A method and system for quantifying temporary industrial expansion based on a zero-inflated poisson model

Abstract

The application discloses a kind of temporary industry expansion quantification method and system based on zero inflation poisson model, and wherein method includes: constructing double random process data forced orthogonal division mechanism, and original installation flow data, enterprise operation data and macroeconomic monitoring data based on causal attribute physical isolation are isolated into absolute zero state vector pool and continuous variation expectation vector pool;Based on the absolute zero state vector pool, the physical constraint characteristic variable representing the extreme value of power grid equipment is extracted, and based on the continuous variation expectation vector pool, the behavior causal characteristic variable representing the thermal inertia and economic game of power grid is extracted;The purpose of the present application is to solve the problem that the existing power industry expansion capacity prediction method is difficult to distinguish the double zero value generation mechanism, the parameter estimation is biased and lacks business interpretability when dealing with temporary industry expansion behavior with zero inflation characteristics due to the lack of double causal constraints of physical limit and economic game.

Description

Technical Field

[0001] This invention relates to the technical field of information technology, specifically to a temporary business expansion quantization method and system based on a zero-inflated Poisson model. Background Technology

[0002] With the profound transformation of the global energy structure and the deepening of power system reform, the power system has shifted from a traditional single generation, transmission, distribution, and consumption model to a dynamic and interconnected modern energy system. Against this backdrop, applications for temporary capacity adjustments by industrial and commercial users involve multiple business dimensions, including temporary electricity use for high-voltage meters, capacity reduction and restoration, temporary capacity reduction, suspension, and restoration. These applications directly impact short-term load fluctuations at the grid's end and the redundancy level of transformer capacity. At the micro level, they reflect the decision-making logic of enterprises facing market fluctuations, operational strategy adjustments, and cash flow constraints, becoming a reference indicator for measuring regional economic prosperity, industrial operating efficiency, and the precision of power resource allocation. However, in existing power marketing management and load forecasting practices, the assessment of capacity expansion typically relies on historical averages, traditional time series models, or linear regression analysis. These methods, based on the assumption of continuous distribution, provide a reference during periods of stable grid operation, but they show limitations when dealing with temporary capacity expansion in the face of increased market uncertainty and diversified business operations. The root cause is that temporary capacity expansion data statistically exhibits a "zero inflation" phenomenon, meaning that a large number of zero observations exist within the observation period. Traditional models struggle to distinguish between "structural zeros" caused by physical capacity depletion or funding shortages and "random zeros" driven by arbitrage opportunities. They also fail to integrate physical limits such as transformer temperature rise and thermal inertia of power grid equipment with economic game-theoretic motives such as arbitrage of basic electricity fees by enterprises. They mostly perform statistical fitting in low-dimensional spaces, making it difficult to uncover the causal driving chains behind complex high-dimensional factors. This leads to biased parameter estimations when facing low-frequency, high-impact capacity fluctuations, and the prediction accuracy and explanatory power are insufficient to meet the needs of modern smart distribution networks for refined capacity management, real-time early warning, and resource allocation. Summary of the Invention

[0003] This invention provides a method and system for quantifying temporary business expansion based on a zero-inflation Poisson model. The aim is to solve the problems of existing power business expansion capacity prediction methods, which, when dealing with temporary business expansion behaviors with zero-inflation characteristics, lack dual causal constraints of physical limits and economic game theory, make it difficult to distinguish the double zero value generation mechanism, have biased parameter estimation, and lack business interpretability.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A temporary business expansion quantification method based on a zero-inflated Poisson model includes: constructing a forced orthogonal partitioning mechanism for dual stochastic process data, physically isolating the original application data, enterprise operation data, and macroeconomic monitoring data based on causal attributes into an absolute zero-state vector pool and a continuously varying expectation vector pool; extracting physical constraint characteristic variables representing the extreme values ​​of power grid equipment based on the absolute zero-state vector pool, and extracting behavioral causal characteristic variables representing the thermal inertia and economic game of the power grid based on the continuously varying expectation vector pool; constructing a zero-inflated Poisson quantification model based on physical and economic anchors, and quantifying the physical constraint characteristic variables... The zero-inflated Poisson quantification model is injected into the logistic regression process as an extreme value penalty term to determine the probability of structural zero values. The behavioral causal feature variables are injected into the Poisson counting process of the zero-inflated Poisson quantification model to determine the expected mean. The maximum likelihood estimation method with economic arbitrage directional gradient constraints is used to solve the parameters of the zero-inflated Poisson quantification model, and dynamic self-healing redistribution of model features is triggered based on the statistical decay protocol. The absolute probability of temporary business expansion by enterprises is calculated using the solved zero-inflated Poisson quantification model, and a real-time warning result with reverse causal tracing is output when the warning threshold is exceeded.

[0005] In one aspect of the invention, in the mechanism for constructing a forced orthogonal partition of data in a dual random process: The absolute zero state vector pool specifically stores variables that determine whether an enterprise has the objective physical basis and financial boundaries for business expansion. The continuously changing expectation vector pool specifically stores variables that determine the specific intensity of business expansion chosen by the enterprise under the premise of having the capability. Before data partitioning, physical invariance verification and outlier correction are performed. The capping method correction function is used to replace extreme outliers that deviate from the historical mean by more than the standard deviation threshold. Cross-domain dimensional standardization is performed on heterogeneous features to block collinear interference between zero-inflation features and non-zero features.

[0006] In one aspect of the invention, the physical constraint characteristic variable includes the transformer physical capacity redundancy limit, which is obtained by calculating the difference between the total rated nameplate capacity of the transformers currently in operation and the maximum measured peak active load after conversion by the average power factor over multiple past sampling periods, and then dividing it by the total rated nameplate capacity. The behavioral causal characteristic variables include the standard deviation of physical load margin based on transformer temperature rise thermal inertia. This is obtained by calculating the difference between the product of the transformer's total rated capacity and the transformer's temperature rise thermal inertia coefficient and the actual apparent power within a set rolling window period, and then extracting the standard deviation of the difference sequence from the mean.

[0007] In one aspect of the invention, the behavioral causal characteristic variables further include the expected difference in basic electricity arbitrage and a bounded rationality text mapping value based on the Poisson coefficient of variation: The expected difference in basic electricity arbitrage is obtained by multiplying the target change capacity value actually reported to the power grid by the basic electricity standard unit price of the transformer capacity, and then subtracting the implicit friction costs covering switching outages and equipment storage. The bounded rationality text mapping value based on Poisson variation coefficient is obtained by capturing the thousandth percentile standardized word frequency of short-term performance-oriented keywords in corporate disclosure texts and through a nonlinear transformation mapping equation with intercept term bias parameter and feature mapping weight parameter.

[0008] In one aspect of the invention, the construction of the zero-inflation Poisson quantization model based on physical and economic anchors: The logarithmic probability equation of the state probability in the logistic regression process is forcibly introduced with a logarithmic extreme value penalty term of the physical capacity redundancy limit of the transformer. When the physical capacity redundancy limit of the transformer approaches zero, the probability of the enterprise falling into a structural zero state in which it is unable to make any internal flexible business expansion adjustments is forcibly pushed to 100%. The log-linear correlation equation of the Poisson counting process introduces an indicator function constructed from the expected difference in basic electricity arbitrage. The indicator function activates the positive amplification weight of the bounded rationality text mapping value based on the Poisson coefficient of variation to the expected mean of Poisson if and only if the expected difference in basic electricity arbitrage is greater than zero.

[0009] In one aspect of the invention, the method of solving the parameters of the zero-inflated Poisson quantization model using the maximum likelihood estimation method with economic arbitrage directional gradient constraints, and the dynamic self-healing redistribution of model features triggered based on the statistical decay protocol: The aforementioned arbitrage unidirectional gradient constraint means that when calculating the first-order partial derivative of the objective log-likelihood function with respect to the short-sighted bias activation weights using the quasi-Newton method, the verification is performed through the indicator function, allowing gradient updates only when the expected difference in basic electricity arbitrage is greater than zero; otherwise, the gradient update for this dimension is forced to zero. The dynamic self-healing redistribution based on the statistical decay protocol triggering model features refers to the calculation of the comprehensive Vuong statistic with a fixed step size. When the moving average of the comprehensive Vuong statistic for multiple consecutive periods is detected to decay below the warning line, the hard boundary between the absolute zero state vector pool and the continuously changing expectation vector pool is opened up. The failed macroscopic environmental resistance features are degraded and released into the Poisson counting process, and the marginal contribution is re-evaluated through the Lagrange multiplier method to complete the reshaping of the causal chain.

[0010] In one aspect of the invention, after the parameters are solved, sub-models of the zero-inflation Poisson quantization model are independently constructed and solved for different business expansion behaviors; the business types include at least temporary power supply for high-voltage meter installation, capacity reduction recovery, temporary capacity reduction, suspension, suspension recovery, and temporary power supply for meter installation; wherein, for temporary power supply for high-voltage meter installation, the partial regression coefficients of the physical load margin standard deviation based on the thermal inertia of transformer temperature rise are extracted for physical limit quantization analysis, and for temporary capacity reduction, the partial regression coefficients of the bounded rationality text mapping values ​​based on the Poisson coefficient of variation are extracted for economic arbitrage quantization analysis.

[0011] In one aspect of the invention, the output includes a real-time early warning result with reverse causal tracing: The reverse causal tracing refers to the system automatically extracting the primary contributing factor that causes the sharp increase in probability when the calculated absolute probability exceeds the warning threshold, and marking in the structured warning report whether the primary contributing factor is due to the compression of the thermal inertia margin of the underlying equipment or the activation of the short-sighted bias of corporate executives under the existence of real arbitrage space; if the dynamic self-healing redistribution is triggered during system operation, a macro-constraint degradation indicator is added to the report.

[0012] In another aspect, the present invention also provides a temporary expansion quantization system based on a zero-inflation Poisson model for implementing the above-described method, the system comprising: The data forced isolation module is used to construct a forced orthogonal partitioning mechanism for dual random process data, which physically isolates the accessed multi-source data into an absolute zero state vector pool and a continuously changing expectation vector pool. The causal feature mining module is used to extract physical constraint feature variables representing the extreme values ​​of power grid equipment based on the absolute zero state vector pool, and to extract behavioral causal feature variables representing the thermal inertia and economic game of the power grid based on the continuously changing expectation vector pool. The physical anchoring model construction module is used to construct a zero-inflated Poisson quantization model based on physical and economic anchors, and to inject the physical constraint feature variables and behavioral causal feature variables into the logistic regression process and the Poisson counting process, respectively. The parameter self-healing solution module is used to solve parameters using the maximum likelihood estimation method with economic arbitrage directional gradient constraints, and to trigger dynamic self-healing redistribution of model features based on the statistical decay protocol. The closed-loop early warning decision module is used to calculate the absolute probability of a company engaging in temporary business expansion activities, and outputs real-time early warning results with reverse causal tracing when the early warning threshold is exceeded.

[0013] In one aspect of the invention, the parameter self-healing solution module runs in a central processing unit configured with a floating-point arithmetic unit that supports advanced vector extension instruction sets. When calculating the approximate matrix of the second-order partial derivative of the target log-likelihood function, it performs task-level parallel splitting through a multi-threaded framework and supports non-collinear concurrent reading of the absolute zero state vector pool and the continuously changing expectation vector pool under a hierarchical architecture of redundant independent disk arrays.

[0014] Compared with the prior art, the present invention has the following beneficial effects: First, this invention uses a dual random process data forced orthogonal partitioning mechanism to block collinear interference between zero-inflation features and non-zero-variation features from the underlying architecture, thus improving the feature dilution problem caused by traditional full data fusion.

[0015] Secondly, a zero-inflated Poisson quantization model based on physical and economic anchors is constructed to map causal mechanisms. Physical constraints characterizing the extreme values ​​of power grid equipment are injected into the logistic regression process as extreme value penalty terms, while behavioral causal characteristics are injected into the Poisson counting process as gating. This scheme mathematically distinguishes between "structural zero values" caused by the depletion of equipment's physical limits and "random zero values" caused by a lack of arbitrage incentives, thus avoiding prediction biases that violate common sense in electrical physics and microeconomic rationality.

[0016] Furthermore, by employing the maximum likelihood estimation method with gradient constraints based on economic arbitrage directionality, statistical correlations without economic support are filtered out, improving the robustness of parameter fitting. A dynamic self-healing redistribution mechanism for model features triggered by a statistical decay protocol endows the system with cross-cycle adaptability to automatically degrade failing constraints and reshape causal chains under changes in the macro-environment, reducing the cost of offline retraining.

[0017] Finally, the system outputs real-time early warning results with reverse causal tracing, overcoming the limitation of traditional predictions that only output probability values. This solution can reveal whether the direct driver triggering business expansion changes is the thermal inertia boundary of physical equipment or short-sighted arbitrage by enterprise management, achieving a functional upgrade from passive numerical alarms to proactive health diagnosis. It provides a decision-making foundation with both physical attributes and economic explanatory power for the refined capacity control and flexible scheduling of smart distribution networks. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0019] Figure 1This is a flowchart of a temporary quantization method based on a zero-inflated Poisson model according to the present invention.

[0020] Figure 2 This is a flowchart illustrating step 1 of a temporary expansion quantization method based on a zero-inflation Poisson model according to the present invention.

[0021] Figure 3 This is a flowchart illustrating step 2 of a temporary expansion quantization method based on a zero-inflation Poisson model according to the present invention.

[0022] Figure 4 This is a flowchart illustrating step 3 of a temporary quantization method based on a zero-inflated Poisson model according to the present invention.

[0023] Figure 5 This is a flowchart illustrating step 4 of a temporary quantization method based on a zero-inflated Poisson model according to the present invention.

[0024] Figure 6 This is a flowchart illustrating step 5 of a temporary quantization method based on a zero-inflated Poisson model according to the present invention.

[0025] Figure 7 This is a flowchart illustrating step 6 of a temporary quantization method based on a zero-inflated Poisson model according to the present invention. Detailed Implementation

[0026] The present invention will be further described below with reference to embodiments. These embodiments are merely some, not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the protection scope of the present invention.

[0027] Please see Figure 1 As shown in the figure, this embodiment discloses a temporary load quantification method based on a zero-inflation Poisson model. At the macro level, this method first abandons the traditional full data fusion and forcibly orthogonally divides multi-source data into an absolute zero-state vector pool and a continuously changing expectation vector pool. Then, it extracts constraint features with power grid physical limits and causal features with economic game theory rationality. Next, it uses the above features as anchor points and injects them into the logistic regression process and Poisson counting process of the zero-inflation Poisson quantification model. Finally, through constrained parameter solving and dynamic self-healing of features, it outputs real-time early warning results with reverse causal traceability capabilities. This architecture isolates the zero-inflation bias from the underlying mathematical logic and solves the prediction distortion problem caused by traditional load quantification methods ignoring physical limits and arbitrage motives.

[0028] The following details each implementation step of this method.

[0029] Step S1: Construct a forced orthogonal partitioning mechanism for dual stochastic process data This step aims to physically isolate the raw application data, enterprise operating data, and macroeconomic monitoring data based on causal attributes. Step S1 is specifically broken down into the following sub-steps: Please see Figure 2 As shown, sub-step S1.1: Multi-source heterogeneous data acquisition and spatiotemporal logic mapping The specific technical problem to be solved in this step is the misalignment between time windows and physical states of multi-source heterogeneous data.

[0030] The specific solution is as follows: The system obtains the original application flow data through a data interface, extracts the target enterprise's original operating capacity, applied change capacity, and total operating capacity, and performs spatiotemporal mapping according to a unified quarterly sampling frequency. The change logic formula is as follows:

[0031] The specific physical meanings and units of each variable in the formula are as follows: Indicates enterprise In the Total operating capacity for the sampling period, in kilovolt-amperes; Indicates enterprise In the The original operating capacity at the beginning of the sampling period, in kilovolt-amperes; Indicates enterprise In the The actual change in requested capacity during the sampling period, in kilovolt-amperes (kVA). This item takes a negative value when the service type is capacity reduction or suspension; and a positive value when the service type is capacity increase or recovery.

[0032] Assume that in the second sampling period, a company's initial capacity was 2000 kVA, and it applied for a capacity reduction of 500 kVA in that period. Substitute into the formula to calculate: kVA. The intermediate result was confirmed as 1500 kVA.

[0033] Regarding parameter traceability: The above basic capacity parameters are all directly derived from the meter archive database of the provincial power grid marketing management system. They are retrieved offline through the application programming interface (API) and snapshotted and fixed on a quarterly basis to ensure the objectivity and authenticity of the data source.

[0034] The computational logic of this step solves the technical problem of state conflict caused by inconsistent data sampling frequencies, and realizes the logical alignment of heterogeneous data on the same time scale.

[0035] Sub-step S1.2: Physical invariance verification and outlier correction The specific technical problem to be solved in this step is the interference of extreme numerical jumps caused by meter malfunctions or criterion fine-tuning on subsequent probability distributions.

[0036] The specific solution is as follows: Before the data enters the feature vector pool, a physical invariance check based on the Laida criterion is performed. The system employs a piecewise function-based Winsorize correction logic.

[0037] The specific physical meanings and units of each variable in the formula are as follows: This indicates the company after corrective action. In the The target verification value for the period (such as the actual quarterly electricity consumption) is determined by the specific indicator. Indicates enterprise In the The original target verification numerical observations for the period; Indicates enterprise The arithmetic mean of the target value over several consecutive sampling periods in the past; It represents the standard deviation of the target numerical sequence within the same time window.

[0038] Assume a company's average electricity consumption over the past four quarters is 50,000 kWh, with a standard deviation of 2,000 kWh. Due to a metering anomaly, the reported electricity consumption for the current period is 60,000 kWh. Substituting this into the piecewise function condition, the upper threshold is... kilowatt-hours. Since 60000 is greater than 56000, the first segmentation condition is triggered, and the output is calculated. Kilowatt-hours.

[0039] Regarding the parameter origin: the threshold multiplier of 3 is derived from the statistical confidence interval setting of the standard normal distribution. This parameter was verified by offline data fitting of the historical load jump distribution pattern of the provincial power grid over the past 10 years, confirming that it can cover 99.7% of the normal physical fluctuation boundary.

[0040] The computational logic of this step solves the technical problem of divergence in model parameter estimation caused by non-operational anomaly data, providing a stable data foundation for feature extraction.

[0041] Sub-step S1.3: Cross-domain dimensional standardization The specific technical problem to be solved in this step is the huge order of magnitude difference between financial data and grid capacity data.

[0042] The specific solution is to apply an improved standardization operator to the corrected heterogeneous feature stream.

[0043]

[0044] The specific physical meanings and units of each variable in the formula are as follows: Represents the standardized dimensionless eigenvalues; This represents the original feature verification value of the input operator, with the unit depending on the original physical quantity; This represents the arithmetic mean of the specific feature across the entire training sample set; It represents the standard deviation of that specific feature across the entire training sample set.

[0045] Assume an original feature value of 120, a sample set mean of 100, and a standard deviation of 10. Substitute these values ​​into the formula to calculate: .

[0046] Regarding parameter origination: mean with standard deviation All of these calculations rely on offline statistical analysis of the full historical dataset, and global parameter updates are performed at the beginning of each complete computation cycle.

[0047] The computational logic of this step solves the technical problem of model weight allocation failure caused by inconsistent dimensions of multidimensional features.

[0048] Sub-step S1.4: Forced orthogonal partitioning of data The specific technical problem to be solved in this step is the confusion between collinearity interference caused by conventional feature stacking and the zero-value generation mechanism.

[0049] The specific solution is as follows: the full standard feature stream processed through steps S1.1 to S1.3 is forcibly allocated to two physically isolated storage pools based on a preset causal attribute dictionary. First, an absolute zero state vector pool is constructed. This pool specifically stores variables that determine whether a company possesses the objective physical basis and financial boundaries for business expansion. If the characteristic attribute belongs to the asset-liability category, current ratio category, industry pressure category, or transformer rated constraint category, it is written into this pool.

[0050] Secondly, a continuously changing expectation vector pool is constructed. This pool specifically stores variables that determine the specific intensity of business expansion chosen by the enterprise given its capabilities. If the feature attribute belongs to the categories of order volatility, textual short-sightedness preference, or thermal inertia margin change, it is written into this pool.

[0051] Data separation is performed based on conditional decisions: Assume the feature set includes "debt-to-asset ratio" and "short-term electricity volatility". According to the classification dictionary, the former is routed to the absolute zero state vector pool, and the latter is routed to the continuously changing expectation vector pool.

[0052] The computational logic of this step solves the technical problem of mutual dilution between zero-inflation features and non-zero variation features, and blocks the data collinearity interference between the two-stage stochastic processes in the subsequent zero-inflation Poisson model.

[0053] Step S2: Extract physical causal features and behavioral causal features This step aims to extract constraint features with clear physical boundaries and behavioral features containing economic game-theoretic motivations, based on the isolated dual vector pool. For clarity, this section first elaborates on the sub-steps (sub-steps S2.1 to S2.2) for mining physical causal feature variables.

[0054] Please see Figure 3 As shown, sub-step S2.1: Quantification and extraction of the physical capacity redundancy limit of the transformer. The specific technical problem to be solved in this step is that existing technologies rely solely on superficial financial liquidity data to judge the state of a company, and cannot determine from the objective perspective of the physical limits of the underlying equipment whether the company truly has the physical foundation to carry out business expansion activities.

[0055] The specific solution is as follows: The system extracts the target enterprise's equipment file data and historical high-frequency load data from the absolute zero state vector pool, and calculates the transformer physical capacity redundancy limit. The calculation logic formula is as follows:

[0056] The specific physical meanings and units of each variable in the formula are as follows: Characterizing enterprises In the The transformer physical capacity redundancy limit for each sampling period is a dimensionless percentage value. Characterizing enterprises The total rated nameplate capacity of transformers currently in operation on the grid, in kilovolt-amperes (kVA). ); Characterizing enterprises past The maximum measured peak active load captured by the metering system within each sampling period, in kilowatts (kW). ); Characterizing enterprises The average power factor within the corresponding observation period is a dimensionless value.

[0057] Assume the total rated nameplate capacity currently operating on the network for the enterprise being evaluated. It is 1000 kVA. Over the past four quarters (i.e....) The peak active load is 800 kW, and the average power factor during this cycle is... The value is 0.95. Substituting into the formula for step-by-step calculation: First, calculate the peak apparent power after conversion as... kilovolt-amperes. Then, substituting into the master formula, the redundancy limit is calculated: The final physical capacity redundancy limit was determined to be 15.79%.

[0058] Regarding parameter tracing: Time window parameters The value is typically set to 4 (corresponding to a complete calendar year). This parameter is determined by performing an STL seasonal decomposition algorithm on the historical load curves of a large number of industrial users and analyzing the electricity consumption cycle patterns offline. The aim is to fully cover the peak of all seasonal production loads of enterprises throughout the year. Average power factor It is directly taken from the integral ratio of active and reactive power collected at high frequency from the smart energy meter.

[0059] The computational logic of this step solves the technical problem that pure financial data models are prone to producing false predictions. By establishing physical rigid boundaries, it accurately quantifies the extreme state of enterprise equipment reaching its capacity limit, providing an objective physical basis for subsequent model determination of "structural zero value".

[0060] Sub-step S2.2: Calculation of the standard deviation of physical load margin based on transformer temperature rise thermal inertia The specific technical problem to be solved in this step is that traditional statistical fluctuation indicators (such as power variance) cannot distinguish between normal shift production fluctuations and abnormal fluctuations when equipment is approaching the danger zone of overload damage.

[0061] The specific solution is as follows: Based on the data in the continuously varying expected vector pool, and combined with an electrical engineering thermodynamic model, the system calculates the standard deviation of the physical load margin based on the thermal inertia of transformer temperature rise. The calculation logic formula is as follows:

[0062] The specific physical meanings and units of each variable in the formula are as follows: Characterizing enterprises In the The standard deviation of physical load margin for each sampling period, in kilovolt-amperes (kVA). ); The rolling window period is defined as the number of sampling periods. Characterizing enterprises The total rated capacity of the transformer, in kilovolt-amperes (kVA). ); The thermal inertia coefficient characterizes the temperature rise of a transformer and is used to characterize the physical tolerance of a transformer to not suffer substantial thermal damage under short-term overload. It is a dimensionless constant. Characterizing enterprises Within the scrolling window The actual apparent power load for each cycle, in kilovolt-amperes (kVA). ); Characterizing enterprises exist The arithmetic mean of the transformer thermal inertia margin over a period of time, expressed in kilovolt-amperes (kVA). ).

[0063] Set time window Rated capacity of a certain enterprise 1000 kVA, thermal inertia coefficient It is calibrated to 1.1 (i.e., allowing 10% short-term overload). The rigid boundary for thermal damage is thus defined as... kilovolt-amperes. Assuming apparent power over the past four quarters. The measured values ​​were 900, 1050, 950, and 1000 kVA, respectively. Substituting these values ​​into the formula, the calculations were performed step by step: First, calculate the physical margin for each period. The values ​​are 200, 50, 150, and 100 kVA, respectively.

[0064] Secondly, calculate the arithmetic mean. 1000 kVA.

[0065] Then, calculate the cumulative value of the variance deviation term: .

[0066] Finally, divide by degrees of freedom And take the square root: The result obtained is 64.55 kVA.

[0067] Regarding parameter tracing: thermal inertia coefficient The value is strictly set according to the "Operating Regulations for Power Transformers" and the insulation heat resistance class (such as Class A, Class B, and Class F) indicated on the nameplate of the target transformer. In actual implementation, this coefficient is obtained through offline calibration by building a thermodynamic equivalent circuit simulation model of transformer oil temperature and top winding temperature rise, ensuring the scientific attributes of the parameter rather than subjective assumptions.

[0068] The computational logic of this step solves the technical problem of the disconnect between data appearance fluctuations and the underlying equipment security status, enabling the model to capture emergency expansion signals that enterprises are forced to initiate in order to avoid thermal damage red lines with extreme sensitivity, thereby improving the depth and accuracy of dynamic feature mining.

[0069] Sub-step S2.3: Quantification and extraction of the expected difference in basic electricity price arbitrage The specific technical problem to be solved in this step is that existing load forecasting models usually treat changes in enterprise capacity (demand) as a pure random walk or pure production demand, completely ignoring the core economic motivations that drive enterprises to carry out capacity reduction operations, which leads to forecasting bias when the model is predicting specific policy periods with large arbitrage opportunities.

[0070] The specific solution is as follows: The system extracts the target enterprise's electricity bill ledger data and production scheduling data from the continuously changing expected vector pool, constructs a basic electricity arbitrage verification operator, and calculates the expected difference in basic electricity arbitrage. The calculation logic formula is as follows:

[0071] The specific physical meanings and units of each variable in the formula are as follows: Characterizing enterprises In the The expected absolute cost savings difference for the target business expansion behavior (such as temporary capacity reduction) performed in each sampling period, in yuan; Characterizing enterprises In the The actual capacity reduction target value reported to the power grid (or calculated by system simulation) within each sampling period, in kilovolt-amperes (kVA). ); This represents the current standard unit price of basic electricity charges for transformer capacity in the provincial power grid where the enterprise is located (if the sampling period is quarterly, then it is the monthly standard multiplied by 3), in yuan / kVA (yuan / ); Characterizing enterprises The total implicit friction costs incurred during this business expansion operation, including power outage switching, equipment storage and maintenance, and short-term production line shutdowns, are expressed in yuan.

[0072] Assume the company being evaluated plans to [do something] in the current quarter (i.e., the [number]th quarter) (Periodic) Apply for temporary capacity reduction. The target capacity reduction value declared to the power grid. The capacity is 2000 kVA. The basic electricity rate stipulated by the local provincial power grid, calculated based on transformer capacity, is 30 yuan / kVA per month, corresponding to a quarterly (3-month) cycle price. The cost is 90 yuan / kVA. The system calculates the comprehensive implicit friction cost of switching operations and production line shutdowns based on the company's historical data. The amount is 45,000 yuan. Substituting into the formula and performing step-by-step calculations: First, calculate the gross electricity savings as... Yuan; then, after deducting implicit friction costs, the result is... The final expected arbitrage difference in basic electricity costs is 135,000 yuan.

[0073] Regarding parameter traceability: Periodic standard unit price It is directly traced back to the latest "Provincial Power Grid Sales Electricity Price List" issued by the National Development and Reform Commission or local price bureaus and updated in real time through hard coding in the system. Implicit friction costs. Instead of subjective estimation, the system extracts the standard operating hours during each power outage and switching operation of the enterprise over the past three years through the Enterprise Resource Planning (ERP) system, multiplies them by the enterprise's average net profit per hour for that quarter, and adds the standard power operation and maintenance outsourcing service fee, and then uses a weighted fitting to determine an objective constant.

[0074] The computational logic of this step solves the technical problem of load forecasting being divorced from the foundation of microeconomics. By introducing a rigorous marginal revenue game formula, the behavior of "whether a company reduces capacity" is anchored from a random probability event to a deterministic decision based on a clear economic arbitrage space.

[0075] Sub-step S2.4: Calculation of bounded rationality text mapping value based on Poisson variation coefficient The specific technical problem to be solved in this step is that traditional natural language processing (NLP) methods often only output broad sentiment scores when processing enterprise text. These scores lack a clear causal physical relationship with the underlying power physics operations, causing "text features" to become black box parameters with no interpretability in rigorous power grid engineering predictions.

[0076] The specific solution is as follows: The system uses a web crawler module to obtain publicly available quarterly operating reports or internal management meeting minutes from enterprises, and extracts bounded rationality text mapping values ​​based on the Poisson coefficient of variation. This step is divided into two-stage calculation logic: First, the system is configured with a specific "short-term performance-oriented keyword dictionary," and the frequency of occurrences of this dictionary in the text is counted, and the thousandths standardized word frequency is calculated:

[0077] The specific physical meanings and units of each variable in the formula are as follows: Characterizing enterprises In the The thousandths normalized word frequency of the target words in the quarterly text is a dimensionless thousandths value; The total absolute frequency of exact matches in the text to the "short-term performance-oriented keyword dictionary" (such as "short-term returns" and "immediate results") is expressed in times. The effective total vocabulary of a text after removing punctuation and regular stop words is represented by words.

[0078] Subsequently, the system employs the standard Logistic activation function operator to transform the word frequency features into a mapping bias that controls the subsequent variation of the Poisson distribution mean:

[0079] The specific physical meanings and units of each variable in the formula are as follows: The text mapping value based on the Poisson coefficient of variation is used to represent the bounded rationality of the text. This indicator is written as a core driving feature into the continuously varying expectation vector pool, and its value range is [value range missing]. , dimensionless constant; Represents the base of the natural logarithm (approximately 2.71828). The bias parameter characterizing the intercept term is a dimensionless constant; The weight parameters representing the word frequency feature mapping are dimensionless constants; Characterizes the thousandths standardized word frequency obtained from the pre-calculation.

[0080] Assuming the system retrieves a company's quarterly management analysis report, the total effective vocabulary after removing stop words is as follows: The dictionary contains 5000 words. The absolute frequency of matches found in the dictionary is determined by the word segmentation tool. It is 15 times. Substitute into the formula from the first step of the calculation: .

[0081] Assuming the system has preset intercept term bias parameters Feature mapping weight parameters .Will Substitute the equations into the second step of the mapping process and perform step-by-step calculations: First, calculate the exponent terms. Then the natural index was calculated. Finally, solve for the mapping value: The final bounded rationality text mapping value is 0.6011.

[0082] Regarding parameter sourcing: The "Short-Term Performance-Oriented Keyword Dictionary" was jointly developed by an expert database of industrial economics, excluding commonly used high-frequency terms. Intercept term bias parameter. With feature mapping weight parameters The parameters are not randomly set, but rather the global optimal parameters are extracted by the system through collecting five years of annual report text data from 800 manufacturing companies and their corresponding business expansion and change frequencies, and then performing offline high-dimensional space fitting using a supervised learning algorithm. This ensures the convergence of the mapping function and its industrial applicability.

[0083] The computational logic of this step solves the technical problem of converting the psychological characteristics of senior executives into engineering calculation parameters. Through rigorous nonlinear mapping equations, the abstract "short-sighted bias" of management is quantified into a specific multiplier that can directly drive model fluctuations, thus achieving a successful landing of soft management characteristics in the hard-core quantitative model of the power grid.

[0084] Please see Figure 4 As shown, step S3: Construct a zero-inflated Poisson quantization model based on physical and economic anchors. This step aims to integrate isolated feature vectors into a single probabilistic mathematical space through a two-stage stochastic process, and to forcibly hard-code the physical laws of the power grid and corporate arbitrage rationality into the model architecture using mathematical penalties and gating mechanisms. Step S3 is specifically broken down into the following sub-steps: Sub-step S3.1: Construction of the dual marginal probability distribution of the zero-inflated Poisson quantization model The specific technical problem to be solved in this step is that traditional single-modal distribution models (such as simple Poisson distribution or normal distribution) cannot simultaneously handle the mixed distribution characteristics of a large number of structural zero values ​​and scattered non-negative integer counts in temporary business expansion data, resulting in overall prediction distortion.

[0085] The specific solution is as follows: The system establishes a basic mathematical framework for a dual stochastic process. The actual temporary business expansion declaration capacity of enterprises is defined as the explained variable, and its corresponding marginal probability distribution function is calculated in two branches:

[0086]

[0087] The specific physical meanings and units of each variable in the formula are as follows: Characterizing enterprises In the The absolute probability of the actual quarterly reported change capacity being strictly zero is a dimensionless percentage value. Characterizing enterprises In the The actual quarterly reporting capacity is a positive integer. The probability of occurrence is a dimensionless percentage value; Positive integer values ​​representing temporary capacity expansion, in kilovolt-amperes (kVA). ); The first-order parameter characterizing the double stochastic process, namely the "structural zero state probability" in which a firm is unable to expand its business, is a dimensionless parameter. The second-order parameter characterizing a doubly stochastic process, namely the "expected mean" in a Poisson counting process, is expressed in units of... Consistent; Represents the base of the natural logarithm (approximately 2.71828). Characteristic Value The factorial operator.

[0088] Assuming that after subsequent parameter mapping, the probability of a certain enterprise falling into a structurally zero state is... Poisson expected mean Substituting into the formula, we can calculate the probability that business expansion will not occur: Calculate the probability of a 1 kVA expansion: The intermediate result has a zero probability of approximately 65.41%, and the probability of a non-zero value decreases according to a Poisson distribution.

[0089] Regarding parameter sourcing: The basic probability distribution framework follows rigorous mathematical statistics principles, with preset constants. Provided by the mathematical library that comes with the system's computing kernel, the overall architecture of the model is directly hard-coded into the floating-point arithmetic unit of the application server.

[0090] The computational logic of this step solves the problem of single-fit failure caused by extreme data sparsity, and achieves complete decoupling of the two heterogeneous physical processes of "never causing business expansion" and "randomly causing business expansion of a specific scale" in the underlying mathematical space.

[0091] Sub-step S3.2: Mapping the logistic regression process based on physical extremum penalty The specific technical problem to be solved in this step is that the traditional logistic regression (Logit) probabilistic model relies solely on soft data fitting and lacks the rigid physical constraints of the underlying power grid equipment, making it easy to predict capacity expansion actions that violate common sense in electrical engineering.

[0092] The specific solution is as follows: The system constructs a mapping relationship for structural zero-value probabilities, and injects physical constraint feature variables from the absolute zero state vector pool as extremum penalty terms into the logistic regression process. The log-odds equation for state probabilities is forcibly transformed into:

[0093] The specific physical meanings and units of each variable in the formula are as follows: Characterizes the structural zero probability of the target being solved; Characterized by the natural logarithm function; The global constant intercept term characterizing the logistic regression process; Characterizes the regular feature matrix extracted from the absolute zero state vector pool (such as industry pressure). Characteristic correspondence The parameter vector of the feature matrix; The weight representing the forced physical extreme value penalty is a dimensionless, extremely positive number. The physical capacity redundancy limit of the transformer obtained in the preceding step S2.1 is represented by a value. consecutive decimals; Characterizes the lower bound fault-tolerant minimum constant.

[0094] Assume the system calculates the basic items based on the routine characteristics of a certain enterprise. The enterprise is in a high-load, dangerous state, with its physical capacity redundancy limit exceeded. (Only 1% redundancy remains). The system has a preset penalty weight. Fault tolerance constant .

[0095] Substitute into the formula to calculate the penalty term: .

[0096] Calculate the logarithmic odds: .

[0097] Inverse probability calculation : This value approaches 1.0 (100%) infinitely.

[0098] Regarding parameter origination: Physical extreme value penalty weights It is not generated by spontaneous model learning, but rather by forced offline calibration to a definite, extremely large positive number through data from historical test bench power-off experiments and extreme power grid operating conditions; minimal constant. This is taken from the single-precision floating-point overflow prevention lower limit of the IEEE754 standard of the underlying hardware chip to prevent the system from throwing a logarithmic field negative infinity exception.

[0099] The computational logic of this step completely solves the problem of over-limit prediction technology under pure data-driven approach. Its effect is closed-loop: when the physical capacity redundancy limit of the transformer approaches zero, it forces the probability of the enterprise falling into a structural zero state in which it is unable to make any internal flexible business expansion adjustments to 100%.

[0100] Sub-step S3.3: Mapping the Poisson counting process based on economic arbitrage gating The specific technical problem to be solved in this step is that the traditional Poisson counting model, after reading textual characteristics such as short-sightedness of management, will indiscriminately amplify the count value, and will be unable to distinguish "random operations under unprofitable circumstances", thus making the results lack economic basis.

[0101] The specific solution is as follows: the system constructs a log-linear correlation equation for the expected mean and injects the previously extracted behavioral causal feature variables into it, and implements restricted driving by constructing a gating mechanism.

[0102]

[0103] The specific physical meanings and units of each variable in the formula are as follows: The mean expectation of the Poisson process being solved is represented by kilovolt-amperes (kVA). ); The constant intercept term characterizing the Poisson counting process; The matrix representing the conventional driving variables (such as the standard deviation of physical load margin) in the continuously varying expected vector pool. ); Characteristic correspondence The parameter vector; Characterizing the short-sighted bias activation weight constant; Characterizing the bounded rationality text mapping value based on the Poisson coefficient of variation, for Dimensionless number; This is a conditional indicator function that outputs the value 1 when the internal Boolean logic is true, and outputs the value 0 otherwise. The expected difference in basic electricity price arbitrage is represented in yuan. Individual fixed effects parameters characterizing the inherent attributes of the processing firm; Characterize the yearly fixed-effects parameters for handling macroeconomic cycles; Characterizes the white noise term with random perturbation.

[0104] Assuming a combination of basic terms The total result is 1.5. System calibration. Text mapping value .

[0105] Scenario 1: If the arbitrage difference is calculated Yuan, no arbitrage opportunity. Indicator function. Calculate the logarithmic expectation: Solve for the expected capacity 1000 kVA.

[0106] Scenario 2: If the arbitrage difference is calculated There is arbitrage opportunity for the unit. Indicator function Calculate the logarithmic expectation: Solve for the expected capacity kilovolt-amperes. Intermediate results show an exponential difference in driving effect.

[0107] Regarding parameter tracing: Short-sighted bias activation weights These are the weight parameters that are fixed after the model performs independent gradient fitting and convergence on historical high-frequency operation records that exceed the arbitrage threshold in the offline full dataset; indicator function. These are logic gate operators, directly integrated into the processor's branch jump instruction set.

[0108] The computational logic of this step solves the technical problem of blind interference of executives' psychological textual features on the model. Through hard logic gate restrictions, the indicator function activates the positive amplification weight of the bounded rationality text mapping value based on the Poisson coefficient of variation on the expected mean if and only if the expected difference of basic electricity arbitrage is greater than zero, thus establishing the real economic foundation of non-zero count values.

[0109] Please see Figure 5 As shown, step S4: Parameter solving and dynamic self-healing redistribution of the zero-inflated Poisson quantization model. This step aims to solve for the parameters of the established model using a low-level algorithm with rigid economic constraints, and to endow the model with cross-cycle adaptability, preventing the statistical model from becoming distorted under changes in the macroeconomic environment. This step is specifically broken down into the following two sub-steps: Sub-step S4.1: Solving the maximum likelihood estimation with gradient constraints for economic arbitrage symmetry. The specific technical problem to be solved in this step is that the traditional parameter solving process only pursues the minimization of variance at the mathematical level, which can easily lead to overfitting that violates economic common sense, causing the model to identify erroneous data jumps as true patterns.

[0110] The specific solution is as follows: The system constructs the log-likelihood function through the application server and uses a quasi-Newton method (such as the BFGS algorithm) for multi-threaded iterative solution. When calculating the parameter gradient, an economic arbitrage-like gradient constraint is forcibly injected. (Log-likelihood function) The expression is:

[0111] The specific physical meanings and units of each variable in the formula are as follows: The objective log-likelihood value to be maximized is represented by the log-likelihood value. The matrix representing the parameters to be estimated, which includes all weights; Operators that sum over a sample set with zero observation capacity; An operator that sums a sample set with a positive integer observation size; Characterizes the probability of structural zero states; The expected mean of Poisson is represented by kilovolt-amperes (kVA). ); Positive integer measured values ​​representing temporary expansion capacity, in kilovolt-amperes (kVA). ).

[0112] Solving for activation weights for bounded rationality text mapping values When the partial derivative is , the system executes the following gradient gating logic:

[0113] The specific physical meanings and units of each variable in the formula are as follows: Characterizing the log-likelihood function with respect to the weights The mathematical gradient; Characterized by the expected difference in basic electricity price arbitrage The conditional indicator function.

[0114] Suppose that in a certain parameter fitting iteration, the product of the preceding gradient components is calculated for a sample of a certain company. The value is 0.85. The system retrieves the data for this company. If there is no arbitrage space, then the indicator function is... Substituting into the formula, the final gradient is obtained. Weight Stop updating this data dimension.

[0115] Parameter Origin: Initial search step size and convergence minimum precision of the quasi-Newton method (set to) The configuration is directly fixed by the underlying mathematical optimization algorithm library, without relying on manual intervention.

[0116] The computational logic of this step solves the technical problem of data fitting deviating from the business reality, and ensures that the generation of non-zero parameters strictly follows the microeconomic arbitrage logic.

[0117] Sub-step S4.2: Model dynamic self-healing redistribution based on statistic decay protocol The specific technical problem to be solved in this step is that when a systemic change occurs in the macroeconomic environment, the physical and financial constraints that originally led enterprises to a zero state are assimilated, and the solidified model boundaries produce cross-cycle prediction biases.

[0118] The specific solution is as follows: the system risk warning engine calculates the comprehensive Vuong statistic on a quarterly rolling basis during operation to monitor the effectiveness of the dual random process relative to the single Poisson process.

[0119]

[0120] The specific physical meanings and units of each variable in the formula are as follows: Characterizing the first The comprehensive Vuong statistic for each evaluation period; The total number of samples within the characterization validation window; The arithmetic mean of the pointwise log-likelihood ratio deviations representing the dual-mode and single-mode models; The sample standard deviation characterizes the log-likelihood ratio deviation.

[0121] The system executes the following state machine decision: when three consecutive cycles... At this time, the feature redistribution protocol is automatically triggered. The system temporarily establishes the physical isolation hard boundary between the absolute zero state vector pool and the continuously changing expectation vector pool; it transfers the failed macroscopic constraint features to the Poisson counting process; and it uses the Lagrange multiplier method to re-evaluate their marginal contribution to the mean of the Poisson expectation.

[0122] Assume the system calculates in the 8th, 9th, and 10th quarters. The values ​​were 1.82, 1.75, and 1.60, respectively. The system determined that the values ​​for three consecutive quarters were all less than or equal to 1.96. The system immediately issued a boundary clearing command, routing the feature data originally belonging to the absolute zero state vector pool to the continuously changing expectation vector pool for feature reshaping.

[0123] Parameter Origin: The judgment threshold of 1.96 is taken from the 95% confidence interval critical value of the standard normal distribution, which is an objective axiomatic parameter for statistical hypothesis testing, ensuring the scientific rigor of the self-healing triggering protocol.

[0124] The computational logic of this step solves the technical problem that static models are difficult to cross economic cycles, and realizes online causal chain reconstruction of the system without interrupting service.

[0125] Please see Figure 6 As shown, after completing the model dynamic self-healing redistribution based on the statistical decay protocol in step S4, this implementation method enters the core analysis stage of application implementation - step S5: performing physical-economic causal analysis of the zero-inflated Poisson model (ZIP) for different types of temporary business expansion behaviors.

[0126] Different business expansion behaviors (e.g., expansionary temporary power supply for high-voltage meter installation and contractionary temporary capacity reduction) are fundamentally opposed in terms of physical motivation and financial constraints. If all data is mixed into a single model for fitting, positive and negative driving factors will cancel each other out, leading to the fallacy of "parameter averaging". Therefore, this implementation decomposes the system into six independent sub-models (i.e., sub-steps S5.1 to S5.6), by reading the partial regression coefficients of specific feature variables ( and The direction and significance of electricity consumption can be accurately determined to reveal the underlying mechanism of enterprise electricity consumption decisions.

[0127] To facilitate a unified definition, the expected capacity under different business scenarios The analytical mapping equation is as follows:

[0128] The specific physical meanings and units of each variable in the formula are as follows: Enterprises predicted by the characterization model In the The expected capacity value for specific business expansion activities occurring in a quarter, in kilovolt-amperes (kVA). ); The probability of the constrained optimal structural zero value calculated from the parameter matrix obtained by the maximum likelihood estimation method is a dimensionless percentage value. The mean of the gated optimal Poisson expectation calculated from the parameter matrix obtained by the maximum likelihood estimation method, in kilovolt-amperes (kVA). ).

[0129] Sub-step S5.1: Quantitative analysis of the physical limits of high-voltage meter temporary power supply (HVTU) Temporary power supply for high-voltage meter installations typically occurs in expansion scenarios where companies face sudden large orders or are commissioning new production lines. In the HVTU sub-model, the set of continuously varying expected variables... The "Standard deviation of physical load margin based on transformer temperature rise thermal inertia" The partial regression coefficient of ") Significantly positive.

[0130] Since the thermal inertia coefficient of the transformer has been calculated in step S2.2 Forced injection of this feature results in a tightly closed loop at the physical level. When The surge means that the actual load of enterprises is frequently approaching or even exceeding the physical red line of equipment thermal damage. This "hard" physical signal of equipment endangerment constitutes the direct and primary physical motivation to trigger HVTU for emergency capacity expansion.

[0131] Sub-step S5.2: Macroscopic constraints and self-healing analysis of volume reduction recovery (CRR) Capacity recovery refers to the expansion behavior of companies that, after reducing capacity, apply to restore their original capacity as the market recovers. The logistic regression process of the ZIP model accurately captures the dual market and financial resistance in this recovery process.

[0132] In the absolute zero state vector pool In this context, the coefficient between the total number of companies in the industry (representing competitive pressure) and the number of days of cash and cash equivalents turnover (representing liquidity depletion) is... All are significantly positive. This reveals the rigid constraint boundary of the recovery: when the industry is crowded and companies' own capital recovery is extremely slow, it is extremely difficult for companies to restart idle capacity to seize market share, thus being firmly locked into the model. The system is in a "constantly zero state" (i.e., unable to recover). When a drastic change in the macroeconomy triggers the feature redistribution protocol in step S4.2, the system can automatically degrade these failed macroeconomic constraint features to ensure the cross-cycle dynamic accuracy of CRR probability prediction.

[0133] Sub-step S5.3: Quantitative Analysis of Economic Arbitrage in Temporary Capacity Reduction (TCR) Temporary downsizing is a defensive contraction strategy proactively implemented by companies when facing cost pressures or a period of order vacuum. In this sub-model, the economic features introduced in steps S2.3 and S2.4 demonstrate the core explanatory power.

[0134] The system calculates the "bounded rationality text mapping value" during the Poisson counting process. The partial regression coefficients of ")" are used to quantify the contribution weight and driving polarity of short-term performance orientation to business expansion behavior. Because the model applies an economic arbitrage unidirectional gradient constraint during parameter solving (step S4.1), i.e. The system can automatically identify the true causal mapping between textual features and actual expansion / reduction decisions. This analysis completely eliminates the illusions caused by pure data jumps, transforming the psychological textual features of executives into extremely accurate financial game decision parameters, achieving a dual quantitative diagnosis of both the objective economic environment and subjective management strategies.

[0135] Sub-steps S5.4 and S5.5: Profit Buffer Effect of Suspension (SUS) and Suspension Resumption (SR) Suspension and resumption are usually a high-frequency business combination, often seen in asset-heavy processing and manufacturing companies that are greatly affected by fluctuations in raw material prices or have unstable supply chains.

[0136] By comparing these sub-models, the system reveals the stabilizing effect of the "financial profit buffer pool." This is achieved through the absolute zero state vector pool during the Suspension (SUS) behavior. In China, the operating index The coefficient is significantly negative. This indicates that companies with a high operating index (i.e., those with sufficient real operating cash flow to support their book profits) have enough "financial redundancy" to absorb the financial friction costs caused by short-term order gaps, without having to frequently initiate equipment shutdowns to save on minor transformer basic electricity costs. Conversely, companies with low operating cash flow (i.e., those with low operating cash flow) have a high operating index. For companies with extremely low electricity costs, even the smallest fluctuations can trigger mandatory equipment shutdowns.

[0137] Sub-step S5.6: Analysis of Market Pricing Constraints on Temporary Metered Units (MTUs) Ordinary temporary electricity metering often occurs in peripheral auxiliary projects of small and medium-sized enterprises that lack long-term expansion expectations or in production rushes for short-term orders.

[0138] In this embodiment, the Lerner index approximation for individual stocks is extracted from the absolute zero state vector pool. It exhibits a very strong negative constraint on the probability of MTU occurrence. ). Analyzing from the underlying logic of industrial economics: Lower-priced firms indicate a lack of pricing power in a red ocean market, resulting in thin profit margins. These meager profits cannot cover the high sunk costs of long-term, formal power infrastructure (such as dedicated substation construction). Therefore, through rigorous mathematical proof, the model seamlessly links the "weak pricing power" of micro-enterprises in the industry chain with their unavoidable reliance on a "low-barrier, low-fixed-investment, guerrilla-style" strategy for end-point power access.

[0139] Through the classification and quantification in steps S5.1 to S5.6 above, this invention elevates rigid data mining to a deeper understanding of a company's physical limits and economic arbitrage behavior. It not only outputs cold, hard capacity prediction values, but also utilizes causal links to produce a holistic diagnostic report on the company's operational health and risk resistance capabilities.

[0140] Please see Figure 7 As shown, step S6: Computational hardware architecture mapping and quantization verification of ultra-large-scale datasets. This step aims to deploy the constructed Zero-Inflated Poisson (ZIP) temporary industrial expansion model in a production-level power grid information system and verify its innovation and technical advantages over traditional methods using real datasets. Step S6 is specifically broken down into the following sub-steps: Sub-step S6.1: Compute hardware concurrent architecture mapping The specific technical problem to be solved in this step is that when processing data from tens of millions of power users in a provincial power grid, the complex maximum likelihood iteration calculation of the double stochastic process takes too long and cannot meet the performance requirements of real-time early warning.

[0141] The specific solution is as follows: the system performs a deep mapping of the computational logic at the hardware architecture level. The iterative process of maximum likelihood estimation (especially the calculation of the Hessian matrix) is decomposed into multi-threaded tasks.

[0142] Specifically, the application server is configured with a central processing unit (CPU) that supports advanced vector extension instruction sets (such as AVX-512). When calculating the second-order partial derivative of the target log-likelihood function, the enterprise sample set is divided into multiple non-overlapping data chunks using a hash consistency algorithm through the OpenMP multi-threaded framework. Each CPU physical core is independently responsible for accumulating the gradient and Hessian matrix components of one data chunk, followed by a fast reduction operation in the shared L3 cache.

[0143] Meanwhile, the underlying architecture employs a redundant array of independent disks (RAID10). The absolute zero-state vector pool and the continuously changing expectation vector pool are mapped to different stripes of the array, ensuring that no I / O bottlenecks or collinearity blocking occur during multi-threaded concurrent reads.

[0144] The implementation of this step solves the technical problem of low efficiency in solving large-scale nonlinear models and provides hardware computing power support for the system's online real-time rolling prediction.

[0145] Sub-step S6.2: Reverse tracing and real-time early warning closed loop The specific technical problem to be solved in this step is that traditional load forecasting systems only generate an alarm value, and decision-makers cannot know the specific business reasons that caused the alarm, so they cannot take targeted intervention measures.

[0146] The specific solution is as follows: When the system calculates a certain enterprise (such as enterprise...) The absolute probability of a high-voltage metered temporary power utilities (HVTU) occurring in the next quarter. When the probability exceeds the warning threshold (set to 65%), not only will the probability value be output, but "reverse causal tracing" will also be enforced.

[0147] The source tracing logic is as follows: First, the system extracts the expected feature vector of continuous changes in the current time window of the enterprise. and their corresponding partial regression coefficients .

[0148] Subsequently, the logarithm of the mean of the Poisson expectation for each feature was calculated. Contribution component (i.e.) ).

[0149] Finally, the feature with the greatest contribution is selected as the primary cause of early warning, and its absolute probability is output to the power grid marketing service dashboard.

[0150] For example, the system not only alerts that "Company A has a 78% probability of experiencing a high-voltage power outage," but also traces the source to point out that "the primary cause is the surge in the standard deviation of physical load margin to the danger zone (contribution 62%), and the secondary cause is the positive turnaround in the expected difference in basic electricity arbitrage, which activates short-term decision-making (contribution 28%)." If the current cycle triggers a feature redistribution protocol, a "macroeconomic constraint degradation" label is added to a prominent position in the report.

[0151] The computational logic of this step solves the technical problem of the lack of business interpretability in the system's early warning system, and realizes an intelligent leap from "knowing what" to "knowing why".

[0152] Sub-step S6.3: Quantization Validation Example for Ultra-Large-Scale Datasets To demonstrate the significant advancements of this method compared to existing technologies, this embodiment extracts comprehensive, fusion-based data from 100,000 high-voltage power users across 12 consecutive quarters of a provincial power grid for empirical benchmarking. In this sample, "zero value" records (those without any temporary business expansion) account for a high proportion of 74.5%.

[0153] Control group setup: No physical orthogonal partitioning was performed, and no physical capacity extremum penalty was introduced. ) and economic arbitrage gate control ( The traditional zero-inflation Poisson model.

[0154] Experimental setup: The complete quantification method provided in this implementation method (integrating all physical and economic anchors and dynamic self-healing mechanisms) was adopted.

[0155] Verification indicators and effect projection: Log-Likelihood Improvement: The control group suffered from spurious fitting of the pure data, with a total log-likelihood value of [value missing]. The experimental group benefited from the precise interception of structural zeros by the physical hard boundary, resulting in a significant increase in the log-likelihood value. .

[0156] Improvement rate of goodness of fit This demonstrates the immense value of physical rigid constraints in statistical fitting.

[0157] The mean squared error (MSE) of the predicted residuals decreased: For high-frequency operations (such as temporary capacity reduction), the control group, unable to identify "blind fluctuations in textual characteristics without arbitrage opportunities," had an MSE as high as [missing value]. The experimental group, by incorporating the unidirectional gradient constraint of economic arbitrage, completely filtered out invalid predictions, reducing the MSE to [value missing]. .

[0158] Error reduction rate .

[0159] The effectiveness of the dynamic self-healing mechanism (Vuong test): In the 8th quarter of the empirical period, the province experienced a macro-credit tightening. The false alarm rate in the control group surged from an average of 5% to 35%. However, in the experimental group, at the beginning of the 9th quarter, the Vuong statistic fell below 1.96, automatically triggering feature reassignment (step S4.2), degrading invalid cash flow features. After feature reshaping, the false alarm rate in the experimental group was successfully suppressed back below 5.8% in subsequent quarters.

[0160] Through the extrapolation and verification using the aforementioned ultra-large-scale dataset, it has been verified that the zero-inflation Poisson quantization model constructed by introducing the limits of underlying electrical physics and microeconomic arbitrage game can greatly improve the accuracy, robustness, and business interpretability of power industry expansion forecasts, demonstrating outstanding substantive features and significant technological advancements.

[0161] In some embodiments, a large industrial manufacturing enterprise is located in the midstream of the industrial chain, with a total rated nameplate capacity of 3150 kVA operating on the grid. During the second sampling period, due to a seasonal contraction in orders, the enterprise submits a temporary capacity reduction application to the grid. The enterprise's initial capacity in the system is 3150 kVA, and the actual requested capacity change is -630 kVA. The system calculates its total operating capacity as 3150 kVA plus -630 kVA, equaling 2520 kVA, through underlying mapping logic, achieving spatiotemporal alignment. Simultaneously, the data integration module monitors the enterprise's current original active power observation value as 142,000 kWh. Since the arithmetic mean of the enterprise's power consumption over the past four periods is 120,000 kWh, and the series standard deviation is 5,000 kWh, the system performs a capped physical invariance check. The upper limit of the threshold is calculated as 120,000 plus 3 multiplied by 5,000, which equals 135,000 kWh. Since 142,000 is greater than 135,000, the upper limit correction piecewise function is triggered, and the output verification value is 135,000 kWh. Subsequently, this value enters the cross-domain dimensionality standardization operator, and is substituted with the full sample mean of 100,000 kWh and the standard deviation of 20,000 kWh. The dimensionless standard eigenvalue is calculated to be 1.75, which is then automatically routed by the system to either the absolute zero state vector pool or the continuously changing expectation vector pool, thus completing the forced orthogonal partitioning of the underlying heterogeneous data.

[0162] In the in-depth analysis of physical causal characteristics, the system further quantifies the enterprise's physical boundaries and equipment thermal safety status. The maximum measured peak active load over the past four cycles was extracted as 1500 kW, with an average power factor of 0.95. The system calculates the apparent peak power as 1500 divided by 0.95, approximately 1578.94 kVA. Substituting this into the transformer physical capacity redundancy limit formula, the limit is calculated as 3150 minus 1578.94, divided by 3150, equaling 0.4987. This indicates that the enterprise still retains approximately 0.4987% (49.87%) of ample capacity redundancy, providing a physical basis for capacity reduction. Subsequently, the system quantifies its temperature rise thermal inertia, setting the thermal inertia coefficient to 1.1, meaning it can tolerate 10% short-term overload, with an absolute thermal damage boundary of 3465 kVA. The apparent power values ​​for the past four periods were 1200, 1400, 1600, and 1500 kVA, respectively. Subtracting these values ​​one by one yielded a physical load margin sequence of 2265, 2065, 1865, and 1965 kVA, with an arithmetic mean of 2040 kVA. The cumulative variance was calculated to be 87500. Dividing this by 3 degrees of freedom and taking the square root, the standard deviation of the physical load margin based on transformer temperature rise thermal inertia was found to be 170.78 kVA.

[0163] In the economic game theory and behavioral causal feature extraction stage, the system calculates the real arbitrage space and the rational boundary of senior management behind this capacity reduction behavior. The company's declared target capacity reduction is 630 kVA, the local power grid's basic quarterly electricity price is 90 yuan per kVA, and the comprehensive implicit shutdown and switching friction cost is calculated to be 15,000 yuan. Substituting into the arbitrage verification operator, the expected difference is 630 multiplied by 90 minus 15,000, which equals 41,700 yuan. Since 41,700 is strictly greater than 0, the existence of absolute arbitrage space is confirmed. At the same time, the crawler module crawls the company's quarterly financial report, and after cleaning, the effective total vocabulary is 8,000 words, with a total frequency of 24 hits of short-term performance-oriented keywords. Substituting into the formula, the thousandth percentile standardized word frequency is calculated to be 3. The system retrieves the bias parameter of -3.85 and the weight parameter of 1.42 from the offline fitting, substitutes them into the mapping equation to calculate the exponent term as negative -3.85 plus 1.42 multiplied by 3, resulting in -0.41. Taking the natural constant to the power of -0.41 gives approximately 0.6636. Further, the bounded rationality text mapping value based on the Poisson coefficient of variation is calculated as 1 divided by 1 plus 0.6636, which is approximately 0.6011. This value highly sensitively characterizes the degree of short-sighted bias exhibited by management in order to embellish financial statements.

[0164] In constructing the probability mapping for the zero-inflated Poisson quantization model, the system first forces physical constraints into the logistic regression process to solve for the structural zero-state probability. The system extracts the conventional peripheral feature vectors to obtain an initial linear combination value of -8.0, presets a maximum / extreme value penalty weight of 10, and sets the overflow prevention minimum constant to 0.00001. Substituting these into the logarithmic probability equation to perform the extreme value penalty term calculation: -10 multiplied by the natural logarithm (containing 0.4987 plus 0.00001), i.e., -10 multiplied by -0.695, equals 6.95. The total logarithmic probability is -8.0 plus 6.95, equaling -1.05. Through inverse exponential inversion, the structural zero-state probability is obtained as 1 divided by 1 plus the natural constant raised to the power of 1.05, resulting in 0.259. This figure indicates that, with a physical extreme redundancy of 0.4987, the absolute probability that the company is deemed incapable of expanding its business is only 25.9 percent. It is not forcibly locked in a physical zero state, leaving ample room for maneuver in the Poisson count terms.

[0165] Simultaneously, the system initiates the mean calculation of the Poisson counting process within the continuously changing expectation vector pool. The system adds the extracted physical load margin standard deviation matrix (170.78) along with the fixed effects term, calculating a basic linear intercept combination value of 6.0. The system retrieves the short-sighted bias activation weight constant of 2.0 and substitutes the text mapping value of 0.6011. At this point, the system's underlying economic arbitrage unidirectional gradient constraint triggers the gating logic: since the basic electricity arbitrage expectation difference of 41,700 yuan is strictly greater than 0, the indicator function's judgment condition is fully met, and a hard output of the value 1 is generated. Substituting into the logarithmic linear correlation equation, the expected logarithmic mean is 6.0 plus 2.0 multiplied by 0.6011 multiplied by 1, totaling 7.2022. Through the underlying anti-overflow transformation of the natural exponent, the underlying expected mean parameter of the Poisson process is approximately 1342.38 kVA. This process demonstrates that, stimulated by the existence of objective arbitrage profits, the short-sighted textual characteristics of management are systematically and compliantly transformed into positive multipliers, amplifying the theoretically expected mean of capacity reduction by enterprises.

[0166] In the final decision output and system closed-loop self-healing phase, the system integrates dual stochastic processes to obtain a comprehensive predicted value for the expected capacity. Substituting the obtained zero-state probability of 0.259 and the Poisson expected mean of 1342.38 into the expected mapping equation of the comprehensive marginal probability distribution, the comprehensive expected capacity value for a company experiencing temporary capacity reduction is calculated as the difference between 1 and 0.259 multiplied by 1342.38, approximately equal to 994.70 kVA. Since this expected value and the calculated probability of the business occurrence far exceed the system's early warning threshold, the platform immediately outputs a reverse tracing alarm, including those driven by basic electricity arbitrage and short-sighted executive preferences. However, after the system had been running continuously for 12 quarters, the region experienced a comprehensive industrial restructuring and production restrictions. The system extracts 1000 samples and calculates the comprehensive Vuong statistic in a rolling manner. It then extracts the mean deviation (0.05) and standard deviation (0.86) of the log-likelihood ratio between the two models. Substituting these values ​​into the formula, the current statistic is calculated as √1000 × 0.05 / 0.86, approximately 1.838. Since 1.838 falls below the system's rigid warning line of 1.96 for discriminant advantage, the system immediately and automatically triggers a statistic decay self-healing protocol. At this point, the system proactively bridges the hard boundary between the absolute zero-state vector pool and the continuously changing expectation vector pool, degrading macro-constraint indicators such as competitive pressure into the Poisson term and re-performing Lagrange multiplier re-evaluation. This dynamic reorganization of the model architecture is completed in millisecond-level concurrent computation, ensuring the causal fitting accuracy of the system when navigating complex economic scenarios.

[0167] It should be noted that all preset thresholds, preset constants, and mapping parameters not directly calculated involved in the technical solution of this invention specifically include the Raida criterion threshold multiplier 3, the time window parameter N (with a value of 4), and the transformer temperature rise thermal inertia coefficient. Hidden friction costs Intercept term bias parameter Feature mapping weight parameters Extreme value penalty weight Lower bound fault-tolerant minimum constant Short-sighted bias activation weight constant Quasi-Newton method converges to minimal accuracy The Vuong statistic warning line of 1.96 and the high-voltage meter temporary power consumption probability warning threshold of 65% were not determined by human subjective setting, but were obtained offline through a series of data mining and optimization processes based on a limited number of historical objective data. The specific data processing and value determination process is illustrated below: The system extracts a limited historical dataset from a provincial power grid over the past 5 years, containing 5000 real capacity change records and corresponding enterprise financial characteristics. K-fold cross-validation is used to partition the dataset, and a joint loss function containing the aforementioned parameters to be estimated is constructed on the training set. The system uses a numerical model; taking the determination of the probability warning threshold as an example, it logically aligns the binary labels (0 or 1) of actual capacity changes in historical data with the predicted probability values ​​of the initial output of the model. A grid search method is used to slide and set a temporary warning threshold within the 50% to 90% range with a step size of 1%, and the corresponding prediction F1 score is calculated for each threshold. Data processing results show that when the sliding threshold is 65%, the harmonic mean of precision and recall, the F1 score, reaches its highest peak of 0.887. Therefore, the optimal objective setting for the system's warning threshold is 65%. Similarly, for the bias parameter... Weight parameters and The system uses the gradient descent algorithm to find the parameter convergence point that minimizes the Akaike Information Criterion (AIC) in multiple iterations. The warning line of 1.96 and the threshold multiplier of 3 are obtained by extracting the critical values ​​of the confidence interval of historical sample distribution. After all parameters have undergone the above-mentioned optimization numerical processing based on a finite number of historical data-driven iterations, they are finally solidified into the preset parameter values ​​that are directly called in this technical solution, thereby ensuring the scientificity, feasibility and robustness of the overall calculation solution in industrial application environment.

[0168] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A temporary quantization method for business expansion based on a zero-inflated Poisson model, characterized in that, include: A forced orthogonal partitioning mechanism for dual random process data is constructed, which physically isolates the original application data, enterprise operation data, and macroeconomic monitoring data into an absolute zero state vector pool and a continuously changing expectation vector pool based on causal attributes. Based on the absolute zero state vector pool, physical constraint feature variables characterizing the extreme values ​​of power grid equipment are extracted, and behavioral causal feature variables characterizing the thermal inertia and economic game of the power grid are extracted based on the continuously changing expectation vector pool. A zero-inflated Poisson quantization model based on physical and economic anchors is constructed. The physical constraint feature variables are injected as extreme value penalty terms into the logistic regression process of the zero-inflated Poisson quantization model to determine the probability of structural zero values. The behavioral causal feature variables are injected into the Poisson counting process of the zero-inflated Poisson quantization model to determine the expected mean. The parameters of the zero-inflation Poisson quantization model are solved using the maximum likelihood estimation method with gradient constraints of economic arbitrage homogeneity, and dynamic self-healing redistribution of model features is triggered based on the statistical decay protocol. The absolute probability of a company engaging in temporary business expansion is calculated using the solved zero-inflated Poisson quantization model, and a real-time warning result with reverse causal traceability is output when the warning threshold is exceeded.

2. The temporary quantization method based on the zero-inflated Poisson model according to claim 1, characterized in that, In the mechanism for forcibly orthogonally partitioning data in the construction of a dual random process: The absolute zero state vector pool specifically stores variables that determine whether an enterprise has the objective physical basis and financial boundaries for business expansion. The continuously changing expectation vector pool specifically stores variables that determine the specific intensity of business expansion chosen by the enterprise under the premise of having the capability. Before data partitioning, physical invariance verification and outlier correction are performed. The capping method correction function is used to replace extreme outliers that deviate from the historical mean by more than the standard deviation threshold. Cross-domain dimensional standardization is performed on heterogeneous features to block collinear interference between zero-inflation features and non-zero features.

3. The temporary quantization method based on the zero-inflated Poisson model according to claim 1, characterized in that, The physical constraint characteristic variables include the transformer physical capacity redundancy limit, which is obtained by calculating the difference between the total rated nameplate capacity of the transformers currently in operation and the maximum measured peak active load after conversion by the average power factor in the past multiple sampling periods, and then dividing it by the total rated nameplate capacity. The behavioral causal characteristic variables include the standard deviation of physical load margin based on transformer temperature rise thermal inertia. This is obtained by calculating the difference between the product of the transformer's total rated capacity and the transformer's temperature rise thermal inertia coefficient and the actual apparent power within a set rolling window period, and then extracting the standard deviation of the difference sequence from the mean.

4. The temporary quantization method based on the zero-inflated Poisson model according to claim 3, characterized in that, The behavioral causal characteristic variables also include the expected difference in basic electricity arbitrage and the bounded rationality text mapping value based on the Poisson coefficient of variation: The expected difference in basic electricity arbitrage is obtained by multiplying the target change capacity value actually reported to the power grid by the basic electricity standard unit price of the transformer capacity, and then subtracting the implicit friction costs covering switching outages and equipment storage. The bounded rationality text mapping value based on Poisson variation coefficient is obtained by capturing the thousandth percentile standardized word frequency of short-term performance-oriented keywords in corporate disclosure texts and through a nonlinear transformation mapping equation with intercept term bias parameter and feature mapping weight parameter.

5. The temporary quantization method based on the zero-inflated Poisson model according to claim 4, characterized in that, In constructing the zero-inflated Poisson quantization model based on physical and economic anchors: The logarithmic probability equation of the state probability in the logistic regression process is forcibly introduced with a logarithmic extreme value penalty term of the physical capacity redundancy limit of the transformer. When the physical capacity redundancy limit of the transformer approaches zero, the probability of the enterprise falling into a structural zero state in which it is unable to make any internal flexible business expansion adjustments is forcibly pushed to 100%. The log-linear correlation equation of the Poisson counting process introduces an indicator function constructed from the expected difference in basic electricity arbitrage. The indicator function activates the positive amplification weight of the bounded rationality text mapping value based on the Poisson coefficient of variation to the expected mean of Poisson if and only if the expected difference in basic electricity arbitrage is greater than zero.

6. The temporary quantization method based on the zero-inflated Poisson model according to claim 5, characterized in that, The method utilizes maximum likelihood estimation with gradient constraints based on economic arbitrage directionality to solve for the parameters of the zero-inflation Poisson quantization model, and triggers dynamic self-healing redistribution of model features based on a statistical decay protocol. The aforementioned arbitrage unidirectional gradient constraint means that when calculating the first-order partial derivative of the objective log-likelihood function with respect to the short-sighted bias activation weights using the quasi-Newton method, the verification is performed through the indicator function, allowing gradient updates only when the expected difference in basic electricity arbitrage is greater than zero; otherwise, the gradient update for this dimension is forced to zero. The dynamic self-healing redistribution based on the statistical decay protocol triggering model features refers to the calculation of the comprehensive Vuong statistic with a fixed step size. When the moving average of the comprehensive Vuong statistic for multiple consecutive periods is detected to decay below the warning line, the hard boundary between the absolute zero state vector pool and the continuously changing expectation vector pool is opened up. The failed macroscopic environmental resistance features are degraded and released into the Poisson counting process, and the marginal contribution is re-evaluated through the Lagrange multiplier method to complete the reshaping of the causal chain.

7. The temporary quantization method based on the zero-inflated Poisson model according to claim 4, characterized in that, After the parameters are solved, sub-models of the zero-inflation Poisson quantization model are independently constructed and solved for different business expansion behaviors. The business types include at least temporary power supply for high-voltage meter installation, capacity reduction and recovery, temporary capacity reduction, suspension, suspension and recovery, and temporary power supply for meter installation. Among them, for temporary power supply for high-voltage meter installation, the partial regression coefficients of the physical load margin standard deviation based on the thermal inertia of transformer temperature rise are extracted for physical limit quantization analysis, and for temporary capacity reduction, the partial regression coefficients of the bounded rationality text mapping values ​​based on the Poisson coefficient of variation are extracted for economic arbitrage quantization analysis.

8. The temporary quantization method based on the zero-inflated Poisson model according to claim 6, characterized in that, The output includes real-time early warning results with reverse causal tracing: The reverse causal tracing refers to the system automatically extracting the primary contributing factor that causes the sharp increase in probability when the calculated absolute probability exceeds the warning threshold, and marking in the structured warning report whether the primary contributing factor is due to the compression of the thermal inertia margin of the underlying equipment or the activation of the short-sighted bias of corporate executives under the existence of real arbitrage space. If the dynamic self-healing reassignment is triggered during system operation, a macro-constraint degradation flag is added to the report.

9. A temporary quantization system based on a zero-inflated Poisson model, used to implement the method as described in any one of claims 1-8, characterized in that, The system includes: The data forced isolation module is used to construct a forced orthogonal partitioning mechanism for dual random process data, which physically isolates the accessed multi-source data into an absolute zero state vector pool and a continuously changing expectation vector pool. The causal feature mining module is used to extract physical constraint feature variables representing the extreme values ​​of power grid equipment based on the absolute zero state vector pool, and to extract behavioral causal feature variables representing the thermal inertia and economic game of the power grid based on the continuously changing expectation vector pool. The physical anchoring model construction module is used to construct a zero-inflated Poisson quantization model based on physical and economic anchors, and to inject the physical constraint feature variables and behavioral causal feature variables into the logistic regression process and the Poisson counting process, respectively. The parameter self-healing solution module is used to solve parameters using the maximum likelihood estimation method with economic arbitrage directional gradient constraints, and to trigger dynamic self-healing redistribution of model features based on the statistical decay protocol. The closed-loop early warning decision module is used to calculate the absolute probability of a company engaging in temporary business expansion activities, and outputs real-time early warning results with reverse causal tracing when the early warning threshold is exceeded.

10. A temporary quantization system based on a zero-inflated Poisson model according to claim 9, characterized in that: The parameter self-healing solution module runs in a central processing unit configured with a floating-point arithmetic unit that supports advanced vector extension instruction sets. When calculating the second-order partial derivative approximation matrix of the target log-likelihood function, it performs task-level parallel splitting through a multi-threaded framework and supports non-collinear concurrent reading of the absolute zero state vector pool and the continuously changing expectation vector pool under a hierarchical architecture of redundant independent disk arrays.

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