RFID-based fixed asset life cycle management system

Abstract

This invention relates to the field of fixed asset management technology, and more particularly to a fixed asset lifecycle management system based on RFID. The system includes: collecting identification events of assets at different locations or in different scenarios through RFID identification; performing time-series modeling according to asset dimensions and constructing behavioral feature data; generating lifecycle state candidate results based on the behavioral features; and having an independent state evolution control module confirm the legality according to preset evolution rules, thereby triggering corresponding asset management operations. The system also combines management operations and confirmation status to provide feedback and optimize relevant rules, achieving closed-loop control of fixed asset lifecycle management. In this invention, by using RFID collection and time-series modeling, the asset state is continuously determined and constrained, forming a structured management mechanism with unidirectional data flow, solving the problem of inconsistent asset state identification caused by traditional static ledgers and manual inventory.

Description

Technical Field

[0001] This invention relates to the field of fixed asset management technology, and in particular to a fixed asset lifecycle management system based on RFID. Background Technology

[0002] As the scale of assets of enterprises and institutions continues to expand, the types and quantities of fixed assets continue to grow. Assets cover a variety of types, including office equipment, production equipment, special instruments and public facilities, and are used in different departments, regions and even across locations. Fixed assets involve different management entities and business processes in multiple stages, such as procurement, use, transfer, maintenance, idleness and disposal. This makes the timeliness, accuracy and consistency requirements of fixed asset management constantly higher, and the overall management complexity increases significantly.

[0003] Traditional fixed asset management mostly uses static ledgers or manual periodic inventory checks. Due to the lack of a unified judgment logic for the continuous changes in asset status, inconsistencies in asset status identification are caused. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides an RFID-based fixed asset lifecycle management system, which aims to improve the problem of inconsistent asset status identification caused by the traditional fixed asset management methods that mostly rely on static ledgers or manual periodic inventory checks.

[0005] This invention provides the following technical solution: a fixed asset lifecycle management system based on RFID includes: The RFID identification and data acquisition module is used to uniquely identify fixed assets and collect RFID identification events in different locations or scenarios. The behavior timing modeling module is used to sort the RFID identification events by time according to the asset dimension and construct asset behavior feature data within a preset time window. The asset lifecycle status determination module is used to generate candidate status determination results from a preset lifecycle status set based on the asset behavior feature data. The lifecycle state evolution control module is used to confirm the legality of the candidate judgment results based on preset evolution rules and the candidate judgment results; the evolution rules include allowing transition paths, prohibiting unreasonable jumps, continuous state confirmation, abnormal rollback and final state freezing mechanism; The asset management strategy linkage module is used to trigger corresponding asset management operations based on the life cycle state confirmed by the life cycle state evolution control module. The parameter feedback and rule optimization module is used to adjust the time series modeling, state determination and evolution rules based on the lifecycle status of asset management operations or confirmations. The data flow between the modules follows the sequence of RFID identification event collection, behavior feature construction, status determination, status evolution confirmation, management operation triggering, and parameter feedback adjustment. The asset lifecycle status determination module and the lifecycle status evolution control module are set up independently, and the data is only transmitted in this order.

[0006] By adopting the above technical solution, and by setting up an RFID identification and data acquisition module, an identification behavior time sequence modeling module, an asset lifecycle status determination module, and a lifecycle status evolution control module, and by carrying out unidirectional data flow in the order of RFID identification event collection, behavior feature construction, status determination, and status evolution confirmation, a structured judgment and constraint control of the fixed asset status change process can be achieved. This improves the problem that traditional fixed asset management, which mostly uses static ledgers or manual periodic inventory, lacks a unified judgment logic for the continuous change process of asset status, resulting in inconsistent asset status identification.

[0007] Furthermore, in the RFID identification and data acquisition module, the steps of uniquely identifying fixed assets and collecting RFID identification events in different locations or scenarios include: Configure a unique RFID tag for each fixed asset; Deploy distributed RFID reader / writer nodes for asset identification; When an asset enters or passes through the RFID node coverage area, the distributed RFID reader / writer node reads the RFID tag information and generates an identification event. The generated identification events are standardized, including recording asset identifiers, node identifiers, identification timestamps, and identification location or business scenario information; The standardized recognition events are transmitted to the recognition behavior timing modeling module or stored in a temporary data buffer for subsequent module processing.

[0008] Furthermore, in the behavior timing modeling module, the step of sorting the RFID identification events by asset dimension includes: Receive identification event streams from the RFID identification and data acquisition module; The identification events are grouped according to the asset identification information, with each asset forming a separate event set; Within the event set of each asset, events are sorted in ascending order based on their identified timestamps; The sorted set of recognized events is used as input for subsequent feature construction.

[0009] Furthermore, in the behavior time-series modeling module, the step of constructing asset behavior feature data within a preset time window includes: Define a fixed-length time window or a sliding time window for each asset; The sorted recognition events are divided into corresponding time windows; For each event set within a time window, extract asset behavior characteristics, including the total number of events, consecutive identification interval, number of cross-region identifications, and the time of the first and last identifications; The extracted asset behavior feature data is output to the asset lifecycle status determination module.

[0010] Furthermore, in the asset lifecycle status determination module, the step of generating candidate status determination results from the preset lifecycle status set includes: Establish a preset lifecycle state set, which includes the state of pending accounting, normal use, high-intensity use, idle, inefficient, pending disposal, and exited. Establish judgment rules for each lifecycle state, and the judgment rules are associated with the asset behavior characteristic data; The asset behavior characteristic data is matched with the lifecycle status determination rules; Generate the state candidate determination result, including the corresponding candidate state and its determination information; The state candidate determination result is output to the life cycle state evolution control module.

[0011] Furthermore, in the lifecycle state evolution control module, the step of confirming the legality of the candidate determination result based on the preset evolution rules and the state candidate determination result includes: The state candidate determination result is compared with the preset allowed transfer path; Check the consistency of the state candidate determination results within a continuous time window; The state candidate determination result is confirmed according to a preset duration threshold. Perform rollback processing on the state candidate determination results of short-term anomalies; The final state candidate determination results that are scrapped or have been exited are frozen; Output the confirmed lifecycle status to the asset management strategy linkage module.

[0012] Furthermore, in the asset management strategy linkage module, the step of triggering the corresponding asset management operation based on the lifecycle state confirmed by the lifecycle state evolution control module includes: Receive a confirmed lifecycle status, which is provided by the lifecycle status evolution control module; Search for matching strategies in a predefined set of management strategies, which includes allocation strategies, reuse strategies, maintenance or inspection strategies, disposal approval strategies, and ledger cancellation strategies corresponding to different lifecycle states. Management operations are triggered based on the matched management policy, including generating operation reminders, sending approval requests, or updating asset ledgers. Output the management operation information triggered.

[0013] Furthermore, in the parameter feedback and rule optimization module, the step of adjusting the time-series modeling, state determination, and evolution rules based on the lifecycle state of asset management operations or confirmations includes: Receive asset management operation information triggered by the asset management strategy linkage module; Receive the lifecycle state confirmed by the lifecycle state evolution control module; Match the lifecycle state with the corresponding set of rule parameters; Adjust the rule parameters of the behavior recognition time-series modeling module; Adjust the rule parameters of the asset lifecycle status determination module; Adjust the rule parameters of the lifecycle state evolution control module; Save the adjusted rule parameters to the corresponding module; Generate rule update records for system monitoring or further optimization.

[0014] Furthermore, the asset lifecycle state determination module and the lifecycle state evolution control module are implemented independently. The state candidate results output by the determination module are only used as inputs to the evolution control module, and the determination module and the evolution control module do not share internal state variables or execution logic.

[0015] Furthermore, in the lifecycle state evolution control module, the preset evolution rules further include: setting a manual confirmation and freezing mechanism for scrapped or exited states; allowing rollback for short-term abnormal candidate states; and performing multi-window consistency confirmation for persistent state candidate results.

[0016] The present invention has the following beneficial effects: 1. In this invention, by setting up an RFID identification and data acquisition module, an identification behavior time sequence modeling module, an asset life cycle status determination module, and a life cycle status evolution control module, and by performing unidirectional data flow in the order of RFID identification event acquisition, behavior feature construction, status determination, and status evolution confirmation, a structured determination and constraint control of the fixed asset status change process can be realized. This improves the problem that traditional fixed asset management mostly adopts static ledgers or manual periodic inventory methods, which lack a unified judgment logic for the continuous change process of asset status, resulting in inconsistent asset status identification.

[0017] 2. In this invention, by decoupling the functions between the asset lifecycle state determination module and the lifecycle state evolution control module, and by having the lifecycle state evolution control module confirm the legality of the state candidate determination results according to the preset evolution rules, a unified constraint on the state transition path, persistence conditions and abnormal situations is achieved. This improves the problem that traditional asset state determination is mostly based on single identification results or simple threshold rules, which lacks a constraint mechanism on the rationality of state evolution, resulting in frequent fluctuations in asset state.

[0018] 3. In this invention, by setting up an asset management strategy linkage module and a parameter feedback and rule optimization module, and triggering asset management operations based on the confirmed life cycle status, while adjusting the parameters of time series modeling, status determination and evolution rules, a closed-loop correlation mechanism between asset status determination and management strategy is formed. This improves the problem that traditional fixed asset management mostly adopts a management method with fixed rules that remain unchanged for a long time. Due to the lack of a rule adjustment mechanism based on actual management results, the management strategy does not match the actual asset status. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the architecture of the RFID-based fixed asset lifecycle management system proposed in this invention; Figure 2 This is a flowchart illustrating the RFID-based fixed asset lifecycle management method proposed in this embodiment. Detailed Implementation

[0020] The technical solutions in 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1: In the first embodiment of the present invention, the present invention provides a fixed asset lifecycle management system based on RFID, such as... Figure 1 As shown, it includes an RFID identification and data acquisition module, which is used to uniquely identify fixed assets and collect RFID identification events in different locations or scenarios; Furthermore, in the RFID identification and data acquisition module, the steps for uniquely identifying fixed assets and collecting RFID identification events in different locations or scenarios include: Configure a unique RFID tag for each fixed asset; Deploy distributed RFID reader / writer nodes for asset identification; When an asset enters or passes through the RFID node coverage area, the distributed RFID reader / writer nodes read the RFID tag information and generate an identification event. The generated identification events are standardized, including recording asset identifiers, node identifiers, identification timestamps, and identification location or business scenario information; The standardized recognition events are transmitted to the recognition behavior timing modeling module or stored in a temporary data buffer for subsequent module processing.

[0022] Specifically, the RFID identification and data acquisition module achieves unique identification of fixed assets and records their operational processes by binding radio frequency tags to them and collecting identification events. This is based on a collaborative mechanism between RFID tags and distributed RFID reader / writer nodes. First, each fixed asset is equipped with a corresponding RFID tag, which stores at least the asset identification information used to distinguish different fixed assets. This asset identification information serves as the basic input data for subsequent identification event association and processing. Then, distributed RFID reader / writer nodes are deployed at the physical locations or business scenarios where the fixed assets may appear. Each reader / writer node has a node identifier and corresponds to a specific deployment location or business scenario, used to read the RFID tags when the asset enters its radio frequency coverage area. When a fixed asset enters or passes through the coverage area of ​​any reader / writer node, the reader / writer node reads the RFID tag information via radio frequency communication. The system generates an RFID identification event by combining the asset identification information with the node identification of the current node and the identification trigger time. The identification timestamp is generated by the local clock of the read / write node or a unified system time source, and the identification location or business scenario information is determined by the node deployment attributes. After generating the identification event, the system performs standardization processing to form a structured data record. The structured data includes at least the asset identification, node identification, identification timestamp, and identification location or business scenario information. All of the above data are directly derived from the RFID tag content, read / write node configuration parameters, and the time information when the identification occurs. The standardized identification event is the output of this module and is transmitted to the identification behavior timing modeling module or temporarily stored in a temporary data buffer. It is used for subsequent processing of the identification event by asset dimension time sorting and behavioral feature construction, thereby providing basic data input for subsequent asset lifecycle status analysis.

[0023] The behavior time-series modeling module is used to sort RFID identification events by time according to asset dimension and build asset behavior feature data within a preset time window; Furthermore, in the behavior timing modeling module, the steps for chronologically sorting RFID identification events by asset dimension include: Receive identification event streams from the RFID identification and data acquisition module; The identification events are grouped according to the asset identification information, with each asset forming a separate event set; Within the event set of each asset, events are sorted in ascending order based on their identified timestamps; The sorted set of recognized events is used as input for subsequent feature construction.

[0024] Specifically, the temporal modeling module for identification behavior aggregates identification events from the RFID identification and data acquisition module based on asset dimensions and performs time-ordered processing to achieve temporal modeling of fixed asset identification behavior. This is based on event grouping and time-ordering rules. The module first receives the identification event stream output by the RFID identification and data acquisition module. The event stream consists of multiple structured identification events, each containing at least the following fields: asset identifier, identification timestamp, and node identifier. The asset identifier originates from the RFID tag, and the identification timestamp originates from the time information generated by the read / write node when identification occurs. Subsequently, the identification event stream is grouped according to the asset identifier within the event, so that identification events with the same asset identifier are grouped into the same event set, thus logically forming a single... The module identifies a sequence of events for each fixed asset. After grouping the events, for any set of events corresponding to a fixed asset, the event set is sorted chronologically based on the timestamps contained in the identified events. The sorting rule is to arrange the events in ascending order from smallest to largest timestamp, ensuring that the sorted set of identified events satisfies the temporal sequence. The timestamps are used as input parameters for sorting, and their values ​​are directly obtained when the identified events are generated. After sorting, the sorted set of identified events is the output of this module. This output maintains temporal continuity and sequential consistency on an asset-by-asset basis, serving as the basic input for constructing asset behavior feature data within a preset time window. This supports further analysis of the behavioral characteristics of fixed assets, such as identification frequency, activity continuity, and spatial changes, in different time periods.

[0025] Furthermore, in the behavior time-series modeling module, the steps for constructing asset behavior feature data within a preset time window include: Define a fixed-length time window or a sliding time window for each asset; The sorted recognition events are divided into corresponding time windows; For each event set within a time window, extract asset behavior characteristics, including the total number of events, consecutive identification interval, number of cross-region identifications, and the time of the first and last identifications; The extracted asset behavior feature data is output to the asset lifecycle status determination module.

[0026] Specifically, the behavior timing modeling module, after sorting RFID identification events by asset dimension, constructs asset behavior feature data through time window division and statistical feature extraction. This is achieved through time-constrained event aggregation and feature calculation. For each fixed asset, a preset time window is defined based on its corresponding sorted identification event sequence. The time window can be a fixed-length time interval or a sliding time interval that moves continuously along the time axis. The start and end times of the time window are determined by the identification timestamps in the identification events. Subsequently, identification events belonging to the same fixed asset and whose identification timestamps fall within the same time window are grouped into an event set, forming a local event sequence corresponding to that time window. Based on this, behavior feature extraction processing is performed on the event set within each time window. The total number of events is calculated from the number of events identified within the window. The consecutive identification interval is determined by the sequence of timestamp differences between adjacent identified events. The number of cross-region identifications is obtained by statistically analyzing the number of node identifier changes of adjacent identified events in the event set. The first identification time and the last identification time are taken as the minimum and maximum values ​​of the identification timestamps within the time window, respectively. The input data for all the above features are derived from the sorted sequence of identified events and are directly calculated from the event field. After feature extraction, the asset behavior feature data corresponding to each time window is used as the output result. This output result is organized with assets and time windows as indexes and is transmitted to the asset lifecycle status determination module for subsequent determination of lifecycle status based on changes in asset behavior, thereby providing structured, time-related behavioral feature input for asset status analysis.

[0027] The asset lifecycle status determination module is used to generate candidate status determination results from a preset lifecycle status set based on asset behavior feature data. Furthermore, in the asset lifecycle status determination module, the step of generating candidate status determination results from the preset lifecycle status set includes: Establish a preset lifecycle state set, which includes the states of pending accounting, normal use, high-intensity use, idle, inefficient, pending disposal, and exited. Define judgment rules for each lifecycle stage, and link these judgment rules to asset behavior characteristic data; Match asset behavior characteristics data with the rules for determining each lifecycle state; Generate state candidate determination results, including the corresponding candidate states and their determination information; The state candidate determination results are output to the life cycle state evolution control module.

[0028] Specifically, the asset lifecycle status determination module uses rule matching and candidate generation to determine the lifecycle status based on asset behavior feature data. It maps structured behavior features to a preset lifecycle status set and generates candidate determination results. First, a preset lifecycle status set is established, consisting of states such as pending accounting, normal use, high-intensity use, idle, inefficient, pending disposal, and exited. Each lifecycle status corresponds to a set of determination rules associated with the asset behavior feature data. The asset behavior feature data, as input, is output by the behavior timing modeling module. It includes features such as the total number of events within a preset time window, continuous identification interval, cross-region identification count, and the first and last identification times. These features are directly derived from the timestamps and node identification information of RFID identification events. In the status determination... During the determination process, the asset behavior feature data formed by an asset within a corresponding time window is matched one by one with the judgment rules corresponding to each life cycle state. The matching process can be represented as a conformity judgment of a given asset behavior feature vector with the constraints of each state rule. When the asset behavior feature meets the rule constraints of a certain life cycle state, a candidate judgment result corresponding to that life cycle state is generated. The generated state candidate judgment result is used as the output result, which at least includes the candidate life cycle state identifier and the feature matching information related to the judgment, and is used to characterize the life cycle state that the asset may be in within the current time window. Subsequently, the state candidate judgment result is transmitted to the life cycle state evolution control module as input data for subsequent confirmation of state legality and state evolution control based on evolution rules, thereby realizing the decoupling of life cycle state determination and state evolution control.

[0029] The lifecycle state evolution control module is used to confirm the legality of the candidate judgment results based on the preset evolution rules and the candidate judgment results; the evolution rules include allowing transition paths, prohibiting unreasonable jumps, continuous state confirmation, abnormal rollback and final state freezing mechanism; Furthermore, in the lifecycle state evolution control module, the steps for confirming the legality of the candidate determination results based on the preset evolution rules and the state candidate determination results include: The state candidate determination result is compared with the preset allowed transition path; Check the consistency of state candidate determination results within a continuous time window; The status candidate determination results are confirmed based on the preset duration threshold. Rollback processing is performed on the short-term abnormal state candidate determination results; The final state candidate determination results that are scrapped or have been withdrawn are frozen; Output the confirmed lifecycle status to the asset management strategy linkage module.

[0030] Specifically, the lifecycle state evolution control module uses the asset's confirmed lifecycle state at the previous moment and the state candidate determination results output by the asset lifecycle state determination module as input data. The state candidate determination results consist of candidate state identifiers and their corresponding determination time series information. This time series information originates from the mapping results of asset behavior characteristic data within a continuous collection period. The module first constrains and judges the transition relationship between the current candidate state and the previously confirmed state based on a preset set of lifecycle state evolution rules. The evolution rules include at least a set of allowed transition paths to limit the logical order of state transitions and rules prohibiting unreasonable jumps to exclude state abrupt changes inconsistent with the actual use logic of the asset. Based on this, the module statistically analyzes the consistency of candidate states for the same asset within a continuous time window. When a candidate state remains consistent within a continuous sampling period and its duration reaches a preset state continuity confirmation threshold, the candidate state is considered to be in a state of consistency. The lifecycle state evolution control module generates a confirmed state result when the state meets the evolution stability requirements. When a candidate state appears only for a short period of time and does not reach the duration threshold, the candidate result is rolled back to the previous confirmed state according to the anomaly rollback rule to reduce the impact of short-term anomaly identification on the lifecycle determination result. For candidate states that have been determined to be scrapped or have been withdrawn, a final state freezing mechanism is triggered when the final state determination condition is met, so that the asset will no longer participate in the state transition judgment in the subsequent evolution process, thereby avoiding the final state asset being repeatedly included in the evolution process. Finally, the lifecycle state evolution control module outputs the lifecycle confirmed state after legality verification and evolution rule constraints. This confirmed state, as the only valid lifecycle identifier of the asset at the current stage, is transmitted to the asset management strategy linkage module to drive the generation of subsequent management actions, including maintenance plan adjustments, allocation strategy restrictions, and disposal process triggering, thereby realizing the continuous connection and consistency control between the state determination result and the management decision execution in asset lifecycle management.

[0031] Furthermore, in the lifecycle state evolution control module, the preset evolution rules further include: setting a manual confirmation and freezing mechanism for scrapped or exited states; allowing rollback for short-term abnormal candidate states; and performing multi-window consistency confirmation for persistent state candidate results.

[0032] Specifically, the lifecycle state evolution control module takes the confirmed lifecycle state of the asset in the previous evolution cycle and the state candidate judgment results output by the asset lifecycle state judgment module as input. The state candidate judgment results are derived from the continuous analysis of asset behavioral characteristic data over time. This behavioral characteristic data is extracted from operation records, timestamp information, and usage frequency statistics generated during the asset's procurement, use, maintenance, transfer, and disposal processes. The lifecycle state evolution control module first constrains the legality of the transfer relationship between the current candidate state and the previously confirmed state according to preset allowed transfer path rules. If a candidate state does not meet the predetermined state evolution order, it is judged as an unreasonable jump and not confirmed. Under the premise of meeting the transfer path constraints, the consistency of the candidate state across multiple consecutive time windows is jointly judged by analyzing the frequency and duration of the candidate state's occurrence in different time windows. The system calculates that when a candidate state remains consistent for at least two time windows and its cumulative duration is not less than a preset continuous confirmation threshold, a state confirmation result is generated. Otherwise, the candidate state is considered a short-term anomaly and is rolled back to the previous confirmed state according to the anomaly rollback rule. When the candidate state is a scrapped state or an exited state, a manual confirmation freezing mechanism is triggered after the evolution rule constraints are met. The state is finally locked through a manual confirmation signal, so that the asset will no longer participate in the state update judgment in the subsequent evolution process, thus forming a final state freeze result. The life cycle state evolution control module finally outputs the life cycle confirmation state after rule verification, multi-window consistency confirmation, and necessary manual confirmation processing. This confirmation state is transmitted to the asset management strategy linkage module as the asset's currently unique and valid life cycle identifier to trigger the corresponding management strategy execution logic, so as to ensure the continuity, traceability, and consistency of the asset life cycle state evolution results at the management decision level.

[0033] The asset management strategy linkage module is used to trigger corresponding asset management operations based on the lifecycle state confirmed by the lifecycle state evolution control module. Furthermore, in the asset management strategy linkage module, the steps to trigger the corresponding asset management operation based on the lifecycle state confirmed by the lifecycle state evolution control module include: Receive the confirmed lifecycle status, which is provided by the lifecycle status evolution control module; Search for matching strategies in a predefined set of management strategies. The set of management strategies includes allocation strategies, reuse strategies, maintenance or inspection strategies, disposal approval strategies, and ledger cancellation strategies that correspond to different lifecycle states. Management actions are triggered based on the matched management policy. These actions include generating action reminders, sending approval requests, or updating asset ledgers. Output the management operation information triggered.

[0034] Specifically, the asset management strategy linkage module takes the confirmed lifecycle status output by the lifecycle status evolution control module as input. This confirmed status is formed by continuously judging and verifying the evolution of asset behavior characteristic data, reflecting the actual use and management stage of the asset within the current management cycle. The asset management strategy linkage module first receives and parses the lifecycle status identifier, and uses the lifecycle status as an index condition to perform a matching search in a pre-built set of management strategies. The set of management strategies is pre-configured by asset management rules, and different lifecycle statuses are associated with management strategy types such as allocation, reuse, maintenance, inspection, disposal approval, and ledger cancellation. After completing the strategy matching, the asset management... The strategy linkage module generates corresponding management operation instructions based on the matching results. These instructions are triggered by the asset lifecycle status and, in conjunction with basic asset attribute information, generate operation reminders, approval requests, or ledger update instructions. The basic asset attribute information originates from the initial asset filing and historical management records. Subsequently, the generated management operation instructions are output to the corresponding management execution unit or business system to drive subsequent asset allocation, maintenance work order generation, disposal approval process initiation, or asset ledger status updates. This achieves a closed-loop linkage between asset lifecycle status and management behavior, ensuring that the lifecycle status determination results are effectively applied in asset management practice and form traceable management operation records.

[0035] The parameter feedback and rule optimization module is used to adjust the time series modeling, state determination and evolution rules based on the lifecycle status of asset management operations or confirmations. The data flow between the modules follows the sequence of RFID identification event collection, behavioral feature construction, status determination, status evolution confirmation, management operation triggering, and parameter feedback adjustment. The asset lifecycle status determination module and the lifecycle status evolution control module are set up independently, and the data is only transmitted in this order. Furthermore, in the parameter feedback and rule optimization module, the steps for adjusting the time-series modeling, state determination, and evolution rules based on the lifecycle status of asset management operations or confirmations include: Receive asset management operation information triggered by the asset management strategy linkage module; Receive the lifecycle state confirmed by the lifecycle state evolution control module; Match the lifecycle state with the corresponding set of rule parameters; Adjust the rule parameters of the behavior recognition time-series modeling module; Adjust the rule parameters of the asset lifecycle status determination module; Adjust the rule parameters of the lifecycle state evolution control module; Save the adjusted rule parameters to the corresponding module; Generate rule update records for system monitoring or further optimization.

[0036] Specifically, the parameter feedback and rule optimization module takes the asset management operation information output by the asset management strategy linkage module and the lifecycle state confirmed by the lifecycle state evolution control module as inputs. The asset management operation information comes from executed or triggered management actions such as allocation, maintenance, disposal, or ledger updates. The confirmed lifecycle state comes from the final state identifier after performing evolution rule verification on the candidate state results. Based on the input information, the parameter feedback and rule optimization module matches the lifecycle state with a pre-established set of rule parameters. These rule parameter sets correspond to the rule configurations for the three stages: behavior timing modeling, lifecycle state determination, and lifecycle state evolution control. Their content comes from the system's initial configuration and historical operation records. After matching, the parameter feedback and rule optimization module, based on the consistency between the confirmed lifecycle state and the actual management operation, optimizes the behavior timing... The time window length or event statistics threshold parameters in the sequential modeling phase are adjusted, the feature judgment conditions used to distinguish different states in the life cycle state determination phase are modified, and the continuous confirmation conditions or rollback conditions in the life cycle state evolution control phase are updated. The adjustment process is implemented by replacing rule parameters or updating values, without involving retrospective modification of existing identification events or historical state results. Subsequently, the adjusted rule parameters are written to the rule configuration storage area of ​​the corresponding module, so that the subsequent RFID identification event processing, behavioral feature construction, state determination and evolution confirmation processes are executed according to the updated rules. At the same time, rule update records containing rule adjustment time, adjustment object and associated life cycle state are generated for system operation monitoring and subsequent optimization analysis, thereby forming a parameter feedback closed loop based on management results and state confirmation results, improving the continuous adaptability and stability of the asset life cycle management process.

[0037] The asset lifecycle state determination module and the lifecycle state evolution control module are implemented independently. The state candidate results output by the determination module are only used as inputs to the evolution control module, and the determination module and the evolution control module do not share internal state variables or execution logic.

[0038] Specifically, the asset lifecycle state determination module and the lifecycle state evolution control module are implemented separately with functional decoupling. The asset lifecycle state determination module generates candidate state determination results based on input asset behavior characteristic data, according to a preset lifecycle state set and corresponding determination rules. These candidate state determination results are output in the form of structured state identifiers and associated determination information, which can be represented as a candidate state set. This set only reflects the possible lifecycle states obtained based on behavior characteristics within the current time window and does not include historical state information or evolutionary constraint information. The lifecycle state evolution control module independently receives the candidate state determination results as external input and performs a legality confirmation process based on its own maintained preset evolution rules. These evolution rules describe the permissible transition relationships and persistence constraints between lifecycle states, and their rule parameters only contain... The storage and operation are integrated within the lifecycle state evolution control module. During operation, the state determination module does not access, read, or modify evolution rule parameters or historical confirmation states. During data flow, the state candidate determination result serves as the only data object transmitted across modules. Its transmission form does not include intermediate variables, feature calculation processes, or determination logic within the state determination module. This allows the lifecycle state evolution control module to complete the confirmation process based solely on the input state candidate result and its own rules when performing continuous state confirmation, abnormal rollback, or final state freeze processing. Finally, it outputs the confirmed lifecycle state for use by the subsequent asset management strategy linkage module. Through the above method, the independent settings of state determination and state evolution control at the data, rule, and execution logic levels are achieved, ensuring clear functional boundaries at each stage and facilitating stable system operation and subsequent rule adjustments.

[0039] Example 2: A fixed asset lifecycle management method based on RFID, including the following steps: S1. Uniquely identify fixed assets with RFID and collect corresponding RFID identification events when fixed assets are in different locations or scenarios. S2. Sort RFID identification events by asset dimension and construct asset behavior feature data based on the sorted RFID identification events within a preset time window. S3. Based on asset behavior characteristic data, generate candidate state determination results from a preset lifecycle state set; S4. Based on the preset evolution rules and the state candidate determination results, the legality of the state candidate determination results is confirmed. The evolution rules include allowing transition paths, prohibiting unreasonable jumps, continuous state confirmation, abnormal rollback and final state freezing mechanism. S5. Based on the legally confirmed lifecycle status, trigger the corresponding asset management operation; S6. Adjust the rules for constructing asset behavior characteristics, determining life cycle status, and evolution rules based on asset management operations or confirmed life cycle status.

[0040] In the scenario of centralized fixed asset management across multiple industrial parks for large group enterprises, these enterprises typically have numerous and complex fixed assets located in various office parks, production plants, and warehousing areas. These assets frequently undergo changes in location, usage intensity, and status during daily use. Traditional management methods relying on manual registration or periodic inventory checks struggle to reflect the actual usage status of assets in a timely and accurate manner. This can lead to problems such as assets being considered in use despite being idle for extended periods, assets not being disposed of promptly after being phased out, and abnormal asset transfers being difficult to detect in a timely manner, thus affecting asset utilization efficiency and the accuracy of management decisions. To address these issues, this invention employs an RFID-based fixed asset lifecycle management method, the process of which is as follows: Figure 2 As shown. The specific implementation process of this method is as follows.

[0041] In step S1, fixed assets within the enterprise are uniquely identified by RFID, and corresponding RFID identification events are collected when the fixed assets are in different locations or business scenarios. By binding RFID tags to the assets and deploying RFID reading and writing nodes at park entrances and exits, warehouses, production areas and office areas, the identification events of assets in different locations and scenarios can be automatically collected, so that the actual flow and use of assets can be continuously recorded, thereby avoiding the problem of asset status lag caused by relying solely on manual ledgers.

[0042] In step S2, RFID identification events are sorted by asset dimension and asset behavior feature data is constructed based on the sorted RFID identification events within a preset time window. By sorting the identification events of the same asset within a continuous time period and extracting behavioral features such as identification frequency, continuous identification interval and cross-region identification number within the time window, the usage activity and spatial changes of the asset at different stages can be expressed in the form of structured features, providing a stable and comparable input basis for subsequent status analysis.

[0043] In step S3, based on asset behavior feature data, candidate status judgment results are generated in a preset life cycle status set. By matching the behavior feature data of the asset formed within the time window with the judgment rules corresponding to each life cycle status, the status of the asset that may be in the state of pending accounting, normal use, idle, inefficient or pending disposal is initially identified. This transforms the asset status judgment from experience-based judgment to rule-based analysis based on behavior data, thereby improving the objectivity and consistency of status recognition.

[0044] In step S4, based on the preset evolution rules and the state candidate determination results, the legality of the state candidate determination results is confirmed. Through evolution rules such as allowing transfer paths, continuous state confirmation, and abnormal rollback, candidate states that are abnormal or unreasonable in the short term are constrained and verified, so that the changes in the asset life cycle state are in line with the actual management logic, and frequent state switching caused by short-term identification fluctuations are avoided, thereby ensuring the stability and traceability of the life cycle state results.

[0045] In step S5, based on the legally confirmed lifecycle status, the corresponding asset management operation is triggered. The confirmed lifecycle status is used as the basis for management decisions, and management operations such as maintenance inspection, transfer and reuse or disposal approval are automatically matched and triggered, so that asset management behavior can be executed synchronously with the actual status of assets, reducing manual intervention and improving asset management response efficiency.

[0046] In step S6, based on asset management operations or confirmed lifecycle states, the rules for constructing asset behavior characteristics, lifecycle state determination rules, and evolution rules are adjusted. By feeding back the management execution results and lifecycle states to the rule configuration layer, the time window parameters, determination thresholds, or evolution conditions are modified, enabling the system to continuously optimize the determination logic according to actual operating conditions. This forms a closed-loop adjustment mechanism for asset lifecycle management, improving adaptability and stability in long-term operation.

[0047] By applying this method to the aforementioned cross-park fixed asset management scenario, the entire process of asset identification, behavior analysis, status determination, management linkage, and rule optimization is automated and data-driven, effectively solving the problems of opaque status, delayed response, and insufficient basis for management decisions in traditional fixed asset management.

[0048] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fixed asset lifecycle management system based on RFID, characterized in that, include: The RFID identification and data acquisition module is used to uniquely identify fixed assets and collect RFID identification events in different locations or scenarios. The behavior timing modeling module is used to sort the RFID identification events by time according to the asset dimension and construct asset behavior feature data within a preset time window. The asset lifecycle status determination module is used to generate candidate status determination results from a preset lifecycle status set based on the asset behavior feature data. The lifecycle state evolution control module is used to confirm the legality of the candidate judgment results based on preset evolution rules and the candidate judgment results; the evolution rules include allowing transition paths, prohibiting unreasonable jumps, continuous state confirmation, abnormal rollback and final state freezing mechanism; The asset management strategy linkage module is used to trigger corresponding asset management operations based on the life cycle state confirmed by the life cycle state evolution control module. The parameter feedback and rule optimization module is used to adjust the time series modeling, state determination and evolution rules based on the lifecycle status of asset management operations or confirmations. The data flow between the modules follows the sequence of RFID identification event collection, behavior feature construction, status determination, status evolution confirmation, management operation triggering, and parameter feedback adjustment. The asset lifecycle status determination module and the lifecycle status evolution control module are set up independently, and the data is only transmitted in this order.

2. The RFID-based fixed asset lifecycle management system according to claim 1, characterized in that, In the RFID identification and data acquisition module, the steps of uniquely identifying fixed assets and collecting RFID identification events in different locations or scenarios include: Configure a unique RFID tag for each fixed asset; Deploy distributed RFID reader / writer nodes for asset identification; When an asset enters or passes through the RFID node coverage area, the distributed RFID reader / writer node reads the RFID tag information and generates an identification event. The generated identification events are standardized, including recording asset identifiers, node identifiers, identification timestamps, and identification location or business scenario information; The standardized recognition events are transmitted to the recognition behavior timing modeling module or stored in a temporary data buffer for subsequent module processing.

3. The RFID-based fixed asset lifecycle management system according to claim 1, characterized in that, In the behavior timing modeling module, the step of sorting the RFID identification events by asset dimension includes: Receive identification event streams from the RFID identification and data acquisition module; The identification events are grouped according to the asset identification information, with each asset forming a separate event set; Within the event set of each asset, events are sorted in ascending order based on their identified timestamps; The sorted set of recognized events is used as input for subsequent feature construction.

4. The RFID-based fixed asset lifecycle management system according to claim 1, characterized in that, In the behavior time-series modeling module, the step of constructing asset behavior feature data within a preset time window includes: Define a fixed-length time window or a sliding time window for each asset; The sorted recognition events are divided into corresponding time windows; For each event set within a time window, extract asset behavior characteristics, including the total number of events, consecutive identification interval, number of cross-region identifications, and the time of the first and last identifications; The extracted asset behavior feature data is output to the asset lifecycle status determination module.

5. The RFID-based fixed asset lifecycle management system according to claim 1, characterized in that, In the asset lifecycle status determination module, the step of generating candidate status determination results from a preset lifecycle status set includes: Establish a preset lifecycle state set, which includes the state of pending accounting, normal use, high-intensity use, idle, inefficient, pending disposal, and exited. Establish judgment rules for each lifecycle state, and the judgment rules are associated with the asset behavior characteristic data; The asset behavior characteristic data is matched with the lifecycle status determination rules; Generate the state candidate determination result, including the corresponding candidate state and its determination information; The state candidate determination result is output to the life cycle state evolution control module.

6. The RFID-based fixed asset lifecycle management system according to claim 1, characterized in that, In the lifecycle state evolution control module, the step of confirming the legality of the candidate determination result based on the preset evolution rules and the state candidate determination result includes: The state candidate determination result is compared with the preset allowed transfer path; Check the consistency of the state candidate determination results within a continuous time window; The state candidate determination result is confirmed according to a preset duration threshold. Perform rollback processing on the state candidate determination results of short-term anomalies; The final state candidate determination results that are scrapped or have been exited are frozen; Output the confirmed lifecycle status to the asset management strategy linkage module.

7. The RFID-based fixed asset lifecycle management system according to claim 1, characterized in that, In the asset management strategy linkage module, the step of triggering the corresponding asset management operation based on the lifecycle state confirmed by the lifecycle state evolution control module includes: Receive a confirmed lifecycle status, which is provided by the lifecycle status evolution control module; Search for matching strategies in a predefined set of management strategies, which includes allocation strategies, reuse strategies, maintenance or inspection strategies, disposal approval strategies, and ledger cancellation strategies corresponding to different lifecycle states. Management operations are triggered based on the matched management policy, including generating operation reminders, sending approval requests, or updating asset ledgers. Output the management operation information triggered.

8. The RFID-based fixed asset lifecycle management system according to claim 1, characterized in that, In the parameter feedback and rule optimization module, the steps of adjusting the time-series modeling, state determination, and evolution rules based on the lifecycle state of asset management operations or confirmations include: Receive asset management operation information triggered by the asset management strategy linkage module; Receive the lifecycle state confirmed by the lifecycle state evolution control module; Match the lifecycle state with the corresponding set of rule parameters; Adjust the rule parameters of the behavior recognition time-series modeling module; Adjust the rule parameters of the asset lifecycle status determination module; Adjust the rule parameters of the lifecycle state evolution control module; Save the adjusted rule parameters to the corresponding module; Generate rule update records for system monitoring or further optimization.

9. The RFID-based fixed asset lifecycle management system according to claim 1, characterized in that, The asset lifecycle state determination module and the lifecycle state evolution control module are implemented independently. The state candidate results output by the determination module are only used as inputs to the evolution control module, and the determination module and the evolution control module do not share internal state variables or execution logic.

10. The RFID-based fixed asset lifecycle management system according to claim 1, characterized in that, In the lifecycle state evolution control module, the preset evolution rules further include: setting a manual confirmation and freezing mechanism for scrapped or exited states; allowing rollback for short-term abnormal candidate states; and performing multi-window consistency confirmation for continuous state candidate results.

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