A digital twin driven college asset whole-process collaborative management system

Abstract

The application relates to the technical field of college asset management, and discloses a digital twin driven college asset whole-process collaborative management system, which comprises a business mapping module, a collaborative arbitration module and a logic verification module. The business mapping module is used for calling an administrative state vector and a physical state vector of a target asset, establishing an associated mapping relationship between the two to construct a logic entity. The collaborative arbitration module is used for determining a confidence decay rate according to a business intensity level, and correcting a benchmark confidence to generate a real-time confidence. The logic verification module is used for comparing the real-time confidence with a preset threshold when an administrative business request is received, so as to trigger a physical perception instruction on demand or directly feed back a management decision result. The application converts a college business rhythm into a monitoring prior weight, accurately matches a supervision intensity with an asset circulation risk, and reduces an on-campus wireless network bandwidth load in an administrative resting period.

Description

Technical Field

[0001] This invention belongs to the field of college asset management technology, and in particular relates to a digital twin-driven collaborative management system for the entire process of college assets. Background Technology

[0002] Current institutional asset management typically employs an administrative approval workflow coupled with a static accounting database model. This model assumes logical synchronization between administrative instructions and the physical state of assets. However, as institutional business density increases, the administrative operation nodes and physical displacement nodes exhibit inherent asynchronous characteristics. This results in a lack of effective conflict arbitration mechanisms during the logical window between the issuance of approval instructions and the physical feedback, leading to technical problems such as discrepancies between accounts and physical assets and failures in asset tracking. Regarding the synchronization requirements at the physical level, adopting a single path of increasing the frequency of physical sensing results in high hardware deployment costs and induces bandwidth congestion in wireless communication networks. Furthermore, because existing solutions lack deep integration with business logic such as academic scheduling and research cycles, the system struggles to identify the non-uniform distribution of asset flow risks over time. This leads to redundant detection loads during periods of business inactivity, while the lack of prior weight guidance during high-frequency turnover periods prevents the on-demand allocation of regulatory resources.

[0003] Besides the mismatch between management processes and physical perception response dimensions, data control methods have shortcomings in handling high-concurrency requests. For example, Chinese invention patent CN121217822A discloses a water conservancy IoT concurrent processing method and device, which realizes data hierarchical processing through three-dimensional value scoring and self-learning mechanism to alleviate the pressure of high-load processing. However, in the scenario of university asset management, the general concurrent processing mode relies on historical access popularity and real-time load status feedback for adjustment. It cannot perceive the impact of strong prior business rules such as teaching schedules and scientific research cycles on asset liquidity. Asset status data is isolated and treated as data packets to be processed for throughput control. It lacks the logical alignment and arbitration capabilities between administrative ownership and physical trajectory. When faced with cross-departmental malicious occupation of assets or complex transfer business, it cannot fundamentally solve the problem of low regulatory efficiency caused by the mismatch between administrative approval flow and the dynamic of physical assets.

[0004] Therefore, the technical problem to be solved by this invention is how to provide a digital twin-driven collaborative management system for the entire process of school assets that can sense business rhythms and realize asynchronous consistency arbitration of states, so as to solve the problem of low regulatory efficiency caused by the mismatch between administrative approval flow and the dynamics of physical assets. Summary of the Invention

[0005] This invention provides a digital twin-driven collaborative management system for the entire process of school assets, comprising:

[0006] The business mapping module is used to retrieve the administrative status vector and physical status vector of the target asset, and establish the association mapping between the administrative status vector and the physical status vector in the digital twin space to construct a logical entity representing the consistency status of the target asset's accounts and physical assets. The administrative status vector includes the administrative authorization period, the identifier of the management unit to which it belongs, and the business popularity factor determined by the academic affairs scheduling data. The physical status vector includes the timestamp of the last physical observation and spatial coordinate data.

[0007] The collaborative arbitration module is used to determine the baseline confidence level of a logical entity based on the physical state vector, and to determine the confidence level decay rate based on the business popularity factor. The confidence level decay rate is used to perform time-shift correction processing on the baseline confidence level to generate the real-time confidence level. The business popularity factor corresponds to the predicted displacement frequency of the target asset in the current administrative cycle. The higher the business popularity factor, the greater the confidence level decay rate.

[0008] The logical verification module is used to retrieve the real-time confidence level and compare it with a preset verification threshold when an administrative approval request for a target asset is received. If the real-time confidence level is lower than the verification threshold, a physical perception command is generated to trigger the update of the physical state vector and perform a physical verification action for the target asset. If the real-time confidence level is not lower than the verification threshold, the system automatically freezes the state evolution of the logical entity and directly feeds back the decision result for the administrative approval request.

[0009] Preferably, the processing logic for determining the business popularity factor by the collaborative arbitration module is as follows: retrieve business scheduling data of the administrative region to which the target asset belongs through the data synchronization interface, identify the teaching business level or scientific research business level corresponding to the current timestamp; retrieve the corresponding quantitative intensity coefficient in the preset intensity mapping table according to the business level; determine the business popularity factor based on the quantitative intensity coefficient, and use the business popularity factor to calculate the confidence decay rate, so as to accelerate the decay of real-time confidence during periods with higher teaching business level or scientific research business level.

[0010] Preferably, the system also includes a supplementary verification module, whose input is connected to the logical verification module. When the real-time confidence level is lower than the verification threshold and physical perception instruction feedback is missing, the module extracts the set of associated assets of the target asset in the digital twin space, obtains the operational status data of active entities in the set of associated assets, calculates the logical confidence level compensation increment by analyzing the operational status data of active entities and the administrative association characteristics of the target asset, and uses the logical confidence level compensation increment to perform incremental correction on the real-time confidence level.

[0011] Preferably, the system also includes a risk assessment module, used to calculate the risk of ownership drift based on the physical state vector and the administrative state vector: extracting the authorized geographical area defined by the administrative state vector and determining the geometric center of the authorized geographical area; calculating the spatial deviation of the spatial coordinate data in the physical state vector relative to the geometric center; if the spatial deviation exceeds the preset risk boundary and the residence time of the target asset in the unauthorized area exceeds the preset time threshold, the risk assessment module generates an ownership anomaly warning signal.

[0012] Preferably, the collaborative arbitration module calculates the real-time confidence level. The logic follows the formula below: ,in, As the baseline confidence level, For the preset attenuation coefficient, As a business popularity factor, This represents the time difference between the current time and the previous physical observation timestamp, with the unit of time difference being [unit missing]. , It is a natural constant.

[0013] Preferably, when the business mapping module establishes a logical entity, it performs chain-based tracing of administrative ownership: obtaining the administrative department in charge of the target asset, the person in charge of using it, and the identifier of the scientific research project currently being undertaken; and performing topological inclusion relationship verification between the jurisdiction of the administrative department and the spatial coordinate data in the physical state vector to determine the legality of the administrative ownership of the logical entity.

[0014] Preferably, the system also includes a load sensing module for monitoring the real-time load status of the university's wireless communication network: when the real-time load status exceeds a preset congestion threshold, the load sensing module sends a threshold adjustment signal to the logic verification module; in response to the threshold adjustment signal, the logic verification module lowers the value of the verification threshold, so as to reduce the frequency of physical sensing command generation by relaxing the confidence tolerance when the network is congested.

[0015] Preferably, the system also includes a trajectory recording module, which stores real-time confidence level, business popularity factor and related data pairs of administrative approval requests at a preset sampling frequency to record the state evolution trajectory of logical entities; the state evolution trajectory is used to build an asset operation efficiency model and evaluate the cost of maintaining consistency between accounts and physical assets under different administrative cycles.

[0016] Preferably, when the logic verification module outputs the decision result, it executes the following feedback logic: the quantitative value of the real-time confidence level is attached as a confidence level label to the feedback message of the administrative approval request; if the real-time confidence level is within a preset safety range, the feedback message includes an automatic approval suggestion; if the real-time confidence level is within a preset risk assessment range, the feedback message includes a manual review instruction.

[0017] Preferably, the system also includes a data synchronization interface for heterogeneous data interaction with external academic affairs management systems and scientific research project management systems; the data synchronization interface uses a metadata conversion template to convert the received business scheduling data into a business state machine model that can be recognized by the collaborative arbitration module, so as to realize the feedback guidance of administrative business rhythm on asset management logic.

[0018] Compared with existing technologies, the digital twin-driven collaborative management system for the entire process of school assets of this invention has the following advantages:

[0019] 1. In the collaborative management of the entire process of school assets, the system establishes a virtual entity that maps the management attributes of physical assets. By introducing a collaborative weight factor that decays over time, the real-time nature of physical observation is represented, which solves the asynchronous conflict between administrative approval instructions and the physical state of assets in the time dimension. The collaborative arbitration engine triggers the verification of logical confidence based on the received administrative approval request, and only activates the physical detection instruction when the confidence is lower than the threshold. This logical closed loop of on-demand perception ensures that management decisions are always based on the true consistency between accounts and physical assets.

[0020] 2. The system obtains academic affairs scheduling and scientific research project cycle data of colleges and universities through the business mapping unit, extracts the business heat factor that reflects the predicted value of asset displacement frequency, and uses this factor to dynamically correct the attenuation slope of the weight factor. This mechanism of transforming the pre-scheduling information of the administrative dimension into the prior weight of monitoring intensity enables the system's regulatory intensity to be accurately matched with the risk of non-homogeneous business flow within the colleges and universities. Under the premise of ensuring the regulatory intensity of sensitive nodes, it reduces the internal wireless communication bandwidth load generated by redundant detection during the administrative rest period.

[0021] 3. The associated witness module extracts the administrative or physical association characteristics between the target asset and its associated asset set. In extreme scenarios where direct detection feedback of the target asset is missing, incremental compensation of logical confidence is performed by utilizing the operational status of active entities. This mechanism utilizes the logical deduction capability of multi-source business evidence within the institution to avoid interruption of asset management continuity caused by local network signal blind spots and improve the management resilience of the system in complex building environments. Attached Figure Description

[0022] Figure 1 This is a flowchart of the digital twin-driven asset management business collaboration and logic verification process of the present invention;

[0023] Figure 2 This invention relates to the overall architecture and network topology of the collaborative management system for the entire process of college assets. Detailed Implementation

[0024] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0025] It should be noted that all directional and positional terms used in this invention, such as: up, down, left, right, front, back, vertical, horizontal, inner, outer, top, bottom, transverse, longitudinal, center, etc., are only used to explain the relative positional relationship and connection between components in a specific state (as shown in the accompanying drawings). They are only for the convenience of describing this invention and do not require that this invention be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the descriptions of "first," "second," etc., in this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0026] In the description of this invention, unless otherwise explicitly specified and limited, the terms installation, connection, and linking should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to the internal communication between two components. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0027] In the description of this specification, references to the terms "an embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0028] This invention provides a digital twin-driven collaborative management system for the entire process of school assets, including a business mapping module, a collaborative arbitration module, and a logical verification module. The business mapping module retrieves administrative data and IoT sensing data of the target asset, establishes an association mapping between the administrative state vector in the administrative data and the physical state vector in the IoT sensing data in the digital twin space, and generates a logical entity representing the consistency status of the asset's accounting records. The collaborative arbitration module calculates the real-time confidence level over time. When receiving administrative business requests, the logic verification module verifies the information based on real-time confidence levels. Compared with the preset verification threshold The quantitative comparison results determine the feedback management decision or trigger physical sensing commands to achieve a match between regulatory intensity and asset transfer risk; the business mapping module performs digital modeling for the target asset; and extracts administrative authorization period, management unit identifier, and business popularity factor determined by business scheduling data from the university asset database. The system constructs an administrative status vector; simultaneously, the business mapping module retrieves the physical observation timestamps and spatial coordinate data of the target asset through the Internet of Things access network to construct a physical status vector; the system performs chain-based tracing of administrative ownership to obtain the administrative department in charge of the target asset, the person in charge of use, and the identification of the scientific research project undertaken; it performs topological inclusion relationship verification between the jurisdiction of the administrative department and the spatial coordinates in the physical status vector to determine the administrative ownership status of the logical entity.

[0029] Business popularity factor The predicted displacement frequency of the target asset within the current administrative cycle is used to reflect the expected impact of academic and research activities on asset liquidity. This predicted displacement frequency is not based on current real-time observation, but rather on the historical average displacement frequency of similar businesses seven natural days prior to the current time point, extracted from the academic affairs management system via a data synchronization interface. The system updates the historical sample values ​​in the intensity mapping table every 24 hours using a sliding window algorithm, thus giving the business heat factor of the current cycle a priori predictive attribute based on statistical regularities, rather than relying on immediate physical feedback. The system constructs the intensity mapping table by retrieving spatial coordinate data of the target asset within a preset historical observation period and calculating the average displacement frequency per unit hour. The average displacement frequency The data is divided into five discrete gradients based on numerical values. The arithmetic mean of all observed samples within each gradient is used as the corresponding quantitative intensity coefficient. The first gradient corresponds to the statutory holiday administrative rest state, and the fifth gradient corresponds to the high-frequency flow state during the scientific research peak. The business mapping module retrieves the quantitative intensity coefficient from the intensity mapping table based on the current business scheduling data level to determine the business heat factor. The collaborative arbitration module performs asynchronous state consistency arbitration based on management weight decay; this module determines the baseline confidence level of logical entities based on the last update time of the physical state vector. And based on business popularity factors Determine the confidence decay rate; real-time confidence The calculation formula is as follows: ,in, For real-time confidence level, The baseline confidence level is set based on the quality of physical observations, and its initial value is set to [value] in this embodiment. , A preset attenuation coefficient is used, and it is set based on asset class to characterize the dynamic frequency differences of different types of assets in physical space. As a business popularity factor, The time difference between the current time and the physical observation timestamp, in units of 1. , It is a natural constant.

[0030] The collaborative arbitration module sets differentiated preset attenuation coefficients for different asset categories. The system categorizes assets into fixed assets, restricted mobility assets, and frequently traded assets; fixed assets have a preset depreciation coefficient. Set as Frequently traded assets have a preset decay coefficient. Set as Automatically load the corresponding preset attenuation coefficient by identifying the target asset class label. Calculate real-time confidence level At that time, preset attenuation coefficient Make real-time confidence The decay slope over time is matched with the physical flow characteristics of the asset. The collaborative arbitration module retrieves business scheduling data of the administrative region to which the target asset belongs through the data synchronization interface, identifies the teaching or scientific research business level corresponding to the current timestamp, and retrieves the quantitative intensity coefficient in the preset intensity mapping table to determine the business heat factor. The system retrieves academic scheduling data from heterogeneous databases via a data synchronization interface. It then uses a metadata conversion template to perform feature extraction of unstructured scheduling fields. The template includes a pre-defined mapping form containing course numbers, laboratory identifiers, and task density. The system parses the text stream data exported from the academic management system using regular expressions and fills it into specified attribute positions in the mapping form to construct a set of business events with discrete feature values. The collaborative arbitration module determines the quantitative intensity coefficient corresponding to each business level based on the participant density and equipment usage load parameters in the business event set, and obtains the business heat factor based on the current timestamp's state position in the business state machine model. The instantaneous value of .

[0031] When the logic verification module receives an administrative approval request for the target asset, it retrieves the real-time confidence level. and the preset verification threshold Perform the comparison; if the real-time confidence level is... Below the verification threshold This module generates physical sensing commands to trigger updates to the physical state vector and perform physical checks; if the real-time confidence level... Not lower than the verification threshold The system freezes the state evolution of logical entities and directly feeds back the decision results of administrative approval requests; the system also includes a supplementary verification module; and it provides real-time confidence levels. Below the verification threshold Furthermore, when physical perception command feedback is missing, the supplementary verification module extracts the set of associated assets of the target asset within the digital twin space and obtains the operational status data of active entities in this set. The supplementary verification module analyzes the operational status data of active entities and the administrative correlation characteristics of the target asset, and calculates the logical confidence compensation increment. The calculation method is as follows: ,in, To compensate for the incremental logical confidence level, The confidence mean of active entities in the associated asset set. This is the historical logical deviation coefficient between the target asset and the active entity; the collaborative arbitration module uses the logical confidence compensation increment to perform incremental correction on the real-time confidence to maintain the continuity of management logic.

[0032] The risk assessment module calculates ownership drift risk based on physical and administrative state vectors. This module extracts the authorized geographical area defined by the administrative state vector and determines its geometric center. The risk assessment module calculates the spatial deviation of the spatial coordinate data in the physical state vector relative to the geometric center. If the spatial deviation exceeds a preset risk boundary and the target asset's residence time in an unauthorized area exceeds a preset time threshold, the risk assessment module generates an ownership anomaly warning signal. The collaborative arbitration unit generates ownership restructuring or physical recovery orders based on the resource utilization efficiency indicators of relevant administrative departments. The system obtains the administrative authorization period from the administrative state vector and executes the regulatory sensitivity compensation procedure. The collaborative arbitration module calculates the time slack between the current observation time and the authorization deadline. And dynamically adjust the real-time confidence level based on this margin. The decay characteristics, when the time margin When the value decreases to within a preset time window, the system uses real-time confidence levels. The calculation formula introduces a constant term correction factor, causing the confidence trajectory to exhibit a non-linear accelerated decay trend, thus determining that the state change sensitivity of logical entities when facing the risk of overdue asset transfer is higher than that of the normal authorization cycle; the load perception module monitors the real-time load status of the university's wireless communication network; when the real-time load status exceeds the preset congestion threshold, the load perception module sends a threshold adjustment signal to the logic verification module; the logic verification module responds to the threshold adjustment signal by lowering the verification threshold. The value is adjusted to relax the confidence tolerance during network congestion, thereby reducing the frequency of physical sensing command generation.

[0033] The load awareness module executes based on measured bandwidth utilization. Verification threshold Dynamic correction: Monitoring the actual bandwidth utilization of traffic data from the core switch of the university's wireless communication network. Actual bandwidth utilization continued seconds exceeded Congestion initiation threshold At that time, the logic verification module uses the formula Calculate the verification threshold correction increment Load response coefficient for The logic verification module utilizes the correction increment. Reduce the initial verification threshold Relax the logical entity confidence tolerance boundary, reduce the frequency of physical sensing command issuance, and allow network load to fall back to the congestion initiation threshold. The following steps are taken: The initial threshold is restored; the behavior interaction module sends a status verification micro-instruction to the management terminal within the asset coverage area, receives the identity feature vector from the response metadata, and calculates its correlation matching degree with the preset authorization weight in the administrative status vector; when the matching degree is lower than the preset security boundary, the system marks the corresponding asset as having abnormal ownership; the trajectory recording module stores the real-time confidence level at a preset sampling frequency. Business popularity factor In addition, it records the state evolution trajectory of logical entities by linking administrative approval requests to assess the cost of maintaining consistency between accounts and reality under different administrative cycles.

[0034] Example 1: In university laboratories during peak periods of major scientific research projects, the frequency of target asset turnover increases due to the intensive nature of research tasks. The system faces the challenge of maintaining consistency between records and physical assets through full-scale probing under limited network bandwidth. The collaborative arbitration module retrieves the laboratory's research schedule data via a data synchronization interface, identifies the current business level, and obtains the corresponding quantitative intensity coefficient from the intensity mapping table, thus assigning a business heat factor. Set as The business mapping module retrieves the administrative status vector of a precision analysis instrument within the region, obtains the physical observation timestamp, and calculates the time difference. for The system calculates the real-time confidence level. Approximately 0.165, real-time confidence level The calculation formula is as follows: ,in, For real-time confidence level, The baseline confidence level is and its value is , The preset attenuation coefficient is given by the following value: , As a business popularity factor, The time difference between the current time and the physical observation timestamp, in units of 1. , It is a natural constant.

[0035] The logic verification module will calculate the real-time confidence level. Compared with the preset verification threshold Compare and determine the real-time confidence level. If the value falls below a preset verification threshold, a physical sensing command is issued to trigger the physical sensing action of the analyzer. The physical state vector responds to the physical sensing command to update the data. The system acquires the spatial coordinate data of the analyzer and displays the real-time confidence level. Reset to This enables real-time maintenance of the consistency between accounts and physical assets during periods of high asset liquidity. This process utilizes business activity factors. By adjusting the confidence decay logic, the business intensity characteristics in the administrative management logic are transformed into the trigger frequency that drives physical sensing actions, realizing an on-demand sensing loop based on management rhythm. The association between administrative data flow and physical sensing flow resolves the conflict between response sluggishness and bandwidth load caused by fixed-frequency detection in school asset management, enabling non-uniform allocation of management resources in physical and logical spaces. The logical entity state of the target asset in the business context is consistent with the administrative authorization intent. The logic verification module uses existing processing units to control the risk of asset transfer.

[0036] Example 2: In a test environment simulating the complex administrative management cycle of a university, the system faces the challenge of balancing the bandwidth usage of on-campus wireless communication with the accuracy of maintaining consistency between asset records and physical assets under different business intensities. The test platform uses the administrative database generated by the university's asset management center as the data source, and IoT data is collected by low-power wireless positioning base stations distributed on the experimental floors. Physical sensing signals are simulated by superimposing Gaussian white noise with a signal-to-noise ratio of 20dB to reflect electromagnetic interference in the campus environment; core parameter verification thresholds... The setting depends on the balance between management sensitivity and bandwidth resources. As the verification threshold under standard operating conditions, the business popularity factor is used. The gradient change was used to verify the system's adaptive response capability to administrative rhythms.

[0037] The experimental group implemented the business popularity factor. The test platform implemented an on-demand sensing procedure, while the control group used a static detection procedure with a fixed frequency of 10 minutes. The test monitored the real-time confidence evolution trend under different business intensity levels and the final discrepancy between accounting and physical assets. The test platform recorded the state data of the target asset within the digital twin space and calculated the real-time confidence of intermediate variables through a collaborative arbitration module. And based on real-time confidence level With verification threshold The quantization comparison results generate physical sensing commands, where the attenuation coefficient for Baseline confidence level for Real-time confidence level The calculation formula is as follows: ,in, For real-time confidence level, As the baseline confidence level, For the preset attenuation coefficient, As a business popularity factor, The time difference between the current time and the physical observation timestamp, in units of 1. .

[0038] Table 1: Comparison of Experimental Data under Different Business Tiers

[0039]

[0040] See Table 1, when the business popularity factor Tiered levels of statutory holidays At that time, the calculated real-time confidence level In the high-value range, the system identifies administrative resting states and suppresses physical sensing actions, and the bandwidth utilization rate decreases from that of the control group. Down to The discrepancy between the book and the actual inventory remained at With the business popularity factor Elevate to the peak level of scientific research Real-time confidence level Accelerated decay at the same time difference The logic verification module determines that the value is below the verification threshold and issues a physical sensing command, increasing the sensing trigger frequency from 6.0 times / hour in the control group to 8.6 times / hour, thus capturing the physical traces of highly liquid assets; in terms of business popularity factors achieve Under excessive operating conditions, the bandwidth utilization rate exhibits a non-linear growth trend and tends to saturate, indicating that the physical detection logic has reached the inflection point of system throughput, and further increases will not be effective. The contribution rate of the value to the discrepancy between accounting and actual inventory decreased; experimental data confirmed the business popularity factor. With real-time confidence The correlation relationship enables logical modeling of the administrative management rhythm. The intermediate feature data generated by the collaborative arbitration module guides physical sensing resources to concentrate on high-risk business periods, solving the problem of redundant load generated by fixed frequency detection during the rest period and the contradiction of slow response during peak periods. By comparing the deviation error between the experimental group and the control group, this invention reduces the bandwidth occupation during the administrative rest period while maintaining the continuity of asset management logic under key business nodes, realizing the dynamic allocation of school management resources in the logical space and physical space.

[0041] Example 3: This example combines Figures 1 to 2 A description of the digital twin-driven collaborative management system for the entire process of school assets, such as... Figure 1 As shown, the target asset data source provides administrative status vectors and physical status vectors. After the system performs the vector retrieval operation, it is input into the business mapping module to establish an association mapping relationship and construct a logical entity. The arbitration module determines the attenuation rate based on the business intensity of the logical entity and corrects it to generate a real-time confidence level. At the same time, the administrative business request triggers the logical verification process. The logical verification module receives the real-time confidence level and performs operations to compare the real-time confidence level with the threshold and trigger instructions or feedback as needed. When the confidence level is less than the threshold, the system triggers perception and generates a physical perception instruction. When the confidence level is greater than or equal to the threshold, the system performs direct feedback and outputs the management decision result.

[0042] like Figure 2 As shown, the IoT access network infrastructure includes IoT gateways and wireless positioning base stations. Controlled physical assets include target assets and positioning tag sensors. The user interaction layer includes management terminals and behavior interaction modules. The above components are connected to the core business server cluster via wireless or wired networks and the campus intranet. The core of the digital twin management system runs within the cluster, which integrates business mapping modules, risk assessment modules, collaborative arbitration modules, load perception modules, logical verification modules, and supplementary verification modules. The core business server cluster is connected to an external system integration server through business data synchronization, including data synchronization interfaces, academic affairs management systems, and scientific research project management systems. At the same time, the core business server cluster is connected to a data storage server through data access, which stores an asset account database and a digital twin status database.

[0043] Example 4: In a scenario involving a university research building containing an electromagnetic shielding laboratory, the target asset enters a physical sensing signal blind zone and is in a high-frequency administrative cycle. The system faces a conflict between a lack of sensing feedback and maintaining regulatory intensity. The business mapping module obtains scheduling data from the university management system and determines the business heat factor based on discrete business levels. Business popularity factors corresponding to major projects for Simultaneously, the supplementary verification module defines a radius centered on the spatial coordinates of the target asset's last update. The system selects assets belonging to the same research project group within a spatial region, combined with the research project identifier in the administrative state vector, to form a set of associated assets. It then identifies active entities within the set that have updated physical state vectors. The system executes a deviation coefficient calibration procedure based on historical observation errors, calculating the target asset's previous... The arithmetic mean of the distance residuals between the physical observation coordinates and the geometric center of the administratively authorized area within a natural day is used to determine the historical logical deviation coefficient. The value of .

[0044] A precision spectrum analyzer caused a signal interruption when it entered a shielded laboratory during a major project. The system identified its operational heat factor. for Its associated asset set includes three active experimental power sources, and the system obtains the average confidence level of the active entities. for Combined with historical logical deviation coefficient for The logical confidence compensation increment was calculated. Logical confidence compensation increment The calculation formula is as follows: ,in, To compensate for the incremental logical confidence level, The confidence mean of active entities in the associated asset set. The historical logical deviation coefficient is used; the logical verification module uses the compensation increment to correct the real-time confidence level. The corrected confidence level meets the requirements for consistency between accounts and physical assets, and the system maintains the normal circulation of the asset in the digital twin space. By deconstructing the administrative business logic and physically anchoring the accompanying evidence chain, the perception loss caused by local environmental interference is transformed into inference based on business rules. Under the premise of ensuring the intensity of supervision, the dependence on a single sensor link is eliminated, and the continuity of management logic is improved.

[0045] Example 5: In a newly deployed IoT access network at a university training base, the service mapping module's execution cycle before system operation is... The static benchmark calibration procedure for one calendar day involves continuously observing the standard calibration assets within the controlled area during the administrative rest period to obtain the sampling standard deviation and data loss rate of the physical state vector, and then using the physical observation quality function to calculate the benchmark confidence level. Initial settings, baseline confidence level The calculation formula is as follows: ,in, As the baseline confidence level, The first environmental sensitivity weighting coefficient is preset and its value is fixed. , The second environmental sensitivity weighting coefficient is preset and its value is fixed. , The standard deviation of spatial coordinate sampling within the calibration period. To minimize data packet loss, the calibration procedure specifies that the sampling point should be no less than [a certain percentage]. And the data completeness is higher than Once the determination is complete, the system generates an initial state baseline for the specific physical environment.

[0046] For the identity feature verification step in the behavior interaction module, the collaborative arbitration module calls the job level and asset requisition history data in the school's personnel management system to establish a mapping model from management unit identifier to authorization weight. By executing the identity comparison task in the management terminal, the mean of the initial matching residual is obtained, and the preset authorization weight of the identity feature vector is set according to the reciprocal of the mean of the initial matching residual obtained by the identity comparison task. This makes the judgment boundary value of the verification micro-instruction correspond to the regulatory tolerance value of the management level. The system converts the administrative authorization intention into a measurable weight matrix parameter and generates the initial weight distribution for the identity feature vector.

[0047] Example 6: During the operation of the large-scale asset monitoring system, the load sensing module executes an adjustment signal calibration procedure based on traffic density. During peak business periods, the load sensing module monitors the bandwidth utilization of the campus network's core switches. And calculate the correction increment for the verification threshold. Among them, the correction increment The calculation formula is as follows: ,in, This is the correction increment for the verification threshold. The load response coefficient is a fixed value. , To measure actual bandwidth utilization, The congestion initiation threshold is set to a value of 1. When the measured bandwidth utilization The time exceeding the congestion initiation threshold for a sustained period of time At that time, the load sensing module sends an adjustment signal containing a correction increment to the logic verification module, causing the logic verification module to reduce the verification threshold. The value.

[0048] In the ownership drift determination within the risk assessment module, the system executes a risk boundary calibration procedure based on physical beacon sampling residuals, deploying calibration assets at the boundary locations of the authorized geographic area and collecting data on these calibration assets. The risk assessment module calculates the variance of the sampling residuals based on the spatial coordinate data of the sampling points generated internally. The spatial risk boundary is determined based on the variance of the sampling residuals. The numerical value, spatial risk boundary The calculation formula is as follows: ,in, As the boundary of spatial risks, The maximum length of the asset's physical dimensions. The variance of the sampling residual is used; the system sets a dwell time threshold based on the longest duration of a single teaching task determined by the academic affairs schedule; the risk assessment module quantifies the error displacement caused by positioning drift; and an ownership anomaly warning signal is generated based on the physical track deviation after excluding sampling errors.

[0049] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A digital twin-driven collaborative management system for the entire process of school assets, characterized in that, include: The business mapping module is used to retrieve the administrative status vector and physical status vector of the target asset, and establish the association mapping between the administrative status vector and the physical status vector in the digital twin space to construct a logical entity representing the consistency status of the target asset's accounts and physical assets. The administrative status vector includes the administrative authorization period, the identifier of the management unit to which it belongs, and the business popularity factor determined by the academic affairs scheduling data. The physical status vector includes the timestamp of the last physical observation and spatial coordinate data. The business mapping module retrieves the physical observation timestamps and spatial coordinate data of the target asset through the IoT access network to construct a physical state vector; The collaborative arbitration module is used to determine the baseline confidence level of a logical entity based on the physical state vector, and to determine the confidence level decay rate based on the business popularity factor. The confidence level decay rate is used to perform time-shift correction processing on the baseline confidence level to generate real-time confidence level. The business heat factor corresponds to the predicted displacement frequency of the target asset within the current administrative cycle. This displacement frequency prediction is not based on the current real-time observation, but on the historical average displacement frequency of the same type of business extracted from the academic affairs management system seven natural days prior to the current time point through the data synchronization interface. The higher the business heat factor, the greater the confidence decay rate. The collaborative arbitration module calculates real-time confidence levels. The logic follows the formula below: ,in, As the baseline confidence level, For the preset attenuation coefficient, As a business popularity factor, This represents the time difference between the current time and the previous physical observation timestamp, with the unit of time difference being [unit missing]. , It is a natural constant; The logical verification module is used to retrieve the real-time confidence level and compare it with a preset verification threshold when an administrative approval request for a target asset is received. If the real-time confidence level is lower than the verification threshold, a physical sensing command is generated to trigger the update of the physical state vector and perform a physical verification action for the target asset. The physical state vector responds to the physical sensing command to complete the data update. If the real-time confidence level is not lower than the verification threshold, the system automatically freezes the state evolution of the logical entity and directly feeds back the decision result for the administrative approval request.

2. The digital twin-driven collaborative management system for the entire process of school assets according to claim 1, characterized in that, The collaborative arbitration module determines the business popularity factor by retrieving the teaching schedule data of the administrative region to which the target asset belongs through the data synchronization interface, identifying the teaching business level or scientific research business level corresponding to the current timestamp, and retrieving the corresponding quantitative intensity coefficient in the preset intensity mapping table according to the business level. The business popularity factor is determined based on the quantitative intensity coefficient, and the confidence decay rate is calculated using the business popularity factor.

3. The digital twin-driven collaborative management system for the entire process of school assets according to claim 1, characterized in that, The system also includes a supplementary verification module, whose input is connected to the logic verification module. This module extracts the set of accompanying assets of the target asset within the digital twin space when the real-time confidence level is below the verification threshold and physical perception command feedback is missing. The supplementary verification module delineates a radius centered on the last updated spatial coordinates of the target asset. In the spatial region, combined with the research project identifier in the administrative status vector, assets belonging to the same research project group are selected to form a set of associated assets. The operational status data of active entities in the associated asset set are obtained, and active entities with physical status vector updates are identified in the set. By analyzing the operational status data of active entities and the administrative correlation characteristics of target assets, the logical confidence compensation increment is calculated. The calculation method is as follows: ,in, To compensate for the incremental logical confidence level, The confidence mean of active entities in the associated asset set. The historical logical deviation coefficient between the target asset and the active entity, and the coefficient calculated by the target asset in the previous... The arithmetic mean of the distance residuals between the physical observation coordinates and the geometric center of the administratively authorized area within a natural day is used to determine the historical logical deviation coefficient. The value of is used to perform incremental correction on the real-time confidence using logical confidence compensation increment.

4. The digital twin-driven collaborative management system for the entire process of school assets according to claim 1, characterized in that, The system also includes a risk assessment module, which is used to calculate the risk of ownership drift based on the physical state vector and the administrative state vector: extract the authorized geographical area defined by the administrative state vector and determine the geometric center of the authorized geographical area; calculate the spatial deviation of the spatial coordinate data in the physical state vector relative to the geometric center; If the spatial deviation exceeds the preset risk boundary and the target asset stays in the unauthorized area for a period of time exceeding the preset time threshold, the risk assessment module generates an ownership anomaly warning signal.

5. The digital twin-driven collaborative management system for the entire process of school assets according to claim 1, characterized in that, When the business mapping module establishes a logical entity, it performs chain-based tracing of administrative ownership: obtaining the administrative department in charge of the target asset, the person in charge of using it, and the identifier of the scientific research project it is currently undertaking; and performing topological inclusion relationship verification between the jurisdiction of the administrative department and the spatial coordinate data in the physical state vector to determine the legality of the administrative ownership of the logical entity.

6. The digital twin-driven collaborative management system for the entire process of school assets according to claim 1, characterized in that, The system also includes a load sensing module for monitoring the real-time load status of the university's wireless communication network. When the real-time load status exceeds a preset congestion threshold, the load sensing module sends a threshold adjustment signal to the logic verification module. In response to the threshold adjustment signal, the logic verification module lowers the value of the verification threshold to reduce the frequency of physical sensing command generation by relaxing the confidence tolerance when the network is congested.

7. The digital twin-driven collaborative management system for the entire process of school assets according to claim 1, characterized in that, The system also includes a trajectory recording module, which stores real-time confidence levels, business popularity factors, and related data pairs of administrative approval requests at a preset sampling frequency to record the state evolution trajectory of logical entities. The state evolution trajectory is used to build an asset operation efficiency model and assess the cost of maintaining consistency between accounts and physical assets under different administrative cycles.

8. The digital twin-driven collaborative management system for the entire process of school assets according to claim 1, characterized in that, When the logic verification module outputs the decision result, it executes the following feedback logic: the quantitative value of the real-time confidence level is attached as a confidence level label to the feedback message of the administrative approval request; if the real-time confidence level is within the preset safety range, the feedback message includes an automatic approval suggestion. If the real-time confidence level is within the preset risk assessment range, the feedback message will include a manual review instruction.

9. A digital twin-driven collaborative management system for the entire process of school assets according to claim 1, characterized in that, The system also includes a data synchronization interface for heterogeneous data interaction with external academic affairs management systems and scientific research project management systems. The data synchronization interface uses a metadata conversion template to convert the received business scheduling data into a business state machine model that can be recognized by the collaborative arbitration module.

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