A closed-loop risk control method for transformation of financial compliance verification

Abstract

The application relates to the technical field of financial services, and discloses a closed-loop risk control method for transformation financial compliance verification, which comprises the following steps: acquiring real-time sampling power data of a rolling unit of a financing subject, extracting a transient power envelope curve and calculating an autocorrelation function, identifying the first wave peak position of the positive delay axis of the autocorrelation function to invert a characteristic beat frequency, and calling a benchmark energy efficiency atlas to match a dynamic energy efficiency benchmark vector; and generating a compliance verification judgment result by calculating an energy efficiency deviation index, wherein the application establishes a mapping relationship between the physical work intensity of production equipment and financial compliance indexes, reduces the verification lag caused by discrete sampling in a traditional audit mode, enables the risk control system to have continuous monitoring capability on production behavior in a financing period, identifies the default risk hidden by the financing subject by switching the production mode, and realizes deep coupling between the financial risk control logic and the physical production process.

Description

Technical Field

[0001] This invention relates to a closed-loop risk control method for compliance verification in transitional finance, belonging to the field of financial services technology. Background Technology

[0002] Currently, in the transformation of financial business, financial institutions usually implement floating interest rate incentive policies based on the carbon emission reduction reports submitted by the financing entities. Existing compliance verification mainly relies on the annual or quarterly environmental reports submitted by the financing entities to determine the interest rate adjustment range in the financing agreement.

[0003] Existing discrete verification methods lead to a decoupling of financial risk control logic from industrial production processes at the spatiotemporal level. This makes it impossible for the system to monitor the intensity of the financing entity's production activities outside the verification window in real time. When the financing entity is not in the verification period, due to the lack of continuous monitoring methods, the system finds it difficult to establish a physical causal relationship between energy consumption intensity and actual output. Simply increasing the reporting frequency or deploying external hardware sensors to strengthen risk control results in high operating costs. Existing linear risk control logic cannot distinguish between reasonable energy efficiency fluctuations caused by process switching and energy efficiency anomalies caused by actual default. For example, Chinese invention patent CN121213242A discloses an AI knowledge service system and method based on a financial intelligent context protocol. It optimizes the efficiency of financial information processing and improves professionalism through time-series control, regulatory verification, and terminology disambiguation mechanisms. The core logic is based on the semantic parsing of secondary text data from documents and reports. When faced with the original physical characteristics of transient power envelopes in industrial sites, the method lacks a deep perception of the causal law between underlying mechanical physical work and actual output, making it difficult to identify default risks concealed by financing entities through production mode switching.

[0004] Therefore, how to map the physical energy intensity of the production site to the compliance evaluation indicators of the financial sector, and to construct a closed-loop verification system with physical constraints, is the technical problem that this invention aims to solve. Summary of the Invention

[0005] To address the problems raised in the background section, the technical solution of this invention is as follows: A closed-loop risk control method for compliance verification in transitional finance, comprising the following steps: Step S1: Obtain real-time sampled power data of the main drive system of the rolling mill of the financing entity during the financing period, wherein the sampling frequency of the real-time sampled power data is not less than 50Hz; Step S2: Extract the power envelope of the real-time sampled power data through a sliding window of a preset length to generate a transient power envelope curve characterizing the operating intensity of the rolling mill. Step S3: Calculate the autocorrelation function of the transient power envelope curve within a preset time domain interval, and identify the position of the first peak of the autocorrelation function on the positive delay axis; Step S4: Determine the characteristic cycle frequency of the current rolling condition based on the delay time corresponding to the first peak position, wherein the characteristic cycle frequency is set as the reciprocal of the delay time; Step S5: Call the preset benchmark energy efficiency map generated by cluster analysis based on historical production data and power consumption data, and use the characteristic cycle frequency to match the dynamic energy efficiency benchmark vector corresponding to the current rolling condition. The benchmark energy efficiency map is used to record the theoretical power consumption distribution of the rolling mill under different characteristic cycle frequencies. Step S6: Calculate the energy efficiency deviation index between the real-time value of the transient power envelope curve and the corresponding theoretical value in the dynamic energy efficiency benchmark vector. Compare the energy efficiency deviation index with the preset compliance threshold. If the energy efficiency deviation index exceeds the preset compliance threshold, generate a non-compliance judgment result and corresponding risk control instructions for the financing cycle.

[0006] Preferably, in the process of performing step S2, the power envelope extraction includes: determining the local maximum sequence of real-time sampled power data within a sliding window of a preset length, and performing cubic spline interpolation on the local maximum sequence to generate a transient power envelope curve; wherein, the time span of the sliding window of the preset length is set to 2 to 5 times the single rolling cycle of the rolling mill; step S2 also includes performing signal noise reduction on the real-time sampled power data, filtering out interference components higher than 20Hz through a preset low-pass filter.

[0007] Preferably, the compliance verification judgment result in step S6 is generated in the following way: based on the formula Calculate the energy efficiency deviation index, among which, For energy efficiency deviation indicators, This represents the real-time power consumption value of the transient power envelope curve at the characteristic clock frequency. This is the theoretical benchmark value in the dynamic energy efficiency benchmark vector; when the energy efficiency deviation index exceeds the preset compliance threshold three times in a row, the financing entity is determined to be in a non-compliant state, and a corresponding risk control instruction is generated.

[0008] Preferably, before matching the dynamic energy efficiency benchmark vector corresponding to the current rolling condition in step S5, the method further includes: real-time monitoring of the root mean square value change of real-time sampled power data within a preset time window; if the root mean square value is continuously lower than the preset no-load threshold and the characteristic cycle frequency is 0, the rolling mill is determined to be in standby mode, and the generation of compliance verification judgment results is suspended.

[0009] Preferably, the steps for constructing the benchmark energy efficiency map include: acquiring energy efficiency observation data of the rolling mill unit during historical production cycles and production data imported through the production management system; extracting the characteristic cycle frequency corresponding to different production volumes from the energy efficiency observation data, establishing a mapping relationship table with the characteristic cycle frequency as the index and the theoretical power consumption per unit production volume as the element; performing cluster analysis on the mapping relationship table to determine the optimal energy efficiency boundary of the rolling mill unit under different production loads, so as to generate the benchmark energy efficiency map.

[0010] Preferably, after step S6, the method further includes: associating the financing entity with the corresponding energy efficiency level based on the numerical distribution of the energy efficiency deviation index; when the compliance verification result indicates that the financing entity should maintain the highest energy efficiency level within a preset period, generating an interest return incentive instruction and sending the interest return incentive instruction to the financial settlement interface.

[0011] Preferably, the real-time sampled power data is obtained by collecting transient current and voltage signals from the power distribution side of the rolling mill by the power monitoring terminal and calculating them. The real-time sampled power data is encapsulated into a data message with a digital signature and transmitted to the central server through an encrypted tunnel.

[0012] Preferably, the process of generating compliance verification results also includes: obtaining the annual carbon emission data disclosed by the financing entity in an external database; and conducting correlation analysis between the annual carbon emission data and the periodic integral value of the energy efficiency deviation indicator to determine whether the financing entity has engaged in production mode switching behavior outside the audit window period.

[0013] Preferably, it also includes: establishing a feature waveform anomaly library for real-time sampled power data; if a regular disturbance is detected in the autocorrelation function at an unexpected delay point, a data authenticity warning signal is generated, and when generating the compliance verification judgment result, the energy efficiency deviation index is weighted and corrected by the data authenticity warning signal.

[0014] Preferably, the compliance verification result serves as an energy efficiency optimization feedback signal, used to dynamically correct the production scheduling parameters or energy consumption constraint thresholds of the rolling mill. Simultaneously, the compliance verification result is introduced as a trigger variable into the smart contract of the financial evidence storage platform, through which the floating interest rate clause in the financing agreement is automatically settled and dynamically adjusted.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In the closed-loop risk control of compliance verification in the transformation of finance, by establishing a dynamic mapping relationship between real-time power consumption data and output value data, the physical work intensity of production equipment is directly converted into compliance evaluation indicators at the financial level. This reduces the verification lag caused by discrete sampling in the traditional audit model, enabling the risk control system to have the ability to continuously monitor the production behavior of the financing entity during the non-verification period and identify default risks concealed by temporary production mode switching.

[0016] 2. By using the autocorrelation analysis of the real-time power curve to determine the rolling cycle frequency, the financial verification benchmark is transformed from a static threshold to a dynamic envelope range that fluctuates with the production rhythm. This enables the risk control logic to understand the semantics of the underlying industrial production, distinguishing between reasonable energy efficiency fluctuations caused by product variety switching and substantive defaults caused by equipment performance degradation, and avoiding the generation of erroneous interest rate increase signals when enterprises produce high value-added products.

[0017] 3. Introduce a time-domain correlation consistency index for active and reactive power, and combine it with the pulse coupling characteristics of characteristic emission concentrations to construct a cross-dimensional physical source arbitration mechanism. This will enable data falsification to simultaneously meet the physical constraints of independent power metering, financial reporting, and environmental monitoring, thereby raising the technical threshold and economic cost for financing entities to fabricate false compliance performance through financial statement manipulation. Attached Figure Description

[0018] Figure 1 This is a flowchart of the closed-loop risk control process for real-time power acquisition and energy efficiency analysis in this invention. Figure 2 This is a schematic diagram illustrating the interaction of functional modules and the data flow of the closed-loop risk control system of the present invention. Detailed Implementation

[0019] To make the technical problems to be solved, the technical solutions and the beneficial effects of the present invention clearer, the present invention will be described in detail below with reference to specific embodiments; it should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the scope of protection of the present invention.

[0020] This invention provides a closed-loop risk control method for compliance verification in transitional finance, comprising a production energy efficiency characteristic acquisition stage, an operating condition characteristic frequency inversion stage, a dynamic energy efficiency benchmark matching stage, and a risk state machine adjudication stage. In the data flow, the power monitoring terminal acquires real-time sampled power data of the main drive system of the rolling mill of the financing entity as the raw input. After signal deconstruction and autocorrelation analysis, characteristic beat frequencies representing the production rhythm are extracted, and then correlated with a preset benchmark energy efficiency spectrum to obtain a dynamic energy efficiency benchmark vector. The real-time energy efficiency deviation index is calculated to drive the compliance state machine to perform state transitions, and credit adjustment instructions are automatically output to the financial settlement interface. In the credit lifecycle management of transitional finance, the financing entity may adjust production load to conceal the true emission intensity during non-audit windows, leading to the failure of discrete verification. To address this challenge, the method of this invention executes a procedure for acquiring real-time sampled power data of the main drive system of the rolling mill of the financing entity during the financing cycle. During this process, the power monitoring terminal collects transient current and voltage signals from the distribution side of the rolling mill and performs calculations according to the transient active power calculation formula, wherein the sampling frequency of the real-time sampled power data... The frequency is set to be no less than 50Hz, which can capture the instantaneous energy pulse characteristics of the mill bite and tail swing. The raw power data collected is encapsulated into a data message with a digital signature and transmitted to the central server through an encrypted tunnel, thus constructing the first characteristic stream of the physical energy intensity of the production site for the financial risk control system. This procedure establishes a continuous data source with physical constraints to identify the behavior of financing entities using production mode switching to cover up default risks.

[0021] For real-time sampling power data specifications, sampling time The system acquires transient voltage and current signals and calculates active power. It encapsulates the continuous active power sequence within a preset time window, along with corresponding device timestamps and digital signature information, into data packets conforming to power communication protocols. These packets are then uploaded to the central server via a physically isolated encrypted tunnel. Upon receiving the packets, the system verifies the signatures and checks the continuity of the timestamps. If a data packet is lost or signature verification fails, a retransmission procedure is triggered, and the current financing cycle's verification and judgment result is marked as pending, ensuring the authenticity and non-repudiation of the risk control instruction decision-making basis. Addressing the inrush current and high-frequency electromagnetic noise interference present in industrial environments, the system needs to extract feature envelopes that reflect the operating intensity of the rolling mill. The method adopts... A power envelope is extracted from the real-time sampled power data using a sliding window of a preset length to generate a transient power envelope curve. The specific implementation path is as follows: determine the local maximum sequence of the real-time sampled power data within the sliding window of the preset length, perform cubic spline interpolation, and fit to generate a smooth transient power envelope curve. To ensure that the envelope completely covers the dynamic characteristics of a single rolling cycle, the time span of the sliding window of the preset length is set to 2 to 5 times the single rolling cycle of the rolling mill. A preset low-pass filter is used to filter out interference components higher than 20Hz, converting the transient power signal into an overall characteristic stream reflecting the rolling load intensity, thereby reducing the randomness error of discrete sampling in the traditional audit mode.

[0022] In metal rolling processes, product variety switching or thickness adjustments can cause nonlinear drift in energy efficiency benchmarks, leading to false alarms in static threshold determination. To address this issue, this invention introduces a rolling cycle frequency inversion procedure based on autocorrelation analysis. The system calculates the autocorrelation function of the transient power envelope curve within a preset time domain interval, and the calculation formula is as follows: ,in, For the transient power envelope curve in The value of the moment. As a delay point, the value is taken upwards to a preset time domain interval. The system retrieves the total number of sampling points on the positive delay axis. The local maxima of the autocorrelation function are found, and the position of the first peak of the autocorrelation function on the positive delay axis is identified; based on the delay time corresponding to the position of the first peak... Determine the characteristic cycle frequency of the current rolling condition. ,in, The time delay corresponding to the location of the first peak must satisfy the following conditions: ,in The minimum mechanical motion cycle of the rolling mill is preset to eliminate high-frequency noise interference; the characteristic cycle frequency is set to... The unit is Hz; and the position of the first peak of the autocorrelation function on the positive delay axis is identified; based on the delay time corresponding to the first peak position... Determine the characteristic cycle frequency of the current rolling condition. The calculation formula is: ,in, Characteristic beat frequency, unit: ; The time delay corresponding to the first peak of the autocorrelation function on the positive delay axis, in units of . When the financing entity produces high-value-added thin plates, the rolling speed and frequency are relatively high, corresponding to... Shorter, obtained from inversion The numerical value increases; this procedure achieves automatic understanding of the semantics of underlying industrial production, used to distinguish between energy efficiency fluctuations caused by product switching and breaches of contract caused by equipment performance degradation; for the procedure for identifying the first peak position of the positive delay axis of the autocorrelation function, a delay time search interval matching the rated operating cycle of the main drive system of the rolling mill is selected, the starting boundary is set to 0.5 times the duration of a single rolling reciprocating motion at the maximum design speed of the system, and the ending boundary is set to 1.5 times the corresponding duration at the minimum design speed of the system. By searching for local maximum values ​​within the closed interval formed by the starting boundary and the ending boundary, irregular high-frequency pulse interference introduced by high-power frequency converters in the production site is eliminated, making the characteristic cycle frequency... The only reference is the mechanical energy pulse cycle formed when the workpiece enters and leaves the rolling mill.

[0023] After obtaining the characteristic beat frequency, the system needs to establish a matching dynamic verification benchmark; to this end, the method of this invention calls a preset benchmark energy efficiency spectrum and utilizes the characteristic beat frequency. Matching the dynamic energy efficiency benchmark vector corresponding to the current rolling condition This benchmark energy efficiency map is used to record the theoretical power consumption distribution of rolling mill units under different characteristic cycle frequencies. The construction procedure of the benchmark energy efficiency map includes: acquiring energy efficiency observation data of the rolling mill unit during historical production cycles and output data imported through the production management system; extracting characteristic cycle frequencies corresponding to different outputs from the energy efficiency observation data; establishing a mapping relationship table with characteristic cycle frequencies as the index and theoretical power consumption per unit output as the element; and performing cluster analysis on the mapping relationship table to determine the energy efficiency boundary of the rolling mill unit under different production loads. To determine the numerical value, the system retrieves the corresponding theoretical power consumption distribution in the graph; through this procedure, the financial verification benchmark is transformed from a static single dimension into a dynamic envelope interval that fluctuates with the production rhythm; to achieve automatic adjudication of compliance status, the system quantifies the deviation between real-time production performance and the benchmark; based on this, the method of this invention executes the energy efficiency deviation index calculation and state machine transition procedure; the system calculates the energy efficiency deviation index according to the formula: ,in, Energy efficiency deviation index; The transient power envelope curve during the currently detected characteristic beat period The energy integral mean within the time domain is calculated by applying the transient power envelope curve over the time domain interval. The formula for calculating the definite integral by dividing by the period duration is: ,in, for The transient power envelope curve value at time t is used to filter out high-frequency unsteady-state impact interference, thereby characterizing the physical work intensity of the production unit output under that cycle time, thus eliminating random errors caused by instantaneous power fluctuations; The theoretical reference value retrieved from the dynamic energy efficiency reference vector, its unit is... To maintain consistency, both are in kW, when energy efficiency deviates from the target. When the preset compliance threshold is exceeded three times consecutively, the compliance state machine transitions from a compliant state to a non-compliant state and generates a risk control instruction; when energy efficiency deviates from the target... continuous When the preset compliance threshold is exceeded, the compliance state machine transitions from a compliant state to a non-compliant state and generates a risk control instruction. Simultaneously, the system monitors the root mean square (RMS) value of real-time sampled power data within a preset time window. If the RMS value remains below the preset no-load threshold and the characteristic cycle frequency... If the value is 0, the rolling mill is determined to be in standby mode and the judgment is suspended. The physical layer energy consumption intensity is mapped to the financial layer risk weight in real time, shortening the time link from default judgment to pricing adjustment. During the compliance judgment process, this invention accurately identifies default risk by distinguishing between slow drift in mechanical performance and sudden deviations in energy efficiency indicators using a binary identification logic: First, for natural aging of equipment due to mechanical wear or changes in lubrication caused by long-term operation, the system introduces a time decay factor to dynamically correct the energy efficiency benchmark through a baseline energy efficiency spectrum reconstruction procedure based on a sliding time window, achieving adaptive compensation for equipment performance degradation and ensuring that normal production fluctuations do not trigger non-default risk signals; Second, for equipment... For energy efficiency anomalies caused by backup failures, the system uses the correlation consistency index of active and reactive power collected in real time over the time domain to perform fault fingerprinting to identify non-subjective production anomalies. Finally, if and only if the production mode indicated by the characteristic cycle frequency remains stable, but the average real-time power consumption experiences a nonlinear step and the physical fingerprint matches the production characteristics, it is determined to be a substantial default by the financing entity subjectively switching the production mode to conceal the true emission intensity. The criteria for determining the substantial default are: when the production rhythm indicated by the characteristic cycle frequency is in a stable state, the DC component of the transient power envelope curve experiences a nonlinear step that exceeds the process adjustment range, and this step characteristic does not conform to the linear drift law of equipment aging.

[0024] Considering that enterprises may falsify electricity metering data and output value reports, this invention introduces a source arbitration mechanism based on multi-dimensional physical fingerprints; during the compliance determination process, the system simultaneously extracts the active power of the financing entity's electricity consumption unit. With reactive power Based on the inductive load characteristics of the metal rolling mill, a preset power factor is established. Dynamic distribution benchmark; the system calculates the correlation consistency index between active power components and real-time reactive power data in the time domain in real time; if a regular disturbance is detected in the autocorrelation function at an unexpected delay point, or if the power factor waveform does not match the output growth logic declared by the financing entity, a data authenticity warning signal is generated; the annual carbon emission data disclosed by the financing entity in an external database is obtained and correlated with the periodic integral value of the energy efficiency deviation index to determine whether there is a production mode switching behavior outside the audit window period; this procedure increases the technical difficulty for the financing entity to fabricate false compliance performance through the physical constraints of electricity metering, financial declaration, and environmental monitoring; the compliance verification judgment result is introduced into the smart contract interface of the evidence storage platform as a financial risk control variable; the smart contract executes the floating interest rate automatic settlement procedure in the credit agreement according to the state transition path of the judgment result; the processor will process the energy efficiency deviation index With preset compliance threshold The comparison results are written to the contract status bit of the distributed ledger in real time; if the status bit indicates that energy efficiency exceeds the standard within three consecutive production cycles, the contract automatically triggers the interest calculation operator. Update the logic to include the interest rate stipulated in the contract. From the benchmark value Adjusted to ,in The interest rate hike step is determined based on the deviation of energy efficiency level. This procedure establishes the physical constraints of industrial physical characteristics on financial settlement behavior. When the compliance verification result indicates that the financing entity maintains the highest energy efficiency level within a preset period, the system generates an interest return incentive instruction and sends it to the financial settlement interface to execute the automatic settlement of floating interest rate terms. If the judgment result indicates a default, an instruction to increase the interest rate or lock the credit line is automatically triggered. This closed-loop mechanism realizes continuous monitoring of the production behavior characteristics of the financing entity and ensures deep synergy between the financial incentive mechanism and the enterprise's production cycle.

[0025] Example 1: In a specific industrial application scenario, a metal rolling financing entity uses a transitional financial credit product with floating interest rate clauses and faces compliance monitoring challenges due to production mode switching during non-verification periods. Because the financing entity declares the production of high-value-added low-carbon steel, it actually uses high-energy-consuming old-style units for large-scale processing of ordinary carbon steel. This makes traditional auditing methods based on average energy consumption unable to identify the financing entity's use of product switching to conceal production line energy efficiency degradation. In response to the above objective conditions, the power monitoring terminal uses a sampling frequency of 100Hz. The system acquires real-time sampled power data from the main drive system of the rolling mill; it extracts the transient power envelope curve using a sliding window with a span of three times the cycle length of a single rolling cycle, calculates the autocorrelation function of the curve within a preset time domain interval, and identifies the position of the first peak on the positive delay axis, where the delay time corresponding to this peak position is determined. The characteristic beat frequency is 2.5s. Determined by the following formula: ,in, Characteristic beat frequency, unit: ; The time delay corresponding to the first peak of the autocorrelation function on the positive delay axis, in units of . .

[0026] The system utilizes the inversion obtained Characteristic beat frequency Matching the dynamic energy efficiency baseline vector in the baseline energy efficiency map The theoretical power consumption per unit output at this frequency is the reference value. for At the same time, the system extracts the real-time power consumption value from the transient power envelope curve. for Energy efficiency deviation from the target Calculated using the following formula: ,in, Energy efficiency deviation index; This is a real-time power consumption value, in units of... ; This is the theoretical benchmark value, in units of When energy efficiency deviates from the target When the value is 0.46 and exceeds the preset compliance threshold three times consecutively, the compliance state machine transitions from the compliance state to the default state, and automatically triggers the credit settlement interface to execute the interest rate increase instruction. Through the synergy of the production energy efficiency feature collection stage and the operating condition feature frequency inversion stage, the feature cycle frequency extracted by the system provides a physically deterministic input premise for energy efficiency benchmark matching, thereby eliminating the verification benchmark drift caused by production mode switching. By using the rolling cycle as a physical fingerprint to establish a causal relationship between energy consumption intensity and the production process, the technical means by which the financing entity conceals its default behavior become ineffective. Finally, the verification result establishes the fact of the financing entity's default during the non-verification period, ensuring the targeted allocation of transformation financial credit funds.

[0027] Example 2: In a test scenario for compliance monitoring of transitional financial loans, the rolling mill of the financing entity faces production cycle switching and electromagnetic noise interference in the industrial environment during the financing period. The test, through the construction of a verification platform integrating power monitoring terminals and credit verification modules, confirms the effectiveness of the risk control method in identifying non-compliant energy use behavior. The test data comes from the operating power sequence of the main drive system of the 2050mm cold continuous rolling mill in the metal rolling production base. To simulate the non-ideal characteristics of the industrial environment, Gaussian white noise with a signal-to-noise ratio of 25dB is actively superimposed on the original signal. The sampling frequency of the real-time power data in the test... The sampling frequency is set to 100Hz. This parameter is used to balance the accuracy of capturing the high-frequency transient characteristics of the signal with the data processing load of the central server; The settings follow the sampling law. To ensure no spectral aliasing occurs and to accurately reconstruct the power envelope waveform with a frequency upper limit of 20Hz, the sampling frequency is... The frequency must be greater than twice the highest frequency of the signal. Considering the resolution limit of the monitoring instrument, 100Hz is set as the operating value. Simultaneously, the preset length of the sliding window is set to 10 seconds, which is used to cover the energy pulse range from bite to tail in a single rolling cycle of the rolling mill. The experiment is divided into a sample group and a control group. The control group uses a monthly average energy consumption verification method based on discrete time points. During the experiment, by adjusting the reduction rate and work roll speed of the rolling mill, multiple levels of production intensity gradients are set to simulate different degrees of energy efficiency deviation risk. The characteristic cycle frequency is determined by calculating the autocorrelation function of the transient power envelope curve. This allows for matching with the dynamic energy efficiency benchmark vector in the benchmark energy efficiency map. .

[0028] Table 1: Example of Comparison of Compliance Verification Data under Different Production Conditions Referring to Table 1, when the operating condition changes from number 2 to number 3, the characteristic cycle frequency is... Depend on Rise to The system automatically matches the corresponding theoretical benchmark value. Depend on Synchronized adjustment to This causes energy efficiency to deviate from the target. Maintaining the value below 0.05 reduces the probability of misjudgment due to accelerated production pace; while in operating conditions 5 and 6, the financing entity maintains the same production pace, but the real-time power consumption value... Rising; data shows that when energy efficiency deviates from the target... After exceeding 0.8, the incremental increase in the credit risk weight of the financing entity tends to level off; by introducing autocorrelation analysis to invert the rolling cycle, the sample group of this invention filters out the interference of industrial noise on energy efficiency judgment and maintains the stability of verification in the energy consumption benchmark drift caused by product variety switching; compared with the 35% false alarm rate generated by the control group when the production intensity changes suddenly, the default identification accuracy of the sample group of this invention is no less than 98% during the financing cycle; the closed-loop risk control mechanism transforms the physical variables on the production side into compliance evidence on the financial side, realizing the coupling of financial credit management and industrial operation logic.

[0029] Example 3: This example combines Figures 1 to 2 This section describes a closed-loop risk control method for compliance verification in transitional finance, such as... Figure 1As shown, starting from step S1, real-time sampled power data of the main drive system of the rolling mill of the financing entity is acquired during the financing period. The sampling frequency of the real-time sampled power data is not less than 50Hz. Then, the process proceeds to step S2, where the power envelope of the real-time sampled power data is extracted through a sliding window of a preset length to generate a transient power envelope curve characterizing the operating intensity of the rolling mill. Next, step S3 is executed to calculate the autocorrelation function of the transient power envelope curve within a preset time domain interval and identify the position of the first peak of the autocorrelation function on the positive delay axis. Based on this position information, the process proceeds to step S3. Step S4: Determine the characteristic cycle frequency of the current rolling condition based on the delay time corresponding to the first peak position, where the characteristic cycle frequency is set as the reciprocal of the delay time; after obtaining the frequency parameter, proceed to step S5: call the preset benchmark energy efficiency map generated by cluster analysis, use the characteristic cycle frequency to match the corresponding dynamic energy efficiency benchmark vector, and obtain the theoretical power consumption distribution under the corresponding cycle frequency; finally, proceed to step S6: calculate the energy efficiency deviation index between the real-time value and the theoretical value and compare it with the preset compliance threshold. If it exceeds the threshold, generate the non-compliance judgment result and risk control instruction for the financing cycle.

[0030] like Figure 2 As shown, this closed-loop risk control system for financial compliance verification constructs an operating environment connecting the financing entity's operations and maintenance personnel with financial compliance auditors. The financing entity's operations and maintenance personnel are associated with a real-time power data acquisition module, which has an inclusion relationship with the production condition feature identification module. The data flow of the production condition feature identification module points to the energy efficiency deviation calculation module, establishing a basis relationship between the two. During operation, the energy efficiency deviation calculation module performs a benchmark data call operation and establishes a data connection with an external benchmark energy efficiency spectrum library. The output results of the energy efficiency deviation calculation module are transmitted to the compliance verification report generation module, which ultimately establishes an interactive connection with the financial compliance auditor.

[0031] Example 4: In a specific scenario of energy efficiency supervision of cold rolling mills, the production line of the financing entity experiences mechanical wear due to long-term operation, causing its basic energy efficiency benchmark to shift over time. If a static verification benchmark is used, the system may trigger a non-default risk warning signal due to energy efficiency reduction caused by the natural aging of equipment. To address this challenge, the system executes a dynamic reconstruction of the benchmark energy efficiency map and a compliance threshold calibration procedure. By eliminating the black box of the mechanism through a defined data processing path, the system ensures that the verification results are anchored to the actual engineering boundaries of the financing entity. The system acquires the energy efficiency historical data of the financing entity's rolling mill for the 720 hours prior to the financing cycle. This historical data includes real-time sampled power data and production data. Based on the global equipment timestamp, the system performs precise time-series synchronization alignment between the macroscopically discrete production data and the underlying high-frequency real-time sampled power data, constructing a mapping-level synchronous observation sequence. For each characteristic cycle frequency... The system divides the frequency range in 0.05Hz increments, extracts power sample sets within the same frequency range, calculates the distribution density of this sample set in terms of power consumption per unit output, and selects the value at which the cumulative percentage of distribution density reaches 10% as the dynamic energy efficiency benchmark vector at that frequency. The calibration procedure utilizes low-energy consumption samples from industrial production to reflect the physical facts of the equipment's optimal state, extracts the energy efficiency boundary that the financing entity can achieve under this specific production rhythm, and thus constructs a dynamic energy efficiency map with physical constraints. To determine the preset compliance threshold for compliance judgment, the system calculates the deviation residual sequence of real-time power consumption values ​​in the historical sample set relative to the dynamic energy efficiency benchmark vector, and uses this sequence to fit a Gaussian distribution model, extracting the mean of the distribution model. with standard deviation Among them, the preset compliance threshold The calculation formula is as follows: ,in, To preset compliance thresholds; This represents the deviation from the mean of the residual sequence; The standard deviation of the residual sequence is used to determine this parameter. This parameter determination procedure balances energy efficiency fluctuations caused by random disturbances in the production process with systematic biases caused by substantive violations, ensuring that energy efficiency deviations from the target are minimized under normal production fluctuations. The probability of exceeding the threshold is less than 5%, thus establishing a statistically based verification benchmark.

[0032] During continuous system operation, the aging of the transmission system causes a unidirectional drift in the mean of real-time sampled power data. The system then executes a spectrum reconstruction procedure based on a sliding time window. Using a 30-day rolling cycle, the system introduces a time decay factor to weaken the weight of older data and injects newly collected energy efficiency observation data into the calculation model. Through iterative updates of the components of the dynamic energy efficiency benchmark vector, it achieves adaptive compensation for equipment performance degradation. When energy efficiency deviates from the target... When the mean value shows a linear growth trend over two consecutive rolling cycles, and the characteristic cycle frequency does not change significantly, the system automatically identifies the equipment aging signal and re-executes the aforementioned threshold calibration procedure to generate an updated preset compliance threshold. This eliminates the interference of mechanical performance degradation on the default determination result. A rolling update procedure is executed for the benchmark energy efficiency map, extracting the operating sequence of the financing entity for the preceding 30 natural days and constructing a statistical distribution of unit output power consumption. The system then analyzes the characteristic cycle frequency... The power consumption sample quantile regression analysis selects the power consumption value at the 10th percentile of the probability distribution curve as the optimal energy efficiency benchmark for this operating condition. This value is used to update the theoretical benchmark value in the benchmark energy efficiency map, offsetting the unidirectional drift of the energy consumption baseline caused by mechanical wear or changes in lubrication conditions of the rolling mill's transmission components due to long-term operation, and reducing the energy efficiency deviation index. Reflecting the energy efficiency performance of the financing entity under the current equipment condition, this procedure transforms the vague energy efficiency assessment criteria into a dynamic envelope driven by the financing entity's real-time physical data, reducing the risk of judgment failure due to environmental challenges or equipment condition evolution. Ultimately, the compliance verification judgment results output by the system exclude non-default energy consumption growth, ensuring that risk control instructions are triggered only when the financing entity subjectively changes its production mode or illegally enters high-energy-consuming operating conditions. This closed-loop feedback mechanism confirms that this method can still provide high-quality credit risk decision-making basis through a defined algorithm path in a disturbed industrial environment, achieving a deep alignment between financial verification indicators and the underlying physical production logic.

[0033] Example 5: In a newly constructed rolling mill production line where historical energy efficiency data is lacking, the power monitoring terminal collects transient voltage and current signals from the main drive electrical control side of the unit, drives the rolling mill to operate at calibration points at 50% and 100% load, and calculates the active power components under different load levels. With reactive power components Among them, power factor The calculation formula is as follows: ,in, For power factor, Active power component, unit: , The reactive power component is expressed in units of 1. The determined power factor distribution characteristics are stored in the initialization index of the benchmark energy efficiency map.

[0034] To determine the initial verification baseline, the processor obtains the theoretical minimum specific energy consumption parameters from the rolling mill's technical specifications, and combines them with... The power envelope characteristics at the sampling frequency are used to calculate the initial dynamic energy efficiency benchmark vector through linear regression. During the period During the trial operation period, the causal correlation index between the transient power envelope curve and the production signal is monitored. When it is greater than the preset verification threshold of 0.95, it is determined that the physical fingerprint of the production unit has been completed and the verification logic has been anchored. The verification node sends the initialization completion signal through the smart contract interface of the financial evidence storage platform. The physical operation data of the main drive system of the rolling mill is mapped to the risk adjudication engine of the closed-loop risk control system.

[0035] Example 6: In a steel production base deployment scenario oriented towards multi-unit collaborative operation, the system executes an offline construction procedure for the baseline energy efficiency map and parameter matrix filling. The processor obtains the full-condition operation dataset of the financing entity within 90 calendar days prior to the start date of the financing agreement, which includes the sampling frequency. For a 100Hz historical power sequence and the corresponding production report, the system divides the historical power sequence into discrete sampling segments of 30 minutes in length, and uses autocorrelation analysis to invert and obtain the characteristic beat frequency of each sampling segment. The system follows the characteristic beat frequency The numerical values ​​are used to perform clustering and stratification on the sampled segments, where the average power consumption per unit output of each frequency stratum is... The calculation formula is as follows: ,in This represents the average power consumption per unit output. For sampling fragments Real-time power consumption value within, in units of , For sampling fragments The corresponding real-time output value, in units of , The total number of samples within the frequency stratum is used to determine the minimum unit output power consumption average for each frequency stratum, which is then filled into each feature node of the baseline energy efficiency map.

[0036] To eliminate background noise from non-production energy consumption, the system performs no-load baseline correction on the benchmark energy efficiency spectrum. Under the condition that the main drive system of the rolling mill is idling and there is no metal bite, the processor extracts the average value of the real-time sampled power data within a preset time window as the background power benchmark value. The system will include all characteristic beat frequencies Dynamic energy efficiency benchmark vector under Subtract the background power reference value The corrected energy efficiency mapping table is obtained; during the real-time system verification process, when a characteristic beat frequency is detected... Corresponding delay time When the production halt exceeds the preset threshold by 15 seconds, the compliance state machine automatically pauses the monitoring of energy efficiency deviation indicators. The system performs cumulative calculations to prevent financing entities from diluting average energy efficiency indicators by increasing equipment idling time; it also executes a risk weight correction procedure based on physical fingerprint consistency, and the processor synchronously extracts the active power components generated by the rolling mill. With real-time reactive power data The coupling relationship between the two is verified by using a preset envelope of inductive load characteristics. When a regular disturbance is detected in the autocorrelation function at an unexpected delay point, and the ratio of the active power increment to the reactive power increment corresponding to the disturbance is found to violate the inherent electromagnetic induction physical law of metal rolling machinery, and when the absence of high-frequency secondary harmonic characteristics in the signal or the sudden violation of the envelope energy conservation law at the physical logic level is detected, a data authenticity warning signal is generated. When the system activates the formula Penalty weighting is applied to deviations from energy efficiency targets. This is the revised verification judgment value. This represents the deviation from the original energy efficiency index. The preset risk adjustment weight coefficient is set to a value of 0.3 to 0.6. This procedure establishes the physical correspondence between production activities and energy consumption, so that the compliance verification judgment results output by the risk adjudication engine are only related to the actual production efficiency of the financing entity, realizing the perception of the physical production process characteristics by financial regulatory instructions.

[0037] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A closed-loop risk control method for compliance verification in transitional finance, characterized in that, Includes the following steps: Step S1: Obtain real-time sampled power data of the main drive system of the rolling mill of the financing entity during the financing period, wherein the sampling frequency of the real-time sampled power data is not less than 50Hz; Step S2: Extract the power envelope of the real-time sampled power data through a sliding window of a preset length to generate a transient power envelope curve characterizing the operating intensity of the rolling mill. Step S3: Calculate the autocorrelation function of the transient power envelope curve within a preset time domain interval, and identify the position of the first peak of the autocorrelation function on the positive delay axis; Step S4: Determine the characteristic cycle frequency of the current rolling condition based on the delay time corresponding to the first peak position, wherein the characteristic cycle frequency is set as the reciprocal of the delay time; Step S5: Call the preset benchmark energy efficiency map generated by cluster analysis based on historical production data and power consumption data, and use the characteristic cycle frequency to match the dynamic energy efficiency benchmark vector corresponding to the current rolling condition. The benchmark energy efficiency map is used to record the theoretical power consumption distribution of the rolling mill under different characteristic cycle frequencies. Step S6: Calculate the energy efficiency deviation index between the real-time value of the transient power envelope curve and the corresponding theoretical value in the dynamic energy efficiency benchmark vector. Compare the energy efficiency deviation index with the preset compliance threshold. If the energy efficiency deviation index exceeds the preset compliance threshold, generate a non-compliance judgment result and corresponding risk control instructions for the financing cycle.

2. The closed-loop risk control method for compliance verification in transitional finance according to claim 1, characterized in that, During step S2, the power envelope extraction includes: determining the local maximum sequence of real-time sampled power data within a sliding window of a preset length, and performing cubic spline interpolation on the local maximum sequence to generate a transient power envelope curve; wherein, the time span of the sliding window of the preset length is set to 2 to 5 times the single rolling cycle of the rolling mill; step S2 also includes performing signal noise reduction on the real-time sampled power data, filtering out interference components higher than 20Hz through a preset low-pass filter.

3. The closed-loop risk control method for compliance verification in transitional finance according to claim 1, characterized in that, The compliance verification judgment result is generated in step S6 in the following way: based on the formula ΔE=|(E real -E base ) / E base | Calculate the energy efficiency deviation index, where ΔE is the energy efficiency deviation index, E real E represents the real-time power consumption value of the transient power envelope curve at the characteristic clock frequency. The real-time power consumption value is set as the average active power integral obtained by performing definite integration on the transient power envelope curve within a single cycle corresponding to the characteristic clock frequency. base This is the theoretical benchmark value in the dynamic energy efficiency benchmark vector; when the energy efficiency deviation index exceeds the preset compliance threshold three times in a row, the financing entity is determined to be in a non-compliant state, and a corresponding risk control instruction is generated.

4. The closed-loop risk control method for compliance verification in transitional finance according to claim 1, characterized in that, Before matching the dynamic energy efficiency benchmark vector corresponding to the current rolling condition in step S5, the following steps are also included: real-time monitoring of the root mean square value change of real-time sampled power data within a preset time window; if the root mean square value is continuously lower than the preset no-load threshold and the characteristic cycle frequency is 0, the rolling mill is determined to be in standby mode, and the generation of compliance verification judgment results is suspended.

5. A closed-loop risk control method for compliance verification in transitional finance according to claim 1, characterized in that, The steps for constructing the benchmark energy efficiency map include: acquiring energy efficiency observation data of the rolling mill during historical production cycles and production data imported through the production management system; extracting characteristic cycle frequencies corresponding to different production volumes from the energy efficiency observation data, and establishing a mapping table with characteristic cycle frequencies as the index and theoretical power consumption per unit production volume as the element; performing cluster analysis on the mapping table to determine the optimal energy efficiency boundary of the rolling mill under different production loads, so as to generate the benchmark energy efficiency map.

6. The closed-loop risk control method for compliance verification in transitional finance according to claim 1, characterized in that, Step S6 and beyond also includes: associating the financing entity with the corresponding energy efficiency level based on the numerical distribution of the energy efficiency deviation index; when the compliance verification result indicates that the financing entity should maintain the highest energy efficiency level within a preset period, generating an interest return incentive instruction and sending the interest return incentive instruction to the financial settlement interface.

7. The closed-loop risk control method for compliance verification in transitional finance according to claim 1, characterized in that, Real-time sampling power data is obtained by collecting transient current and voltage signals from the power distribution side of the rolling mill unit through the power monitoring terminal and calculating them. The real-time sampling power data is encapsulated into a data message with a digital signature and transmitted to the central server through an encrypted tunnel.

8. A closed-loop risk control method for compliance verification in transitional finance according to claim 1, characterized in that, The process of generating compliance verification results also includes: obtaining the annual carbon emission data disclosed by the financing entity in external databases; and conducting correlation analysis between the annual carbon emission data and the periodic integral value of the energy efficiency deviation index to determine whether the financing entity has engaged in production mode switching behavior outside the audit window period.

9. A closed-loop risk control method for compliance verification in transitional finance according to claim 1, characterized in that, It also includes: establishing a feature waveform anomaly library for real-time sampled power data; if a regular disturbance is detected in the autocorrelation function at an unexpected delay point, a data authenticity warning signal is generated, and when generating compliance verification judgment results, the energy efficiency deviation index is weighted and corrected by the data authenticity warning signal.

10. A closed-loop risk control method for compliance verification in transitional finance according to claim 1, characterized in that, The compliance verification results serve as energy efficiency optimization feedback signals, used to dynamically correct the production scheduling parameters or energy consumption constraint thresholds of the rolling mill. At the same time, the compliance verification results are introduced as trigger variables into the smart contract of the financial evidence storage platform, through which the floating interest rate clause in the financing agreement is automatically settled and dynamically adjusted.

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