Adaptive data modeling system based on interactive graphical interface
By adjusting the mapping weights and execution step size in real time in an interactive graphical interface adaptive modeling system, the problems of computational model rigidity and topological decoupling in existing technologies are solved, and stability and accuracy are improved in highly dynamic environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WENZHOU INTENET NETWORK INFORMATION TECH CO LTD
- Filing Date
- 2026-03-09
- Publication Date
- 2026-06-05
Smart Images
Figure CN122153208A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of data processing technology for specific computational models, and particularly relates to an adaptive data modeling system based on an interactive graphical interface. Background Technology
[0002] Currently, using interactive graphical interfaces to guide adaptive modeling processes is a major way to improve engineering efficiency. By representing logical operators through visual components, the modeling process can be presented intuitively, and stable data streams can be handled by adjusting specific parameters online. However, when dealing with complex conditions such as highly dynamic sensor networks or multiphase flow reaction processes, existing modeling systems exhibit obvious structural limitations. Typically, the operator connections of the computational graph are fixed in the initialization phase, and the data features are fitted only by iterative updates of weight parameters. In order to maintain the stability of the computational path, this mode sacrifices the model's ability to capture non-stationary sequence mechanism transitions, resulting in significant prediction biases when the model faces nonlinear displacements of the input manifold.
[0003] Besides the rigid architecture, the adaptive mechanism at the control method level also has shortcomings. For example, Chinese invention patent CN116877238A discloses an adaptive data-driven modeling method for a diesel engine selective catalytic reduction system, which updates parameters online through echo state networks and gradient descent strategies. Although such solutions can alleviate error accumulation, they are data fitting and lack a real-time extraction mechanism for the geometric features of the underlying computational manifold. Conventional improvement approaches tend to increase the model depth or stack ensemble operators to improve fitting accuracy, but this leads to a non-linear increase in inference latency. A deeper technical contradiction lies in the lack of a direct physical mapping mechanism between the physical interactive displacement of the graphical interface and the underlying numerical tensor operations. The user's manual intervention cannot be converted into a real-time correction increment of the model topology, resulting in a quasi-decoupled state between the interaction layer and the computation layer, making it difficult to achieve adaptive reconstruction of the topology while ensuring numerical stability.
[0004] Therefore, the technical problem to be solved by this invention is how to construct a dynamic evolution framework that can transform interactive spatial displacement into energy mapping of the underlying computation graph and achieve adaptive reconstruction of computational paths while maintaining numerical stability. Summary of the Invention
[0005] This invention provides an adaptive data modeling system based on an interactive graphical interface, comprising an interactive instruction processing unit, a stability analysis unit, an operator evolution execution unit, and a closed-loop feedback constraint unit. The interactive instruction processing unit is used to obtain the input displacement vector for the graphical interface, define the coordinate space of the graphical interface as a logical mapping space that represents the distribution of data features, and calculate the normal component of the logical node relative to the logical mapping space. The stability analysis unit, connected to the operator evolution execution unit, is used to obtain the Hessian matrix at the logical node association of the underlying computation graph in real time, and calculate the eigenvalues of the Hessian matrix at the logical node association to extract stability features that characterize the convergence state of the model. The operator evolution execution unit is used to carry the underlying computation graph and execute adaptive reconstruction instructions according to the input displacement vector. The operator evolution execution unit is equipped with a feedforward prediction module, which is used to simulate the state response characteristics of the far-end operator of the underlying computation graph based on the topology prediction perturbation sequence before the adaptive reconstruction is executed. The closed-loop feedback constraint unit is used to receive stability feature quantity and state response feature, and calculate the mapping weight of the input displacement vector in the logical mapping space. When the stability feature quantity is greater than the preset stability threshold, the mapping weight is reduced proportionally to reduce the execution step size of adaptive reconstruction. The closed-loop feedback constraint unit is also used to monitor the gradient flow of the underlying computation graph. When the global loss function overflows above a preset overflow threshold, it generates a reverse update resistance instruction in the logical mapping space based on the gradient flow, so as to generate a mapping weight penalty term to limit the update rate of the logical node coordinates.
[0006] Preferably, the closed-loop feedback constraint unit calculates the execution step size according to the following rules. : ,in, The preset base step size constant, The preset manifold sensitivity coefficient, This is a stability characteristic.
[0007] Preferably, the operator evolution execution unit further includes an operator redundancy pruning module. The operator redundancy pruning module is used to calculate the interaction entropy loss based on the product of the user interaction frequency and the operator gradient contribution, and to set the energy level function that decays over time for the non-basic operators in the underlying computation graph. The operator redundancy pruning module is used to switch specific non-basic operators to a bypass state to simplify the computation path when the interaction entropy loss is lower than a preset threshold.
[0008] Preferably, the feedforward prediction module is further configured to generate a logic repulsion vector in the logic mapping space when the oscillation frequency of the remote state variable triggered by the topology change caused by the topology prediction perturbation sequence indicates that the logic node exceeds a preset frequency threshold; the closed-loop feedback constraint unit is further configured to apply the logic repulsion vector as a constraint operator to the mapping weight to block the current displacement update path.
[0009] Preferably, the closed-loop feedback constraint unit is used to map the original pixel displacement output by the interactive instruction processing unit into a logical update increment with weight decay, so that the parameter gradient in the adaptive reconstruction process is maintained within a preset convergence range.
[0010] Preferably, the stability analysis unit is used to extract the rate of change of the Hessian matrix trace of the underlying computation graph during the adaptive reconstruction process, and send the rate of change to the closed-loop feedback constraint unit to generate a time-series adaptive damping factor.
[0011] Preferably, the operator evolution execution unit is used to call the reconstruction operator to perform real-time topology adjustment of the computation path after receiving the logic update increment corrected by the closed-loop feedback constraint unit, so as to fit the nonlinear displacement of the input data manifold.
[0012] Preferably, the interactive instruction processing unit is also used to monitor the movement acceleration of the logic node in real time, and adjust the sampling frequency of the stability analysis unit according to the movement acceleration, so that the sampling frequency is synchronized with the user's operation rhythm; the adjustment range of the sampling frequency is 10Hz to 1000Hz.
[0013] Preferably, the operator redundancy pruning module determines the interaction entropy loss by calculating the product of the user's click frequency on the logical node and the gradient weights of the specific non-basic operators for the descent of the global loss function.
[0014] Preferably, the stability analysis unit is also used to determine whether the underlying computation graph has experienced logical oscillation by monitoring the fluctuation variance of the stability characteristic quantity within a window of 10ms to 50ms, and when the fluctuation variance exceeds the oscillation threshold, the instruction closed-loop feedback constraint unit sets the logical update increment to zero to suspend the current parameter update.
[0015] Compared with existing technologies, the adaptive data modeling system based on an interactive graphical interface of this invention has the following advantages: 1. In adaptive data modeling, the numerical stability of the computational model is aligned with the depth of the interactive intervention path. By introducing the eigenspectral radius parameter of the adjacency matrix of the computational graph into the coordinate update logic of the graphical interface, the step size of the interactive response is modulated in real time by the curvature of the computational graph manifold. When the computational model approaches the instability boundary, the motion damping component generated by the system automatically compresses the displacement step size of the logic node. This mechanism transforms the abstract numerical convergence constraint into an intuitive physical operational resistance, guiding the topology reconstruction action in real time during the interaction process to avoid the numerical explosion interval, and ensuring the numerical continuity and algorithm stability of the computational model during the topology discretization mutation process.
[0016] 2. Self-correction in interactive space driven by residual gradient modeling: By extracting local residual components generated during data modeling, the system transforms them into attractive and repulsive force gradients in the graphical interface space. The system utilizes the intermediate deviation signal generated by the computation container to generate anisotropic virtual reverse torques in the graphical interaction layer. When the user's topology adjustment path violates the underlying manifold law, the node exhibits obvious drag resistance, while the adjustment towards the optimal manifold direction generates a magnetic attraction and guidance effect. This tactile logic correction based on data feedback transforms the modeling system from a one-way operation tool into a two-way logic arbiter, reducing the risk of mismatch between human experience and real mechanisms.
[0017] 3. Zero-delay perception and feedforward protection of local reconstruction risks: By emitting virtual wavefront probe pulses when the logic node is active, the system can simulate the response characteristics of the far-end operators in the computation graph in advance. By utilizing the information overflow during the interaction process, the system can predict the global nonlinear disturbances that may be caused by local topology changes before the model officially refreshes the weights. When the probe signal triggers the far-end state variable to oscillate beyond the threshold, the system generates an instantaneous logic repulsion field to block the current drag path, which solves the lag problem of traditional error feedback and ensures that the long-range dependent logic of the large-scale computation model is always within the convergence boundary during the reconstruction process. Attached Figure Description
[0018] Figure 1 This is a schematic diagram of the logical interaction process and closed-loop feedback control principle of the present invention; Figure 2 This is a schematic diagram of the overall hardware architecture and industrial data link deployment of the present invention. Detailed Implementation
[0019] 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.
[0020] 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.
[0021] 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.
[0022] 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.
[0023] This invention provides an adaptive data modeling system based on an interactive graphical interface, comprising an interactive instruction processing unit, a stability analysis unit, an operator evolution execution unit, and a closed-loop feedback constraint unit. The interactive instruction processing unit is used to obtain the input displacement vector for the graphical interface and define the coordinate space of the graphical interface as a logical mapping space representing the distribution of data features. It also calculates the normal components of logical nodes relative to the logical mapping space. The stability analysis unit is connected to the operator evolution execution unit and is used to obtain the underlying computation graph at the logical node association points in real time. Matrix, and calculate The eigenvalues of the matrix at logical node associations are used to extract stability features that characterize the convergence state of the model. The operator evolution execution unit carries the underlying computation graph and executes adaptive reconstruction instructions based on the input displacement vector, while the closed-loop feedback constraint unit receives stability features. And state response characteristics, and calculate the mapping weights of the input displacement vector in the logical mapping space, in stability feature quantity When the value exceeds a preset stability threshold, the mapping weights are reduced proportionally to decrease the execution step size of adaptive reconstruction. .
[0024] During the interactive modeling process, the interactive instruction processing unit monitors the movement acceleration of the logic nodes in real time and adjusts the sampling frequency of the stability analysis unit based on this acceleration to synchronize the sampling frequency with the user's operation rhythm. The adjustment range of the sampling frequency is [range missing]. to The stability analysis unit stores displacement samples in real time through a circular buffer with a depth of 50 data units. When the interactive instruction processing unit detects that the acceleration of the logical node is less than five units per square second, the system locks the sampling frequency at 10 Hz and simultaneously extends the observation window from 10 milliseconds to 50 milliseconds to ensure that the buffer can retain at least five valid displacement sample points in each statistical period, meeting the minimum sample number requirement for fluctuation variance calculation and eliminating statistical distortion under low-speed operation. When calculating the normal component of the logical node relative to the logical mapping space, the interactive instruction processing unit obtains the set of tangent vectors of the logical node on the current data manifold, extracts the local normal vector of the logical node by executing the Schmitt orthogonalization procedure, and places the input displacement vector in the direction of the local normal vector. The components on the manifold are removed, and only the components of the input displacement vector in the tangent space of the manifold are retained and defined as logical displacement vectors. In subsequent processing, logical update increments are generated based on the mapping weights, thereby achieving real-time alignment between the human-machine intervention path and the geometric constraints of the underlying computational manifold. The interactive instruction processing unit calculates the logical displacement vector, obtains the set of tangent vectors of the logical node in the current data manifold, executes the Schmitt orthogonalization procedure to extract local normal vectors, removes the components of the input displacement vector in the direction of the local normal vectors, retains the components in the tangent space of the manifold and defines them as logical displacement vectors. The closed-loop feedback constraint unit generates logical update increments based on the mapping weights, so that the human-machine intervention path is aligned with the geometric constraints of the underlying computational manifold in real time. The operator evolution execution unit calls the reconstruction operator to adjust the computational path topology in real time.
[0025] The stability analysis unit extracts the underlying computation graph during the adaptive reconstruction process. The rate of change of the matrix trace is sent to the closed-loop feedback constraint unit, which generates a time-adaptive damping factor. The stability analysis unit is also used to monitor stability characteristics. exist to The fluctuation variance within the window determines whether logical oscillations have occurred in the underlying computation graph. When the fluctuation variance exceeds the oscillation threshold, the instruction closed-loop feedback constraint unit sets the logical update increment to zero, thus suspending the current parameter update. The stability analysis unit analyzes the stability characteristics. Quantization involves selecting the Hessian matrix at the logical node of the underlying computation graph, calculating the square root of the sum of squares of each element to obtain the Frobenius norm, and using the Frobenius norm to characterize the curvature intensity of the logical mapping space in the local manifold; this is addressed in relation to the interaction entropy loss. The system counts the click frequency of logical nodes within a 600-second sampling window. With gradient weights The Min-Max normalization method is used to eliminate dimensional differences and obtain normalized parameters. To ensure the accuracy of metabolic threshold determination, the reference set of maximum and minimum values used for normalization is taken from a rolling historical time window of 60 minutes prior to the current sampling time. The system scans the peak click frequency and gradient weight every 100 milliseconds within this time window and stores them in a temporary register. The current original sample value is subtracted from the minimum value in the temporary register and then divided by the difference between the maximum and minimum values to ensure that the normalized parameters participating in the multiplication operation fall within the per-unit value range of 0 to 1. The dimensionless value is calculated based on the product of the normalized parameters. When this value is lower than the 0.15 threshold for three consecutive calculation cycles, the operator redundancy pruning module switches the corresponding non-basic operator to the bypass state by modifying the topology descriptor.
[0026] The operator evolution execution unit includes a feedforward prediction module, which simulates the state response characteristics of the far-end operators in the underlying computation graph based on the topology prediction perturbation sequence before adaptive reconstruction. If the oscillation frequency of the far-end state variables triggered by the topology change indicated by the topology prediction perturbation sequence exceeds a preset frequency threshold, the feedforward prediction module generates a logic repulsion vector in the logic mapping space. The feedforward prediction module then performs topology prediction perturbation simulation, employing a sensitivity analysis method based on first-order Taylor expansion, at the current coordinates of the logic node. The superimposed magnitude is a 1% perturbation vector across the full range of the logical mapping space. Monitoring the characteristics of the remote operator state response caused by this. If the power spectral density of the high-frequency component of the feature reaches more than 1.5 times the background noise benchmark, it is determined that the underlying computation graph has generated logical oscillation. The closed-loop feedback constraint unit reduces the corresponding logical update increment magnitude to 10% of the current value. Before the reconstruction operator starts the topology adjustment action, the numerical stability feedback constraint logic is established. The closed-loop feedback constraint unit uses the logical repulsion vector as a constraint operator to act on the mapping weight to block the current displacement update path. After receiving the logical update increment corrected by the closed-loop feedback constraint unit, the operator evolution execution unit calls the reconstruction operator to perform real-time topology adjustment on the computation path to fit the nonlinear displacement of the input data manifold.
[0027] The closed-loop feedback constraint unit maps the original pixel displacement output by the interactive instruction processing unit into a logical update increment with weight decay, ensuring that the parameter gradient during the adaptive reconstruction process remains within a preset convergence range. The closed-loop feedback constraint unit calculates the execution step size according to the following rules. : ,in, To execute the step size, The preset base step size constant, The preset manifold sensitivity coefficient, To ensure stability, a closed-loop feedback constraint unit monitors the gradient flow of the underlying computation graph. When the global loss function overflows above a preset overflow threshold, a reverse update resistance instruction is generated in the logical mapping space based on the gradient flow to generate a mapping weight penalty term to limit the update rate of logical node coordinates. To optimize computational resource utilization, the operator evolution execution unit also includes an operator redundancy pruning module. This module calculates the interaction entropy loss based on the product of user interaction frequency and operator gradient contribution, and sets energy level functions that decay over time for non-fundamental operators in the underlying computation graph. The operator redundancy pruning module determines the interaction entropy loss by calculating the product of the user's click frequency on logical nodes and the gradient weights of specific non-fundamental operators for the decrease of the global loss function. When the interaction entropy loss is lower than a preset threshold, the operator redundancy pruning module switches specific non-fundamental operators to a bypass state to simplify the computation path. This system solves the structural rigidity problem caused by dynamic system mechanism transitions by mapping the geometric features of the underlying computational manifold to the interaction space in real time, thereby improving the accuracy of data modeling while ensuring the stability of model topology evolution.
[0028] Example 1: In a multiphase flow chemical reaction process modeling scenario involving transitions between laminar and turbulent mechanisms, the system processes nonlinear data streams acquired by multidimensional sensors. Due to abrupt changes in the physical state inside the reactor, the data feature distribution is displaced within the logical mapping space. The operator drags and drops logical nodes through a graphical interface to perform adaptive reconstruction of the underlying computational graph. The interactive instruction processing unit obtains the input displacement vector and simultaneously calculates the normal component of the logical node relative to the logical mapping space. At this time, the stability analysis unit connects to the operator evolution execution unit in real time to extract the logical node association points in the underlying computational graph. The matrix is then used to calculate the distribution of its eigenvalues, from which stability eigenvalues are obtained. In this embodiment, the stability characteristic quantity The measured value is .
[0029] Closed-loop feedback constraint unit based on stability characteristics Real-time adjustment of execution step size Specifically, the execution step size The calculation rules are as follows: ,in, To execute the step size, The basic step size constant is set to , The manifold sensitivity coefficient is set to 1. , The stability characteristic is used to obtain the execution step size at the current time through closed-loop feedback constraint unit calculation. for Before executing the adaptive reconstruction instruction, the feedforward prediction module within the operator evolution execution unit simulates the state response characteristics of the far-end operator in the underlying computation graph based on the topology prediction perturbation sequence. If the oscillation frequency of the far-end state variable obtained from the simulation exceeds a preset frequency threshold, the closed-loop feedback constraint unit generates a reverse update resistance instruction based on the logic repulsion vector to limit the coordinate update rate of the logic node to avoid numerical overflow. The operator evolution execution unit then utilizes the adjusted execution step size... The system uses a reconstruction operator to fit the nonlinear displacement of the input data manifold; the system then uses stability features... Execution step size in the interaction space A nonlinear feedback correlation is established to achieve synergy between the underlying computational manifold characteristics and the human-machine intervention path. After the underlying computational graph completes topology reconstruction, the global loss function is maintained within the convergence range, and the computational model fits the non-stationary data characteristics under turbulent conditions.
[0030] Example 2: In a high-dynamic sensor network modeling experiment for monitoring industrial multiphase flow processes, the system processes a time-series data stream containing nonlinear disturbances generated by a simulation platform, which is solved based on a computational fluid dynamics model. The equations generate a sequence of logical node coordinates simulating the flow field distribution inside the reactor. This experiment verifies the effectiveness of the system in maintaining numerical stability using a closed-loop feedback mechanism when the underlying computational graph faces manifold displacements induced by mechanistic transitions. It examines the real-time modulation of the stability characteristic quantity K on the adaptive reconstruction step size. During the experimental parameter setting phase, the sampling frequency... The value depends on the movement acceleration of the logical node. The technical trade-off lies in ensuring the capture of transient features of topological changes while reducing computational overhead. The decision rule is set as follows: when the movement acceleration of the logical node... Exceed Sampling frequency per square second It tends towards the upper limit of the value range, that is In the engineering example of this experiment, the acceleration of logical node movement was considered. for The sampling frequency will be calculated based on the operating condition of one square second. Set as At the same time, the basic step size constant will be... Set as manifold sensitivity coefficient Set as To simulate measurement errors under industrial electromagnetic conditions, the signal-to-noise ratio of the actively superimposed signal source in the test signal source is [value missing]. Gaussian white noise and frequency of Power frequency interference was monitored, and the feature quantities extracted by the stability analysis unit were used; see Table 1, where the control group used a fixed execution step size and lacked stability-based feature quantities. Feedback constraints.
[0031] Table 1: Comparison of stability performance between experimental and control groups under different mechanistic transition intensities Analysis of the data in Table 1 shows that as the mechanism transition speed increases from... Increase to The stability characteristic quantities calculated by the experimental group through the stability analysis unit Depend on Growth to The closed-loop feedback constraint unit will execute the step size accordingly. from Dynamically reduced to Make the global loss function Always maintained at The following interval; in contrast, the control group maintains a fixed execution step size, but under high-speed transition conditions, it cannot suppress the underlying computation graph. Matrix eigenvalue overflow leads to global loss function Growth to Numerical divergence occurs; further investigation is needed regarding the manifold sensitivity coefficient. Stress testing is performed on the boundary values of the given values. Reduce to At that time, the system's damped response to changes in manifold curvature is insufficient, leading to a decrease in the global loss function under high dynamic drag. The growth rate changes from linear to exponential overflow, and the numerical stability criterion is lost; when Increase to At this time, the mapping weights are excessively penalized, the shift updates of logical nodes exhibit saturation, and the execution step size... Always lower This causes the modeling path to fail to follow the displacement increment of the mechanism transition; experimental results show that by mapping the geometric features of the underlying computational manifold to the feedback step size constraint of the interaction space, the computational model has the ability to adaptively reconstruct when dealing with non-stationary data streams.
[0032] Example 3: This example combines Figures 1 to 2 Describe an adaptive data modeling system based on an interactive graphical interface, such as... Figure 1As shown, the user or graphical interface generates an input displacement vector and transmits it to the A1 interactive instruction processing unit. This unit is responsible for calculating the normal component and the original pixel displacement in the logical mapping space and sending it to the A3 closed-loop feedback constraint unit. On the other hand, it monitors the movement acceleration in real time to adjust the sampling frequency and transmits it to the A2 stability analysis unit. The A2 stability analysis unit extracts the Hessian matrix and gradient flow data unidirectionally from the underlying computation graph, and then calculates the stability feature quantity K and the rate of change of the matrix trace, and sends them to the A3 closed-loop feedback constraint unit. The A3 closed-loop feedback constraint unit calculates the execution step size S based on the received parameters. Its calculation logic follows the formula... The generated mapping weights are used to output logic update increments to the A4 operator evolution execution unit. The A4 operator evolution execution unit integrates feedforward prediction simulation, redundancy pruning and reconstruction functions, and feeds back state response characteristics and logic exclusion vectors to the A3 closed-loop feedback constraint unit to form a constraint closed loop. At the same time, it outputs topology reconstruction instructions and operator bypass switching signals to the underlying computation graph, and finally completes the real-time update of operators, topology and weights in the underlying computation graph.
[0033] like Figure 2 As shown, the system hardware deployment is divided into three main parts: operator interaction terminal, adaptive modeling core server, and industrial field data layer. The operator interaction terminal consists of a workstation, a PC host, and human-computer input devices such as a mouse and touch screen. The host runs an interactive graphical interface program containing an interactive command processing unit component, which is responsible for capturing displacement and acceleration, and exchanging interactive commands and visualization streams with the adaptive modeling core server through a high-speed network connection. The adaptive modeling core server, as a computing cluster, is equipped with a parallel computing acceleration card to handle video memory and tensor operations, and runs the core computing runtime environment. It integrates operator evolution execution services including feedforward prediction, redundancy pruning, stability analysis services, matrix feature extraction, and closed-loop feedback constraint services such as weight adjustment and step size calculation. It also mounts a low-level computational graph data storage module for storing model topology and parameter weights. The server receives real-time nonlinear data streams from the industrial field data layer through an industrial bus or optical fiber. This data layer consists of a high-dynamic sensor network including multiphase flow, temperature, and pressure monitoring, and a data acquisition gateway responsible for signal conditioning and buffering.
[0034] Example 4: In a long-cycle modeling scenario of a distributed industrial gas monitoring sensor network, the system continuously processes data from... The high-dimensional non-stationary data stream uploaded from each sampling point, after the physicochemical reaction process in the monitored area enters a stable cyclic state, generates redundancy in the logical path of the non-fundamental operators used to capture transient change characteristics in the underlying computation graph. The operator redundancy pruning module in the operator evolution execution unit performs path metabolism, and the operator redundancy pruning module obtains the interaction frequency for the logical nodes. and the gradient weights of specific non-fundamental operators for descent of the global loss function. Interaction frequency For users Within the sliding observation window of seconds, targeting the first The ratio of the cumulative number of drag or click operations performed by each logical node to the total window duration, gradient weights. The operator's redundancy pruning module calculates the interaction entropy loss by providing a normalized score of the gradient magnitude output during backpropagation in the underlying computation graph. The calculation rules are as follows: ,in, For interaction entropy loss, interaction entropy loss To characterize the coupling between interactive activation states and model numerical contributions, the system introduces an energy level function. Real-time evaluation of the survival probability of operators, energy level function The calculation formula is as follows: ,in, For the first The current energy level value of each operator, Set the survival weights during operator initialization as follows: , The time decay factor, This is the duration of silence for the operator since it was last activated by an interactive command.
[0035] To determine the time decay factor The system executes parameter calibration procedures during the debugging phase to determine the value of the computation graph container under different conditions. The memory replacement rate under a given value determines the equilibrium point, when the time decay factor... Set as At that time, the decay curve of the energy level function matches the steady-state period of the sensor signal, and the operator redundancy pruning module monitors the cross-entropy loss. With preset metabolic threshold The logical relationship, if the interaction entropy loss continuous If a computation cycle falls below the metabolic threshold, the non-fundamental operator is determined to enter a low-contribution state. The operator redundancy pruning module redirects the input port of the non-fundamental operator to the corresponding bypass node by modifying the topology descriptor of the underlying computation graph, thus putting the operator into a bypass state. This process is repeated when the large-scale distributed modeling task reaches... During the hourly phase, the operator redundancy pruning module identifies and masks redundancy. A redundant operator with low interaction and low gradient contribution reduces the memory footprint of the computation graph container from... Reduce to The system's global loss function The fluctuation amplitude remained at Within the convergence domain, the operator redundancy pruning module couples user interaction features with model numerical contributions, achieving dynamic metabolism of computational paths while maintaining the fitting accuracy of the underlying computational manifold geometric features.
[0036] Example 5: In a scenario where a monitoring system for high-viscosity polymer polymerization is deployed, the system processes sensor data with nonlinear rheological characteristics. Due to differences in the physicochemical states at different reaction stages, the feedforward prediction module within the operator evolution execution unit needs to perform a benchmark calibration process for the current operating environment to generate a topology prediction perturbation sequence. The system selects data from the underlying computation graph... Each logical node is sequentially subjected to a probe displacement vector. The stability analysis unit captures the output response of the far-end operator in the underlying computation graph in real time. Thus, the perturbation mapping matrix is constructed. This matrix is used to describe the degree of correlation between the logical node displacement and the state of the remote operator.
[0037] During calibration, the displacement detection step size is set to the full range of the logical mapping space. Proportion, by executing continuous Subsequent displacement detection cycles acquire convergence region characteristic data, and the stability analysis unit calculates the response cycle for each response period. The rate of change of the matrix trace is stored in the topology prediction perturbation sequence; when the closed-loop feedback constraint unit detects the global loss function during the calibration phase... The average response deviation is lower than the preset error threshold. At that time, the system will map the current disturbance matrix. It is solidified as the computational benchmark for the feedforward prediction module, enabling the system to predict long-range disturbances caused by changes in local nodes during the subsequent interactive modeling stage.
[0038] Example 6: At the deployment site of the polymerization process monitoring system, the system executes a parameter calibration procedure to determine the manifold sensitivity coefficient. The value of is selected by the interactive instruction processing unit as the reference input data, which is the steady-state data stream of the reactor under no-load cyclic state. A set of probe displacement sequences covering the entire range of the logic mapping space is generated, and the output of the stability analysis unit at different probe points is recorded. Rate of change of matrix trace, calculate global loss function For execution step size The second derivative is obtained, and the critical point where its value changes sign is identified. The local curvature mean corresponding to this critical point is compared with the basic step size constant. The ratio is determined as the manifold sensitivity coefficient. In the actual deployment parameter calibration procedure, the system applies analog voltage pulses with a step size of one millivolt to the logic node under no-load conditions, records the slope of the change of the global loss function within fifty consecutive sampling periods, and multiplies its arithmetic mean by a decay gain of 0.2 as the initial storage value of the manifold sensitivity coefficient. If the execution step size is detected to trigger the saturation limit for three consecutive periods in subsequent operation, the coefficient is finely adjusted downward in a step unit of 0.01 until the step size returns to the range of 30% to 70% of the basic step size constant.
[0039] In real-time monitoring scenarios involving high-frequency environmental noise, the stability analysis unit extracts the underlying computation graph before and after executing the adaptive reconstruction instruction. The rate of change of the matrix trace is used as an input variable by the closed-loop feedback constraint unit in a preset damping mapping function to determine the time-series adaptive damping factor. The rate of change of the matrix trace increases beyond a preset slope threshold. At this time, the system instruction operator evolution execution unit redirects the current computation path to a backup operator branch with higher numerical stability by modifying the topological descriptor of the underlying computation graph. This enables the modeling process to maintain the global loss function by dynamically adjusting the weight propagation path when faced with local numerical oscillations caused by sudden changes in the mechanism. The system ensures stable evolution; for the feedforward prediction module within the operator evolution execution unit, the system determines a preset frequency threshold by analyzing the spectral distribution of the far-end state variables in the underlying computation graph during historical stable operating cycles, and calculates the far-end state variables in... The spectrum within each sampling period is used to extract the frequency component where the power spectral density reaches its maximum value. Use it as the background noise benchmark and according to its A preset frequency threshold is set by a factor of 1.5. When the oscillation frequency of the far-end state variable indicated by the topology prediction perturbation sequence within the prediction window reaches the preset frequency threshold, the closed-loop feedback constraint unit reduces the magnitude of the logic update increment to 1 / 3 of the current value. The ratio is used to suppress tensor overflow, thereby establishing a defensive boundary for numerical stability while ensuring the sensitivity of computational path reconstruction.
[0040] 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. An adaptive data modeling system based on an interactive graphical interface, characterized in that, It includes an interactive instruction processing unit, a stability analysis unit, an operator evolution execution unit, and a closed-loop feedback constraint unit: The interactive instruction processing unit is used to obtain the input displacement vector for the graphical interface, define the coordinate space of the graphical interface as a logical mapping space that represents the distribution of data features, and calculate the normal component of the logical node relative to the logical mapping space. The stability analysis unit, connected to the operator evolution execution unit, is used to obtain the Hessian matrix at the logical node association of the underlying computation graph in real time, and calculate the eigenvalues of the Hessian matrix at the logical node association to extract stability features that characterize the convergence state of the model. The operator evolution execution unit is used to carry the underlying computation graph and execute adaptive reconstruction instructions according to the input displacement vector. The operator evolution execution unit is equipped with a feedforward prediction module, which is used to simulate the state response characteristics of the far-end operator of the underlying computation graph based on the topology prediction perturbation sequence before the adaptive reconstruction is executed. The closed-loop feedback constraint unit is used to receive stability feature quantity and state response feature, and calculate the mapping weight of the input displacement vector in the logical mapping space. When the stability feature quantity is greater than the preset stability threshold, the mapping weight is reduced proportionally to reduce the execution step size of adaptive reconstruction. The closed-loop feedback constraint unit is also used to monitor the gradient flow of the underlying computation graph. When the global loss function overflows above a preset overflow threshold, it generates a reverse update resistance instruction in the logical mapping space based on the gradient flow, so as to generate a mapping weight penalty term to limit the update rate of the logical node coordinates.
2. The adaptive data modeling system based on an interactive graphical interface according to claim 1, characterized in that, The closed-loop feedback constraint unit calculates the execution step size according to the following rules. : ,in, The preset basic step size constant, The preset manifold sensitivity coefficient, This is a stability characteristic.
3. The adaptive data modeling system based on an interactive graphical interface according to claim 1, characterized in that, The operator evolution execution unit also includes an operator redundancy pruning module. The operator redundancy pruning module is used to calculate the interaction entropy loss based on the product of the user interaction frequency and the operator gradient contribution, and to set the energy level function that decays over time for the non-basic operators in the underlying computation graph. The operator redundancy pruning module is used to switch specific non-basic operators to a bypass state to simplify the computation path when the interaction entropy loss is lower than a preset threshold.
4. The adaptive data modeling system based on an interactive graphical interface according to claim 1, characterized in that, The feedforward prediction module is also used to generate a logic repulsion vector in the logic mapping space when the oscillation frequency of the remote state variable triggered by the topology change caused by the topology prediction perturbation sequence indicates that the logic node exceeds a preset frequency threshold; the closed-loop feedback constraint unit is also used to apply the logic repulsion vector as a constraint operator to the mapping weight.
5. The adaptive data modeling system based on an interactive graphical interface according to claim 1, characterized in that, The closed-loop feedback constraint unit is used to map the original pixel displacement output by the interactive instruction processing unit into a logical update increment with weight decay, so that the parameter gradient during the adaptive reconstruction process is maintained within a preset convergence range.
6. The adaptive data modeling system based on an interactive graphical interface according to claim 1, characterized in that, The stability analysis unit is used to extract the rate of change of the Hessian matrix trace of the underlying computation graph during the adaptive reconstruction process, and send the rate of change to the closed-loop feedback constraint unit to generate the time-series adaptive damping factor.
7. The adaptive data modeling system based on an interactive graphical interface according to claim 1, characterized in that, The operator evolution execution unit is used to call the reconstruction operator to perform real-time topology adjustment of the computation path after receiving the logic update increment corrected by the closed-loop feedback constraint unit, so as to fit the nonlinear displacement of the input data manifold.
8. The adaptive data modeling system based on an interactive graphical interface according to claim 1, characterized in that, The interactive instruction processing unit is also used to monitor the movement acceleration of the logical node in real time, and adjust the sampling frequency of the stability analysis unit according to the movement acceleration so that the sampling frequency is synchronized with the rhythm of user operation; the adjustment range of the sampling frequency is 10Hz to 1000Hz.
9. The adaptive data modeling system based on an interactive graphical interface according to claim 3, characterized in that, The operator redundancy pruning module determines the interaction entropy loss by calculating the product of the user's click frequency on logical nodes and the gradient weights of specific non-basic operators with respect to the global loss function.
10. The adaptive data modeling system based on an interactive graphical interface according to claim 1, characterized in that, The stability analysis unit is also used to determine whether the underlying computation graph has experienced logical oscillations by monitoring the fluctuation variance of stability characteristic quantities within a window of 10ms to 50ms. When the fluctuation variance exceeds the oscillation threshold, the instruction closed-loop feedback constraint unit sets the logical update increment to zero to suspend the current parameter update.