the group identifier stored in the RFID tag is obtained. According to the group identifier, the RFID tag is read in groups using a preset group priority. The reading results are compared and verified by group to obtain the verification result. The group identifier is pre-written according to the drug category attribute or usage frequency attribute. S26: Based on the verification result, the tag data of the successfully read RFID tag is taken as the initial result.

[0124] Short command: This refers to a brief command sent by the RFID reader before actual reading, used to quickly estimate the number of tags in the RFID field. For example, the reader sends an interrogation command without requiring complete data reading, only triggering the tags to return a random response signal. The data source for this short command is a pre-set instruction set of the system, used to obtain a preliminary estimate of the number of tags before the anti-collision process is initiated.

[0125] Random response signal: This refers to a short echo signal generated by an RFID tag according to probabilistic rules after receiving a short command from a reader, used to indicate its presence to the reader. For example, after receiving a short command, the tag returns a response containing a temporary identifier within a preset time slot with a certain probability. The data source for this random response signal is the response decision generated by the tag's internal random number generator.

[0126] Response count: This refers to the total number of random response signals received by the RFID reader within a preset time window after sending a short command. For example, the reader waits 50ms after sending a short command and records the number of response signals returned by all tags during this period. This response count data comes from the counting statistics of tag echo signals received by the reader's receiving channel.

[0127] Number of tags to be read: This refers to the total number of RFID tags that need to be identified in the current radio frequency field, used to guide the selection and parameter configuration of anti-collision strategies. For example, in a batch reading scenario for pharmaceuticals entering a warehouse, it is estimated that there are approximately 200 tagged pharmaceuticals in the current area. The data source for this number of tags to be read is the estimated total number of tags calculated by the system based on the number of responses using a preset mathematical model.

[0128] Preset threshold: This refers to a pre-defined threshold number of tags used to distinguish between low-concurrency and high-concurrency scenarios. For example, the system pre-configures 20 tags as a threshold; when the number of tags to be read is less than 20, it is considered a low-concurrency scenario, and when it is greater than or equal to 20, it is considered a high-concurrency scenario. The data source for this preset threshold is a parameter pre-configured by the system based on the performance intersection of the binary search tree algorithm and the dynamic time-slotted ALOHA algorithm.

[0129] Binary Search Tree (BST) algorithm: This is a deterministic collision avoidance algorithm that recursively splits the search space by sending bit query commands layer by layer, based on the conflicting bit information returned by the tags, ultimately accurately identifying the unique identifier of each tag. For example, after the reader sends a query command, it determines the bitmask for the next query based on the bit conflict information returned by the tags, gradually narrowing the identification range until a tag is uniquely identified. The data source for this BST algorithm is a pre-built collision avoidance algorithm library in the system, used to ensure 100% recognition accuracy when the number of tags is small.

[0130] Dynamic Slotted ALOHA Algorithm: This is a probabilistic anti-collision algorithm that dynamically adjusts the frame length (number of time slots) to adapt to the number of tags to be identified. Tags randomly select time slots within a frame to transmit data, thereby reducing the probability of collisions and improving channel utilization. For example, when the estimated number of tags is 100, the system sets the frame length to 100 time slots. Tags randomly select responses within each time slot, and the reader statistically analyzes the status of each time slot and dynamically adjusts the frame length for the next round based on collision data. The data source for this Dynamic Slotted ALOHA algorithm is a pre-built anti-collision algorithm library used to maximize read throughput when the number of tags is large.

[0131] Optimal number of time slots: This refers to the theoretically optimal frame length value calculated based on the estimated number of tags to be read, minimizing the tag collision probability and maximizing channel utilization. For example, when the number of tags to be read is N, the system calculates the optimal number of time slots as M≈N through a mathematical model, minimizing the overall loss between collision and idle time slots. The data source for this optimal number of time slots is the frame length configuration value dynamically calculated by the system based on the tag number and collision probability model.

[0132] Frame length: This refers to the total number of time slots allocated to the RFID reader during a round of anti-collision reading, used to limit the random selection range of the tag's response. For example, if the system sets the frame length to 256 time slots, the tag will randomly select one time slot between 0 and 255 to respond. The data source for this frame length is the reading parameters dynamically configured by the system based on the optimal number of time slots.

[0133] Collision rate: This refers to the proportion of time slots where tag collisions occur during a single read operation out of the total number of time slots. It quantifies the severity of tag conflicts under the current frame length configuration. For example, in a frame with 100 time slots, if 25 time slots experience collisions, the collision rate is 25%. This collision rate is calculated by the reader based on statistics of the success, idle, and collision states of each time slot.

[0134] Group identifier: This refers to a classification mark pre-written into a specific storage area of ​​the RFID tag to identify the business group to which the drug belongs. For example, when drugs are put into storage, the system writes the corresponding group code to the tag based on the drug category (narcotic drugs, general drugs) or usage frequency (high frequency, medium frequency, low frequency). The data source for this group identifier is the configuration information written by the backend platform during tag initialization based on the drug's category or usage frequency attributes.

[0135] Preset grouping priorities refer to the pre-defined order in which drug groups are read, guiding the execution order of group reading. For example, the system sets "Emergency Drugs Group" as the highest priority, "High-Frequency Drugs Group" as the second highest priority, and "Regular Drugs Group" as the lowest priority. The data source for these preset grouping priorities is a priority mapping table pre-configured by the system based on drug management requirements and business processes.

[0136] Verification results refer to the conformity assessment information obtained after comparing and verifying the results read by group. For example, the system compares the read drug label groups with a preset group list item by item, generating a list of missing drugs, a list of redundant drugs, and a list of drugs in the wrong position. The data source for this verification result is the comparison report generated after comparing the group reading results with the preset group list.

[0137] Tag data refers to drug-related information stored in RFID tags, including unique drug identifiers, group identifiers, and other business attributes. For example, the electronic product code area of ​​the tag stores a unique code combining "generic drug name-specification-batch number-serial number," while the user storage area stores group identifiers and expiration information. This tag data originates from the raw data stored in each storage area of ​​the tag chip and is parsed by the reader after successful reading.

[0138] In step S21, the radio frequency identification reader sends a short command to trigger the tag to return a random response signal in a probabilistic manner. After counting the number of responses, the total number of tags to be read in the current radio frequency field is estimated by a preset tag number estimation model, which is used as the number of tags to be read.

[0139] The tag quantity estimation model is based on probabilistic and statistical principles: after the RFID reader sends a short command, the tags respond randomly with a preset probability p (e.g., 0.25). Let the number of responses be R, then the number of tags to be read, N ≈ R / p. To improve accuracy, the system can send multiple rounds of short commands and take the average, or use maximum likelihood estimation for correction. For example, if the number of responses in one round is 50, and the preset response probability is 0.25, then the estimated N ≈ 200. This model determines the optimal response probability through offline calibration to minimize the estimation error. The basis for selecting the preset probability (e.g., balancing estimation accuracy and response conflicts) is provided, along with simple formulas or steps for multi-round estimation or maximum likelihood estimation.

[0140] For example, with a preset response probability p=0.25, and assuming the number of responses counted within a time window after the RFID reader sends a short command is R, the estimation formula for the number of tags N to be read is: N≈R / p. To improve estimation accuracy, the system continuously executes three rounds of short command statistics, takes the average of the response counts, and substitutes this average as R into the formula to calculate N. If the response counts for the three rounds are 48, 52, and 50 respectively, then the average response count = 50, and the estimated number of tags to be read N = 50 / 0.25 = 200.

[0141] In step S22, when the number of tags to be read is less than a preset threshold, the estimated number of tags to be read is compared with the preset threshold. If it is determined to be a low-concurrency scenario, the binary search tree algorithm is switched to recursively split the search space by sending bit query instructions layer by layer to achieve accurate identification of tags.

[0142] In step S23, when the number of tags to be read is greater than or equal to the preset number threshold, the system compares the estimated number of tags to be read with the preset number threshold. If it is determined to be a high-concurrency scenario, it switches to the dynamic time-slot ALOHA algorithm to achieve fast reading of a large number of tags through a random time-slot allocation mechanism.

[0143] In step S24, when using the dynamic time-slot ALOHA algorithm to probabilistically read the RFID tags, the theoretically optimal number of time slots is calculated based on the estimated number of tags as the initial frame length. During the reading process, the collision rate is monitored in real time, and the frame length of subsequent rounds is dynamically increased or decreased according to the trend of the collision rate, so that the system can quickly converge to the optimal reading state.

[0144] The system throughput is maximized when the frame length (number of time slots) equals the number of tags to be read. Specifically, let the estimated number of tags be N, then the optimal number of time slots M is the integer closest to N. For example, when N=87, M=87. This principle stems from the Poisson distribution model: when M=N, the probability of a successful time slot is highest (approximately 36.8%), and the overall loss of collision and idle time slots is minimized, thus achieving maximum reading efficiency per unit time. The system dynamically configures the frame length parameter accordingly.

[0145] The Poisson distribution model is used to describe the probability distribution of tags randomly selecting time slots. When the frame length (number of time slots) equals the number of tags to be read, the probability of a successful time slot is the highest, and the overall loss from collisions and idle time slots is the lowest, thus achieving the maximum read throughput.

[0146] For example, based on the Poisson distribution model, the read throughput is maximized when the frame length (number of time slots) M is equal to the number of tags to be read N. Therefore, the optimal number of time slots M_opt is calculated as: M_opt = N. When N = 200, the initial frame length M_opt is set to 200.

[0147] The "dynamically adjust the frame length of the next reading based on the collision rate" has the following adjustment function: when the collision rate C is higher than the threshold of 0.3, M_new = M_current (1+C); When the collision rate C is below the threshold of 0.1, M_new=M_current (1-C / 2). For example, if the current frame length M_current=200 and the collision rate C=0.25, then the next frame length M_new=200. (1+0.25)=250.

[0148] In step S25, during the reading process, the group identifier is carried in the reading instruction as a filtering condition. The tags in each group are woken up in turn according to the preset group priority. After the reading is completed, the actual reading result of the group is compared with the preset list of items to be stored, and a verification result is generated.

[0149] A pre-configured list of medications expected to be stored in a specific storage unit (such as a medicine cabinet compartment) or group. For example, a drawer in a fentanyl cabinet might be pre-configured to hold 5 vials of fentanyl injection; this list includes the medication name, specifications, and quantity. The data source is a pre-configured list generated based on prescriptions or inventory settings when medications are received into the warehouse. This list is used to compare with actual readings to verify the accuracy of medication storage.

[0150] In step S26, the tag data that has been successfully read is selected based on the verification results, and the data that failed to be read or failed the verification is removed. The successfully read tag data is then summarized to form an initial result for subsequent deduplication processing.

[0151] This embodiment achieves end-to-end optimization of the RFID tag reading process through the synergistic effect of a dynamic time slot allocation strategy, a hybrid anti-collision strategy, and a group reading strategy. The dynamic time slot allocation strategy dynamically adjusts the frame length based on the number of tags to be read and corrects it in real time according to the collision rate, enabling the system to maximize channel utilization in high-concurrency scenarios. The hybrid anti-collision strategy intelligently switches between deterministic and probabilistic algorithms based on a tag quantity threshold, ensuring recognition accuracy in low-concurrency scenarios and prioritizing reading throughput in high-concurrency scenarios. The group reading strategy reads and compares tags in batches according to preset priorities based on group identifiers, enabling the system to prioritize the identification needs of high-priority drugs and complete compliance verification in real time during the reading process. Therefore, through the deep coupling of these three strategies, this application ensures accurate identification during low-concurrency periods and efficient reading during high-concurrency periods in application scenarios with drastic fluctuations in the number of drug tags. It also achieves hierarchical response and real-time verification based on service priorities, significantly improving the adaptability, efficiency, and accuracy of RFID reading.

[0152] Please see Figure 4 In one embodiment, the step of performing deduplication and fusion processing on the initial result based on a multi-dimensional dynamic deduplication strategy to obtain deduplicated event records includes: S31: Based on the unique drug identification identifier corresponding to the radio frequency identification tag in the initial result, perform a first deduplication process on the initial result to obtain a first intermediate result; S32: Based on the geographical location node and business logic corresponding to the first intermediate result, perform deduplication and fusion processing on the first intermediate result to obtain the second intermediate result; S33: Based on the operator identity information corresponding to the second intermediate result, filter the reading events in the second intermediate result that do not conform to the preset operation process to obtain the deduplication event record.

[0153] A unique identifier for a drug is a combination code stored in the electronic product code area of ​​an RFID tag, used to uniquely identify a single drug. For example, this identifier uses a four-level coding structure of "generic name of drug - specification - production batch number - single-vial serial number", such as "fentanyl injection - 0.1mg - 20250101 - 000123". The data for this unique identifier comes from the solidified information written to the tag by the coding workstation when the drug enters the warehouse.

[0154] The first deduplication process involves filtering redundant data generated in the initial results due to the same label being repeatedly reported during a single read, using the drug's unique identifier as the primary key. For example, if the same drug is reported twice consecutively by the reader during batch reading, the system determines it as duplicate data based on its unique identifier and retains only the record from the first read. The data source for this first deduplication process is the deduplication logic executed by the system based on the database's primary key uniqueness constraints.

[0155] The first intermediate result refers to the initial result after deduplication of the drug's unique identifier. It is used for subsequent integration of geographical location nodes and business logic. For example, the initial result contains 200 read records, which are merged into 180 unique drug-specific records after the first deduplication process.

[0156] Geographic location node: This refers to the physical location identifier of an RFID reading event, used to characterize the business node in the drug circulation process. Examples include "Level 1 Drug Warehouse," "Level 2 Central Pharmacy," "Fentanyl Cabinet," "Operating Room Drug Room," and "Empty Bottle Recycling Device." The data source for this geographic location node is the node identifier pre-configured by the system based on the reader deployment location.

[0157] Business logic refers to the business rules and state transition constraints that should be followed when medicines flow between different geographical locations, guiding the fusion processing of read events. For example, after a medicine leaves the "primary warehouse," it should be transferred to the "secondary pharmacy" for storage; after leaving the "secondary pharmacy," it should be transferred to the "operating room" for use. The state cannot be skipped or reversed. The data source for this business logic is the state machine model and flow rules preset by the system according to the medicine management process.

[0158] The second intermediate result refers to the intermediate result after deduplication and fusion processing based on geographic location nodes and business logic. It is used for subsequent compliance filtering of operator identity information. For example, if the same medication is repeatedly read by multiple nearby readers within a short period, the system merges them into a single valid read record based on the priority of the geographic location nodes. The data source for this second intermediate result is the dataset output by the system after performing spatial deduplication and business association merging on the first intermediate result.

[0159] Operator identity information refers to the operator's identification information obtained through biometric authentication or ID card authentication and linked to the drug reading event. For example, if a pharmacist completes identity authentication via fingerprint recognition before picking up medication, the system will forcibly associate the pharmacist's employee ID with the subsequent drug reading event. The data source for this operator identity information is the identity credential returned by the identity authentication system when the operator logs in or authorizes the transaction.

[0160] Pre-defined operating procedures refer to the pre-established operational norms and compliance requirements in drug management, used to verify the compliance of read events. For example, the retrieval of fentanyl-related drugs requires a two-person operation procedure, meaning that two operators must complete identity authentication before the cabinet can be opened to retrieve the medication. The data source for this pre-defined operating procedure is a pre-configured process rule library based on drug management regulations and hospital rules.

[0161] In step S31, the unique drug identification identifier corresponding to each read record is extracted from the initial result. The identifier is used as the primary key to match the records in the database. Duplicate records with the same identifier are merged or discarded, and only a single valid record is retained to form the first intermediate result.

[0162] In step S32, the geographical location node where each reading event occurs in the first intermediate result is identified. Combined with the preset drug circulation business logic, the events of the same drug being read by multiple nodes in the adjacent spatiotemporal range are spatially deduplicated and state-fused to form a second intermediate result that conforms to the business process.

[0163] In step S33, the drug reading event in the second intermediate result is bound to the operator's identity information. The event is verified for compliance according to the preset operation process. Abnormal events that do not meet the requirements of dual operation, single authorization or operation time limit are filtered out. The events that pass the verification are recorded as deduplicated events.

[0164] This embodiment employs a multi-dimensional dynamic deduplication strategy, performing layer-by-layer deduplication and fusion of the initial results from three dimensions: unique drug identifier, geographic location nodes and business logic, and operator identity information. The first deduplication process uses the unique drug identifier as the primary key for precise matching, eliminating redundant data caused by repeated reporting of the same tag during a single read. The second deduplication process combines geographic location nodes and business logic for spatial deduplication and state fusion, merging records of the same drug repeatedly read by multiple neighboring nodes within a short period into a single valid event, ensuring that state transitions conform to the business process. The third layer filters read events that do not conform to the preset operating procedures based on operator identity information, eliminating abnormal data from a behavioral compliance perspective, ensuring that every record entering the database corresponds to a genuine, legal, and complete business operation. Therefore, through the synergistic effect of these three deduplication mechanisms, this application achieves high fidelity and data uniqueness in drug information reading, providing a reliable data foundation for accurate traceability and management throughout the entire drug lifecycle.

[0165] In one embodiment, the step of performing a first deduplication process on the initial result based on the unique drug identification identifier corresponding to the radio frequency identification tag in the initial result to obtain a first intermediate result includes: S311: Extract the unique drug identification identifier stored in the electronic product code area of ​​the RFID tag from the initial result, wherein the unique drug identification identifier includes a combination code of the generic name, specification, production batch number and single serial number of the drug; S312: Using the unique identifier of the drug as the primary key, retrieve the corresponding detailed drug information and the current status of the drug from the database of the backend platform; S313: Perform anomaly filtering on the initial result based on the retrieved current state of the drug, wherein the anomaly filtering includes: if the current state of the drug does not match the business process to which the current read event corresponding to the current state of the drug belongs, then discard the data in the initial result that does not match the business process; if the current state of the drug matches the business process to which the current read event corresponding to the current state of the drug belongs, then retain the data in the initial result that matches the business process. S314: The initial result after anomaly filtering is used as the first intermediate result.

[0166] The Electronic Product Code (EPC) area refers to the dedicated storage area within the RFID tag chip used to store the unique identification information of an item. Its data structure conforms to the global standard for EPC codes. For example, the EPC area of ​​a tag stores a 96-bit binary code, including a header, manufacturer code, product code, and serial number fields. After parsing, the complete identification information of the drug is obtained. The data source for this EPC area is the fixed data written by the coding workstation during tag initialization.

[0167] Detailed drug information refers to the complete drug profile information stored in the database, linked to the drug's unique identifier. This includes the manufacturer, expiration date, storage conditions, and drug instructions. For example, based on the drug's unique identifier, a search reveals that this vial of fentanyl injection is manufactured by a pharmaceutical company, has an expiration date of December 2026, and requires storage at 2-8℃. This detailed drug information originates from the basic data synchronized from the production management system to the backend platform database when the drug is received into the warehouse.

[0168] Current drug status: This refers to the latest business status identifier of a drug throughout its entire lifecycle, used to characterize the drug's current operability and circulation stage. For example, current drug status includes "Pending warehousing," "In stock," "Dispatched," "Verified," and "Destroyed." The data source for this current drug status is the latest status field recorded in the backend platform database, updated in real time after each business operation.

[0169] The business process to which the current read event belongs refers to the type of business operation and the current stage of the process corresponding to this RFID read event, used for matching and verification with the current status of the medicine. For example, if the read event occurs at the "empty bottle recycling device" node, the corresponding business process is "empty bottle recycling," requiring the current status of the medicine to be "verified" or "used." The data source for the business process to which the current read event belongs is the business type determined by the system based on the geographical location node where the read event occurred and the triggering conditions.

[0170] Anomaly filtering refers to a mechanism that selectively retains or discards read data in the initial result based on the matching result between the current status of the drug and the business process to which the current read event belongs. For example, if a drug with a status of "destroyed" is read in the empty bottle recycling process, the system will discard the read data because its status does not match the recycling business process. The data source for this anomaly filtering is the filtering logic executed by the system based on the status-process matching rules.

[0171] In step S311, the data of the electronic product code area corresponding to each read record in the initial result is parsed, and the combination code of the generic name, specification, production batch number and single serial number of the drug is extracted according to the preset coding rules to form a unique identification of the drug.

[0172] In step S312, the extracted unique drug identifier is used as the query key, and an exact match search is performed in the backend platform database to obtain the detailed drug information and current status associated with the identifier.

[0173] In step S313, the business process to which the current read event belongs (that is, the business process to which the current read event belongs corresponding to the current state of the drug) is obtained, and the retrieved current state of the drug is matched and verified with the business process: if the current state of the drug does not match the requirements of the business process, the read event is determined to be an abnormal event, and the data corresponding to the abnormal event in the initial result is discarded; if they match, the data of the read event in the initial result is retained.

[0174] S314: Summarize all read event data retained after exception filtering to form the first intermediate result, which is used for subsequent integration and processing of geographic location nodes and business logic.

[0175] This embodiment achieves initial quality screening of read data from a business semantic level by retrieving the current status of a drug using its unique identifier as the primary key and performing anomaly filtering based on the business process to which the current read event belongs. Since the unique identifier of a drug uses a combination of "generic name-specification-batch number-serial number," it can uniquely identify a single drug and associate it with its complete lifecycle information. Because the system uses this identifier as the primary key to retrieve the current status of the drug, it can obtain the latest business status of the drug recorded in the database. Because the system matches and verifies the current status of the drug with the business process to which the current read event belongs, data is only retained when the status matches the process. This pre-filters invalid read events caused by abnormal statuses (such as misreading destroyed drugs or duplicate releases of cancelled drugs) before the data enters the subsequent deduplication process. Therefore, through the above-mentioned status-process matching and verification mechanism, this application fundamentally avoids data interference from processed drugs in normal business processes, significantly improving the data quality and business accuracy of drug information reading.

[0176] In one embodiment, the step of filtering read events in the second intermediate result that do not conform to the preset operation process based on the operator identity information corresponding to the second intermediate result to obtain the deduplicated event record includes: S331: Bind the operator's identity information in the second intermediate result with the drug reading data of the current reading event in the second intermediate result to form a human-drug association record. The operator's identity information includes the identity identifier of at least one operator obtained through biometric authentication or identity card authentication. S332: According to the preset operation process, perform process compliance verification on the human-drug association record to obtain the compliance verification result, wherein the preset operation process includes one or more of the following: a two-person operation process, a single-person authorization operation process, and an operation time limit process; S333: When the compliance verification result concludes that the compliance is not compliant, the data in the second intermediate result that concludes that the compliance verification is not compliant will be filtered out and discarded; S334: When the compliance verification result is compliant, the data in the second intermediate result that is compliant is retained, and the operator's identity information is added to the second intermediate result.

[0177] Operator identity information refers to data obtained through biometric authentication or identification card authentication, used to identify the personnel performing drug operations. For example, before picking up medication, a pharmacist completes authentication via fingerprint recognition, and the system records the pharmacist's employee number, name, and authentication time. The data source for this operator identity information is the identity credentials returned by the identity authentication system when the operator logs in or authorizes the process.

[0178] Biometric authentication refers to a technical means of identity verification based on inherent physiological characteristics of the human body. For example, fingerprint recognition, facial recognition, and iris recognition are used to confirm the identity of an operator. The data source for this biometric authentication is the comparison result returned by the biometric data collection terminal connected to the backend platform during operator authentication.

[0179] Identity card authentication refers to a technical means of verifying identity based on a physical card held by an operator. For example, RFID (Radio Frequency Identification) work cards, IC cards, or magnetic stripe cards are swiped on a card reader to confirm the operator's identity. The data source for this identity card authentication is the authentication result reported to the backend platform after the card reader reads the identity identifier stored in the card.

[0180] Current read event: refers to a business instance corresponding to a tag data read operation completed by an RFID reader at a specific point in time. For example, when a pharmacist takes a vial of fentanyl injection from a fentanyl cabinet, the reader reads the drug tag and generates a read record containing the read time, geographical location node, and drug identifier. The data source for this current read event is the original read record reported by the reader after completing the tag read.

[0181] Drug reading data refers to the set of drug-related information read and parsed from the tag by an RFID reader. For example, drug reading data includes the unique drug identifier extracted from the electronic product code area, the group identifier read from the user storage area, and the expiration date information. The data source for this drug reading data is the parsing result output by the reader after successfully reading the tag.

[0182] Person-Drug Association Record: This refers to a data record formed by binding the operator's identity information with the drug reading data of the current reading event, used to trace the responsible party for each drug operation. For example, the system associates pharmacist A's employee number with the label data of the fentanyl injection solution they retrieved, forming a complete record containing the operator, drug identifier, operation time, and operation node. The data source for this person-drug association record is the data structure generated by the system after binding the operator's identity information with the drug reading data.

[0183] Pre-defined operating procedures refer to a set of pre-defined operating specifications and compliance requirements in drug management operations, used to verify the compliance of human-drug association records. For example, the retrieval of fentanyl-related drugs requires a two-person operating procedure, meaning that two operators must complete identity verification before the cabinet can be opened to retrieve the medication. The data source for this pre-defined operating procedure is a pre-configured process rule library in the system based on drug management regulations and hospital rules and regulations.

[0184] The two-person operation procedure refers to the compliance requirements that require two operators to be present simultaneously and complete their respective identity verifications before performing any drug handling. For example, the dispensing of fentanyl-type narcotic drugs requires a doctor and a nurse to complete fingerprint authentication separately, and the system can only authorize the opening of the cabinet after both individuals have passed identity verification. The data for this two-person operation procedure comes from the system's preset process configuration according to the narcotic drug management regulations.

[0185] Single-person authorization operation process: This refers to the compliance requirements that require only one operator to complete identity authentication and obtain authorization before performing drug operations. For example, the issuance of ordinary drugs only requires a single pharmacist to swipe their card for authentication. The data source for this single-person authorization operation process is the process configuration preset by the system according to the drug classification management requirements.

[0186] Operation time limit procedures refer to compliance requirements that mandate medication transactions to be completed within a specified time window. For example, the operating room medication retrieval process requires that the entire process, from opening the cabinet to medication use, must be completed within 30 minutes; exceeding this timeframe necessitates re-verification. The data for these operation time limit procedures comes from time window parameters preset by the system based on business operation specifications.

[0187] Process compliance verification refers to the process of comparing and verifying each item of the human-drug association records according to a preset operating procedure to determine whether the current reading event complies with the prescribed operating specifications. For example, the system verifies whether a fentanyl dispensing event has identity authentication records for both operators A and B, and whether the time interval between the two authentications is within the allowable range. The data source for this process compliance verification is the verification conclusion generated by the system after matching the human-drug association records with the preset operating procedure rules.

[0188] Compliance verification result: This refers to the compliance judgment conclusion obtained after verifying the compliance of the current read event execution process. For example, the verification result includes statuses such as "Dual authentication passed", "Operation time limit exceeded", and "Single operation unauthorized". The data source for this compliance verification result is the Boolean value or status code output by the system according to the verification rules.

[0189] In step S331, the system obtains the identity information of the operator corresponding to each reading event in the second intermediate result, and associates and binds the identity information with the drug reading data of the same event to form a complete record of the traceable operator (that is, the human-drug association record).

[0190] In step S332, the compliance rules configured in the preset operation process are obtained, and the number of operators, identity authentication methods, operation time windows and other elements in the human-drug association record are compared and verified item by item to generate compliance verification results.

[0191] In step S333, when the compliance verification result is non-compliant, the system identifies the read event that fails the compliance verification and removes all data corresponding to the read event from the second intermediate result, without proceeding to the subsequent processing flow.

[0192] In step S334, when the compliance verification result is compliant, the system identifies a read event indicating that the compliance verification result is passed, retains the data of the event, and writes the bound operator identity information as an additional field of the record into the second intermediate result.

[0193] This embodiment achieves precise control over drug operation data from a behavioral compliance perspective by binding operator identity information with drug reading data and filtering out reading events that do not conform to the pre-set operation process after compliance verification. Since operator identity information is obtained through biometric authentication or ID card authentication, the authenticity and non-repudiation of the operator's identity are ensured. Because the system binds operator identity information with drug reading data to form a human-drug association record, each drug operation can be accurately traced to the specific responsible person. Because the system performs compliance verification on the association record according to two-person operation procedures, single-person authorized operation procedures, or operation time limit procedures, only operations that fully comply with pre-set specifications are retained; otherwise, they are directly filtered and discarded. Therefore, through the above-mentioned strong human-drug binding and process compliance verification mechanism, this application ensures the strict implementation of management systems such as two-person operation from both technical and procedural perspectives. Any non-compliant operation cannot generate valid data records, effectively preventing the illegal loss of drugs and operational violations, and providing reliable technical support for the compliant management of drugs throughout their entire lifecycle.

[0194] It is understandable that if both step S13 ("when the signal strength indicator value is lower than the first threshold, or the signal-to-noise ratio is lower than the second threshold, increase the transmission power of the RFID reader according to the preset power increase configuration") and step S5 ("when the interference source analysis result includes medical device interference, detect the interference intensity of the medical device interference, and when the interference intensity exceeds the preset interference threshold, reduce the transmission power of the RFID reader") are triggered simultaneously, the conflict handling rule of "medical device coexistence" taking precedence over "signal quality optimization" means that when strong medical device interference is detected, the power reduction operation is performed first, even if a weak signal tag exists at this time. In the presence of medical device interference, the system prioritizes electromagnetic compatibility, and the adjustment of transmission power is subject to the constraint of not interfering with the normal operation of medical devices. When the signal quality is insufficient, compensation is made through other means (such as adjusting the antenna beam direction or switching to a better frequency).

[0195] Please see Figure 5 This application provides a radio frequency adaptive acquisition and multi-dimensional deduplication system for drug information, the system comprising: Radio frequency identification tag 801 is attached to the drug packaging to store drug information; The RFID reader / writer 802 is communicatively connected to the RFID tag; The backend platform 803 is connected to the radio frequency identification reader.

[0196] The system is configured to implement the steps of the radio frequency adaptive acquisition and multi-dimensional deduplication reading method for drug information described above.

[0197] This embodiment employs a dynamic environment-adaptive RF signal acquisition strategy, enabling the RFID reader to sense changes in the electromagnetic environment in real time and dynamically adjust its operating frequency and transmission power. This maintains a stable and reliable signal link even in complex interference environments, improving the success rate and stability of drug label signal acquisition. Building upon this, by combining a dynamic time slot allocation strategy, a hybrid anti-collision strategy, and a group reading strategy, the system dynamically optimizes the reading method based on the actual number and distribution characteristics of tags. This increases throughput in high-concurrency scenarios and ensures accurate identification in low-concurrency scenarios, providing complete and accurate initial results for subsequent processing. Furthermore, through a multi-dimensional dynamic deduplication strategy, the system identifies and merges redundant data layer by layer, using the drug's unique identifier, geographical location node, business logic, and operator identity information as dimensions. This effectively eliminates duplicate records and improves the uniqueness and accuracy of deduplication event records. Therefore, through end-to-end collaborative optimization from signal acquisition to data processing, this application achieves high reliability, high fidelity, and data uniqueness in drug information reading.

[0198] In one embodiment, the system further includes: a drug storage unit; The drug storage unit adopts a metal shielding structure to form a Faraday cage shielding structure, which is used to confine the radio frequency identification signal inside the cabinet. The drug storage unit includes a cabinet and a cabinet door that covers the cabinet. A conductive material is provided at the contact point between the cabinet door and the cabinet, and the conductive material is used to eliminate signal leakage at the gap when the cabinet door is closed. The inner wall of the cabinet is lined with electromagnetic wave absorbing material to suppress signal reflection and standing wave formation inside the cabinet; The medicine storage unit is also equipped with a cabinet door status sensor, which is used to detect the opening and closing status of the cabinet door in real time. The RFID reader is communicatively connected to the cabinet door status sensor and is used to dynamically adjust the transmission power of the RFID reader based on the switch status signal collected by the cabinet door status sensor. Specifically, when the switch status signal indicates that the cabinet door is closed, the RFID reader uses a first transmission power to read the RFID tag; when the switch status signal indicates that the cabinet door is open, the RFID reader uses a second transmission power to read the RFID tag, and the second transmission power is less than the first transmission power.

[0199] A medicine storage unit is a physical cabinet structure used to store medicines. It integrates an RFID reader and antenna to enable real-time inventory and retrieval records of the medicines inside. Examples include fentanyl cabinets, high-value consumable cabinets, and smart medicine cabinets. The data source for this medicine storage unit is the hardware configured during system deployment; its structure and parameters are preset by the equipment manufacturer.

[0200] Metal shielding structure: This refers to the main body structure of a cabinet constructed using conductive metal materials. It utilizes the reflection and absorption characteristics of metal on radio frequency signals to form electromagnetic shielding. For example, galvanized steel sheet can be used as the cabinet material, with its thickness and conductivity meeting the requirements for radio frequency signal attenuation. The data for this metal shielding structure comes from the specifications of the metal materials selected during equipment manufacturing and the structural design parameters.

[0201] Faraday cage shielding structure: This refers to a closed cavity structure formed by continuous conductive material, which can confine electromagnetic fields inside the cavity and block external electromagnetic fields from entering. For example, the cabinet and door of a pharmaceutical storage unit are made of metal and electrically connected to form a complete Faraday cage, confining RFID signals inside the cabinet. The data for this Faraday cage shielding structure comes from the physical shielding characteristics constituted by the cabinet's metal material, thickness, and joint treatment process.

[0202] Conductive materials: These are elastic materials with good electrical conductivity installed at the contact points between the cabinet door and the cabinet body. They are used to fill gaps and establish continuous electrical contact when the cabinet door is closed. Examples include conductive foam, metal springs, and conductive rubber. The data on conductive materials comes from the type of conductive material selected during equipment manufacturing and the parameters of its installation location.

[0203] Electromagnetic wave absorbing material: This refers to a functional material laid on the inner wall of a cabinet that converts the energy of incident electromagnetic waves into heat energy, thereby suppressing signal reflection and standing wave formation. The data for this electromagnetic wave absorbing material comes from the type, thickness, and placement parameters of the absorbing material selected during equipment manufacturing.

[0204] Cabinet door status sensor: This refers to a sensing device installed between the cabinet door and the cabinet body of a pharmaceutical storage unit to detect the opening or closing status of the cabinet door in real time. Its output signal is directly transmitted to an RFID reader or system controller via an electrical connection. For example, magnetic switches, micro switches, and photoelectric sensors can all be used as cabinet door status sensors. The data source for this cabinet door status sensor is the switching signal output by the sensor when the cabinet door is opened or closed, which is collected in real time by the reader via a wired interface.

[0205] Switch status signal: This refers to the electrical signal output by the cabinet door status sensor, which characterizes the current physical state of the cabinet door. For example, the sensor outputs a high-level signal when the cabinet door is closed and a low-level signal when the cabinet door is open. The data source for this switch status signal is the real-time level change generated by the sensor based on the opening and closing status of the cabinet door.

[0206] First transmit power: This refers to the transmit power value used by the RFID reader when the cabinet door status signal indicates that the cabinet door is closed. For example, in the closed storage state with the cabinet door closed, the reader uses a transmit power of 30dBm to perform a comprehensive inventory of the tags inside the cabinet. The data source for this first transmit power is the power parameters pre-configured by the system based on the cabinet's shielding performance and tag distribution.

[0207] The second transmit power refers to the transmit power value used by the RFID reader when the cabinet door status signal indicates that the cabinet door is open. This value is lower than the first transmit power. For example, when the cabinet door is open, the reader uses a transmit power of 20dBm to cover only the opening area of ​​the cabinet door. The data for this second transmit power comes from the power parameters pre-configured by the system based on signal leakage suppression requirements when the door is open.

[0208] The main body of the cabinet is constructed of metal. Conductive material is installed at the contact point between the cabinet door and the cabinet body. When the cabinet door is closed, the conductive material fills the gap to form a continuous electrical contact, thus constructing a complete electromagnetic shielding cavity.

[0209] Electromagnetic wave absorbing material is laid on the inner wall of the cabinet to convert the radio frequency signal irradiated onto the inner wall into heat energy and dissipate it, thereby reducing the multipath effect and reading blind zone caused by multiple signal reflections.

[0210] A magnetic switch or micro-motion sensor is installed between the cabinet door and the cabinet body to collect the opening and closing status of the cabinet door in real time and output the corresponding switch status signal to the radio frequency identification reader.

[0211] The cabinet door status sensor is connected to the RFID reader via an electrical interface. It converts the cabinet door opening and closing status into a switch signal and outputs it to the reader in real time. After receiving the signal, the reader dynamically adjusts the transmission power according to the preset power control logic: when the signal indicates that the cabinet door is closed, the first transmission power is used to conduct a comprehensive inventory of the tags inside the cabinet; when the signal indicates that the cabinet door is open, the reader switches to the second transmission power for reading, and the second transmission power is less than the first transmission power.

[0212] This embodiment achieves precise spatial constraint and dynamic control of RFID signals through a power adjustment mechanism that links the metal shielding structure, conductive materials, electromagnetic wave absorbing materials, and cabinet door status of the drug storage unit. When the cabinet door is closed, the metal shielding structure and conductive materials form a Faraday cage shielding structure, effectively confining the RFID signal inside the cabinet and preventing signal leakage that could lead to misreading of external tags. The electromagnetic wave absorbing material on the inner wall of the cabinet suppresses signal reflection and standing wave formation, resulting in a more uniform and stable radio frequency field inside the cabinet. The reader dynamically adjusts its transmission power based on the switch status signal collected by the cabinet door status sensor. When the cabinet door is closed, a first transmission power is used to ensure efficient inventory checks inside the cabinet; when the cabinet door is open, a lower second transmission power is used to cover only the opening area, preventing signal leakage when retrieving medication. Therefore, through the synergistic effect of the physical shielding and dynamic power control, this application achieves intelligent linkage between the drug storage unit and the system: "high-power precise inventory checks when the door is closed, low-power medication retrieval when the door is open," eliminating signal crosstalk and misreading problems at the source and significantly improving the data accuracy of drug storage and retrieval.

[0213] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0214] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0215] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0216] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for radio frequency adaptive acquisition and multi-dimensional deduplication of drug information, characterized in that, The method is applied to a radio frequency adaptive acquisition and multi-dimensional deduplication reading system for drug information. The system includes: a radio frequency identification reader, a back-end platform, and a radio frequency identification tag set on the drug. The back-end platform is communicatively connected to the radio frequency identification reader. The method includes: Based on a dynamic environment adaptive radio frequency signal acquisition strategy, the radio frequency identification reader is controlled to acquire the signal of the radio frequency identification tag; the radio frequency identification reader is controlled to scan the signal strength within a preset working frequency band, generate an environmental noise spectrum diagram, and analyze the interference signal based on the environmental noise spectrum diagram to obtain the interference source analysis result. Based on the interference source analysis result, the frequency point with the least interference is dynamically selected as the target frequency point. Based on the interference source analysis results and the preset interference source and frequency band mapping library, a candidate frequency point set is generated; When the interference source analysis results include WiFi interference, the channel information of the WiFi interference is parsed according to the interference source analysis results to obtain the center frequency, and the candidate frequency point set is updated by adopting a strategy to avoid the center frequency. When the interference source analysis results include Bluetooth interference or microwave oven interference, the harmonic frequency band is calculated based on the power intensity of the Bluetooth interference or the power intensity of the microwave oven interference in the interference source analysis results, and the weight of the frequency points falling into the harmonic frequency band is reduced in the candidate frequency point set. When the interference source analysis results include interference from medical devices, frequency points that conflict with the operating frequency band of the medical device interference are excluded from the candidate frequency point set based on the interference source analysis results. Furthermore, the weights of frequency points in the candidate frequency point set that are far from the operating frequency band of the medical device interference are increased. Based on the analysis results of the interference sources, the interference mode of the current environment is determined; If the interference mode includes a single mode, then the frequency hopping strategy is determined based on the time characteristics of the interference source analysis results. If the time characteristics are continuous interference, then the frequency hopping strategy is determined to be active frequency hopping. If the time characteristics are pulse interference, then the interval of the pulse interference is obtained, and the communication quality of the current frequency point is evaluated within the interval. If the communication quality meets the preset conditions, then the current frequency point of the RFID reader is taken as the target frequency point. If the communication quality does not meet the preset conditions, then the frequency hopping strategy is determined to be passive frequency hopping. If the interference mode includes a mixed mode, then the interference source combination mode at the next moment is predicted based on the interference mode, and the frequency hopping strategy is determined based on the predicted interference source combination mode. When the frequency hopping strategy is active frequency hopping or passive frequency hopping, the communication quality of the candidate frequency points in the candidate frequency point set is measured to obtain the measured quality index. Based on the measured quality index and the preset comprehensive scoring model, the optimal frequency point is selected from the candidate frequency point set as the target frequency point. Based on the target frequency, the radio frequency identification reader is controlled to collect the signal of the radio frequency identification tag, and the signal strength indication value and signal-to-noise ratio of the signal collected by the radio frequency identification tag are monitored in real time; When the signal strength indication value is lower than the first threshold, or the signal-to-noise ratio is lower than the second threshold, the transmit power of the radio frequency identification reader is increased according to the preset power increase configuration until the signal strength indication value or the signal-to-noise ratio meets the preset quality conditions. When the signal strength indication value is higher than the third threshold, the transmit power of the radio frequency identification reader is reduced according to the preset power reduction configuration; Based on the dynamic time slot allocation strategy, the hybrid anti-collision strategy and the group reading strategy, the acquired signals are read to obtain the initial results; Based on a multi-dimensional dynamic deduplication strategy, the initial results are deduplicated and fused to obtain deduplicated event records. When the interference source analysis results include medical device interference, the interference intensity of the medical device interference is detected. When the interference intensity exceeds a preset interference threshold, the transmission power of the RFID reader is reduced until the transmission power is lower than the target power. The target power is an upper limit of the transmission power that the RFID reader can use to prevent the medical device from interfering with its normal operation, which is dynamically determined based on the operating frequency band and interference intensity of the medical device interference.

2. The radio frequency adaptive acquisition and multi-dimensional deduplication method for drug information according to claim 1, characterized in that, The steps for reading the acquired signals and obtaining initial results based on the dynamic time slot allocation strategy, hybrid anti-collision strategy, and group reading strategy include: Send a short command, count the number of random response signals received corresponding to the short command, use the number of responses as the response count, and estimate the number of tags to be read in the current radio frequency field based on the response count; When the number of tags to be read is less than a preset threshold, a binary search tree algorithm is used to deterministically read the RFID tags. When the number of tags to be read is greater than or equal to the preset number threshold, the dynamic time-slot ALOHA algorithm is used to read the radio frequency identification tags probabilistically. When using the dynamic time-slot ALOHA algorithm to probabilistically read the RFID tag, the optimal number of time slots is calculated based on the number of tags to be read, and the reading of the RFID tag is performed with the optimal number of time slots as the frame length. The proportion of collision time slots to total time slots during the reading process is counted as the collision rate, and the frame length of the next round of reading is dynamically adjusted based on the collision rate. During the reading process, the group identifier stored in the RFID tag is obtained. Based on the group identifier, the RFID tag is read in groups according to a preset group priority. The reading results are compared and verified by group to obtain the verification result. The group identifier is pre-written according to the drug category attribute or usage frequency attribute. Based on the verification result, the tag data of the successfully read RFID tag is taken as the initial result.

3. The radio frequency adaptive acquisition and multi-dimensional deduplication method for drug information according to claim 1, characterized in that, The steps of performing deduplication and fusion processing on the initial results based on the multidimensional dynamic deduplication strategy to obtain deduplicated event records include: Based on the unique drug identification identifier corresponding to the radio frequency identification tag in the initial result, the initial result is subjected to a first deduplication process to obtain a first intermediate result; Based on the geographical location node and business logic corresponding to the first intermediate result, the first intermediate result is deduplicated and merged to obtain the second intermediate result; Based on the operator's identity information corresponding to the second intermediate result, the reading events in the second intermediate result that do not conform to the preset operation process are filtered to obtain the deduplicated event records.

4. The radio frequency adaptive acquisition and multi-dimensional deduplication method for drug information according to claim 3, characterized in that, The step of performing a first deduplication process on the initial result based on the unique drug identification identifier corresponding to the RFID tag in the initial result to obtain a first intermediate result includes: Extract the unique drug identification identifier stored in the electronic product code area of ​​the RFID tag from the initial result, wherein the unique drug identification identifier includes a combination code of the drug's generic name, specifications, production batch number and single-unit serial number; Using the drug's unique identifier as the primary key, the corresponding detailed drug information and current drug status are retrieved from the database of the backend platform. The initial result is filtered for anomalies based on the retrieved current state of the drug. The anomaly filtering includes: if the current state of the drug does not match the business process to which the current read event corresponding to the current state of the drug belongs, then the data in the initial result that does not match the business process is discarded; if the current state of the drug matches the business process to which the current read event corresponding to the current state of the drug belongs, then the data in the initial result that matches the business process is retained. The initial result after anomaly filtering is used as the first intermediate result.

5. The radio frequency adaptive acquisition and multi-dimensional deduplication method for drug information according to claim 3, characterized in that, The step of filtering read events in the second intermediate result that do not conform to the preset operation process based on the operator identity information corresponding to the second intermediate result to obtain the deduplicated event record includes: The operator's identity information in the second intermediate result is bound to the drug reading data of the current reading event in the second intermediate result to form a human-drug association record. The operator's identity information includes the identity identifier of at least one operator obtained through biometric authentication or identity card authentication. According to the preset operation process, the human-drug association record is subjected to process compliance verification to obtain the compliance verification result. The preset operation process includes one or more of the following: a two-person operation process, a single-person authorization operation process, and an operation time limit process. When the compliance verification result concludes that the compliance is not met, the data in the second intermediate result that concludes that the compliance verification is not met will be filtered out and discarded. When the compliance verification result is compliant, the data in the second intermediate result that is compliant is retained, and the operator's identity information is added to the second intermediate result.

6. A radio frequency adaptive acquisition and multi-dimensional deduplication system for drug information, characterized in that, The system includes: Radio frequency identification (RFID) tags are attached to drug packaging to store drug information. An RFID reader / writer is communicatively connected to the RFID tag; The backend platform is communicatively connected to the RFID reader / writer; The system is configured to implement the steps of the radio frequency adaptive acquisition and multi-dimensional deduplication reading method for drug information as described in any one of claims 1 to 5.

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