A multi-band RFID-based asset intelligent inventory method and system

Abstract

The application discloses an asset intelligent inventory method and system based on a multi-frequency band RFID, relates to the field of data identification, and comprises a binding module, a deployment module and the like. The binding module is used for allocating a globally unique identification code to an asset to be managed, writing the code and asset static information into a multi-frequency band anti-interference RFID tag, and establishing a unique binding relationship between a tag ID and the asset identification code. The deployment module is used for deploying a fixed reader / writer and a mobile terminal compatible with UHF and NFC dual-frequency bands, constructing a global coverage cooperative sensing network, and supporting the reception and transmission of asset identification signals. The application can dynamically optimize scanning frequency and power, adapt to complex environments, improve identification stability, reasonably distribute transmission tasks through a load balancing mechanism, avoid node congestion, and ensure second-level accurate database updating through multi-layer data verification and quality rating.

Description

Technical Field

[0001] This invention relates to the field of data identification technology, specifically to an intelligent asset inventory method and system based on multi-band RFID. Background Technology

[0002] With the ongoing digital transformation of enterprises, various technology application models have emerged in the asset management field, including manual inventory methods relying on paper lists or Excel spreadsheets, barcode / QR code technology requiring close-range, one-to-one scanning, and RFID systems in their initial applications. Among these, manual inventory and barcode / QR code technology have inherent limitations in their operational processes, while existing RFID systems mostly adopt an "offline collection, post-collection" model. Furthermore, ordinary RFID tags are prone to signal attenuation on metal surfaces or in liquid environments, and lack effective integration with core enterprise business systems such as HIS and ERP, making it difficult to meet the diverse needs of modern asset management.

[0003] Patent application No. 202310868131.0 discloses a robot asset inventory method based on multi-sensor fusion. This application aims to solve the problems of "in the prior art, asset inventory is mostly carried out by manually scanning with handheld RFID receivers, and then manually registering the asset information into the computer for comparison. This process is time-consuming, labor-intensive, and prone to missed detection and false detection. At the same time, traditional asset inventory methods lack dynamic management. When there are frequent asset transfers, the inventory personnel are not familiar with the new changes in the asset status, resulting in the industry pain point of unclear fixed asset location and inaccurate asset tag data. The efficiency and reliability of the inventory work are difficult to guarantee, causing management difficulties. Traditional asset inventory methods mostly rely on manual memorization of asset locations. In the production line environment, there are many types of production line equipment, and it is necessary to record the equipment usage time, equipment type and other attributes. In this environment, the distribution range of line assets is wide and the density is high. Once an asset change occurs, the inventory personnel cannot quickly handle the situation, which seriously affects the work efficiency."

[0004] However, the problems of low efficiency and high error rate of manual inventory in traditional asset management, inconvenient operation of barcodes / QR codes, and the lagging data updates, poor environmental adaptability, and information silos of existing RFID systems have not been properly resolved.

[0005] To address this, we propose a method and system for intelligent asset inventory based on multi-band RFID. Summary of the Invention

[0006] In view of the above-mentioned shortcomings of the existing technology, the present invention provides an intelligent asset inventory method and system based on multi-band RFID, which can effectively solve the problems of the existing technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions; This invention discloses an intelligent asset inventory system based on multi-band RFID, comprising: The system comprises four modules: a binding module, a deployment module, and a recording module. The binding module assigns a globally unique identifier to each asset and writes the identifier and static asset information into a multi-band anti-interference RFID tag to establish a unique binding relationship between the tag ID and the asset identifier. The deployment module deploys fixed readers and mobile terminals compatible with both UHF and NFC dual-bands to build a comprehensive collaborative sensing network that supports the reception and transmission of asset identifier signals. The aggregation module captures the identifier codes and associated information sent by the asset RFID tags in real time through the sensing network, simultaneously collecting periodic data from fixed readers and active scanning data from mobile terminals. The update module parses, deduplicates, and verifies the collected identifier data, driving the asset dynamic database to update second-level based on the identifier code. The linkage module uses a standardized interface to enable bidirectional interaction between the dynamic data associated with the asset identifier code and external business systems, supporting information exchange between identifier parsing data and business processes. The recording module integrates multi-band identifier data, performs coarse-grained indoor positioning of assets based on identifier codes using TDOA or RSSI, and simultaneously retains a complete workflow record associated with the identifier code. The binding module is interconnected with the deployment module via a local area network. The deployment module is interconnected with the aggregation module and the update module via a local area network. The aggregation module and the update module are interconnected with the linkage module via a local area network. The linkage module is interconnected with the recording module via a local area network.

[0008] Furthermore, the multi-band anti-interference RFID tag integrates a multi-dimensional environmental sensing unit and a hardware encryption unit. The multi-dimensional environmental sensing unit collects environmental parameters in real time during asset storage and use, and the hardware encryption unit encrypts the core asset information stored on the tag based on a preset encryption algorithm. The encryption verification process is as follows: ; In the formula: This is for encrypting and verifying the response value; For hash functions; A unique identifier for RFID tags; Pre-allocate a dedicated key; M is the current timestamp; M is the verification threshold; This is an XOR operation; The data is the normalized data of environmental parameters collected by the multi-dimensional environmental sensing unit; Only when Information within the tag is only allowed to be read when it matches the verification value pre-calculated by the hardware encryption unit.

[0009] Furthermore, the collaborative sensing network has a built-in dynamic load balancing mechanism that calculates the load coefficient of each read / write node in real time and adjusts the data transmission priority and data forwarding path according to the load coefficient. The load factor ; In the formula: Let be the load factor of the i-th read / write node; These are the weighting coefficients; This represents the number of tags currently being concurrently identified by the i-th node; Let i be the amount of data to be transmitted currently. Let i be the length of the current cache queue. This represents the maximum number of concurrent tags supported for the i-th digit. This represents the single transmission amount corresponding to the i-th maximum data transmission bandwidth. Let i be the maximum capacity of the i-th cache queue; when When the load exceeds the preset threshold, the system automatically diverts the data transmission tasks exceeding the preset load threshold to adjacent low-load nodes.

[0010] Furthermore, the mobile terminal dynamically optimizes the scanning frequency and scanning power based on the signal strength attenuation characteristics of the asset tag and the environmental interference coefficient: ; In the formula: The optimal scanning frequency for the i-th mobile terminal; This is the reference value for the scanning frequency; This is a reference value for the tag signal strength. The strength of the tag signal currently captured by the i-th mobile terminal; The environmental interference coefficient is the location of the i-th mobile terminal. This represents the maximum value of the environmental interference coefficient. Interference adaptation coefficient; The optimal scanning power for the i-th mobile terminal; This is the reference value for scanning power.

[0011] Furthermore, the asset dynamic database is a distributed database used to store static basic information, dynamic status data and full-process transfer records corresponding to asset identification codes in real time. The update module first performs parsing, deduplication, and verification operations on the collected identification data, then performs quality rating on the verified data, and executes a differentiated update strategy based on the rating results: Data parsing: Extract key fields from the identification data, including asset codes, environmental parameters, and timestamps, and convert them into a preset standardized data format; Data deduplication: A deduplication index is built based on the asset identification code and collection timestamp to remove duplicate data collected for the same asset within a preset time window; Data verification: Verify data integrity through hash verification, synchronously verify data validity based on static asset information comparison, and mark abnormal data with format errors or information mismatches; Quality rating: ; In the formula: This represents the quality level value of the i-th identifier data. For quality assessment weighting coefficients; The total number of key fields in the i-th data entry; This represents the total number of key fields. This is the verification error value for the i-th data item; The maximum allowable verification error value; The effective capture duration for the i-th data item; The total scan time for the i-th data item; is the deduplication validity coefficient of the i-th data. It takes a value of 1 when there is no duplicate data, and takes a value according to the proportion of valid data after deduplication when there is duplicate data. when When the quality exceeds the first-level quality threshold, the asset dynamic database is directly driven to perform real-time updates; when... When the data falls between the primary and secondary quality thresholds, a fusion verification is performed using historical data from the same source before updating; when... When the data quality falls below the secondary quality threshold, a secondary data collection is triggered on the mobile terminal or fixed reader until the data quality meets the update requirements.

[0012] Furthermore, the linkage module, through a standardized interface, facilitates bidirectional interaction between the dynamic data associated with the asset identification code and external business systems. It interfaces with different external business systems via adaptive protocol parsing, and the calculation formula is as follows: ; In the formula: The protocol compatibility between the system and the j-th external business system; n is the number of dimensions matching key protocol fields. This is the attribute parameter of the k-th protocol field in the system; This refers to the attribute parameter of the k-th protocol field of the j-th external business system. when When the data exceeds the adaptation threshold, bidirectional data interaction is directly performed through a standardized interface; when... When the data is below the adaptation threshold, the identifier parsing data is format converted, field mapped and rate adapted according to the protocol specifications of the external business system to generate a data format that matches the requirements of the target system, and the data conversion log is stored synchronously.

[0013] Furthermore, the recording module improves the indoor positioning accuracy of assets by fusing phase difference and time difference data from signals received by multiple readers to correct errors in the initial positioning results based on TDOA or RSSI. The formula for calculating the positioning error correction is as follows: ; In the formula: This is the positioning error correction amount; This is the phase difference correction coefficient; is the average phase difference when multiple readers receive the same tag signal; c is the electromagnetic wave propagation speed; f is the carrier frequency of the RFID tag signal; This is the time difference correction factor; This represents the average time difference between multiple readers receiving the same tag signal.

[0014] Furthermore, the multi-band anti-interference RFID tag dynamically adjusts its energy supply strategy based on the tag's remaining battery power and signal transmission requirements: ; In the formula: The real-time energy supply for the tag; The remaining battery level of the tag; For data transmission energy allocation coefficient and active state energy allocation coefficient; This represents the amount of data currently to be transmitted. This refers to the maximum amount of data that a tag can transmit in a single transaction. The duration of activity of a tag within a work cycle; The working cycle of the label; when When the battery level exceeds the preset sufficient power threshold, sufficient power is allocated to data transmission and active status according to the above formula; when... When the battery level is between the sufficient and low thresholds, appropriately reduce the energy supply for non-critical data transmission, prioritizing the transmission of core data such as identification codes; when... When the battery level drops below the low battery threshold, the energy-saving mode is activated, and core data transmission is only performed when a reader wake-up signal is received. At the same time, the low battery warning unit built into the tag sends a power alarm message to the system to remind staff to replace the tag power supply in time.

[0015] On the other hand, an asset intelligent inventory method based on multi-band RFID includes: A globally unique identifier code is assigned to each asset to be managed. The code and the asset's static information are written into a multi-band anti-interference RFID tag integrating a multi-dimensional environmental sensing unit and a hardware encryption unit, establishing a unique binding relationship between the tag ID and the asset identifier code. Fixed readers and mobile terminals compatible with both UHF and NFC dual-bands are deployed to build a collaborative sensing network with full coverage and a built-in dynamic load balancing mechanism to serve the reception and transmission of asset identifier signals. The identifier code and associated information of the asset RFID tag are captured in real time through the collaborative sensing network, and the periodic data of the fixed reader and the active scanning data of the mobile terminal with dynamically optimized scanning parameters are collected simultaneously. The collected identifier data is parsed, deduplicated, and verified, and a quality rating is performed. Based on the rating results, a differentiated update strategy is implemented to drive the asset dynamic database to update in seconds. Through standardized interfaces and adaptive protocol parsing methods, bidirectional interaction is carried out between the asset identifier code associated dynamic data and external business systems, enabling the interoperability of identifier parsing data and business process information. Multi-band identifier data and multi-reader signal phase difference and time difference data are integrated, and TDOA or RSSI combined with error correction is applied to perform coarse-grained indoor positioning of assets, while simultaneously retaining the entire process flow record.

[0016] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects: This invention supports dual-band collaborative sensing, enabling comprehensive and efficient signal acquisition. It integrates environmental parameter acquisition with hardware encryption technology to ensure the security of core asset information. By dynamically optimizing scanning frequency and power, it adapts to complex environments and improves identification stability. Furthermore, it rationally allocates transmission tasks through a load balancing mechanism to avoid node congestion. The data undergoes multi-layer verification and quality rating to ensure accurate database updates within seconds. Simultaneously, it improves indoor positioning accuracy through phase difference and time difference correction. Adaptive protocol adaptation enables smooth interoperability with external systems. On the other hand, it adds a dynamic energy allocation strategy to the tags to extend their lifespan. It also retains complete asset transfer records for easy traceability, effectively improving the intelligence level and management efficiency of asset inventory, while reducing labor costs and data errors. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of an intelligent asset inventory system based on multi-band RFID. Figure 2This is a flowchart illustrating an intelligent asset inventory method based on multi-band RFID. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] The present invention will be further described below with reference to embodiments.

[0021] Example 1: This embodiment presents an intelligent asset inventory system based on multi-band RFID, such as... Figure 1 As shown, it includes: The binding module is used to assign a globally unique identifier code to the assets to be managed, and write the code and the asset's static information into a multi-band anti-interference RFID tag to establish a unique binding relationship between the tag ID and the asset identifier code; Multi-band anti-interference RFID tags integrate a multi-dimensional environmental sensing unit and a hardware encryption unit. The multi-dimensional environmental sensing unit collects environmental parameters in real time during asset storage and use, while the hardware encryption unit encrypts the core asset information stored on the tag based on a preset encryption algorithm. The encryption verification process is as follows: ; In the formula: This is for encrypting and verifying the response value; For hash functions; A unique identifier for RFID tags; Pre-allocate a dedicated key; M is the current timestamp; M is the verification threshold; This is an XOR operation; The data is the normalized data of environmental parameters collected by the multi-dimensional environmental sensing unit; During encryption verification, the UHF band transmits the tag ID and exclusive key core factor, while the NFC band supplements the transmission of environmental parameters and timestamp verification factor. The verification value is generated only after the dual-band factors are verified synchronously. At the same time, the weight of environmental parameters is dynamically adjusted according to the asset usage frequency, with the weight of high-frequency assets increasing by 50%, achieving dual encryption protection of "frequency band division of labor + scenario adaptation". To ensure the security of core asset information within RFID tags, the above formula combines the tag's unique identifier, pre-assigned exclusive key, current timestamp, and normalized environmental parameters through an XOR operation, then processes them using a hash function and takes the modulus. Tag information is only allowed to be read when the result matches the verification value pre-calculated by the hardware encryption unit. Through the fusion and standardized encryption of multi-dimensional key information, security risks such as information forgery and leakage are effectively resisted. During the encryption verification process, after the tag is activated by the reader, it immediately collects the current environmental parameters and quantifies them into an environmental fingerprint code. Simultaneously calculate the current time slice index. The method for quantifying environmental parameters is as follows: the temperature range is divided into several intervals, and the width of each interval is... (e.g., 5°C), current temperature When it falls in the k-th interval, the temperature fingerprint code Humidity and vibration are quantified using similar methods; environmental fingerprints are synthesized through bitwise operations. The tag transmits environmental fingerprint codes via the NFC band. and time slice index The data is sent to the reader in plaintext, while the tag's internal hardware encryption unit calculates a pre-verification value using the stored tag ID, exclusive key, time slice index, and environmental fingerprint. and will It is also sent to the reader; the reader receives the data sent by the tag. , and Then, query the system database for the tag corresponding to... and (The key is assigned by the system during tag initialization and securely stored in the database), and the verification value is calculated using the same hash algorithm. The reader will calculate the result. Send with tag A comparison is performed, and access to the core asset information within the tag is only allowed if the two are completely identical; otherwise, access is denied and an anomaly log is recorded. Time slice index The calculation method is as follows: divide continuous time into time slices of fixed width, where the width of each time slice is... Set to 5 seconds, the time slice index corresponding to the current timestamp. To tolerate minor clock deviations between the tag and the reader, the tag calculates not only the current time slice when calculating the pre-verification value. corresponding It also calculates adjacent time slices simultaneously. ) corresponding and This forms a triple verification candidate set; the label indexes the current time slice. The three candidate verification values ​​are sent to the reader, which calculates based on its own clock. The verification value corresponding to the closest time slice index is selected for comparison. When the deviation between the tag clock and the reader clock is within ± When the time slice indices calculated by both are the same or adjacent within the second range, the verification is successful. Security guarantee mechanism: Although and Transmitted in plaintext, but due to the exclusive key Stored only within the tag's internal hardware encryption unit and system security database, even if an attacker intercepts it... , and Furthermore, it's impossible to deduce the key or forge the verification value of other tags (due to the one-way nature of hash functions); the introduction of environmental fingerprints further enhances security: even if an attacker clones the tag ID and key, they cannot accurately replicate the temperature, humidity, vibration, and other parameters of the tag's current environment, thus preventing the forgery of the tag's calculated values ​​under different environments. The difference from the original label caused verification failure. The quantization interval design ensures that minor fluctuations in parameters within the same environment (such as a temperature change from 23℃ to 24℃) do not alter the fingerprint code, avoiding verification failures caused by real-time changes in environmental parameters. Simultaneously, environmental changes across intervals (such as moving from indoor 25℃ to outdoor 5℃) will cause significant changes in the fingerprint code, effectively preventing unauthorized reading of the label in different environments. The advantages of environmental fingerprint quantization are: fluctuations in environmental parameters within the same interval do not change the fingerprint code, resolving the technical contradiction of verification failure caused by real-time changes; it preserves the environment binding characteristic, so when the asset moves from its original environment to an abnormal environment, the fingerprint code changes, making it impossible for attackers to forge; the quantization interval width can be dynamically adjusted according to the asset value, with high-value assets using smaller intervals for strong environment binding and low-value assets using larger intervals to improve fault tolerance; the advantage of time window segmentation verification is: tolerance... The clock deviation of one second (e.g., ±5 seconds) effectively solves the technical problem of verification failure caused by clock asynchrony; the ability to prevent replay attacks by retaining the timestamp means that the verification value intercepted by the attacker will only be valid if the timestamp is retained. Valid within the window; expires automatically after timeout. Only when Information within the tag is only allowed to be read when it matches the verification value pre-calculated by the hardware encryption unit; The environmental parameters collected by the multi-dimensional environmental sensing unit include at least temperature, humidity, and vibration intensity. Hash functions that conform to the cryptographic standards promulgated by the State Cryptography Administration or internationally accepted cryptographic standards, and possess collision resistance, one-wayness, and high avalanche effect, including SM3, SHA-256, and SHA-512; The deployment module is used to deploy fixed readers and mobile terminals compatible with both UHF and NFC dual-band signals to build a collaborative sensing network with full coverage to support the reception and transmission of asset identification signals. The collaborative sensing network has a built-in dynamic load balancing mechanism that calculates the load factor of each read and write node in real time and adjusts the data transmission priority and data forwarding path according to the load factor. Load factor ; In the formula: Let be the load factor of the i-th read / write node; These are the weighting coefficients; This represents the number of tags currently being concurrently identified by the i-th node; Let i be the amount of data to be transmitted currently. Let i be the length of the current cache queue. This represents the maximum number of concurrent tags supported for the i-th digit. This represents the single transmission amount corresponding to the i-th maximum data transmission bandwidth. Let i be the maximum capacity of the i-th cache queue; Among them, the dynamic load balancing mechanism combines the overlap of UHF / NFC dual-band signal coverage to allocate traffic priority, giving priority to high real-time asset data (such as maintenance work order data) to low-load nodes in the dual-band coverage overlap area; at the same time, it adjusts the transmission queue according to the asset data priority, with emergency data transmission having a 3-level higher priority than ordinary inventory data. The above formula integrates three core load factors: the number of tags currently being identified concurrently by the read / write nodes, the amount of data to be transmitted, and the length of the cache queue. It calculates the proportion of each factor to the maximum carrying capacity of the corresponding node, and then combines the weight coefficients to obtain the load coefficient. When the load coefficient exceeds the preset threshold, the excess data transmission task is automatically diverted to the adjacent low-load nodes whose remaining carrying capacity meets the requirements. In the end, the load dynamic balance of the entire collaborative sensing network is achieved, avoiding the overload of a single node that leads to a decrease in transmission efficiency. when When the preset load threshold is exceeded, the system automatically diverts the data transmission tasks exceeding the preset load threshold to adjacent low-load nodes; in, The values ​​are all within the range of (0,1). When the target area has dense assets and high label distribution density (such as densely packed warehouse shelves or departments with concentrated medical equipment), the concurrent recognition pressure is high. The larger the value, the lower the value; conversely, when the label distribution is sparse and the concurrent recognition requirement is low, the better. The smaller the value, the better, especially when the asset requires collecting information from multiple dimensions (such as various environmental parameters and full lifecycle status data) and the volume of a single data entry is large. The larger the value, the better, conversely, when the asset information is concise and the data transmission volume is small. The smaller the value, the better. A larger value indicates a more complex network environment, higher data transmission latency leading to cache buildup; conversely, a larger value indicates a smooth network and high cache turnover efficiency. The smaller the value; A low-load node is defined as an adjacent node whose load coefficient is lower than the preset load threshold and whose remaining concurrent tag processing capacity, remaining data transmission bandwidth, and remaining cache capacity all meet the processing requirements of the data to be split. The aggregation module is used to capture the identification codes and associated information sent by asset RFID tags in real time through the sensing network, and simultaneously collect periodic data from fixed readers and active scanning data from mobile terminals. The mobile terminal dynamically optimizes the scanning frequency and scanning power based on the signal strength attenuation characteristics of asset tags and environmental interference coefficients. ; In the formula: The optimal scanning frequency for the i-th mobile terminal; This is the reference value for the scanning frequency; This is a reference value for the tag signal strength. The strength of the tag signal currently captured by the i-th mobile terminal; The environmental interference coefficient is the location of the i-th mobile terminal. This represents the maximum value of the environmental interference coefficient. Interference adaptation coefficient; The optimal scanning power for the i-th mobile terminal; This is the reference value for scanning power; The above formula, based on preset scanning frequency and power reference values, combines the ratio of tag signal strength reference value to the signal strength currently captured by the mobile terminal, and the relationship between environmental interference coefficient and maximum interference coefficient, to calculate the optimal scanning frequency and power. The scanning frequency increases non-linearly with increasing environmental interference (through the interference adaptation coefficient). The strategy involves controlling the growth rate, linearly increasing the scanning power as the signal strength decreases, and appropriately suppressing power growth through environmental interference coefficients to avoid blindly increasing transmission power in high-interference environments, which could exacerbate interference. The design philosophy of this strategy is: when environmental interference... At higher signal strengths, excessively increasing the transmission power can actually exacerbate signal collisions and adjacent channel interference (similar to how honking a horn during traffic jams can worsen the congestion). Therefore, appropriately suppressing power increases while simultaneously increasing the scanning frequency, and employing a strategy of "multiple short pulses" instead of "single long pulses," can effectively reduce the collision rate in densely deployed RFID networks; current signal strength When the value is below a preset threshold, the scanning frequency is increased. and scan power Enhanced signal acquisition capability; when environmental interference occurs When the signal strength exceeds the preset threshold, the frequency and power ratio are dynamically adjusted to reduce the interference, thereby enabling the mobile terminal to adapt to different signal strengths and interference environments, ensuring the stability and effectiveness of tag data collection. in, The quantitative value representing the degree of interference of the environment on the tag signal is obtained by real-time collection of electromagnetic interference intensity, obstruction density and multi-band signal crosstalk at the location through the built-in sensor of the mobile terminal, weighted summation and normalization. ∈ (0,1), the value is larger when the environmental interference type is more complex and the tag signal attenuation rate is faster, and the value is smaller when the environmental interference type is less complex and the tag signal attenuation rate is faster; The update module is used to parse, deduplicatize, and verify the collected identification data, driving the asset dynamic database to update in seconds based on the identification code; The asset dynamic database is a distributed database used to store static basic information, dynamic status data and full-process flow records corresponding to asset identification codes in real time. It supports data writing, querying and updating operations at the second level and has data redundancy backup and fault self-healing capabilities. The update module first performs parsing, deduplication, and verification operations on the collected identification data, then performs quality rating on the verified data, and executes a differentiated update strategy based on the rating results: Data parsing: Extract key fields from the identification data, including asset codes, environmental parameters, and timestamps, and convert them into a preset standardized data format; Data deduplication: A deduplication index is built based on the asset identification code and collection timestamp to remove duplicate data collected for the same asset within a preset time window; Data verification: Verify data integrity through hash verification, synchronously verify data validity based on static asset information comparison, and mark abnormal data with format errors or information mismatches; Quality rating: ; In the formula: This represents the quality level value of the i-th identifier data. For quality assessment weighting coefficients; The total number of key fields in the i-th data entry; This represents the total number of key fields. This is the verification error value for the i-th data item; The maximum allowable verification error value; The effective capture duration for the i-th data item; The total scan time for the i-th data item; is the deduplication validity coefficient of the i-th data. It takes a value of 1 when there is no duplicate data, and takes a value according to the proportion of valid data after deduplication when there is duplicate data. In the quality rating stage, a dual-band data consistency verification factor can be introduced simultaneously. When the deviation of key fields collected by UHF and NFC exceeds 10%, the verification error weight will be automatically increased. In addition, the weight of the effective capture time of assets in operation will be doubled, and the weight of idle assets will be appropriately reduced to ensure that the data quality assessment in dynamic scenarios is accurately adapted to the asset status. The above formula evaluates the quality of the identification data from four core dimensions: completeness of key fields, control of verification error, proportion of effective capture time, and effectiveness of deduplication. The quality level value is obtained by weighted summation of the corresponding calculation factors and weight coefficients. Based on the comparison results between the quality level value and the first and second level quality thresholds, differentiated strategies are executed, such as immediate update, update after verification of historical data from the same source, or triggering secondary collection by mobile terminals and fixed readers, to ensure the accuracy and timeliness of the data in the asset dynamic database. when When the quality exceeds the first-level quality threshold, the asset dynamic database is directly driven to perform real-time updates; when... When the data falls between the primary and secondary quality thresholds, a fusion verification is performed using historical data from the same source before updating; when... When the data quality falls below the secondary quality threshold, the mobile terminal or fixed reader is triggered to perform secondary data collection until the data quality meets the update requirements. in, All are positive numbers, and their sum is 1. The first-level quality threshold is greater than the second-level quality threshold. The fusion verification extracts the historical valid data of the asset within the near preset period, performs weighted fusion on key fields according to time decay weight, calculates the deviation value between the current data and the fusion result. If the deviation value is lower than the preset deviation threshold, the database is updated with the fused data. If the deviation value is higher than the preset deviation threshold, the mobile terminal or fixed reader is triggered to perform targeted secondary collection on the difference fields. The linkage module is used to enable bidirectional interaction between dynamic data associated with asset identification codes and external business systems through standardized interfaces, so as to support the information exchange between identification resolution data and business processes. The linkage module uses a standardized interface to enable bidirectional interaction between the dynamic data associated with the asset identification code and external business systems. It interfaces with different external business systems through adaptive protocol parsing. The calculation formula is as follows: ; In the formula: The protocol compatibility between the system and the j-th external business system; n is the number of dimensions matching key protocol fields. This is the attribute parameter of the k-th protocol field in the system; This refers to the attribute parameter of the k-th protocol field of the j-th external business system. In calculating protocol adaptability Previously, the system automatically performed standardized preprocessing on attribute parameters: data format categories (JSON, XML, CSV, etc.) were encoded into numerical values, field names were matched using semantic similarity algorithms (such as cosine similarity), and transmission rates were normalized by dividing by reference rates to ensure that the units of different types of parameters were consistent before covariance calculation was performed. The above formula is used to achieve smooth data interaction between the system and external business systems. Key attribute parameters such as data format, field length, and transmission rate are used as matching dimensions. The covariance of the attribute parameters of each protocol field between the system and the external business system is calculated as the ratio of the product of the parameter modulus of both parties to quantify the degree of protocol compatibility between the two. When the compatibility is higher than the threshold, bidirectional interaction is directly carried out through a standardized interface. When it is lower than the threshold, format conversion, field mapping and rate adaptation are performed according to the protocol specifications of the external system, and data conversion logs are stored synchronously. In this way, the system can flexibly adapt to the protocol requirements of different external business systems and ensure the information exchange between the identifier resolution data and the business process. when When the data exceeds the adaptation threshold, bidirectional data interaction is directly performed through a standardized interface; when... When the data is below the adaptation threshold, the identifier parsing data is format converted, field mapped and rate adapted according to the protocol specifications of the external business system to generate a data format that matches the requirements of the target system, and the data conversion log is stored synchronously. Among them, the attribute parameters include at least the data format, field length, and transmission rate; The recording module is used to integrate multi-band identification data, perform coarse-grained indoor positioning of assets based on identification codes through TDOA or RSSI, and simultaneously retain the entire process flow record associated with the identification codes; The recording module improves indoor asset positioning accuracy by fusing phase difference and time difference data from signals received by multiple readers and correcting errors in the initial positioning results based on TDOA or RSSI. The formula for calculating the positioning error correction is as follows: ; In the formula: This is the positioning error correction amount; This is the phase difference correction coefficient; is the average phase difference when multiple readers receive the same tag signal; c is the electromagnetic wave propagation speed; f is the carrier frequency of the RFID tag signal; This is the time difference correction factor; The average time difference between multiple readers receiving the same tag signal; To improve the accuracy of indoor asset positioning, the above formula collects the average phase difference and average time difference of signals received from the same tag by multiple readers while obtaining the initial positioning result through TDOA or RSSI. Combined with the electromagnetic wave propagation speed, RFID tag signal carrier frequency and flexibly adjustable correction coefficient, the positioning error correction amount is calculated. Then, the correction amount is superimposed on the initial positioning result to effectively correct the deviation in the initial positioning, making the indoor asset positioning more accurate. The error correction process is as follows: calculate the initial asset positioning result based on TDOA or RSSI, and simultaneously collect the phase difference of multiple readers. With time difference Substitute the data into the above formula to obtain the error correction amount. The initial positioning results were then compared with... By performing reverse overlay, the optimized asset positioning result is obtained; When correcting positioning errors, the phase difference correction weight is enhanced by leveraging the anti-blocking advantage of UHF and the near-field accuracy advantage of NFC to optimize the time difference correction weight. When the asset's moving speed is ≥0.5m / s, mobile terminal inertial navigation data is simultaneously invoked to assist in correction, with a correction coefficient... Real-time improvement of 30%-40%, solving the pain point of mobile asset positioning deviation; in, ∈(0.1,0.9), the value is larger when the reader deployment density is higher and the integrity and consistency of phase difference data acquisition are stronger, and the value is smaller when the density of reader deployment is lower and the consistency of phase difference data acquisition is stronger; ∈(0.1,0.9), the value is larger when the clock synchronization accuracy of the reader is higher and the calibration accuracy of the time difference data is better, and the value is smaller when the clock synchronization accuracy of the reader is lower. The positioning error correction module integrates phase difference. and time difference Before processing the data, a phase-time difference consistency check is performed to eliminate outliers. The theoretical basis for the consistency check is that there is a theoretical relationship between the electromagnetic wave propagation distance d and the phase difference and time difference, and the phase difference can be used to estimate the distance. Distance calculated from time difference Where c is the speed of electromagnetic wave propagation, and f is the carrier frequency of the RFID tag signal; ideally, However, due to phase distortion caused by multipath interference and time measurement errors caused by clock jitter, there may be discrepancies between the two; the phase-time difference consistency index is defined as follows: For the i-th reader, its consistency index calculation formula is: ; in, The distance calculated by the i-th reader / writer based on the phase difference. The distance calculated by the i-th reader / writer based on the time difference; Outlier removal rules: When Time (e.g., threshold) If the deviation exceeds 30% (=0.3), the reader / writer's data is considered abnormal and will not be included in subsequent fusion calculations; Dual-band cross-validation mechanism: For UHF and NFC dual-band readers deployed in the same location, their consistency indices are calculated separately. and The cross-validation rule is: if If both frequency bands are reliable, data can be successfully integrated; otherwise... In this case, only NFC data will be used (leveraging the advantage of short-range accuracy); if Then only UHF data is used (taking advantage of anti-blocking); if If the data at that location is unreliable, its weight is reduced to 0. The advantages of phase-time difference consistency verification are: automatic identification and elimination of abnormal data caused by multipath interference, clock jitter, etc.; demonstrating the synergistic advantages of dual-band mutual verification; clear physical constraints, clear mathematical principles, and conformity to the laws of electromagnetic wave propagation. In this embodiment, the correction coefficient and The measurement uncertainty can be dynamically adjusted based on the measurement uncertainty. The method for calculating the measurement uncertainty is as follows: For N readers participating in the fusion, the formula for calculating the phase difference measurement uncertainty is: ; in The average phase difference collected from N readers; The smaller the value, the more stable the phase measurement; The formula for calculating the uncertainty of time difference measurement is: ; in The average time difference collected from N readers; The smaller the value, the more stable the time difference measurement; Adaptive weight calculation employs an inverse variance weighting method, based on the least squares principle, assigning higher weights to more stable measurements; correction coefficients... and The calculation formula is: ; ; Easy to verify This satisfies the weight normalization requirement; Dual-band differential weighting adjustment: Due to its longer wavelength (915MHz corresponds to approximately 33cm), the UHF band has lower phase measurement accuracy but stronger resistance to obstruction. Larger, leading to Smaller (reduced phase weight); NFC band, due to short-range communication, has less impact from multipath propagation in time difference measurement, but its effective range is short, typically... Smaller, resulting in Larger (increases the weight of time difference); the advantage of adaptive weight adjustment is that it automatically adapts to environmental changes, automatically reduces the phase weight when there is occlusion, and automatically increases the phase weight when there is open space; Regarding movement speed For assets, to avoid abrupt changes in location results, a weighted moving average is used to integrate historical locations. The smoothing formula is as follows: ; in Here are the corrected position coordinates at the current time k. These are the smoothed position coordinates of k-1 at the previous time step. The smoothing coefficient is calculated using the following formula: ; in The current movement speed of the asset. The preset maximum movement speed; when the asset is stationary ( ), Historical location weighting is as high as 80%, significantly smoothing out location jumps; when assets move at high speed ( ), It fully utilizes the current correction position to achieve real-time tracking; Multi-band anti-interference RFID tags flexibly select active, semi-active, or passive tags based on asset value. High-value assets use active dual-band tags to support environmental sensing and encrypted verification; medium- and low-value assets use semi-active or passive UHF tags to reduce costs; the energy supply strategy module is only applicable to active and semi-active tags; the multi-band anti-interference RFID tag dynamically adjusts its energy supply strategy based on the tag's remaining battery power and signal transmission requirements. ; In the formula: The real-time energy supply for the tag; The remaining battery level of the tag; For data transmission energy allocation coefficient and active state energy allocation coefficient; This represents the amount of data currently to be transmitted. This refers to the maximum amount of data that a tag can transmit in a single transaction. The duration of activity of a tag within a work cycle; The working cycle of the label; The above formula is based on the remaining power of the tag, combined with the ratio of the current amount of data to be transmitted to the maximum amount of data that can be transmitted at one time, and the proportion of active time within the working cycle. It regulates the real-time energy supply through two types of allocation coefficients: data transmission and active status. This not only meets business needs but also reasonably manages energy consumption, thereby extending the lifespan of the tag and providing timely reminders for power supply replacement. when When the battery level exceeds the preset sufficient power threshold, sufficient power is allocated to data transmission and active status according to the above formula; when... When the battery level is between the sufficient and low thresholds, appropriately reduce the energy supply for non-critical data transmission, prioritizing the transmission of core data such as identification codes; when... When the battery level is below the low battery threshold, the energy-saving mode is activated, and core data transmission is only performed when a reader wake-up signal is received. At the same time, the low battery warning unit built into the tag sends a power alarm message to the system to remind staff to replace the tag power supply in time. in, The values ​​are all in the range (0,1). The higher the proportion of the current amount of data to be transmitted to the maximum amount of data that can be transmitted in a single transmission by the tag, the higher the data transmission priority. The larger the value, the lower the value; conversely, the lower the value, the higher the percentage of active time for the tag within a work cycle and the stronger the business demand during active periods. The larger the value, the smaller the value; In the energy supply strategy, core data is preferentially transmitted via the NFC low-power band, while non-core data is transmitted via the UHF band; when environmental parameters detect high-risk scenarios such as high temperature (≥60℃) or strong vibration (≥5g), The value is increased to 0.7-0.8 to prioritize core data transmission and trigger high-frequency power alarms. The binding module interacts with the deployment module via the local area network. The deployment module interacts with the aggregation module and the update module via the local area network. The aggregation module and the update module interact with the linkage module via the local area network. The linkage module interacts with the recording module via the local area network.

[0022] In this embodiment, the binding module assigns a globally unique identifier code to the asset to be managed, writes the code and asset static information into a multi-band anti-interference RFID tag to establish a unique binding relationship between the tag ID and the asset identifier code. The deployment module deploys fixed readers and mobile terminals compatible with both UHF and NFC dual-band frequencies to build a collaborative sensing network with full coverage to support the reception and transmission of asset identifier signals. The aggregation module simultaneously captures the identifier codes and associated information sent by the asset RFID tags in real time through the sensing network, and simultaneously collects periodic data from fixed readers and active scanning data from mobile terminals. The update module then parses, deduplicates, and verifies the collected identifier data, driving the asset dynamic database to update second-level based on the identifier code. The linkage module further uses a standardized interface to enable bidirectional interaction between the dynamic data associated with the asset identifier code and external business systems to support information exchange between identifier parsing data and business processes. Finally, the recording module merges the multi-band identifier data and performs coarse-grained indoor positioning of assets based on the identifier code using TDOA or RSSI, while simultaneously retaining the entire process flow record associated with the identifier code.

[0023] In the above embodiments, the system can achieve efficient and accurate asset inventory, synchronize asset dynamic information and transfer records in seconds, significantly improve indoor positioning accuracy, ensure secure and reliable data transmission with strong anti-interference capabilities, adapt to various external business systems to achieve information exchange, reasonably manage tag energy consumption, and reduce redundant data collection and manpower input.

[0024] Application example: To improve the management efficiency of large medical equipment (such as MRI machines, ultrasound diagnostic instruments, infusion pumps, etc.), XX tertiary hospital introduced an intelligent asset inventory system based on multi-band RFID.

[0025] First, the system assigns a globally unique identification code to each medical device through the binding module, such as "MED-2024-A001" or "MED-2024-B123". The code, along with static information such as the device model, purchase date, and rated parameters, is written into a multi-band anti-interference RFID tag that integrates temperature, humidity, and vibration intensity sensing units with hardware encryption units. This establishes a unique binding relationship between the tag ID and the device code. After encryption and verification, the information inside the tag can only be read if the verification result matches.

[0026] Subsequently, the deployment module was used to deploy fixed readers compatible with both UHF and NFC dual-band in areas such as hospital equipment warehouses, outpatient departments, and inpatient wards. Mobile terminals were also provided for equipment management personnel, building a collaborative sensing network with full coverage to support the reception and transmission of medical equipment identification signals.

[0027] In daily management, the aggregation module uses this sensing network to capture the identification codes and associated environmental parameters sent by the RFID tags of the devices in real time, and simultaneously collects periodic data from fixed readers and data actively scanned by management personnel using handheld mobile terminals. When a ward has a high density of medical equipment and the fixed readers are under heavy concurrent identification pressure, the system uses a dynamic load balancing mechanism to calculate that the load factor of the reader is exceeding the limit, and automatically diverts some data transmission tasks to adjacent low-load readers to ensure smooth data transmission. When management personnel are in areas with weak signals or high environmental interference, such as corners where equipment is stored, the mobile terminal automatically optimizes the scanning frequency and power to successfully capture tag signals.

[0028] The update module parses, deduplicates, and verifies the collected data, removes duplicate data from the same device within a short period of time, marks abnormal data, and performs a quality rating. Finally, it concludes that the data quality meets the standards, driving the asset dynamic database to perform second-level updates. For example, the current usage status and location information of "MED-2024-A001" are synchronized to the database in real time.

[0029] The linkage module interfaces with the hospital's equipment management system and maintenance scheduling system through standardized interfaces. After verifying the protocol compatibility, it enables two-way data interaction. When the environmental parameters of a piece of equipment show abnormalities, the relevant information is automatically synchronized to the maintenance scheduling system, generating a maintenance work order. After the maintenance is completed, the maintenance record is also fed back to the asset inventory system, realizing information exchange in the business process.

[0030] The recording module integrates multi-band identification data and uses TDOA technology for coarse-grained indoor positioning. It also incorporates phase and time differences from signals received by multiple readers for error correction, ultimately pinpointing the location of the "MED-2024-B123" ultrasound diagnostic instrument at the nurses' station in Ward 2, 3rd floor of the inpatient department. The module also records the entire process of the device's operation, from warehousing and requisition to routine inspections and maintenance. Furthermore, the RFID tag dynamically adjusts its power supply based on remaining battery power. When the tag's battery level falls below a low-battery threshold, it automatically sends an alarm to alert management to replace the power supply.

[0031] Example 2: At the implementation level, based on Example 1, this example refers to... Figure 2 A further detailed description of the asset intelligent inventory system based on multi-band RFID in Example 1 is provided below: A method for intelligent asset inventory based on multi-band RFID, comprising: Assign a globally unique identifier code to the assets to be managed, write the code and the asset's static information into a multi-band anti-interference RFID tag that integrates a multi-dimensional environmental sensing unit and a hardware encryption unit, and establish a unique binding relationship between the tag ID and the asset identifier code. Deploy fixed readers and mobile terminals compatible with both UHF and NFC frequency bands to build a collaborative sensing network with full coverage and a built-in dynamic load balancing mechanism to serve the reception and transmission of asset identification signals; The identification codes and associated information of asset RFID tags are captured in real time through a collaborative sensing network, and periodic data from fixed readers and active scanning data from mobile terminals with dynamically optimized scanning parameters are collected simultaneously. The system performs parsing, deduplication, and verification operations on the collected identification data and performs quality rating. Based on the rating results, it implements a differentiated update strategy to drive the asset dynamic database to update in seconds. Through standardized interfaces and adaptive protocol parsing methods, the dynamic data associated with asset identification codes is exchanged bidirectionally with external business systems, enabling the interoperability of identification resolution data and business process information. By integrating multi-band identification data and multi-reader signal phase difference and time difference data, TDOA or RSSI is applied in combination with error correction to perform coarse-grained indoor asset positioning, and the entire process flow record is stored simultaneously.

[0032] In summary, the systems and methods described above support dual-band collaborative sensing, enabling comprehensive and efficient signal acquisition. They integrate environmental parameter acquisition with hardware encryption technology to ensure the security of core asset information. By dynamically optimizing scanning frequency and power, they adapt to complex environments and improve identification stability. Furthermore, a load balancing mechanism rationally allocates transmission tasks to avoid node congestion. Data undergoes multi-layer verification and quality rating to ensure accurate database updates at the second level. Indoor positioning accuracy is improved through phase difference and time difference correction. Adaptive protocol adaptation enables smooth interoperability with external systems. On the other hand, dynamic energy allocation strategies are added to tags to extend their lifespan. Complete asset transfer records are retained for easy traceability, effectively improving the intelligence level and management efficiency of asset inventory, while reducing labor costs and data errors.

[0033] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent asset inventory system based on multi-band RFID, characterized in that, include: The binding module is used to assign a globally unique identifier code to the assets to be managed, and write the code and the asset's static information into a multi-band anti-interference RFID tag to establish a unique binding relationship between the tag ID and the asset identifier code; The deployment module is used to deploy fixed readers and mobile terminals compatible with both UHF and NFC dual-band signals to build a collaborative sensing network with full coverage to support the reception and transmission of asset identification signals. The aggregation module is used to capture the identification codes and associated information sent by asset RFID tags in real time through the sensing network, and simultaneously collect periodic data from fixed readers and active scanning data from mobile terminals. The update module is used to parse, deduplicatize, and verify the collected identification data, driving the asset dynamic database to update in seconds based on the identification code; The linkage module is used to enable bidirectional interaction between dynamic data associated with asset identification codes and external business systems through standardized interfaces, so as to support the information exchange between identification resolution data and business processes. The recording module is used to integrate multi-band identification data, perform coarse-grained indoor positioning of assets based on identification codes through TDOA or RSSI, and simultaneously retain the entire process flow record associated with the identification codes.

2. The asset intelligent inventory system based on multi-band RFID according to claim 1, characterized in that, The multi-band anti-interference RFID tag integrates a multi-dimensional environmental sensing unit and a hardware encryption unit. The multi-dimensional environmental sensing unit collects environmental parameters in real time during asset storage and use. The hardware encryption unit encrypts the core asset information stored on the tag based on a preset encryption algorithm. The encryption verification process is as follows: ； In the formula: This is for encrypting and verifying the response value; For hash functions; A unique identifier for RFID tags; Pre-allocate a dedicated key; M is the current timestamp; M is the verification threshold; This is an XOR operation; The data is the normalized data of environmental parameters collected by the multi-dimensional environmental sensing unit; Only when Information within the tag is only allowed to be read when it matches the verification value pre-calculated by the hardware encryption unit.

3. The asset intelligent inventory system based on multi-band RFID according to claim 1, characterized in that, The collaborative sensing network has a built-in dynamic load balancing mechanism that calculates the load coefficient of each read / write node in real time and adjusts the data transmission priority and data forwarding path according to the load coefficient. The load factor ; In the formula: Let be the load factor of the i-th read / write node; These are the weighting coefficients; This represents the number of tags currently being concurrently identified by the i-th node; Let i be the amount of data to be transmitted currently. Let i be the length of the current cache queue. This represents the maximum number of concurrent tags supported for the i-th digit. This represents the single transmission amount corresponding to the i-th maximum data transmission bandwidth. Let i be the maximum capacity of the i-th cache queue; when When the load exceeds the preset threshold, the system automatically diverts the data transmission tasks exceeding the preset load threshold to adjacent low-load nodes.

4. The asset intelligent inventory system based on multi-band RFID according to claim 1, characterized in that, The mobile terminal dynamically optimizes the scanning frequency and scanning power based on the signal strength attenuation characteristics of the asset tag and the environmental interference coefficient. ； In the formula: The optimal scanning frequency for the i-th mobile terminal; This is the reference value for the scanning frequency; This is a reference value for the tag signal strength. The strength of the tag signal currently captured by the i-th mobile terminal; The environmental interference coefficient is the location of the i-th mobile terminal. This represents the maximum value of the environmental interference coefficient. Interference adaptation coefficient; The optimal scanning power for the i-th mobile terminal; This is the reference value for scanning power.

5. The asset intelligent inventory system based on multi-band RFID according to claim 1, characterized in that, The asset dynamic database is a distributed database used to store static basic information, dynamic status data and full-process flow records corresponding to asset identification codes in real time. The update module first performs parsing, deduplication, and verification operations on the collected identification data, then performs quality rating on the verified data, and executes a differentiated update strategy based on the rating results: Data parsing: Extract key fields from the identification data, including asset codes, environmental parameters, and timestamps, and convert them into a preset standardized data format; Data deduplication: A deduplication index is built based on the asset identification code and collection timestamp to remove duplicate data collected for the same asset within a preset time window; Data verification: Verify data integrity through hash verification, synchronously verify data validity based on static asset information comparison, and mark abnormal data with format errors or information mismatches; Quality rating: ; In the formula: This represents the quality level value of the i-th identifier data. For quality assessment weighting coefficients; The total number of key fields in the i-th data entry; This represents the total number of key fields. This is the verification error value for the i-th data item; The maximum allowable verification error value; The effective capture duration for the i-th data item; The total scan time for the i-th data item; is the deduplication validity coefficient of the i-th data. It takes a value of 1 when there is no duplicate data, and takes a value according to the proportion of valid data after deduplication when there is duplicate data. when When the quality exceeds the first-level quality threshold, the asset dynamic database is directly driven to perform real-time updates; when... When the data falls between the primary and secondary quality thresholds, a fusion verification is performed using historical data from the same source before updating; when... When the data quality falls below the secondary quality threshold, a secondary data collection is triggered on the mobile terminal or fixed reader until the data quality meets the update requirements.

6. The asset intelligent inventory system based on multi-band RFID according to claim 1, characterized in that, The linkage module uses a standardized interface to conduct bidirectional interaction between the dynamic data associated with the asset identification code and external business systems. It interfaces with different external business systems through adaptive protocol parsing. The calculation formula is as follows: ； In the formula: The protocol compatibility between the system and the j-th external business system; n is the number of dimensions matching key protocol fields. This is the attribute parameter of the k-th protocol field in the system; This refers to the attribute parameter of the k-th protocol field of the j-th external business system. when When the data exceeds the adaptation threshold, bidirectional data interaction is directly performed through a standardized interface; when... When the data is below the adaptation threshold, the identifier parsing data is format converted, field mapped and rate adapted according to the protocol specifications of the external business system to generate a data format that matches the requirements of the target system, and the data conversion log is stored synchronously.

7. The asset intelligent inventory system based on multi-band RFID according to claim 1, characterized in that, The recording module improves the indoor positioning accuracy of assets by fusing phase difference and time difference data from signals received by multiple readers to correct errors in the initial positioning results based on TDOA or RSSI. The formula for calculating the positioning error correction is as follows: ； In the formula: This is the positioning error correction amount; This is the phase difference correction coefficient; is the average phase difference when multiple readers receive the same tag signal; c is the electromagnetic wave propagation speed; f is the carrier frequency of the RFID tag signal; This is the time difference correction factor; This represents the average time difference between multiple readers receiving the same tag signal.

8. The asset intelligent inventory system based on multi-band RFID according to claim 1, characterized in that, The multi-band anti-interference RFID tag dynamically adjusts its energy supply strategy based on the tag's remaining battery power and signal transmission requirements. ； In the formula: The real-time energy supply for the tag; The remaining battery level of the tag; For data transmission energy allocation coefficient and active state energy allocation coefficient; This represents the amount of data currently to be transmitted. This refers to the maximum amount of data that a tag can transmit in a single transaction. The duration of activity of a tag within a work cycle; The working cycle of the label; when When the battery level exceeds the preset sufficient power threshold, sufficient power is allocated to data transmission and active status according to the above formula; when... When the battery level is between the sufficient and low thresholds, appropriately reduce the energy supply for non-critical data transmission, prioritizing the transmission of core data such as identification codes; when... When the battery level drops below the low battery threshold, the energy-saving mode is activated, and core data transmission is only performed when a reader wake-up signal is received. At the same time, the low battery warning unit built into the tag sends a power alarm message to the system to remind staff to replace the tag power supply in time.

9. The asset intelligent inventory system based on multi-band RFID according to claim 1, characterized in that, The binding module is interconnected with the deployment module via a local area network. The deployment module is interconnected with the aggregation module and the update module via a local area network. The aggregation module and the update module are interconnected with the linkage module via a local area network. The linkage module is interconnected with the recording module via a local area network.

10. A method for intelligent asset inventory based on multi-band RFID, wherein the method is an implementation method of an intelligent asset inventory system based on multi-band RFID as described in any one of claims 1-9, characterized in that, include: Assign a globally unique identifier code to the assets to be managed, write the code and the asset's static information into a multi-band anti-interference RFID tag that integrates a multi-dimensional environmental sensing unit and a hardware encryption unit, and establish a unique binding relationship between the tag ID and the asset identifier code. Deploy fixed readers and mobile terminals compatible with both UHF and NFC frequency bands to build a collaborative sensing network with full coverage and a built-in dynamic load balancing mechanism to serve the reception and transmission of asset identification signals; The identification codes and associated information of asset RFID tags are captured in real time through a collaborative sensing network, and periodic data from fixed readers and active scanning data from mobile terminals with dynamically optimized scanning parameters are collected simultaneously. The system performs parsing, deduplication, and verification operations on the collected identification data and performs quality rating. Based on the rating results, it implements a differentiated update strategy to drive the asset dynamic database to update in seconds. Through standardized interfaces and adaptive protocol parsing methods, the dynamic data associated with asset identification codes is exchanged bidirectionally with external business systems, enabling the interoperability of identification resolution data and business process information. By integrating multi-band identification data and multi-reader signal phase difference and time difference data, TDOA or RSSI is applied in combination with error correction to perform coarse-grained indoor asset positioning, and the entire process flow record is stored simultaneously.

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