A global campus data quality management and control sharing system and method

Abstract

This invention relates to the field of campus resource allocation and monitoring data processing technology, and discloses a campus-wide data quality control and sharing system and method, including: acquiring personnel location trajectory data and building energy consumption time series data; when the timestamp deviation value exceeds a threshold, running a differential compensation function and index correction coefficient for numerical hedging; outputting aligned and reconstructed feature parameters to calculate the resource utilization deviation matrix; performing smooth regression prediction when sampling frames are lost; using data processing tokens to resolve bus read / write conflicts under high concurrency load; and converting the shared data access control boundary into a dynamic routing table control strategy and outputting resource allocation data instructions when the matrix exceeds a safety threshold. This invention resolves the time series feature differences caused by multi-rate asynchronous sampling, suppresses control signal overshoot and decision loop oscillation, improves the convergence speed of control deviation, and ensures system processing efficiency.

Description

Technical Field

[0001] This invention belongs to the field of campus resource allocation and monitoring data processing technology, and in particular relates to a campus-wide data quality control and sharing system and method. Background Technology

[0002] Current conventional management systems employ a method of aggregating structured data from multiple departments. By summarizing multi-source management flow data such as spatial distribution of personnel, facility load, and consumption of public resources, they provide static and periodic report support for management decisions. To improve the timeliness of decision-making, it is usually necessary to perform spatiotemporal alignment and correlation analysis on heterogeneous information collected from across departments in a central processing unit. However, under the dynamic pressure scenarios of dense personnel migration and transient changes, the multi-source data collection and flow mechanism faces defects due to inherent spatiotemporal differences. This is because the collection cycle and physical time lag of administrative data of different dimensions are fundamentally mismatched. Trajectory data has high-frequency transient change characteristics, while physical facility energy consumption data is subject to hard limitations of multi-rate sampling and physical mass and heat transfer time lags, with a collection cycle as long as 15 minutes. This severe mismatch between static physical sampling constraints and the reality of dynamic personnel flow leads to data quality distortion when conventional systems use traditional timestamps for mechanical alignment, inducing decision overshooting and supply-demand oscillations in resource allocation systems.

[0003] To address interference caused by multi-rate sampling, common practices include adding front-end sensor networks to increase the sampling frequency or using conventional moving average filters in the control center to smooth the data flow. However, analysis shows that under high-concurrency dynamic pressure, simply adding physical hardware increases maintenance costs and exacerbates bus concurrency conflicts and data frame loss. Conventional smoothing strategies, while eliminating noise, also erase the true characteristic peaks brought about by sudden flow events, leading to a significant lag in allocation decisions. There are bottlenecks in improvement at the hardware and conventional filtering statistical levels, and existing data quality monitoring solutions at the software level also face the inherent limitation of not being able to adapt to cross-rate asynchronous timing. For example, [the following is an example of a solution] authorized by CN1. Chinese invention patent 20234211B discloses an intelligent data quality monitoring method and system. Anomaly detection and quality scoring rely on feature vector inputs with the same frequency alignment. The system is processed through a pre-trained model and adaptive threshold. The premise for the detection scheme to be valid is that the multi-source data has a consistent reference in the time domain. However, in the face of the mismatch between the microsecond-level transient fluctuations of trajectory data and the minute-level time delays of energy consumption and physical heat transfer in the high-dynamic flow scenario on campus, this technology lacks adaptive conditioning methods for multi-rate heterogeneous time series faults. When heterogeneous data sources with time series deviations are substituted into the anomaly detection and scoring model, false positive overshoots are generated due to the collision of underlying data logic and high-frequency random steps, which induces oscillations in the management decision-making loop.

[0004] Therefore, the technical problem to be solved by this invention is how to provide a highly stable feature flow by the analysis range control unit, and how to combine the index control unit with the spatiotemporal deflection step size vector to perform extrapolation state reconstruction and boundary correction on the energy consumption time series feature flow, and supplement it with the time series sliding window residual compensation operator to eliminate the decision overshoot caused by multi-rate sampling, so as to complete the convergence control of the allocation command. Summary of the Invention

[0005] This invention aims to solve the problems of data quality distortion and the inability to converge resource allocation commands in real time caused by spatiotemporal difference constraints in multi-rate sampling.

[0006] In this technical solution, a campus-wide data quality control and sharing system includes:

[0007] The data acquisition unit, time-series alignment unit, indicator analysis unit, and shared presentation unit transmit data to each other via a data bus; the system controls data quality through the following steps: the data acquisition unit acquires personnel location trajectory data and building energy consumption time-series data;

[0008] The time-series alignment unit extracts the timestamps of personnel location trajectory data and building energy consumption time-series data and calculates the time-series deviation value. When the time-series deviation value exceeds the preset sampling time difference threshold, it runs the time-series sliding window differential compensation function, calculates the index correction coefficient based on the time delay sensitivity, uses the index correction coefficient to perform numerical offsetting on the building energy consumption time-series data, and outputs the alignment reconstruction feature parameters.

[0009] The indicator analysis unit calculates the resource utilization deviation matrix based on the alignment and reconstruction feature parameters. When it is determined that a sampling frame loss has occurred, it uses the weight decay factor to perform smooth regression prediction on the alignment and reconstruction feature parameters in the preceding time window. Under high concurrency load, it constructs a conflict state matrix and allocates data processing tokens to different data processing partitions according to the magnitude of the spatiotemporal deflection step vector to rearrange read and write priorities and resolve bus read and write conflicts.

[0010] The shared presentation unit determines the shared data access control boundary when the resource utilization deviation matrix exceeds the security threshold, transforms the shared data access control boundary into a dynamic routing table control policy, and outputs resource allocation data instructions.

[0011] Preferably, the time-series alignment unit includes the following steps: the time-series alignment unit constructs a time-series sliding window based on the collection cycle of building energy consumption time-series data, and captures the discrete distribution deviation value of the timestamp within the time-series sliding window; the time-series alignment unit calculates the residual compensation gain term based on the discrete distribution deviation value, and superimposes the residual compensation gain term onto the index correction coefficient to numerically correct the resource utilization deviation matrix, thereby eliminating the time-series characteristic differences generated by multi-rate asynchronous sampling and suppressing management decision oscillations.

[0012] Preferably, the indicator analysis unit includes the following steps: when the discrete temporal local density variation rate of personnel location trajectory data exceeds the long-period distribution threshold, the indicator analysis unit determines that a group transient sudden migration event has occurred and triggers the construction of a conflict state matrix; the indicator analysis unit calculates the magnitude of the spatiotemporal deflection step size vector of each data processing partition participating in the concurrent conflict, prioritizes the allocation of data processing tokens to the group of data processing partitions with the largest magnitude, and allocates different delay waiting periods to other data processing partitions through a delay queue to resolve bus read and write conflicts.

[0013] Preferably, the indicator analysis unit includes the following steps: when the energy consumption data acquisition node experiences continuous sampling interruption and the building energy consumption time series data is truncated, the indicator analysis unit fuses the historical benchmark calibration constant vector; the indicator analysis unit uses the weight decay factor to perform a gradual and smooth regression prediction of the alignment reconstruction feature parameters within the preceding time window, maintaining the convergence state of the resource utilization deviation matrix.

[0014] Preferably, the shared presentation unit includes the following steps: the shared presentation unit projects personnel location trajectory data and building energy consumption time series data into a feature space within a preset time window based on a multi-level role permission mechanism, and removes data items that are not related to administrative management; when the resource utilization deviation matrix output by the indicator analysis unit exceeds the safety threshold, the shared presentation unit dynamically determines the boundary of shared data access control rights and restricts unauthorized nodes from accessing the shared data.

[0015] Preferably, the shared presentation unit includes the following steps: In response to the change in the boundary of shared data access control, the shared presentation unit updates the system dynamic routing table control policy and directs the dynamic routing table control policy to the school-level monitoring node and the college-level management node; the shared presentation unit distributes resource allocation data instructions along the transmission path of the dynamic routing table control policy and sends the resource allocation data instructions to the corresponding school-level monitoring node and college-level management node.

[0016] Preferably, the data acquisition unit acquires personnel location trajectory data and building energy consumption time series data in the following ways: receiving administrative benchmark energy consumption data of each building collected by IoT collection nodes deployed in the campus living area at a fixed sampling period of 15 minutes; at the same time, receiving discrete time series spatial migration trajectories of personnel groups transmitted in real time by the campus access control system and wireless network access nodes, and generating personnel location trajectory data representing spatial location evolution.

[0017] Preferably, when calculating the index correction coefficient of the timing alignment unit, a data buffer queue is introduced based on the time delay sensitivity and the collection cycle of building energy consumption time series data. The building energy consumption time series data is cached and conditioned according to the data buffer queue to smooth the timing deviation value and stabilize the output sequence of resource allocation data instructions.

[0018] Preferably, the system also includes a central clock synchronization module; the central clock synchronization module is connected to the data acquisition unit, the timing alignment unit and the index analysis unit through a time synchronization bus, and sends a reference clock synchronization signal to the data acquisition unit, the timing alignment unit and the index analysis unit to unify the clock reference coordinates within the system.

[0019] A method for managing and sharing data quality across an entire campus, used to run an entire campus data quality management and sharing system, includes the following steps:

[0020] Step S1: The data acquisition unit acquires personnel location trajectory data and building energy consumption time series data;

[0021] Step S2: The time-series alignment unit extracts the timestamps of personnel location trajectory data and building energy consumption time-series data and calculates the time-series deviation value. When the time-series deviation value exceeds the preset sampling time difference threshold, the time-series sliding window differential compensation function is run. The index correction coefficient is calculated based on the time delay sensitivity. The index correction coefficient is used to perform numerical offsetting on the building energy consumption time-series data, and the alignment reconstruction feature parameters are output.

[0022] Step S3: The index analysis unit calculates the resource utilization deviation matrix based on the alignment and reconstruction feature parameters. When it is determined that a sampling frame loss has occurred, it uses the weight decay factor to perform smooth regression prediction on the alignment and reconstruction feature parameters in the preceding time window. Under high concurrency load, it constructs a conflict state matrix and allocates data processing tokens to different data processing partitions according to the magnitude of the spatiotemporal deflection step vector to rearrange read and write priorities and resolve bus read and write conflicts.

[0023] In step S4, when the resource utilization deviation matrix exceeds the security threshold, the shared presentation unit determines the shared data access control boundary, transforms the shared data access control boundary into a dynamic routing table control strategy, and outputs resource allocation data instructions.

[0024] Compared with existing technologies, the present invention, a campus-wide data quality control and sharing system, has the following advantages:

[0025] 1. In the sharing and management of data quality across the entire campus, the analysis scope control unit outputs a standard state vector set to the indicator control unit, which continuously monitors the spatial feature point set of the trajectory state vector within the sliding time window, calculates the discrete time series local density variation rate, and intercepts the direct calculation of the indicator matrix when the variation rate exceeds the judgment boundary threshold, blocking the logical collision of multi-source heterogeneous data streams before calculation. The spatiotemporal deflection step size vector is calculated through the excess of the variation rate, and the calibration operator is used as the forward translation step size of the time axis to perform forward extrapolation state reconstruction on the energy consumption time series feature stream to cover the energy consumption spatial mapping boundary, establish bidirectional interlocking and collaboration of multi-source data streams, provide deviation degree matrix calculation, and eliminate indicator decision oscillation.

[0026] 2. The indicator control unit extracts the timestamps of trajectory data and energy consumption data and calculates the difference. When a nonlinear time difference is determined, the time-series sliding window residual compensation operator is activated. Combined with the time delay sensitivity and energy consumption acquisition cycle, the administrative control yield damping term is calculated. The negative feedback hedging and smoothing correction are implemented on the resource utilization deviation matrix to eliminate parasitic interference caused by multi-rate sampling. The dynamic buffer is introduced into the calculation by the synergistic effect of multiple features, so that the adjustment command output fits the resource allocation supply and demand status, reverses the over-adjustment of management decisions caused by the lag in local data updates, and achieves the technical state of monotonic and rapid convergence of allocation commands.

[0027] 3. When communication delays or frame loss in the environmental sensor network cause the energy consumption time-series characteristic stream to be truncated, the adaptive degradation unit integrates the historical benchmark calibration constant vector and uses the decay factor to smoothly degrade the reconstructed characteristic values ​​of the preceding effective time window, maintaining the convergence state of the evaluation index under edge conditions. At the same time, under the high-concurrency load conditions caused by dense migration, the token arbitration unit constructs a conflict state matrix and dynamically adjusts the throughput priority of data flow in each computing power partition according to the modulus of the spatiotemporal deflection step size vector. Data processing tokens are allocated preferentially to partitions with large modulus lengths, and small modulus length partitions implement step-by-step delay waiting. Bus read and write conflicts are eliminated through discretized rule scheduling. Attached Figure Description

[0028] Figure 1 This is a flowchart of the steps of a method for managing and sharing campus data quality across the entire region, as described in this invention.

[0029] Figure 2 This is a unit structure diagram of a campus-wide data quality control and sharing system according to the present invention. Detailed Implementation

[0030] 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.

[0031] A campus-wide data quality control and sharing system includes:

[0032] The data acquisition unit, time-series alignment unit, indicator analysis unit, and shared presentation unit transmit data to each other via a data bus; the system controls data quality through the following steps: the data acquisition unit acquires personnel location trajectory data and building energy consumption time-series data;

[0033] The time-series alignment unit extracts the timestamps of personnel location trajectory data and building energy consumption time-series data and calculates the time-series deviation value. When the time-series deviation value exceeds the preset sampling time difference threshold, it runs the time-series sliding window differential compensation function, calculates the index correction coefficient based on the time delay sensitivity, uses the index correction coefficient to perform numerical offsetting on the building energy consumption time-series data, and outputs the alignment reconstruction feature parameters.

[0034] The indicator analysis unit calculates the resource utilization deviation matrix based on the alignment and reconstruction feature parameters. When it is determined that a sampling frame loss has occurred, it uses the weight decay factor to perform smooth regression prediction on the alignment and reconstruction feature parameters in the preceding time window. Under high concurrency load, it constructs a conflict state matrix and allocates data processing tokens to different data processing partitions according to the magnitude of the spatiotemporal deflection step vector to rearrange read and write priorities and resolve bus read and write conflicts.

[0035] The shared presentation unit determines the shared data access control boundary when the resource utilization deviation matrix exceeds the security threshold, transforms the shared data access control boundary into a dynamic routing table control policy, and outputs resource allocation data instructions.

[0036] Preferably, the time-series alignment unit includes the following steps: the time-series alignment unit constructs a time-series sliding window based on the collection cycle of building energy consumption time-series data, and captures the discrete distribution deviation value of the timestamp within the time-series sliding window; the time-series alignment unit calculates the residual compensation gain term based on the discrete distribution deviation value, and superimposes the residual compensation gain term onto the index correction coefficient to numerically correct the resource utilization deviation matrix, thereby eliminating the time-series characteristic differences generated by multi-rate asynchronous sampling and suppressing management decision oscillations.

[0037] Preferably, the indicator analysis unit includes the following steps: when the discrete temporal local density variation rate of personnel location trajectory data exceeds the long-period distribution threshold, the indicator analysis unit determines that a group transient sudden migration event has occurred and triggers the construction of a conflict state matrix; the indicator analysis unit calculates the magnitude of the spatiotemporal deflection step size vector of each data processing partition participating in the concurrent conflict, prioritizes the allocation of data processing tokens to the group of data processing partitions with the largest magnitude, and allocates different delay waiting periods to other data processing partitions through a delay queue to resolve bus read and write conflicts.

[0038] Preferably, the indicator analysis unit includes the following steps: when the energy consumption data acquisition node experiences continuous sampling interruption and the building energy consumption time series data is truncated, the indicator analysis unit fuses the historical benchmark calibration constant vector; the indicator analysis unit uses the weight decay factor to perform a gradual and smooth regression prediction of the alignment reconstruction feature parameters within the preceding time window, maintaining the convergence state of the resource utilization deviation matrix.

[0039] Preferably, the shared presentation unit includes the following steps: the shared presentation unit projects personnel location trajectory data and building energy consumption time series data into a feature space within a preset time window based on a multi-level role permission mechanism, and removes data items that are not related to administrative management; when the resource utilization deviation matrix output by the indicator analysis unit exceeds the safety threshold, the shared presentation unit dynamically determines the boundary of shared data access control rights and restricts unauthorized nodes from accessing the shared data.

[0040] Preferably, the shared presentation unit includes the following steps: In response to the change in the boundary of shared data access control, the shared presentation unit updates the system dynamic routing table control policy and directs the dynamic routing table control policy to the school-level monitoring node and the college-level management node; the shared presentation unit distributes resource allocation data instructions along the transmission path of the dynamic routing table control policy and sends the resource allocation data instructions to the corresponding school-level monitoring node and college-level management node.

[0041] Preferably, the data acquisition unit acquires personnel location trajectory data and building energy consumption time series data in the following ways: receiving administrative benchmark energy consumption data of each building collected by IoT collection nodes deployed in the campus living area at a fixed sampling period of 15 minutes; at the same time, receiving discrete time series spatial migration trajectories of personnel groups transmitted in real time by the campus access control system and wireless network access nodes, and generating personnel location trajectory data representing spatial location evolution.

[0042] Preferably, when calculating the index correction coefficient of the timing alignment unit, a data buffer queue is introduced based on the time delay sensitivity and the collection cycle of building energy consumption time series data. The building energy consumption time series data is cached and conditioned according to the data buffer queue to smooth the timing deviation value and stabilize the output sequence of resource allocation data instructions.

[0043] Preferably, the system also includes a central clock synchronization module; the central clock synchronization module is connected to the data acquisition unit, the timing alignment unit and the index analysis unit through a time synchronization bus, and sends a reference clock synchronization signal to the data acquisition unit, the timing alignment unit and the index analysis unit to unify the clock reference coordinates within the system.

[0044] A method for managing and sharing data quality across an entire campus, used to run an entire campus data quality management and sharing system, includes the following steps:

[0045] Step S1: The data acquisition unit acquires personnel location trajectory data and building energy consumption time series data;

[0046] Step S2: The time-series alignment unit extracts the timestamps of personnel location trajectory data and building energy consumption time-series data and calculates the time-series deviation value. When the time-series deviation value exceeds the preset sampling time difference threshold, the time-series sliding window differential compensation function is run. The index correction coefficient is calculated based on the time delay sensitivity. The index correction coefficient is used to perform numerical offsetting on the building energy consumption time-series data, and the alignment reconstruction feature parameters are output.

[0047] Step S3: The index analysis unit calculates the resource utilization deviation matrix based on the alignment and reconstruction feature parameters. When it is determined that a sampling frame loss has occurred, it uses the weight decay factor to perform smooth regression prediction on the alignment and reconstruction feature parameters in the preceding time window. Under high concurrency load, it constructs a conflict state matrix and allocates data processing tokens to different data processing partitions according to the magnitude of the spatiotemporal deflection step vector to rearrange read and write priorities and resolve bus read and write conflicts.

[0048] In step S4, when the resource utilization deviation matrix exceeds the security threshold, the shared presentation unit determines the shared data access control boundary, transforms the shared data access control boundary into a dynamic routing table control strategy, and outputs resource allocation data instructions.

[0049] Example 1: In a continuously operating environment of refined campus administrative supervision and data processing, when a large number of people migrate within a pre-set themed teaching simulation space, the discrete temporal spatial migration trajectory data transmitted in real time by the access control system and wireless network access nodes exhibits microsecond-level transient fluctuations. Meanwhile, the building energy consumption time-series characteristic stream collected by IoT collection nodes deployed in the campus living area at a fixed sampling period of 15 minutes exhibits minute-level time lag due to physical heat transfer resistance and firmware sampling period limitations. This spatiotemporal asynchrony caused by differences in the physical intrinsic properties of these heterogeneous data sources results in data quality distortion when centrally spliced ​​and aligned through a public data warehouse. Consequently, it causes overshooting of control signals and oscillations in the management decision loop when calculating resource allocation decision indicators, leading to a deviation between resource allocation and actual supply and demand. To address the temporal characteristic differences caused by the aforementioned multi-rate asynchronous sampling, the analysis range control unit collects discrete temporal spatial migration trajectory data of the population group to generate a personnel location trajectory data stream representing the spatial evolution of their positions. Energy consumption time-series characteristic flow Through its internally configured feature space projection operator within a preset sliding time window The system projects heterogeneous data streams into a feature space to filter out business noise unrelated to administrative management, and outputs a standard state vector set to the indicator control unit. The feature extraction unit within the indicator control unit then isolates the trajectory state vector. Energy consumption time-series characteristic flow The trajectory state vector is continuously monitored by the consistency alignment unit. In the sliding time window The spatial feature point set within the range is used to calculate the discrete-time local density variation rate. ,in, The rate of change of local density in discrete time series. The spatial distribution density of trajectory feature points within the current sliding time window. The spatial distribution density of trajectory feature points within the preceding history comparison window. The sampling time interval for trajectory data.

[0050] When the consistency alignment unit determines the discrete-time local density variation rate Exceeding the preset judgment boundary threshold At this time, the system stops the calculation of subsequent indicator matrices through cascaded control logic to avoid data conflicts between multi-source heterogeneous data streams before indicator calculation, and calculates the spatiotemporal deflection step size vector based on the transient excess of the rate of change. ,in, The spacetime deflection step size vector, This is the response delay correction constant vector determined by the inherent physical heat capacity characteristics of the logistics facilities. The rate of change of local density in discrete time series. To determine the boundary threshold, a spatiotemporal nonlinear consistency calibration operator is constructed, using the spatiotemporal deflection step size vector. As the forward translation step size of the time axis, the energy consumption time series characteristic flow Implement forward extrapolation state reconstruction, and directly cover and rewrite the energy consumption time series feature flow using the calculated expected energy consumption evolution trend feature values. In the current sliding time window The spatial mapping boundary within the system enables time-series synchronization and correlation of discrete high-frequency trajectory data and continuous low-frequency energy consumption data. This transforms the physical hardware sampling differential constraint, which is difficult to handle in traditional paths, into a feedforward decoupled calculation driven by known trajectory mutations. Based on the heat conduction continuity equation and signal sampling theorem, there is a physical scale mismatch between high-frequency transient flow signals and low-frequency thermal inertia heat signals, requiring the establishment of a signal time-domain offset path to calibrate control parameters. When the timing alignment unit is connected to the test environment, the input data bus is connected to an energy meter with 0.5S-level metering accuracy and a wireless network access node with a data throughput latency of less than 50ms. During the initialization phase, the system continuously collects 72 hours of normal personnel flow density data and calculates its statistical standard deviation. The value of three standard deviations was determined as the decision boundary threshold. The value is 5.0 cycles per second. A step power load is introduced in a representative building to measure the time constant required for the indoor temperature response to decay to the derivative of the indoor temperature difference. After normalization, the response delay correction constant vector is determined. The value is 1.25. To eliminate background noise interference caused by multi-factor coupling, the timing alignment unit incorporates a first-order low-pass digital filter. This filter removes outliers and singularities caused by high-frequency communication jitter, thus improving the original building energy consumption timing characteristic stream. The pure physical energy consumption variable is extracted from the data, and a data buffer queue is used to form a data storage address segment that can accommodate two acquisition cycles as a physical delay buffer medium. This physically absorbs the scale difference between minute-level heat conduction delay and microsecond-level bus update frequency. In actual operation, the response delay correction constant vector determined by the inherent physical heat capacity characteristics of the logistics facilities represents the energy change lag time of the building complex due to the heat transfer resistance of the walls, air, and other media on a physical scale. In order to eliminate cross-scale logical gaps in the data flow of the surface computer bus, this invention uses the timer interrupt driver of the underlying operating system kernel to map the second-level or minute-level physical lag time to the task queue processing time limit at the software level. When the physical heat capacity of the logistics facilities is large... The physical lag time is correspondingly extended. The system uses the hardware and software status registers in the data bus controller to linearly scale this physical lag time to a microsecond-level bus communication scheduling window, dynamically adjusting the task processing time slices corresponding to different data processing partitions, making them adaptively adjust between 10 milliseconds and 100 milliseconds. Through this cross-scale time-domain mapping mechanism, the overall thermodynamic physical time lag is transformed into surface-level software token allocation priority control damping, enabling the task execution sequence of the bus scheduling system to achieve time-domain matching with the thermal inertia of the underlying physical entity. On this basis, the index control unit uses the reconstructed energy consumption spatial mapping feature quantity as the input of independent variable to solve for the resource utilization deviation matrix reflecting the matching state between the allocation and actual consumption of resources across the entire campus. ,in, For the resource utilization deviation matrix, To reflect the spatial projection features of students' trajectory needs, This refers to the spatial mapping characteristic of facility energy consumption after forward extrapolation reconstruction using the calibration operator. To configure constant vectors for refined management standards, while energy consumption characteristics continuously change due to network disturbances. In non-ideal edge conditions where one periodic frame is lost, the adaptive degradation unit fuses the historical benchmark calibration constant vector and uses a weight decay factor to perform a smooth regression prediction of the alignment reconstruction feature parameters within the preceding effective time window in order to maintain the convergence state of the deviation matrix. In high-concurrency load conditions, the token arbitration unit constructs the conflict state matrix. Monitor and based on the spatiotemporal deflection step size vector The module length dynamically adjusts the throughput priority for different data processing partitions, and prioritizes the allocation of data processing tokens to partitions with large module lengths to rearrange read and write priorities. This establishes a discrete rule-based data flow interception mechanism before the data is injected into the evaluation matrix. Specifically, each of the aforementioned data processing partitions corresponds one-to-one with a different physical administrative area on campus, including teaching areas, living areas, and office areas. During system operation, the discrete-time local density variation rate is not a single value across the entire domain, but rather a regional characteristic quantity calculated independently and in parallel by the index analysis unit for each physical administrative area. At the same time, the response delay correction constant vector is also independently calibrated according to the different structures and materials of buildings within each physical administrative area. Therefore, when handling high-concurrency loads, the token arbitration unit reads the local density variation rate and the regional response delay correction constant vector corresponding to each independent data processing partition, thereby calculating the spatiotemporal deflection step size vector unique to that data processing partition. When read / write conflicts occur between the data streams of each partition on the bus, the token arbitration unit solves for the geometric magnitude of the spatiotemporal deflection step size vector calculated for each partition, and sorts these magnitudes in descending order through a numerical comparison algorithm. The partition with the largest magnitude value represents the area with the most severe current personnel gathering and energy consumption mismatch. Based on this, the system determines it as the highest priority and prioritizes distributing data processing tokens to it.

[0051] The execution control unit extracts the timestamps of student trajectory data and energy consumption data and calculates the timing deviation value. When the timing deviation exceeds the preset sampling time difference threshold, the timing sliding window differential compensation function is run to calculate the corresponding administrative control yield damping term. ,in, This is a damping term for administrative control concessions. This is the time delay sensitivity adjustment coefficient. This is the absolute difference between the timestamps of the trajectory data and the energy consumption data. The energy consumption data collection cycle is defined, and the damping term is utilized. Resource utilization deviation matrix By implementing negative feedback hedging and smoothing correction to eliminate parasitic interference caused by multi-rate sampling, the shared presentation unit transforms the determined shared data access control boundary into a dynamic routing table control strategy when the deviation matrix exceeds the safety threshold. This strategy then distributes resource allocation data instructions to the corresponding school-level monitoring nodes and college-level management nodes, enabling the control deviation to achieve smooth, monotonically fast convergence without overshoot within 4.1 minutes. This keeps the overall administrative resource misallocation rate at 1.3%, achieving a deterministic control mechanism that offsets administrative flow losses purely through logical topology evolution and lightweight operator scheduling without adding physical sensing hardware. This is to achieve the dynamic routing of the data access control boundary. The specific transformation of the dynamic routing table control policy involves a core network topology controller deployed within the shared presentation unit. When the resource utilization deviation matrix exceeds the security threshold, the shared data access control boundary determined by the system is represented at the software level as an access control list containing node identities and access permission levels. During the transformation process, the network topology controller calls the extended interface of the border gateway protocol to automatically convert the unauthorized node identifiers in the access control list into network layer routing interception entries. Simultaneously, it writes the network addresses of high-privilege school-level monitoring nodes and college-level management nodes into the dynamic routing table of the core switch, configuring them as the next-hop destination addresses for targeted forwarding. Specifically, the controller will... The original default network-wide broadcast routing policy was rewritten as a targeted unicast path based on a specific virtual LAN. By directly modifying the routing table prefix and port forwarding mapping of the core switch, access requests from unauthorized nodes were redirected to the isolation sandbox. Meanwhile, resource allocation data commands were distributed in real-time to the target terminal along the updated dynamic routing table transmission path. Based on the closed-loop negative feedback limiting mechanism in automatic control theory, the control characteristic quantities generated by the preceding calculations were converted into specific physical kinematic constraints on the field actuators. This facilitated the construction of a downlink control closed loop for data commands to the variable frequency central air conditioning system and field actuators such as proportional control valves. The resource allocation data commands output by the shared presentation unit included those targeting the target building. The variable frequency motor speed limit and chilled water proportional control valve opening adjustment of the Yu variable frequency central air conditioning system are controlled by the college-level management node terminal or the school-level monitoring node terminal via Ethernet bus. The digital control limit is parsed using the built-in communication interface, and the speed limit code is converted into an analog current signal of 4 mA to 20 mA, or directly written into a specific register address of the field programmable logic controller. This forces the variable frequency motor to operate within a limited operating range of 30 Hz to 45 Hz, and drives the stepper motor of the proportional control valve to adjust the valve opening damping, so that the actual energy consumption power of the building and the real-time trajectory demand caused by dense personnel movement achieve physical convergence.

[0052] Example 2: The method claimed in this invention is applicable to the processing of multi-source heterogeneous time-series data streams in the refined administrative supervision of the entire campus. The control performance of this invention in a real, non-ideal environment is verified by constructing a dedicated distributed campus bus simulation test platform. The simulation test platform runs based on a distributed discrete event simulation model. The multi-core central processing unit of the computer system allocates physical memory address segments to simulate concurrent read and write operations of multiple campus information collection nodes. The original dataset used in the experiment includes two sets. The first set is a discrete time-series spatial migration trajectory dataset. The data source for this dataset is obtained by collecting data at a sampling rate of 10Hz from an IoT gateway node within a preset themed teaching simulation environment and then de-identifying it. This dataset is used to provide high-frequency, suddenly changing personnel location trajectory data streams. The second set is a dataset of time-series energy consumption characteristics of buildings, which is obtained by reading data from smart three-phase electricity meters installed in the campus living area at a fixed sampling period of 15 minutes. It is used to characterize the low-frequency energy consumption time-series characteristics containing heat transfer and time lag effects. In the design of this experimental scheme, in order to determine the preset sliding time window To determine the optimal parameters, it is necessary to analyze and identify the main technical parameters affecting the window size and the preset sliding time window. The main technical factors for determining the values ​​include the maximum rate of change of campus personnel flow velocity and the time-series characteristic flow of energy consumption. The update baseline period is determined by setting a preset sliding time window. At this time, the core technical issue to be balanced lies in the trade-off between the real-time nature of data updates and the processor addressing overhead during feature projection calculation. When the rate of change of the speed of personnel flow is on the rise and its first derivative is large, in order to ensure the integrity of capturing transient spatial migration features and avoid erasing abrupt feature points due to the time-domain averaging effect caused by an excessively long window, a sliding time window is preset. The value should approach the lower limit of its parameter range. Specifically, the analysis range control unit determines the preset sliding time window based on the period of the highest frequency component of the discrete temporal spatial migration trajectory data stream. With a time limit of 30 minutes, under the aforementioned decision-making rules, this value can capture microsecond-level burst migration features while keeping the memory addressing overhead of a single feature space projection calculation within the processor's preset safe load threshold.

[0053] To verify the anti-interference performance and feasibility of the method of this invention in actual campus networks and physical environments with non-ideal disturbances, this experimental scheme implemented explicit injection of noise and environmental disturbances in the data stream of personnel location trajectories. Energy consumption time-series characteristic flow In the logical channel, Gaussian white noise with a signal-to-noise ratio of 20dB is actively injected to simulate random packet loss and transmission delay perturbations caused by high-density gathering of people in the wireless network access node. For the control group that has not been processed by the method of this invention, the personnel location trajectory data stream containing the above-mentioned Gaussian white noise interference is directly transmitted by the data bus. Energy consumption time-series characteristic flow The data stream of personnel location trajectories is routed to the indicator matrix calculation module. Under the initial state containing the aforementioned noise disturbances, the data stream... The original measurements exhibit high-frequency random steps, and the mean measurement within the sliding window shows a random divergence of 18.3%. Correspondingly, due to the low-frequency energy consumption time-series characteristic flow collected by the smart energy meter... Due to minute-level network latency perturbations, a 15.2-minute timestamp misalignment occurred in the original sampling sequence. Under the operating logic of the control group, the aforementioned heterogeneous data streams, without consistency alignment and state reconstruction, were directly substituted into the index matrix by the processor for scalar solution, resulting in an output resource utilization deviation matrix. It exhibits periodic false positive overshoot, with its maximum waveform amplitude oscillating irregularly between 1.2 and 2.8. The decision loop experiences overshoot divergence due to high-frequency noise amplification. In the operation process of the sample group of this invention, the analysis range control unit receives heterogeneous data streams containing the same noise, and utilizes its characteristic space projection operator within a preset sliding time window of 30 minutes. The system performs smooth projection to filter out temporal glitches caused by Gaussian white noise and outputs the purified standard state vector set to the indicator control unit. The consistency alignment unit inside the indicator control unit retrieves the personnel position trajectory data stream. In the preset sliding time window The spatial feature point set within the area is used to calculate the discrete-time local density variation rate caused by the large-scale concentrated flow of the population. The value was 14.6 per second, which exceeded the preset threshold for judgment. And determine the boundary threshold Set to 5.0 per second, the system triggers the rigid cascaded interception module, pausing the direct matrix calculation of subsequent indicator analysis units. The consistency alignment unit calculates the spatiotemporal deflection step size vector based on the excess rate of change. , where the inherent delay correction constant vector The setpoint was determined to be 1.25 by the physical heat transfer time delay experiment of the logistics facility, and the spatiotemporal deflection step size vector was calculated. The measured value is 3.65. The calibration operator uses this 3.65 as the forward shift step size of the time axis to perform a calibration on the low-frequency energy consumption time series characteristic flow. By implementing forward extrapolation state reconstruction, the reconstructed expected energy consumption trend characteristic value accurately covers the spatial mapping boundary within the current sliding time window, correcting the original 15.2-minute timestamp misalignment due to time lag to within 0.15 minutes.

[0054] Through in-depth demonstration using a multi-dimensional comparative system, to further confirm the synergistic effect of complete feature combinations, a partially missing control group was added to the experiment. Under the premise of maintaining complete consistency in the hardware and software environment and noise injection conditions, the forward extrapolation state reconstruction step in the consistency alignment unit was selectively abolished; that is, the system's local density variation rate... Exceeding the judgment boundary threshold At this time, only the interception action is performed, but the spatiotemporal deflection step size vector is not applied. Corrected energy consumption time-series characteristic flow In the spatial mapping boundary, during the operation of the missing control group, although the false positive overshoot of the high-frequency control signal was suppressed to some extent and the packet throughput queue was not completely blocked, the failure to eliminate the minute-level time delay discontinuity between the low-frequency energy consumption data and the high-frequency trajectory data resulted in a final resource utilization deviation matrix. There is a 24.5% systematic residual between the actual supply and demand matching state and the actual system stability, indicating poor system stability. However, the sample group of this invention, which adopts the complete technical solution of this invention, benefits from the bidirectional interlocking and synergy between the analysis range control unit and the indicator control unit features. Under the same sudden high flow scenario, the resource utilization deviation matrix is ​​significantly reduced. The measured value remained accurate and stable at 1.03, without any decision loop oscillations caused by data quality distortion. This confirmed the synergistic effect between the various features of the complete technical solution. To demonstrate the rationality of the key parameter boundaries, an out-of-range control group was introduced into the experimental design to determine the boundary threshold. Numerical boundary optimization and gradient verification stress tests were performed. In the gradient condition design of this experiment, three decision boundary thresholds were set. The independent value gradients are as follows: the first gradient uses an absolute lower limit value of 1.0 per second, which is below the lower limit of the required protection range; the second gradient uses the normal median value within the protection range, which is 5.0 per second; and the third gradient uses an absolute upper limit value of 20.0 per second, which is above the upper limit of the required protection range. Experimental measurement data show that when determining the boundary threshold... When the absolute lower limit is set to 1.0 per second, the threshold is set too low. Normal small-scale walking or routine movement of people on campus will incorrectly trigger the rigid cascading interception mechanism, causing the system's indicator analysis operator to be frequently suspended, resulting in a negative resource utilization deviation matrix. The update cycle was forced to lengthen from the normal 15 minutes to 125.6 minutes, causing blockage in the processor's task queue and increasing the system's data processing load by 3.2 times. This failed to meet the needs of real-time administrative supervision, especially when determining boundary thresholds. When the absolute upper limit is set to 20.0 per second, the system boundary is set too wide, resulting in a degradation effect. At this point, the sudden spatiotemporal differences caused by large-scale migration cannot effectively trigger cascaded interception, and the system cannot activate the spatiotemporal deflection step size vector when the discrete local density change rate reaches 14.6 per second. The calculation leads to missed detections in the forward extrapolation state reconstruction, resulting in a negative resource utilization deviation matrix in the output. The system exhibited false positive overshoot again, and the control bias failed to converge. Only when the boundary threshold was determined... When the normal median value of 5.0 frames per second is set within the protection range, the system can not only accurately identify sudden large-volume migration conditions, but also precisely activate the spatiotemporal nonlinear consistency calibration operator, enabling the control deviation to achieve smooth convergence within 4.1 minutes. This proves that the numerical range defined by this invention is the optimal working window for balancing the system's processing load and real-time control performance. In the long-term adaptability test of the sample group of this invention facing non-ideal edge conditions, the energy consumption characteristic flow experienced a continuous loss of 3 periodic frames due to simulated network depth perturbation. Specifically, the boundary ranges of the aforementioned quantization parameters and the specific... All values ​​were determined through experimental measurements and engineering simulation tests. The decision boundary threshold was set at 5.0 units per second. Its engineering significance lies in the fact that when the rate of change in campus personnel flow speed is below 5.0 units per second, it is considered normal discrete personnel movement, and the system does not need to intercept or calculate it; if it is above 5.0 units per second, a group migration warning is triggered. If this value is set too low, it will cause frequent system interception, leading to computational blockage. The inherent delay correction constant vector was set to 1.25, which was derived from the statistical mean of the physical heat transfer time delay obtained through multiple rounds of thermal tests on exhaust and heating facilities in a typical teaching building. The latency sensitivity adjustment coefficient, derived from this study, is set to 0.12, with a value range between 0.10 and 0.20. This value was obtained through calibration using command response curves simulated under different network packet loss rates. Values ​​below 0.10 result in insensitivity to timing deviations, leading to delayed command output; values ​​above 0.20 cause over-adjustment of negative feedback, resulting in divergence. The weight decay factor is set to 0.85, determined through long-term edge case simulations. A decay factor below 0.70 causes the prediction curve to decay to zero too quickly, losing its extrapolation significance; a value above 0.95 fails to accurately reflect the actual value. To reflect the current deterioration trend due to missing data, a value of 0.85 is chosen to ensure the smoothest convergence transition of the deviation matrix during three consecutive periods of frame loss. At this point, the adaptive degradation unit automatically reads the historical benchmark calibration constant vector from a specific memory address, using the administrative benchmark energy consumption constant of 120 kW·h for a specific building as the basic physical property benchmark. A preset weight attenuation factor of 0.85 is used to perform a gradually decreasing smooth regression prediction on the alignment and reconstruction feature parameters within the preceding effective time window. As the frame loss time progresses, the system's resource utilization deviation matrix... Under predictive control, smooth convergence was maintained, avoiding system crashes or control strategy divergence caused by data flow interruptions. When constructing the conflict state matrix, the token arbitration unit allocated a dedicated two-dimensional array space in the shared storage area of ​​the distributed campus bus controller. The row and column indices of this two-dimensional array correspond to the various data processing partitions in the campus network. Each intersection element in the matrix uses a binary value to represent whether there is a concurrent conflict state between two corresponding partitions that simultaneously request bus read / write operations. The value 1 represents a conflict, and the value 0 represents no conflict. The data processing partitions are hard-sliced ​​according to the physical access layer switch ports of the campus IoT gateway. The system is divided into 16 independent computing power partitions. Data processing tokens are hardware enable signals or software authorization words distributed by the bus arbitrator. Only partitions that obtain tokens can open bus read / write channels. When reordering read / write priorities, the system calls the internal heap sort algorithm to extract partitions marked as conflicting in the conflict state matrix. It reads the magnitude of the spatiotemporal deflection step vector corresponding to each partition and inputs it into the sorted linked list. The original first-in-first-out queue is adjusted to an absolute priority queue based on the magnitude from largest to smallest, thereby ensuring that data in high-dynamic areas is prioritized for scheduling. At the same time, under high-concurrency load conditions, the token arbitration unit constructs the conflict state matrix. Real-time monitoring of bus concurrency conflicts, based on the calculated spatiotemporal deflection step size vector. The module length of 3.65 dynamically adjusts the throughput priority for different data processing partitions, prioritizing the allocation of data processing tokens to rearrange read and write priorities for partitions with larger module lengths. The execution control unit then extracts the timestamps of student trajectory data and energy consumption data and calculates the timing deviation value. The corresponding administrative control yield damping item is executed. ,in, For the administrative control yield damping term, the time delay sensitivity adjustment coefficient Set to 0.12. The measurement time was 8.4 minutes, and the energy consumption data acquisition cycle was [missing information]. The set value is 15 minutes, and the administrative control setback damping item is calculated. The measured value was 0.067, and this measured value was used to analyze the resource utilization deviation matrix. Implementing smooth hedging correction, the above multi-dimensional experiments and data comparison results show that by analyzing the range control unit to provide a highly stable feature flow, and combining the index control unit with the spatiotemporal deflection step size vector to perform extrapolation state reconstruction and boundary correction on the energy consumption time series feature flow, supplemented by the time series sliding window residual compensation operator to eliminate decision overshoot caused by multi-rate sampling, in the operation process of the time series sliding window residual compensation operator, for the solid superposition operation of the aforementioned residual compensation gain term, its specific path in discrete data stream processing is as follows: the time series alignment unit captures the discrete distribution deviation value of the timestamp within the window and solves for... The residual value is multiplied by a fixed step size using a gain amplifier to obtain the residual compensation gain term. The so-called "solid superposition" refers to the fact that, within the data stack structure of the algorithm execution, this residual compensation gain term is treated as a static, unchanging constant and accumulated arithmetically into the memory data bits of the already calculated index correction coefficient, thus forming an indivisible composite correction factor. This composite correction factor participates in the subsequent numerical hedging calculations as a whole. Through this hard-coded numerical superposition, the index correction coefficient can immediately incorporate the feedback damping of the time-series surface residual, thereby mitigating subsequent resource utilization deviations. When the degree matrix is ​​numerically corrected, it can directly offset the step interference caused by multi-rate asynchronous sampling, suppress the oscillation of management decisions, and objectively and stably control the overall administrative resource misallocation rate at a low level of 1.3%. Here, the aforementioned overall administrative resource misallocation rate is defined in pure textual quantification as: the percentage of the absolute deviation between the actual resource allocation in each physical area of ​​the campus and the actual demand calculated based on personnel location trajectories within a preset 24-hour observation period, divided by the total standard configuration. In order to objectively evaluate it, the experimental platform implemented a standardized pure digital test procedure: by The discrete event simulator automatically exports a dataset containing 10,000 records as input in the background. Without relying on any graphical or visual image evidence, it directly extracts the resource allocation data instruction sequence output by the system through the log text of the data analysis terminal. Then, it performs point-by-point difference calculation on this instruction sequence and the ideal time-delay standard resource allocation scheme, automatically counting the number of abnormal discrete data points that exceed the preset safety error range. The data shows that after introducing the collaborative reconstruction control of this invention, the proportion of discrete data error mismatch points captured after 72 hours of continuous operation remains stable at 1%.Within 3%, this achieves a purely digital self-verification closed loop for the effectiveness of the control technology. This experiment demonstrates that the method claimed in this invention, by reconstructing the control flow and data flow in the logical space, enables the heterogeneous data flow quality control of the system to be independent of upgrades to the front-end physical sensing hardware. It stably reproduces in a real engineering environment filled with network packet loss and sampling delays. The entire experimental process did not involve any diagnostic or treatment conclusions. All outputs to the management terminal were limited to the physical indicators of de-identified data and dimensionless deviation matrices, without any penetrating acquisition of personal privacy data. Ultimately, the determined boundaries of shared data access control rights are transformed into dynamic routing table control strategies and resource allocation data instructions are distributed to the corresponding regulatory node terminals, realizing an efficient and deterministic closed-loop implementation of a campus-wide data quality control and sharing system.

[0055] Example 3: This example combines Figures 1 to 2 This document describes a comprehensive campus data quality control and sharing system and method, such as... Figure 1 As shown, it includes four sequential steps. In step S1, personnel location trajectory data and building energy consumption time series data are acquired. Then, step S2 is performed, where the time series deviation value is calculated and the alignment and reconstruction feature parameters are output. In step S3, the deviation degree matrix is ​​calculated to rearrange priorities and resolve conflicts. Finally, step S4 is performed, where the dynamic routing control strategy is transformed and the allocation command is output.

[0056] like Figure 2 As shown, the execution data acquisition unit acquires personnel and energy consumption time-series data, triggers the time-series alignment unit to run the time-series difference compensation function along the associated path of calculating the time-series deviation value, and further differentiates into three logical branches based on the running state of this function. The first branch directly points to the shared presentation unit to transform into a dynamic routing control strategy along the path of calculating the deviation matrix. The second branch triggers the index analysis unit to intercept the calculation and perform state reconstruction under the condition that the rate of change exceeds the threshold, and also points to the shared presentation unit to transform into a dynamic routing control strategy after the judgment matrix exceeds the safety threshold. The third branch triggers the adaptive degradation unit to perform smooth regression prediction under the condition of encountering data sampling interruption, and finally points to the shared presentation unit to transform into a dynamic routing control strategy along the path of maintaining the convergence state of the matrix.

[0057] Example 4: In the application scenario of implementing comprehensive energy consumption quality control in a campus administrative management system, when the system faces the engineering challenge of misalignment of spatiotemporal characteristics caused by differences in physical sampling frequencies of heterogeneous data sources, which in turn induces overshooting of resource allocation commands, the analysis range control unit performs targeted repair by establishing a mapping chain between the underlying physical signals and the upper-level management parameters, targeting the energy consumption time-series characteristic flow. Personnel location trajectory data stream To address the asynchronous sampling contradiction, the system embeds a time-series alignment operator. Its white-box operation logic is as follows: the indicator analysis unit obtains the energy consumption time-series feature stream from the time-series database. Original sampling timestamp Personnel location trajectory data stream Original sampling timestamp Calculate the instantaneous deviation between the two. And compare this difference with a preset sampling time difference threshold. Numerical comparison is performed, including determining boundary thresholds. With respect to the preset sampling time difference threshold All were determined through offline calibration experiments to ensure that synchronization alignment could be triggered under normal network fluctuations; when the determination At that time, the system triggers the timing sliding window differential compensation function, based on The historical rate of change is extrapolated to calculate the correction offset of energy consumption characteristics, which is then used to adjust the energy consumption characteristics. Perform numerical hedging and output aligned reconstructed feature parameters. .

[0058] The system utilizes index correction coefficients The above hedging process is quantitatively adjusted, including the indicator correction coefficient. ,in, This is the indicator correction coefficient. This is the time delay sensitivity adjustment coefficient, with a value range set from 0.10 to 0.20. This is the absolute difference between the timestamps of the trajectory data and the energy consumption data. The energy consumption data acquisition cycle is set to 15 minutes. This coefficient is used to achieve dynamic and smooth correction of energy consumption characteristics. The system utilizes the reconstructed aligned reconstructed feature parameters. Feature quantity mapped to personnel trajectory space Input the deviation matrix to build the model, and calculate the resource utilization deviation matrix. ,in, For the resource utilization deviation matrix, Spatial projection feature quantity representing the characteristics of personnel trajectory requirements. For aligned and reconstructed feature parameters Mapped spatial characteristics of facility energy consumption A constant vector is configured for refined management standards, thereby enabling the mapping calculation from the underlying physical signals to the management deviation matrix.

[0059] To address the stability requirements for data quality assurance in non-ideal engineering environments, the system is pre-configured with self-healing and compensation procedures for abnormal conditions. This includes measures taken when energy consumption data is continuously lost due to network fluctuations. Every period of frames, the adaptive degradation unit automatically reads the historical benchmark calibration constant vector pre-stored in the memory, uses the administrative benchmark energy consumption constant setting of 120 kW·h for a specific building as the physical property benchmark, and utilizes the weighted attenuation factor. Smooth regression prediction is performed on the feature parameters within the effective time window, where the weight decay factor... The value is set to 0.85 to ensure the deviation matrix... To maintain a smooth transition during data interruptions, and to eliminate concurrent conflicts at the data throughput level, the token arbitration unit is based on the spatiotemporal deflection step size vector. Length of the module Constructing a conflict state matrix Implement throughput priority queuing based on module length weighting for different data processing partitions to ensure high-variability feature streams. Prioritizing the acquisition of data processing tokens establishes a control barrier for logical flow before data injection into the evaluation matrix. Ultimately, the shared presentation unit determines the data based on the resource utilization deviation matrix. The positive and negative polarity of the data offset is controlled by a dynamic routing table strategy, which automatically triggers the targeted push of data allocation instructions. This allows the control deviation to achieve smooth, monotonically fast convergence without overshoot within 4.1 minutes, objectively controlling the misallocation rate of administrative resources across the entire campus to 1.3%. This eliminates the oscillations in decision-making logic caused by asynchronous sampling and realizes a logical closed loop for campus-wide data quality management from physical signal acquisition to administrative resource allocation. Based on the distributed system resource load balancing theory and the time-varying decay law of data, concurrent data streams in multiple regions require spatial weighted allocation and rolling updates of the time-domain baseline. In the scenario of large-scale collaborative deployment across multiple regions of the campus, the indicator analysis unit adopts a multi-partition parallel processing approach. Distributed scheduling is implemented for independent computing power clusters in different geographical regions such as teaching areas, living areas, and research areas. This ensures that the total adjustment amount across the entire region is based on the resource utilization deviation matrix fed back by each region in real time. The eigenvalues ​​are weighted and allocated on demand, prioritizing data processing resources for partitions with larger absolute deviations. The system incorporates a dynamic reconstruction and update mechanism for historical benchmark calibration constant vectors to address environmental baseline drift caused by seasonal changes. A 30-day verification cycle is used, employing a sliding time window with a time decay factor to eliminate outdated energy consumption data. The average actual energy consumption of previous cycles is used as a correction variable for rolling recalibration of basic physical property benchmarks. This is also applied to personnel location trajectory data. During the data collection process, the IoT access node directly strips the personal identification tag and media access control address at the field layer gateway interface, and only transmits the de-identified discrete spatial node count and location flow feature vector to the data acquisition unit. It uses information processing boundary shrinkage control methods to build a privacy defense line for the data flow.

[0060] Example 5: Before deploying the campus-wide data quality control and sharing system in a highly dynamic and interference-prone network environment, the system pre-configures standardized on-site deployment pre-calibration and engineering calibration procedures to eliminate measurement deviations caused by differences in sensor deployment locations and link noise. Specifically, engineering personnel select representative teaching buildings on campus as offline calibration scenarios and use standard physical quantity calibration sources to perform sensitivity consistency calibration on the IoT acquisition nodes deployed at the access layer of each building. This is achieved by synchronously recording the input values ​​of the standard calibration source within a preset time window. Output values ​​of the acquisition node The gain compensation coefficient of each node is calculated. ,in, This is the gain compensation coefficient. Input values ​​for the standard calibration source. To collect the output value of the acquisition node, the system will use this coefficient. It is embedded in the flash memory sector of the local control module and used for real-time correction of power consumption timing characteristics. Quantization error.

[0061] After completing offline calibration, the system performs a logic link stress test before on-site deployment. This test injects simulated high-density burst traffic sequences of personnel into the control bus, monitors the resource utilization of the data processing module and the bus response latency in real time, and sets preset judgment boundary thresholds. The system dynamically calibrates and records the trajectory state vector when transient congestion occurs in the campus wireless network, causing the data link packet loss rate to reach 5%. The density fluctuation slope is determined, and the extreme fluctuation value measured under this condition is set as the dynamic adjustment benchmark for triggering implicit negative event risks. If the statistical deviation of data transmission timing is detected to exceed the preset confidence interval, the system automatically activates the redundancy backup logic. The adaptive degradation unit retrieves the historical benchmark calibration constant vector and performs smooth degradation prediction and weight fusion on the real-time data stream. This ensures that the resource utilization deviation matrix output by the indicator control unit is accurate when the communication link performance undergoes nonlinear degradation. Maintaining logical stability, this procedure ensures hardware compatibility and data processing consistency as the system is deployed from a single teaching building to a campus-wide scale, providing technical support for scientific decision-making based on administrative supervision data.

[0062] Example 6: When the campus data management and sharing system reconstructs the time-series trajectory of energy consumption and personnel flow across the entire campus, in order to address the risk of signal quality degradation caused by inconsistent data acquisition step sizes across heterogeneous nodes, the system executes an offline calibration procedure to establish a physical time-series benchmark. Engineers select teaching and research clusters as benchmark test areas and input a set of standard pulse benchmark signals with fixed frequency and duty cycle to the physical access node of the data center. Record the response delay of each acquisition terminal under different load pressures. The compensation operator was obtained by fitting the data using the least squares regression algorithm. ,in, For timing compensation operators, For the first Channel contribution weights for each acquisition channel. For the first The unit impulse response function of each node is used as the initial physical configuration and stored in the terminal's local firmware for subsequent online monitoring of the personnel trajectory point set. Building energy consumption characteristic flow Perform real-time synchronization calibration.

[0063] To improve the accuracy of identifying risks from implicit negative event recording, the system introduces a deviation feature space threshold judgment model. When the data quality evaluation index... Continuous abnormal fluctuations were detected, and the calculated spatiotemporal deflection step size vector was obtained. normalized modulus When the preset processing boundary is exceeded, the system automatically initiates a self-healing compliance procedure based on the feature envelope of historical data. This procedure will convert the real-time feature vector of the current business data... Compared with the historical baseline feature vector in the cache Differential projection is performed, and an adaptive fusion algorithm is used to reconstruct the state of lost frames or outliers. The reconstructed state feature vectors are then used to perform state reconstruction. ,in, The reconstructed state feature vector contains... A vector of physical characteristic parameters, This is the raw feature vector acquired in real time. This is the baseline feature vector for the same historical period. To dynamically correct the gain coefficient, the value range is set from 0.65 to 0.85. By introducing this correction logic, the system transforms the access control boundary of administrative supervision data into a dynamic routing table control strategy, realizing logical smoothness from physical sampling deviation to management decision matrix. This ensures that under the pressure of complex network communication, the administrative supervision indicators received by the school-level supervision terminal can always be maintained within the mismatch rate threshold range of 1.3%, thereby completing the logical mapping closed loop from the underlying raw business data flow to the high-level administrative management decision data.

[0064] 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 campus-wide data quality control and sharing system, characterized in that, include: The data acquisition unit, time-series alignment unit, indicator analysis unit, and shared presentation unit transmit data to each other via a data bus; The system controls data quality through the following steps: The data acquisition unit acquires personnel location trajectory data and building energy consumption time series data; The time-series alignment unit extracts the timestamps of personnel location trajectory data and building energy consumption time-series data and calculates the time-series deviation value. When the time-series deviation value exceeds the preset sampling time difference threshold, it runs the time-series sliding window differential compensation function, calculates the index correction coefficient based on the time delay sensitivity, uses the index correction coefficient to perform numerical offsetting on the building energy consumption time-series data, and outputs the alignment reconstruction feature parameters. The indicator analysis unit calculates the resource utilization deviation matrix based on the alignment and reconstruction feature parameters. When it is determined that a sampling frame loss has occurred, it uses the weight decay factor to perform smooth regression prediction on the alignment and reconstruction feature parameters in the preceding time window. Under high concurrency load, it constructs a conflict state matrix and allocates data processing tokens to different data processing partitions according to the magnitude of the spatiotemporal deflection step vector to rearrange read and write priorities and resolve bus read and write conflicts. The shared presentation unit determines the shared data access control boundary when the resource utilization deviation matrix exceeds the security threshold, transforms the shared data access control boundary into a dynamic routing table control policy, and outputs resource allocation data instructions.

2. The campus-wide data quality control and sharing system according to claim 1, characterized in that, The time-series alignment unit includes the following steps: The time-series alignment unit constructs a time-series sliding window based on the collection cycle of building energy consumption time-series data, and captures the discrete distribution deviation value of timestamps within the time-series sliding window; The time-series alignment unit calculates the residual compensation gain term based on the discrete distribution deviation value, and superimposes the residual compensation gain term into the index correction coefficient to numerically correct the resource utilization deviation matrix, thereby eliminating the time-series characteristic differences caused by multi-rate asynchronous sampling and suppressing management decision oscillations.

3. The campus-wide data quality control and sharing system according to claim 1, characterized in that, The indicator analysis unit includes the following steps: When the discrete temporal local density variation rate of personnel location trajectory data exceeds the long-period distribution threshold, the indicator analysis unit determines that a group transient sudden migration event has occurred, triggering the construction of a conflict state matrix; the indicator analysis unit calculates the magnitude of the spatiotemporal deflection step size vector of each data processing partition participating in concurrent conflicts, prioritizes the allocation of data processing tokens to the group of data processing partitions with the largest magnitude, and allocates different delay waiting periods to other data processing partitions through delay queues to resolve bus read and write conflicts.

4. The campus-wide data quality control and sharing system according to claim 1, characterized in that, The indicator analysis unit includes the following steps: When the energy consumption data acquisition node experiences continuous sampling interruption and the building energy consumption time series data is truncated, the indicator analysis unit fuses the historical benchmark calibration constant vector; the indicator analysis unit uses the weight decay factor to perform a gradual and smooth regression prediction of the alignment reconstruction feature parameters within the preceding time window, maintaining the convergence state of the resource utilization deviation matrix.

5. A campus-wide data quality control and sharing system according to claim 1, characterized in that, The shared presentation unit includes the following steps: The shared presentation unit projects personnel location trajectory data and building energy consumption time series data into a feature space within a preset time window based on a multi-level role permission mechanism, and removes data items that are not related to administrative management; When the resource utilization deviation matrix output by the indicator analysis unit exceeds the safety threshold, the shared presentation unit dynamically determines the boundary of shared data access control rights and restricts unauthorized nodes from accessing the shared data.

6. The campus-wide data quality control and sharing system according to claim 1, characterized in that, The shared presentation unit includes the following steps: In response to changes in the boundaries of shared data access control, the shared presentation unit updates the system's dynamic routing table control policy, directing the dynamic routing table control policy to the school-level monitoring node and the college-level management node; the shared presentation unit distributes resource allocation data instructions along the transmission path of the dynamic routing table control policy, sending the resource allocation data instructions to the corresponding school-level monitoring node and college-level management node.

7. A campus-wide data quality control and sharing system according to claim 1, characterized in that, The data acquisition unit acquires personnel location trajectory data and building energy consumption time series data in the following ways: it receives administrative benchmark energy consumption data of each building collected by IoT collection nodes deployed in the campus living area at a fixed sampling period of 15 minutes; at the same time, it receives discrete time series spatial migration trajectories of personnel groups transmitted in real time by the campus access control system and wireless network access nodes, and generates personnel location trajectory data representing the evolution of spatial location.

8. A campus-wide data quality control and sharing system according to claim 1, characterized in that, When calculating the index correction coefficient of the timing alignment unit, a data buffer queue is introduced based on the time delay sensitivity and the collection cycle of building energy consumption time series data. The building energy consumption time series data is cached and conditioned according to the data buffer queue to smooth the timing deviation value and stabilize the output sequence of resource allocation data instructions.

9. A campus-wide data quality control and sharing system according to claim 1, characterized in that, The system also includes a central clock synchronization module; the central clock synchronization module is connected to the data acquisition unit, timing alignment unit and index analysis unit through a time synchronization bus, and sends a reference clock synchronization signal to the data acquisition unit, timing alignment unit and index analysis unit to unify the clock reference coordinates within the system.

10. A method for managing and sharing data quality across an entire campus, used to run the system for managing and sharing data quality across an entire campus as described in claim 1, characterized in that, Includes the following steps: Step S1: The data acquisition unit acquires personnel location trajectory data and building energy consumption time series data; Step S2: The time-series alignment unit extracts the timestamps of personnel location trajectory data and building energy consumption time-series data and calculates the time-series deviation value. When the time-series deviation value exceeds the preset sampling time difference threshold, the time-series sliding window differential compensation function is run. The index correction coefficient is calculated based on the time delay sensitivity. The index correction coefficient is used to perform numerical offsetting on the building energy consumption time-series data, and the alignment reconstruction feature parameters are output. Step S3: The index analysis unit calculates the resource utilization deviation matrix based on the alignment and reconstruction feature parameters. When it is determined that a sampling frame loss has occurred, it uses the weight decay factor to perform smooth regression prediction on the alignment and reconstruction feature parameters in the preceding time window. Under high concurrency load, it constructs a conflict state matrix and allocates data processing tokens to different data processing partitions according to the magnitude of the spatiotemporal deflection step vector to rearrange read and write priorities and resolve bus read and write conflicts. In step S4, when the resource utilization deviation matrix exceeds the security threshold, the shared presentation unit determines the shared data access control boundary, transforms the shared data access control boundary into a dynamic routing table control strategy, and outputs resource allocation data instructions.

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