Environment monitoring data dimension standard conversion and consistency check processing system

Abstract

This invention relates to the field of industrial data processing and discloses a system for converting and verifying the dimensional standards of environmental monitoring data. The system includes modules for decoupling operating condition benchmarks, nonlinear dimensional mapping, and topological consistency arbitration. The system acquires the sensing signal sequence and associated operating condition feature vectors to extract a deviation tensor. The mapping module corrects the mapping function of the conversion operator based on the deviation tensor and generates the target data stream. The arbitration module constructs a logical topological constraint graph and calculates the diffusion residual vector. The system establishes a reverse correction loop, reloading the weight parameters to achieve closed-loop consistency correction when residual overflow occurs. This invention utilizes the deviation tensor to compensate for the curvature of the mapping function, eliminating systematic dimensional drift caused by environmental evolution. A self-healing path is established through topological residual feedback to identify physical anomalies and suppress signal source aging interference, ensuring the high robustness and logical consistency of the industrial monitoring data stream.

Description

Technical Field

[0001] This invention belongs to the field of industrial data processing technology, and in particular relates to a system for converting and verifying the dimensional standards of environmental monitoring data. Background Technology

[0002] Currently, chemical industrial parks and manufacturing environments are equipped with high-density environmental monitoring stations to collect raw sampling sequences generated by sensors such as electrochemical, photoionization, and infrared absorption sensors. The data processing system converts the sampling signals output by the sensors into standard dimension target data according to preset mapping operators, providing a closed-loop control basis for emission reduction decisions in industrial production.

[0003] Due to the drastic temperature fluctuations, pressure changes, and humidity interference in industrial environments, sensor response characteristics exhibit nonlinear shifts as the physical environment evolves. Existing technologies generally employ static conversion coefficients, which presuppose a constant mapping between the sensor response and the physical quantity. Although errors can be adjusted by increasing the calibration frequency or relaxing the consistency tolerance threshold, these methods are limited by hardware maintenance costs and struggle to identify systematic deviations in the logic space. When hardware compensation is limited, the industry is exploring the use of software algorithms to correct parameters. For example, Chinese invention patent application CN121186434A discloses an intelligent voltage monitoring system with adaptive calibration capabilities. Using neural network models to learn the nonlinear drift patterns under multi-factor coupling and completing online updates of model parameters through a closed-loop self-learning mechanism, this approach to processing environmental monitoring data has fundamental limitations: it is essentially a pure data-driven model based on single-point sampling, lacking topological constraints on the physical distribution relationships between distributed sensing nodes. In complex industrial scenarios, it is difficult to logically distinguish between sensor performance degradation and local environmental physical anomalies. Furthermore, it does not physically correct the curvature effect of the underlying operating conditions on the dimension conversion operator mapping function, resulting in nonlinear distortion in the system's mapping logic under extreme dynamic conditions, and failing to ensure logical consistency across the global dimension of multi-source heterogeneous data streams.

[0004] Therefore, how to construct a scheme that senses real-time operating conditions and uses physical topological constraints to realize the self-healing reconstruction of the transformation operator, thereby eliminating dimensional mapping distortion and improving data processing consistency, has become the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a system for converting environmental monitoring data to a standard dimension and verifying consistency. The system includes:

[0006] The operating condition reference decoupling module is used to acquire the time-aligned heterogeneous sensing signal sequence and the associated operating condition feature vector, and to extract the operating condition deviation tensor of the heterogeneous sensing signal sequence relative to the reference operating condition.

[0007] The nonlinear dimensional mapping module stores dimensional transformation operators containing adjustable weight parameters and modifies the mapping function of the dimensional transformation operators based on the curvature compensation increment generated by the operating condition deviation tensor, so as to convert heterogeneous sensing signal sequences into standard dimensional target data streams.

[0008] The topology consistency arbitration module is used to construct a logical topology constraint graph based on the physical distribution association between distributed sensing nodes, and to calculate the diffusion residual vector of the standard dimension target data flow in the logical topology constraint graph. The system is configured to establish a reverse correction loop based on the diffusion residual vector: when the diffusion residual vector exceeds the preset confidence interval, the diffusion residual vector is fed back to the nonlinear dimension mapping module to reload the adjustable weight parameters and realize the closed-loop consistency correction of the standard dimension target data flow.

[0009] Preferably, the operating condition reference decoupling module is used to perform logical decoupling in the following manner: Step S11, synchronously acquire industrial production variables and environmental interference variables corresponding to the heterogeneous sensing signal sequence to construct a related operating condition feature vector; Step S12, map the related operating condition feature vector to a preset steady-state operating condition space, and calculate the projection distance of the related operating condition feature vector from the steady-state operating condition space to generate an operating condition deviation tensor characterizing the nonlinear shift of the signal source response; wherein, the operating condition deviation tensor is used to characterize the systematic dimensional drift caused by the evolution of the physical environment in the heterogeneous sensing signal sequence.

[0010] Preferably, the nonlinear dimensional mapping module includes a nonlinear transformation operator library, and the nonlinear dimensional mapping module is used to perform curvature compensation in the following manner: Step S21, matching the target transformation operator in the nonlinear transformation operator library according to the operating condition deviation tensor; Step S22, injecting the operating condition deviation tensor into the target transformation operator, and completing the curvature compensation of the mapping function by adjusting the second derivative characteristics of the target transformation operator.

[0011] Preferably, the topology consistency arbitration module includes a physical distribution constraint model, which is used to transform the physical location association between distributed sensing nodes into diffusion flux constraints in the logical topology constraint graph.

[0012] Preferably, the topology consistency arbitration module is used to identify pseudo-abnormal signals caused by signal source performance degradation by comparing the changing trend of the standard dimensional target data flow between adjacent distributed sensing nodes with the conformity of the diffusion flux constraint under the diffusion constraint environment defined by the logical topology constraint graph.

[0013] Preferably, the system further includes a working condition hedging logic unit, which is used to monitor the convergence speed of the diffusion residual vector in real time during the overload of the adjustable weight parameter.

[0014] Preferably, the working condition hedging logic unit is used to determine the state attribute based on the following arbitration logic: Step S31, if the diffusion residual vector converges to the preset confidence interval within the preset iteration period, it is determined that the deviation of the heterogeneous sensing signal sequence originates from the drift of the dimensional operator, and self-healing correction is completed; Step S32, if the diffusion residual vector remains in a non-converged state within the preset iteration period, it is determined that a physical anomaly has occurred in the local environment and a state characterization signal is output.

[0015] Preferably, the system includes a distributed industrial time-series database, which is used to store the historical evolution trajectory of heterogeneous sensing signal sequences, operating condition deviation tensors, and standard dimension target data streams.

[0016] Preferably, the system is used to analyze historical evolution trajectories to extract the aging trend of sensing terminals, and dynamically adjusts the numerical width of a preset confidence interval when the diffusion residual vector shows a trend of monotonic shift, so that the numerical width stabilizes within a preset range of 5% to 10% as the degree of aging increases.

[0017] Compared with existing technologies, the environmental monitoring data dimension standard conversion and consistency verification processing system of the present invention has the following advantages:

[0018] 1. In the transformation of environmental monitoring data dimensions to standard dimensions, the transformation process is dynamically adaptive to complex industrial conditions. By extracting the characteristic vectors of operating conditions such as temperature, pressure, and humidity in real time and using them as input parameters of the nonlinear mapping engine, the system changes the traditional approach that relies on static transformation coefficients. The mapping operator compensates for the curvature of the mapping function in real time based on the operating condition deviation tensor, so that the dimension transformation logic keeps pace with the evolution of the on-site physical environment, eliminates the systematic dimension drift caused by operating condition fluctuations, and ensures the logical rigor of the transformation of the original sampled values ​​of heterogeneous sensors to standard dimension target data.

[0019] 2. Construct a data consistency self-healing mechanism based on physical space constraints. By transforming the physical location correlation between monitoring points into diffusion flux constraints in the logical topology map, the system elevates consistency verification from simple numerical comparison to trend arbitration that conforms to physical laws. When the trend residual between adjacent monitoring points exceeds the preset confidence interval, the system does not generate isolated abnormal alarms. Instead, it injects the residual vector back into the mapping engine and reconstructs the conversion operator by adjusting the weight parameters of the mapping function. This closed-loop correction path enables the system to have the logical hedging capability against hardware zero-point drift or gain failure, achieving system-level robustness of the data flow without changing the physical characteristics of the sensor.

[0020] 3. Establishing a fundamental identification path between real physical anomalies and sensor performance degradation: This solution utilizes the synergistic effect of topological residual feedback and operator overloading to establish a miniature logic conservation field within the data processing unit. During the operator self-correction process, if the topological residual can converge within a preset period, it indicates that the deviation originates from operator drift and is self-healed by the system; if the residual cannot be eliminated, it is determined that a real physical anomaly has occurred in the local operating condition. This processing mechanism effectively identifies and eliminates false anomaly signals caused by factors such as acid mist corrosion and sensor aging, reducing the false alarm rate of the industrial environmental protection early warning system and improving the reliability of production scheduling decisions. Attached Figure Description

[0021] Figure 1 This is the core architecture and dimension conversion closed-loop logic diagram of the system of this invention;

[0022] Figure 2 This is a flowchart of the data interaction and anomaly identification process of the industrial monitoring station network of the present invention. Detailed Implementation

[0023] 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.

[0024] 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.

[0025] 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 connection of 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.

[0026] 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.

[0027] An environmental monitoring data dimensional standard conversion and consistency verification system, the system comprising:

[0028] The operating condition reference decoupling module is used to acquire the time-aligned heterogeneous sensing signal sequence and the associated operating condition feature vector, and to extract the operating condition deviation tensor of the heterogeneous sensing signal sequence relative to the reference operating condition.

[0029] The nonlinear dimensional mapping module stores dimensional transformation operators containing adjustable weight parameters and modifies the mapping function of the dimensional transformation operators based on the curvature compensation increment generated by the operating condition deviation tensor, so as to convert heterogeneous sensing signal sequences into standard dimensional target data streams.

[0030] The topology consistency arbitration module is used to construct a logical topology constraint graph based on the physical distribution association between distributed sensing nodes, and to calculate the diffusion residual vector of the standard dimension target data flow in the logical topology constraint graph. The system is configured to establish a reverse correction loop based on the diffusion residual vector: when the diffusion residual vector exceeds the preset confidence interval, the diffusion residual vector is fed back to the nonlinear dimension mapping module to reload the adjustable weight parameters and realize the closed-loop consistency correction of the standard dimension target data flow.

[0031] Preferably, the operating condition reference decoupling module is used to perform logical decoupling in the following manner: Step S11, synchronously acquire industrial production variables and environmental interference variables corresponding to the heterogeneous sensing signal sequence to construct a related operating condition feature vector; Step S12, map the related operating condition feature vector to a preset steady-state operating condition space, and calculate the projection distance of the related operating condition feature vector from the steady-state operating condition space to generate an operating condition deviation tensor characterizing the nonlinear shift of the signal source response; wherein, the operating condition deviation tensor is used to characterize the systematic dimensional drift caused by the evolution of the physical environment in the heterogeneous sensing signal sequence.

[0032] Preferably, the nonlinear dimensional mapping module includes a nonlinear transformation operator library, and the nonlinear dimensional mapping module is used to perform curvature compensation in the following manner: Step S21, matching the target transformation operator in the nonlinear transformation operator library according to the operating condition deviation tensor; Step S22, injecting the operating condition deviation tensor into the target transformation operator, and completing the curvature compensation of the mapping function by adjusting the second derivative characteristics of the target transformation operator.

[0033] Preferably, the topology consistency arbitration module includes a physical distribution constraint model, which is used to transform the physical location association between distributed sensing nodes into diffusion flux constraints in the logical topology constraint graph.

[0034] Preferably, the topology consistency arbitration module is used to identify pseudo-abnormal signals caused by signal source performance degradation by comparing the changing trend of the standard dimensional target data flow between adjacent distributed sensing nodes with the conformity of the diffusion flux constraint under the diffusion constraint environment defined by the logical topology constraint graph.

[0035] Preferably, the system calculates the correction increment of the adjustable weight parameters through the following quantization logic. : ,in, The preset convergence factor, For the diffusion residual vector, The modulus of the operating condition deviation tensor. It is 0.001.

[0036] Preferably, the system further includes a working condition hedging logic unit, which is used to monitor the convergence speed of the diffusion residual vector in real time during the overload of the adjustable weight parameter.

[0037] Preferably, the working condition hedging logic unit is used to determine the state attribute based on the following arbitration logic: Step S31, if the diffusion residual vector converges to the preset confidence interval within the preset iteration period, it is determined that the deviation of the heterogeneous sensing signal sequence originates from the drift of the dimensional operator, and self-healing correction is completed; Step S32, if the diffusion residual vector remains in a non-converged state within the preset iteration period, it is determined that a physical anomaly has occurred in the local environment and a state characterization signal is output.

[0038] Preferably, the system includes a distributed industrial time-series database, which is used to store the historical evolution trajectory of heterogeneous sensing signal sequences, operating condition deviation tensors, and standard dimension target data streams.

[0039] Preferably, the system is used to analyze historical evolution trajectories to extract the aging trend of sensing terminals, and dynamically adjusts the numerical width of a preset confidence interval when the diffusion residual vector shows a trend of monotonic shift, so that the numerical width stabilizes within a preset range of 5% to 10% as the degree of aging increases.

[0040] Example 1: This invention provides an environmental monitoring data dimensional standard conversion and consistency verification processing system, which is applied to the automatic environmental quality monitoring network of a large chemical industrial park. In a specific application scenario, the system receives heterogeneous sensing signal sequences from electrochemical sensors and photoionization PID sensors. The operating condition reference decoupling module acquires the timing-aligned sensing signal sequence. and related working condition feature vectors The associated working condition feature vector Including real-time collected temperature, pressure, and humidity data, the operating condition benchmark decoupling module will associate the operating condition feature vector. Mapped to a preset steady-state operating condition space, and the associated operating condition feature vector is calculated. The projected distance from the steady-state operating space is used to extract the operating condition deviation tensor. The deviation tensor under this operating condition Used for quantizing sensing signal sequences Compared to the systematic dimensional drift of the baseline operating condition, the initial electrical parameters output by the multi-source sensors exhibit heterogeneity in physical units and magnitudes. The system reads the original current intensity of the electrochemical sensor and the original voltage amplitude of the photoionization sensor via a hardware acquisition bus. The central processing unit retrieves the corresponding sensor's factory-specified full-scale extreme value data from the memory, performs extreme value normalization division, and converts the acquired absolute physical quantities into dimensionless floating-point arrays with values ​​constrained to the range of 0 to 1. After numerical conversion, the output digital array is written as a heterogeneous sensing signal sequence x(t) into the data register, establishing the baseline input state for subsequent mathematical calculations. The nonlinear dimensional mapping module is based on the operating condition deviation tensor. The generated curvature compensation increment corrects the stored dimension transformation operator mapping function. The nonlinear dimension mapping module matches the target transformation operator in the nonlinear transformation operator library and converts the operating condition deviation tensor. Injecting the target conversion operator, curvature compensation is completed by adjusting the second derivative characteristics of the target conversion operator. When the system performs the underlying mapping association, it is considered that the fluctuation of ambient temperature and humidity will cause nonlinear hindrance to the ion mobility of the electrochemical reaction interface inside the sensor, which in turn causes physical bending deformation of the sensor's true output response curve.

[0041] The processing unit reads the characteristic magnitude of the operating condition deviation tensor and quantizes it into the increment of the quadratic term coefficient in the Taylor expansion of the underlying mapping function. This allows the second derivative of the mathematical mapping curve to accurately simulate and counteract the deformation of the sensor response curve caused by the evolution of the overall physical environment. This establishes a closed-loop logical mechanism for transforming the physical parameters of the overall environment into abstract mathematical curvature parameters. The corrected mapping function follows the following logical relationship: ,in, For standard dimensional target data stream, It is a heterogeneous sensing signal sequence. For the associated working condition feature vector, As a preset basic transformation operator, This is the preset working condition correction factor. For the operating condition deviation tensor, the mathematical operations inside the basic transformation operator f perform decomposition mapping logic. The formula involves physical quantities defined as follows: This indicates that the preset basic gain constant during the device initialization phase has a dimension of one. This indicates that the dimension of the correction increment for calculating the transmission dynamic weight parameters in the reverse correction loop is one. This represents the polynomial fitting calculation terms constructed in the steady-state coordinate system, and the control program extracts them from memory. The variable is added to the base gain constant by performing an algebraic addition instruction. The underlying transformation operator product weight physical numerical reloading is completed, and this mapping process will transform heterogeneous sensing signal sequences. Convert to standard dimension target data stream .

[0042] The topology consistency arbitration module constructs a logical topology constraint graph based on the physical distribution relationships between distributed sensing nodes. This module transforms the physical location relationships between sensing nodes into diffusion flux constraints in the logical topology constraint graph through a physical distribution constraint model. Based on the Gaussian plume physics model, the transport of fluid matter in open space is governed by advection and convection transport laws. The system reads three-dimensional ultrasonic wind field data sent by external meteorological station nodes via a serial communication interface. The computing unit analyzes the data to extract the wind direction vector and calculates the effective wind speed convection component along the straight line connecting adjacent distributed sensing nodes. This three-dimensional ultrasonic wind field data originates from standard ultrasonic anemometers and wind vanes deployed high in the station network. These anemometers utilize the physical principle of the absolute time difference generated when sound waves propagate with and against the wind in a moving air medium to solve for the three-dimensional wind speed vector coordinates in the detection space in real time. After acquiring this vector, the system performs calculations... The unit performs geometric trigonometric function projection dimensionality reduction on the one-dimensional straight axis where the spatial connection between adjacent distributed sensing nodes is pre-determined, and extracts the scalar projection value in this axial direction as the effective wind speed convection component. The logic processing unit calculates the fluid migration feature transmission delay value by dividing the fixed straight-line physical distance between nodes by the effective wind speed convection component. The storage controller opens a sliding data buffer column in the random access memory with a time width equal to the feature transmission delay, extracts the average value of the sampled values ​​within the period of the sliding data buffer column as the alignment reference output to compensate for the inherent time lag of fluid migration in the physical space scale.

[0043] The topology consistency arbitration module compares the trend of target data flow changes between adjacent sensing nodes with the diffusion flux constraints under the diffusion constraint environment defined by the logical topology constraint graph. The degree of compliance is calculated, and the diffusion residual vector of the target data flow in the logical topology constraint graph is calculated. The specific calculation steps are as follows: The computing unit extracts the time-series difference value of the target data stream in standard dimensions between the source sensing node located upwind and the target sensing node located downwind in real time. It then performs an arithmetic subtraction operation with the theoretical concentration decay amount derived from the diffusion flux constraint parameters, and extracts the absolute value of the subtraction result as the final diffusion residual vector value. This accurately transforms the physical dimension of the gas diffusion convection process into a purely numerical dimension algorithm input index for the back-correction loop. When the diffusion residual vector... When the error exceeds the preset confidence interval, the system activates the reverse correction loop, and the system will spread the residual vector. Feedback is sent to the nonlinear dimensional mapping module and processed according to the formula. Calculate the correction increment of the adjustable weight parameters This allows for the reloading of weight parameters in the nonlinear dimensional mapping module, where... This is the adjustment increment for the weight parameters. The preset convergence factor, For the diffusion residual vector, For operating condition deviation tensor The length of the mold, for The operating condition hedging logic unit monitors the diffusion residual vector in real time during parameter overload. The convergence speed, if the diffusion residual vector exist If the system converges to the confidence interval within the preset iteration period, the system determines the sensing signal sequence. The deviation originates from the drift of the dimensional operator and completes self-healing correction, if the diffusion residual vector While maintaining a non-convergent state, the system determines that a physical anomaly has occurred in the local environment and outputs a state characterization signal.

[0044] The system stores the sensing signal sequences through a distributed industrial time-series database. Operating condition deviation tensor and target data stream The historical evolution trajectory is systematically analyzed to extract the aging trend of sensing terminals, when the diffusion residual vector... When a trend of monotonic deviation is observed, the system dynamically adjusts the width of the preset confidence interval to stabilize it within the range of 5% to 10% as the aging degree increases. The constants and confidence interval limits introduced in the aforementioned calculation logic are based on rigorous engineering simulations. The anomaly prevention parameter of 0.001 is forcibly set according to the minimum safe overflow threshold of the floating-point division operation at the bottom layer of the main control microprocessor. The lower limit of the interval value of 5% corresponds to the normal fluctuation tolerance caused by the background high-frequency pulse random noise of the industrial power grid, preventing the system from becoming oversensitive and frequently executing invalid feedback. The upper limit of the interval value of 10% is defined based on the statistical experience value of the extreme background drift rate obtained after two years of full-cycle decay testing of the electrochemical sensor. This ensures that the real high-concentration hazardous gas leakage signal will not be masked by the system's self-healing logic error. The system eliminates the systematic dimensional drift caused by environmental evolution by compensating the curvature of the mapping function through the operating condition deviation tensor, ensuring the logical consistency of the industrial monitoring data stream.

[0045] Example 2: The experimental verification process of the environmental monitoring data dimensional standard conversion and consistency verification system provided by this invention is as follows: The experimental platform is built in a physical experimental chamber containing a distributed photoionization PID sensor and an electrochemical sensor. This experimental chamber is equipped with a simulated working condition system with high-precision gas mixing function, which is used to control the ambient temperature fluctuation within the range of 10℃ to 50℃, with a temperature control accuracy better than ±0.5℃, and can continuously adjust the ambient relative humidity between 30% and 95%. The experimental data source is isobutylene standard gas with a known concentration of 50.0 ppm injected into the sensor array. To establish a comparison benchmark, a control group using static conversion coefficients for dimensional mapping and an experimental group using the system of this invention are set up. The sampling period of the key parameter in this experiment... The value of is determined based on the rate of change of the first derivative of the sensed signal. It is used to balance the integrity of signal capture with the computational load of the system. When the rate of change of environmental parameters exceeds 0.1℃ / min, the system sets the sampling frequency to 5Hz, while the sampling frequency is set to 2Hz under steady-state drift conditions. In addition, in order to simulate industrial electromagnetic environment interference, a random pulse noise with a signal-to-noise ratio of 25dB is superimposed on the power supply end of the sensor during the test, and 50Hz power frequency interference is introduced into the signal transmission link.

[0046] After the experiment was started, the sensing terminal acquired a sequence of heterogeneous sensing signals containing environmental disturbances. During the baseline setting phase, the original PID sensor output voltage obtained by the experimental group shifted from the baseline of 1.25V to 1.48V as the ambient humidity increased from 45% to 85%. If directly converted according to the static coefficient of the control group, the output target data would have a measurement deviation of 18.4%. During the core derivation phase, the experimental group's operating condition baseline decoupling module calculated the associated operating condition feature vector in real time. Furthermore, the operating condition deviation tensor characterizing humidity disturbance is extracted within the steady-state operating condition space. The typical value of the modulus of this tensor, determined by calculation, is 0.21; the nonlinear dimensional mapping module uses this operating condition deviation tensor... A library of nonlinear transformation operators was retrieved, and curvature compensation increments were injected into the mapping function to counteract the nonlinear sensitivity enhancement effect of the electrochemical sensor under high-temperature conditions. Experimental data showed that when the ambient temperature was at the extreme condition of 45℃, the standard dimension target data stream calculated by the experimental group... The output value was 50.7 ppm, while the output value of the control group deviated to 59.2 ppm, proving that the system can offset the perceived physical characteristic deviation by correcting the curvature of the mapping function through the operating condition deviation tensor.

[0047] To verify the gradient law of the effect of the present invention, three damp-heat stress gradients of low, medium and high were further set up in the experiment; when the relative humidity of the environment reached the saturation zone of more than 90%, the sensor sensitivity decay exhibited nonlinear saturation characteristics, and at this time the operating condition deviation tensor The modulus jumps from 0.21 to 0.38; in the value closure stage, the topology consistency arbitration module intervenes to perform secondary verification of the data flow; this module constructs a logical topology constraint graph based on the physical distribution association between adjacent sensing nodes, and the system calculates the diffusion residual vector. The initial value is 0.92; the reverse correction loop will diffuse the residual vector. Feedback is sent to the nonlinear dimensional mapping module and based on the formula. Adjust the weight parameters to make the diffusion residual vector It converges to a confidence interval of 0.03 within 5 logical iterations; where, This is the adjustment increment for the weight parameters. The preset convergence factor, For the diffusion residual vector, For operating condition deviation tensor The length of the mold, The value was 0.001; the final experimental group output showed that during the full-dimensional transformation process, the target data sequence... The root mean square error decreased from 9.42 in the control group to 0.85, and the standard dimension target data stream... The agreement with the standard gas concentration remained above 98.5%. This experiment confirms that the experimental group, when processing multi-source heterogeneous sensing signals, effectively utilized the operating condition deviation tensor extracted by the operating condition reference decoupling module. It can characterize the systematic dimensional shift caused by the evolution of the physical environment; experimental data confirms the effectiveness of the curvature compensation mechanism in the nonlinear dimensional mapping module in eliminating nonlinear interference in industrial sites; in particular, when the monitoring data faces extreme conditions of high humidity above 80%, the consistency deviation of the control group increases monotonically with time, while the experimental group, using the closed-loop correction of the topological consistency arbitration module, successfully controls the logical consistency error of the monitoring data stream to within 2.0%; this result shows that the system provided by this invention can solve the technical problem of inaccurate dimensional mapping and conflict of node data trends in industrial environmental monitoring, and meets the engineering requirements of high-reliability data streams for closed-loop control of industrial emission reduction.

[0048] Example 3: The environmental monitoring data dimension standard conversion and consistency verification processing system provided by this invention is applied to an industrial-grade fixed gas monitoring station network with a continuous operating time of up to 24 months. In this environment, due to the reduction of electrolyte in the electrochemical sensor and the energy decay of the ultraviolet lamp in the PID sensor, heterogeneous sensing signal sequences... The response characteristics deviate from the original dimensional definition, and the operating condition benchmark decoupling module obtains the associated operating condition feature vectors of the monitoring nodes in real time. The projected distance is calculated within the steady-state operating space to determine the operating condition deviation tensor. To reduce the computational overhead of the nonlinear dimensional mapping module in locating target transformation operators in a large number of compensation functions, the system's pre-stored nonlinear transformation operator library adopts a hierarchical storage structure based on curvature features. The nonlinear transformation operator library is divided into several curvature subspaces according to the physical dimensional properties of the sensor. Each curvature subspace contains a set of discretized transformation operators that cover nonlinear response functions in the sensitivity range from 100% to 60% attenuation.

[0049] The nonlinear dimensional mapping module completes operator retrieval through the following steps: calculating the operating condition deviation tensor. The system identifies the first-order slope component and the second-order curvature component; using the identified components as index keys, it locates the corresponding curvature subspace in the nonlinear transformation operator library; it calculates the fitting residuals between the real-time sensing signal sequence and each candidate operator in this subspace using the least squares criterion; during this process, the system selects the target transformation operator with the smallest fitting residual and utilizes the operating condition deviation tensor. Curvature compensation is performed on the second derivative characteristic of this operator, and the specific mapping calculation process follows the logical relationship below: ,in, For standard dimensional target data stream, It is a heterogeneous sensing signal sequence. For the associated working condition feature vector, For the target transformation operator, This is the working condition correction factor. As the operating condition deviation tensor, this mapping process transforms heterogeneous sensing signal sequences Converted to calibrated standard dimension target data stream .

[0050] To address the calibration benchmark shift caused by aging of sensing terminals, the topology consistency arbitration module calculates the diffusion residual vector through the logical topology constraint graph. The system initiates a dynamic parameter calibration process to determine the numerical width of the confidence interval. It then reads the diffusion residual vector of the sensing node over the past 30 monitoring periods from a distributed industrial time-series database. Historical records, and calculation of the sample variance of historical residual data. ,in, For sample variance, To create a diffuse residual vector, the system sets the numerical width of the confidence interval to three standard deviations of the historical residual distribution. This applies when the relative humidity of the environment stabilizes due to production fluctuations. If the system detects the diffusion residual vector above, If the value continues to exceed the dynamic confidence interval, a reverse correction loop is triggered, and the value is adjusted according to the formula. Calculate the correction increment of the weight parameters ,in, To correct the increment, The convergence factor is For the diffusion residual vector, For operating condition deviation tensor The length of the mold, The value is 0.001. By overloading the weight parameters in the nonlinear dimensional mapping module, the system compensates for the dimensional inaccuracies caused by sensor aging, ensuring that the target data sequence is accurate at the end of the equipment maintenance cycle. The consistency deviation is stabilized within 2.5%. This mechanism transforms the sensor hardware calibration requirements into closed-loop logic correction of the system, maintaining the logical consistency of industrial data flow.

[0051] Example 4: When the system faces a multi-source heterogeneous sensing signal sequence When the response characteristics of the nonlinear transformation operator library experience nonlinear degradation due to extended service time, the discretization data filling process is completed through a controlled engineering experimental procedure. This procedure uses a dynamic gas mixing device to generate standard test gases with concentration gradients of 10.0 ppm, 50.0 ppm, and 100.0 ppm in a standard environmental laboratory, and simulates the operating condition fluctuation range between -10℃ and 50℃ with the help of an environmental control system. By acquiring the raw voltage signals collected by electrochemical sensors and PID sensors at different aging stages, the nonlinear deviation of the real-time response value from the standard definition is calculated. Based on this deviation, the corresponding second derivative curvature feature is extracted, and the discretized transformation function after least squares fitting is encoded as a transformation operator. These operators are distributed in the curvature subspace of the nonlinear transformation operator library with a granularity of 1% sensitivity decay rate.

[0052] In data access scenarios for newly built distributed monitoring station networks, the operating condition benchmark decoupling module handles the access of heterogeneous sensing signal sequences. Initialization was completed through a pre-deployed synchronization procedure. This procedure required the system to perform hardware-level clock alignment for each heterogeneous acquisition channel and operating condition sensor in the distributed sensing nodes. By measuring the propagation delay of the signal pulse edge, the heterogeneous sensing signal sequence was... With associated working condition feature vector The phase difference in the time domain is adjusted to below 50ms; after the system connects to the field environment, it synchronously reads the zero-point noise distribution under steady-state operation and stores the calculated average noise power as the initial operating condition offset; the system determines the associated operating condition feature vector by injecting the initial zero-point offset of the zero-point air calibration mapping function. The projection zero point in the steady-state operating space; this pre-procedure transforms distributed asynchronous physical quantities into logical inputs with global temporal correlation, enabling subsequent dimensional conversion processes to have a unified operating condition deviation judgment benchmark.

[0053] Example 5: In the initial deployment of the environmental monitoring station network of a newly built large-scale integrated refining and chemical project, the operating condition reference decoupling module determines the physical boundary of the initial operating condition space through reference calibration before connecting to the real-time sensing link; this procedure requires the system to collect the heterogeneous sensing signal sequences output by each sensing node in a constant temperature and pressure unloaded environment. A feature set of 500 sampling points was extracted, and the mean vector and covariance matrix of the Gaussian mixture model were iteratively fitted using the expectation-maximization algorithm. The central feature vector of the fitted probability density distribution was defined as the origin of the zero point in the steady-state working condition space. The physical distribution constraint model was used to determine the straight-line distance between adjacent sensing nodes. And the diffusion flux constraints in the logical topology constraint graph are calculated according to the decay logic of the diffusion law for the pipeline connection path and the pipeline connection path. Diffusion flux constraint The calculation formula is as follows: ;in, For diffusion flux constraints, This is the environmental response factor, with values ​​ranging from 0.85 to 1.15. As for node spacing, this procedure transforms the discrete physical location distribution into hard constraints in the logical topology constraint graph, establishing a traceable physical consistency judgment benchmark for the topology consistency arbitration module.

[0054] When the system encounters an initial dimensional inaccuracy caused by differences in the sensitivity of different batches of sensors, the operating condition hedging logic unit determines the convergence factor through a closed-loop adaptive method. And the initial weighting parameters of the mapping function; after injecting a standard gas of known concentration, the procedure adjusts the correction increment of the weighting parameters according to a preset step size. And monitor the diffusion residual vector in real time. The evolution trajectory in the logical topological constraint graph; the criterion for determining the convergence state of the system is the diffusion residual vector. If the first derivative remains stable below 0.05 for 10 consecutive sampling periods, the system will calculate the convergence factor. The weight parameters are stored in a distributed industrial time-series database; when sudden disturbances in the field environment, such as air pressure or humidity, cause deviations in the operating conditions, the tensor... When the magnitude step exceeds 0.5, the system triggers a pre-test logic, which calculates the real-time sensing signal sequence. The Mahalanobis distance between the signal source and the eigenvector of the steady-state reference distribution center is used to evaluate the signal source quality. If the Mahalanobis distance exceeds a preset chi-square distribution threshold, the corresponding abnormal impulse noise is automatically removed, thus completing gross error filtering at the signal source level before the mapping function is corrected. After the system completes initial deployment, it enters the running state, and the target data sequence between each sensing node is... The mutual difference after logical consistency correction remains within the 1.0% threshold range.

[0055] 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 environmental monitoring data dimension standard conversion and consistency check processing system, characterized in that, The system includes: The operating condition reference decoupling module is used to acquire the time-aligned heterogeneous sensing signal sequence and the associated operating condition feature vector, and to extract the operating condition deviation tensor of the heterogeneous sensing signal sequence relative to the reference operating condition. The nonlinear dimensional mapping module stores dimensional transformation operators containing adjustable weight parameters and modifies the mapping function of the dimensional transformation operators based on the curvature compensation increment generated by the operating condition deviation tensor, so as to convert heterogeneous sensing signal sequences into standard dimensional target data streams. The topology consistency arbitration module is used to construct a logical topology constraint graph based on the physical distribution association between distributed sensing nodes, and to calculate the diffusion residual vector of the standard dimension target data flow in the logical topology constraint graph. The system is configured to establish a reverse correction loop based on the diffusion residual vector: when the diffusion residual vector exceeds the preset confidence interval, the diffusion residual vector is fed back to the nonlinear dimension mapping module to reload the adjustable weight parameters and realize the closed-loop consistency correction of the standard dimension target data flow.

2. The environmental monitoring data dimension standard conversion and consistency verification processing system according to claim 1, characterized in that, The operating condition reference decoupling module is used to perform logical decoupling in the following way: Step S11, synchronously acquire the industrial production variables and environmental interference variables corresponding to the heterogeneous sensing signal sequence to construct the associated operating condition feature vector; Step S12, map the associated operating condition feature vector to the preset steady-state operating condition space, and calculate the projection distance of the associated operating condition feature vector from the steady-state operating condition space to generate the operating condition deviation tensor characterizing the nonlinear shift of the signal source response; wherein, the operating condition deviation tensor is used to characterize the systematic dimensional drift caused by the evolution of the physical environment in the heterogeneous sensing signal sequence.

3. The environmental monitoring data dimension standard conversion and consistency verification processing system according to claim 1, characterized in that, The nonlinear dimensional mapping module includes a nonlinear transformation operator library, and the nonlinear dimensional mapping module is used to perform curvature compensation in the following manner: Step S21, match the target transformation operator in the nonlinear transformation operator library according to the operating condition deviation tensor; Step S22, inject the operating condition deviation tensor into the target transformation operator, and complete the curvature compensation of the mapping function by adjusting the second derivative characteristics of the target transformation operator.

4. The environmental monitoring data dimension standard conversion and consistency verification processing system according to claim 1, characterized in that, The topology consistency arbitration module includes a physical distribution constraint model, which is used to transform the physical location associations between distributed sensing nodes into diffusion flux constraints in the logical topology constraint graph.

5. The environmental monitoring data dimension standard conversion and consistency verification processing system according to claim 4, characterized in that, The topology consistency arbitration module is used to identify pseudo-abnormal signals caused by signal source performance degradation by comparing the variation trend of standard dimension target data flow between adjacent distributed sensing nodes with the degree of conformity of diffusion flux constraints under the diffusion constraint environment defined by the logical topology constraint graph.

6. The environmental monitoring data dimension standard conversion and consistency verification processing system according to claim 1, characterized in that, The system also includes a working condition hedging logic unit, which is used to monitor the convergence speed of the diffusion residual vector in real time during the overload of the adjustable weight parameter.

7. The environmental monitoring data dimension standard conversion and consistency verification processing system according to claim 6, characterized in that, The working condition hedging logic unit is used to determine the state attribute based on the following arbitration logic: Step S31, if the diffusion residual vector converges to the preset confidence interval within the preset iteration period, it is determined that the deviation of the heterogeneous sensing signal sequence originates from the drift of the dimensional operator, and self-healing correction is completed; Step S32, if the diffusion residual vector remains in a non-converged state within the preset iteration period, it is determined that a physical anomaly has occurred in the local environment and the state characterization signal is output.

8. The environmental monitoring data dimension standard conversion and consistency verification processing system according to claim 1, characterized in that, The system includes a distributed industrial time-series database, which stores the historical evolution trajectory of heterogeneous sensing signal sequences, operating condition deviation tensors, and standard dimension target data streams.

9. The environmental monitoring data dimension standard conversion and consistency verification processing system according to claim 8, characterized in that, The system is used to analyze historical evolution trajectories to extract the aging trend of sensing terminals, and dynamically adjusts the numerical width of the preset information interval when the diffusion residual vector shows a trend of monotonic shift, so that the numerical width stabilizes within the preset range of 5% to 10% as the degree of aging increases.

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