A dynamic quantitative assessment method and system for risks of multi-business enterprises

Abstract

This application relates to a dynamic quantitative assessment method and system for risks of multi-business enterprises, aiming to solve the problems of existing technologies having a single assessment dimension, static assessment, and lack of cross-industry compatibility. The method includes: real-time acquisition of multi-source heterogeneous data and standardized preprocessing; construction of a knowledge base that combines relational and graph databases to achieve digital mapping of multi-business characteristics; performing three-dimensional probability quantification by combining real-time monitoring, historical statistics, and industry benchmarks, dynamically updating weights using Pearson correlation coefficients, and introducing Bayesian model optimization; and significantly improving the accuracy of risk identification and real-time early warning capabilities in multi-business scenarios through the collaboration of the perception layer, data layer, computing layer, and application layer, achieving continuous self-learning and closed-loop optimization of the system.

Description

Technical Field

[0001] This invention belongs to the field of computer technology, specifically relating to a dynamic quantitative assessment method and system for risks of multi-business enterprises. Background Technology

[0002] With the intensification of market competition and the expansion of business scale, the risk management capabilities of comprehensive group enterprises have become a key element to ensure their sustainable development. Such enterprises are usually involved in various business fields such as engineering construction, construction waste disposal and project operation. The complexity and correlation of business logic put forward extremely high requirements for the accuracy of risk identification and quantitative assessment.

[0003] Existing enterprise risk assessment technologies face numerous challenges in multi-business application scenarios: First, risk assessment dimensions are too limited, relying mainly on isolated indicators from specific fields, failing to build a comprehensive evaluation system covering the entire chain of multiple business sectors; second, the assessment process exhibits significant static characteristics, lacking the ability to dynamically process IoT sensing data and external environmental information, making it difficult to capture instantaneous risk fluctuations in business operations; third, risk quantification models lack cross-industry compatibility and dynamic adaptability, failing to effectively align the feature dimensions of multi-source heterogeneous data, resulting in a serious disconnect between assessment results and the actual risk situation of enterprises; finally, the system lacks self-evolution and iteration mechanisms, making it difficult to autonomously optimize through historical feedback and intelligent algorithms when facing the access of new business sectors or drastic environmental changes, easily leading to false alarms or missed alarms. These problems collectively limit enterprises' ability to make scientific decisions in complex environments.

[0004] In view of the shortcomings of traditional risk assessment techniques, such as single assessment dimensions, insufficient real-time response capability, poor model adaptability, and lack of closed-loop optimization mechanism, the dynamic quantitative assessment method and system for risks of multi-business enterprises proposed in this invention are particularly important. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic quantitative assessment method and system for risks of multi-business enterprises, which can effectively solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A dynamic quantitative assessment method for risks in multi-business enterprises includes the following specific steps: Step 1: Constructing a multi-source heterogeneous data perception and preprocessing system: By deploying multimodal IoT sensors in physical scenarios of different business formats, internal perception data containing physical state signals is collected in real time. External perception data including weather warnings, policy and regulatory changes, and industry safety benchmarks is accessed through the application programming interface of a descriptive state transition architecture. The original signals are filtered, denoised, and format-standardized using a standard score standardization algorithm, transforming the multi-business heterogeneous data into a standardized digital signal stream. Step 2: Establishing a multi-dimensional hybrid storage and association mapping knowledge base: A hybrid architecture combining relational databases and graph databases is adopted to structurally store historical risk event case libraries, industry safety standard libraries, risk type dimension libraries, and impact level dimension libraries. By constructing entity relationship mappings between business formats, risk types, feature dimensions, and benchmark parameters, rapid association retrieval and digital mapping of multi-business risk characteristics are achieved. Step 3: Performing dynamic quantitative calculation of three-dimensional risk probability: Based on real-time state monitoring indices, A three-dimensional assessment model is constructed by comparing historical risk statistical indices with industry benchmark indices. An improved Pearson correlation coefficient method is used to dynamically generate real-time weight coefficients based on the company's accumulated data. A Bayesian network model is introduced to map the comprehensive risk index into a probability score by calculating posterior probabilities, achieving autonomous optimization of the assessment logic. Step 4 performs risk severity and business sensitivity assessment: a risk scenario feature vector is constructed using a word frequency inverse file frequency algorithm and one-hot encoding technology. A weighted nearest neighbor algorithm is used to match similar cases in the historical case library to predict the physical loss score. Simultaneously, a business sensitivity parameter is calculated based on a linear regression model. The physical loss score is multiplied by the business sensitivity parameter to obtain the final severity score. Step 5 implements complex event processing and dynamic iterative optimization: a spatiotemporal alignment mechanism is established using complex event processing technology. Time alignment, consistency verification, and frequency adaptive processing are performed on asynchronously arriving multi-source heterogeneous data. A pre-defined rule tree structure is used to determine whether a risk reassessment is triggered, and the assessment results and handling feedback are fed back to the historical library to drive continuous iteration of the algorithm model.

[0007] Preferably, the internal sensing data acquisition process in step 1 employs differentiated deployment strategies for different business scenarios. In the engineering construction scenario, settlement sensors and tilt sensors are prioritized for monitoring structural stability. In the construction waste recycling scenario, vibration sensors and dust concentration sensors are prioritized for monitoring equipment operating status. A sensor calibration module performs uniform accuracy processing on sensor data from different business scenarios to ensure the reliability of the original signals. The external sensing data access interface has a built-in timeout reconnection mechanism to ensure the continuity of the data stream, and data transmission uses an object representation format.

[0008] Preferably, the specific logic of data preprocessing in step 1 includes: for continuous sensor data, outliers are removed using preset statistical criteria, and extreme false alarm data from sensors are identified and filtered. For unstructured text data, keywords are extracted using a word frequency inverse document frequency algorithm from natural language processing, and these keywords are converted into structured feature vectors. For data from the same source across different business sectors, dimensional normalization is used to eliminate dimensional differences in cross-sector comparisons, transforming physical quantities into a unified format signal that can be directly accessed by upper-level computing units.

[0009] Preferably, in step 2, the relational database uses a structured query language database, whose stored historical risk event case library includes fields such as case identifier, business type, risk type, feature vector, physical loss, occurrence time, and processing result. The graph database adopts a native graph storage architecture, and its constructed risk type dimension library defines the risk classification system for each business type, associating each risk type with a specific feature dimension. The impact hierarchy dimension library stores the risk consequence mapping relationship and business type sensitivity benchmark parameters under different business types, supporting fast association query of multi-business type data through graph traversal algorithms.

[0010] Preferably, in step 2, the rule base adopts a combination of key-value pair in-memory database and disk database for persistent storage. The in-memory database is used to store the real-time weight coefficients and trigger thresholds of high-frequency access, while the persistent layer is used to store all rule data and support dynamic updates and version management of rules.

[0011] Preferably, the calculation logic for the real-time status monitoring index in step 3 involves comparing the real-time sensor readings with preset safety thresholds, warning thresholds, and high-risk thresholds, and mapping them to a predetermined numerical range using a piecewise function. The system automatically matches corresponding threshold parameters for different business types from the influence hierarchy dimension library. The historical risk statistics index is calculated based on the frequency of occurrence of the assessed object or similar objects in historical risk events, and an improved weighted nearest neighbor algorithm is used to find similar cases in the historical case library. When calculating case similarity, a business type similarity weight is introduced, and different preset weight values ​​are set according to the correlation between the business type of the case and the target business type. The accuracy of the matching results is ensured by calculating the feature distance.

[0012] Preferably, in step 3, the industry benchmark comparison index is used to compare the current state of the object with the industry average or lifecycle model. For equipment assets, in the engineering construction industry, the runtime is compared with the industry average mean time between failures (MTBF). In the construction waste recycling industry, the equipment failure rate is compared with the industry average failure rate.

[0013] Preferably, the dynamic generation algorithm for the weight coefficients in step 3 is activated after the enterprise's historical data accumulation reaches a preset sample size threshold. The system uses an improved Pearson correlation coefficient to calculate the correlation between real-time data fluctuations, historical failure frequencies, industry benchmark deviations, and accident rate sequences. A dimensional correction term is introduced during the calculation process to eliminate the influence of heterogeneous data. The correlation coefficients for each dimension are obtained by calculating the covariance and standard deviation, and the final weight coefficients are generated after absolute value conversion and normalization. The system maintains an independent weight system for each business type and each specific risk type, and performs automatic updates according to a predetermined time period. An emergency update mechanism is triggered if a major risk event occurs.

[0014] Preferably, the Bayesian network model in step 3 employs a two-stage scoring strategy. In the stage without historical data, a preliminary evaluation is performed using an industry-default weighting and fixed threshold mapping table. During the data accumulation stage, the system learns prior probabilities from historical data and statistically analyzes the distribution of the comprehensive risk index across different score intervals. For multi-business scenarios, prior probabilities are learned separately for each business type to avoid cross-business interference. When a new comprehensive risk index is generated, the system uses a preset probability distribution model to fit historical data, calculates the posterior probability of the current value at each risk level, and selects the level with the highest posterior probability as the final probability score. If probabilities are tied, a business type sensitivity parameter is introduced for weighted judgment.

[0015] Preferably, the feature vector construction process in step 4 includes numerical conversion of classification features such as equipment type and working environment using one-hot encoding; normalization of continuous features such as personnel density and equipment operating time; and keyword extraction and conversion of textual features such as work logs and accident descriptions into high-dimensional vectors. When assessing specific risks, the feature vector fully covers dimensions such as geological type, support type, depth, personnel density, and historical similar events, ensuring that heterogeneous data can uniformly participate in the weighted nearest neighbor algorithm calculation.

[0016] Preferably, in step 4, the weighted nearest neighbor algorithm determines scene similarity by calculating weighted Euclidean distance and introduces a business type feature weight penalty term. Feature weights are automatically assigned according to business type. The algorithm selects a predetermined number of cases with the shortest distance, assigns voting weights based on the reciprocal of the distance, and performs weighted voting on the physical loss scores within a preset scoring interval, using the case with the highest total weight as the prediction result.

[0017] Preferably, in step 4, the physical loss score is automatically mapped from multiple dimensions, including casualties, economic losses, social impact, and business interruption. The casualty dimension is determined using a predetermined version of a target detection algorithm combined with personnel density sensors and video analysis. The economic loss dimension is calculated using an asset ledger interface and a loss accounting model. The social impact dimension is determined by obtaining media exposure data through a public opinion monitoring interface. The business interruption dimension is determined by obtaining the core system downtime data through a business system interface.

[0018] Preferably, the calculation of the business format sensitivity parameter in step 4 is divided into two stages: industry benchmark generation and enterprise dynamic adjustment. The industry benchmark is extracted from an authoritative industry database and outliers are removed using preset statistical criteria and the isolated forest algorithm. A probability distribution model is fitted using maximum likelihood estimation, and a specific statistical value is selected as the benchmark value. In the enterprise dynamic adjustment stage, the correlation between physical loss and actual business impact is established based on a linear regression model, and regression coefficients are calculated. Finally, the business format sensitivity parameter is determined by the ratio of the adjusted benchmark parameter to the original benchmark parameter, and a business format weight adjustment term is introduced for adaptation and optimization.

[0019] Preferably, the spatiotemporal alignment mechanism of the complex event processing technology in step 5 uses Coordinated Universal Time (UTC) as the main timeline, sets a configurable sliding time window, and sets an appropriate preset window duration according to different business formats. A timestamp sorting and caching mechanism is used for out-of-order data, and sorting is completed in a temporary queue. A latency compensation algorithm is used for calibration of asynchronous delayed data.

[0020] Preferably, in step 5, the triggering engine uses a rule tree structure for consistency verification, with rule tree nodes linked by logical operators. When a linked event meets the triggering condition, a reassessment signal is sent to the computation layer. The reassessment process employs a parallel computing mechanism, prioritizing high-risk units. The assessment results are pushed to the management terminal in real time via a message queue, driving the application layer to generate a dynamic risk heatmap. The heatmap marks risk hotspots on a map via a geographic information system interface, supporting color gradient display and multi-dimensional filtering.

[0021] Preferably, the application layer communicates with the computing layer and data layer via a remote procedure call interface, and the data transmission process is encrypted using a high-level encryption standard with a preset bit width. The system has a built-in parameter verification mechanism that automatically verifies the accuracy of business-related sensitivity parameters at predetermined intervals. If the deviation exceeds a preset deviation threshold, a parameter recalculation process is triggered, forming a complete closed loop of data collection, evaluation, feedback, and optimization.

[0022] Compared with the prior art, the present invention has the following beneficial effects: 1. Significantly improves the accuracy of risk assessment in multi-business scenarios This invention effectively solves the bottleneck problem of data alignment in traditional technologies when facing multiple business formats by constructing a unified feature encoding and dimensional normalization scheme for multi-source heterogeneous data. By introducing business format sensitivity parameters and penalty terms, the risk quantification model can be deeply adapted to the physical characteristics and business logic of different industries, avoiding evaluation distortion caused by applying a single algorithm model. Practical application verification shows that this invention significantly improves the accuracy of risk identification in complex multi-business scenarios and greatly reduces the error in determining critical values.

[0023] 2. Extremely strong data-driven dynamic response and real-time early warning capabilities This invention leverages the IoT sensing layer and complex event processing technology to achieve a fundamental shift from periodic manual assessment to continuous dynamic monitoring. Through a dedicated spatiotemporal alignment mechanism and latency compensation algorithm, the system can handle asynchronous, heterogeneous, and out-of-order arriving real-time monitoring data, ensuring the timeliness of risk assessment. The dynamic triggering engine can initiate a reassessment process very quickly after a risk event occurs. Combined with a message queue push mechanism, this allows enterprise managers to obtain sufficient advance warning to identify potential high-risk situations, significantly shortening the time cycle from risk discovery to decision-making and response.

[0024] 3. Excellent system self-learning and model iterative optimization features The dynamic weight generation mechanism and Bayesian network cold-start strategy designed in this invention endow the system with powerful self-evolution capabilities. Through transfer learning technology, the cold-start challenge of lacking historical data support in the initial stage of new business model integration is solved, ensuring that the system can provide reliable evaluation results from the initial phase. As the enterprise's own data accumulates, the system can automatically update the algorithm weights using an improved Pearson correlation coefficient, allowing the model to continuously align with the enterprise's actual risk characteristics. The closed-loop feedback mechanism drives continuous model iteration, ensuring the long-term effectiveness of the evaluation logic in volatile environments.

[0025] 4. High degree of technology applicability and cross-industry adaptability This invention employs a hybrid storage architecture, clearly defining standardized interface protocols between each layer to ensure the system's stability and scalability under large-scale concurrent data processing. The visualization module combines geographic information systems with color gradient mapping to transform complex risk quantification values ​​into intuitive dynamic heatmaps and priority lists. This method is not only suitable for diversified enterprises but can also be seamlessly extended to various multi-business sectors, providing a standardized technical support solution for risk management in multi-business enterprises. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the overall technical solution architecture of the dynamic quantitative assessment method for risks of multi-business enterprises proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework for the dynamic quantitative calculation and severity assessment of three-dimensional risk probability in this invention; Figure 3 This is a logical flowchart of the multi-source heterogeneous data perception preprocessing and multi-dimensional hybrid storage association mapping in this invention. Detailed Implementation

[0028] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0029] Example 1 refer to Figures 1 to 3 This embodiment constructs a dynamic quantitative assessment system for risks of multi-business enterprises. Its hardware architecture adopts a layered distributed deployment mode, aiming to provide comprehensive group enterprises with real-time risk monitoring capabilities covering multiple business formats such as engineering construction and construction waste resource disposal.

[0030] At the system architecture level, the physical sensing layer in this embodiment deploys high-precision capacitive settlement sensors and microelectromechanical system (MEMS) tilt sensors in the engineering construction scenario to monitor the structural stability of the foundation pit and surrounding buildings. The settlement sensor's measurement range is set to 0 to 500 mm, with a resolution of 0.01 mm, and it is connected to the edge computing gateway via an RS485 bus. In the construction waste recycling scenario, the system deploys triaxial piezoelectric vibration sensors and laser scattering dust concentration sensors. The vibration sensor's frequency response range is 0.5 to 10 kHz, used to monitor the operating status of core equipment such as crushers. Each sensing terminal connects to the local area network through an industrial-grade wireless gateway. The gateway integrates a 16-bit analog-to-digital converter chip to ensure that physical signals are converted into high-fidelity digital quantities.

[0031] The system's computing layer is deployed on a high-performance server cluster, with the core processor using a multi-core Xeon series chip and configured with no less than 128GB of synchronous dynamic random access memory. To support computer vision analysis, the system also integrates a graphics processor with a tensor processing unit, specifically for running object detection algorithms. The data layer adopts a hybrid storage scheme, where a relational database cluster is responsible for storing structured historical risk cases, and its data table structure includes fields such as unique case identifier, business type code, risk level, loss amount, and handling time; a native graph database is used to construct a risk knowledge graph, with nodes representing risk entities and edges representing the evolutionary relationships and sensitivity mappings between risks; and an in-memory database is used to cache frequently changing real-time weight coefficients and trigger thresholds to ensure the system's millisecond-level response speed.

[0032] At the workflow deduction level, the system achieves dynamic quantitative assessment of risks across multiple business sectors through the following steps: The first step is multi-source heterogeneous data sensing and preprocessing. The data acquisition module first obtains raw current or voltage signals from various sensors through the edge gateway. For engineering construction scenarios, the system collects settlement data every 60 seconds; for equipment operation scenarios, the sampling frequency is increased to 100 times per second. Simultaneously, the external sensing module uses the application programming interface of the expressive state transition architecture to access typhoon and rainstorm warning data issued by the meteorological bureau once per hour, and simultaneously captures updated safety production regulations from the industry regulatory platform. After receiving these raw signals, the data preprocessing unit first calls the standard fractional normalization algorithm. This algorithm calculates the mean and standard deviation of the signal within a sliding window, converting physical quantities of different dimensions (such as millimeters, milligrams per cubic meter, and degrees Celsius) into dimensionless standardized values. To address potential pulse interference from sensors, the preprocessing unit implements a 3-standard-deviation criterion; if the instantaneous signal value deviates from the mean by more than 3 standard deviations, it is judged as a false alarm and discarded, ensuring that the data input to the computation layer has high confidence.

[0033] The second step involves establishing a multi-dimensional hybrid storage and association mapping knowledge base. The system guides the preprocessed data stream to different storage paths based on business type. The relational database uses structured query language to classify and archive historical risk events. The graph database executes entity extraction logic, associating the risk node "foundation pit collapse" with feature nodes such as "soft soil geology," "support failure," and "heavy rainfall." The impact hierarchy dimension library defines the tolerance levels of different business types to the same risk through preset business type sensitivity parameters. For example, in the engineering construction business, a settlement rate exceeding 5 mm per day is set as extremely high, while in a typical logistics park business, the same level of ground settlement is mapped as medium to low risk.

[0034] The third step involves performing dynamic quantification calculations of three-dimensional risk probability. The risk quantification engine constructs three-dimensional spatial coordinates based on real-time status monitoring indices, historical risk statistical indices, and industry benchmark comparison indices. The real-time status monitoring index is calculated using a piecewise function; when sensor readings are within the safe threshold, the index is 0; when it exceeds the warning threshold but does not reach the high-risk threshold, the index increases linearly between 0.5 and 0.8. The dynamic generation of weighting coefficients is the core of this step, and the system uses a modified Pearson correlation coefficient method for calculation.

[0035] The system extracts real-time monitoring sequences, historical fault sequences, and industry benchmark sequences from the past 12 months from the historical database. Correlation coefficients are derived by calculating the covariance and standard deviation between each sequence and the actual accident incidence rate sequence. The weighting coefficient calculation formula is as follows:

[0036] in, These represent the correlation coefficients between the real-time, historical, and industry dimensions and the accident rate, respectively. This formula ensures that the sum of the weights for the three dimensions is always 1, and the dimension with higher correlation has a stronger dominant position in the evaluation. The calculated weight coefficients are written to the in-memory database in real time for use by the Bayesian network model in the next stage. The Bayesian model calculates the posterior probability by introducing the prior probability (i.e., the industry average risk rate) and the likelihood function (i.e., the probability of the current monitored value occurring).

[0037] The fourth step involves assessing the severity of the risk and its sensitivity to the business type. The system uses a word frequency-inverse document frequency algorithm to extract features from unstructured accident description text, generating a high-dimensional feature vector. A weighted nearest neighbor algorithm searches the historical case database for the five cases with the smallest Euclidean distance to the current scenario. To eliminate differences in business types, the system introduces a business type sensitivity parameter. This parameter is calculated based on a linear regression model, with physical loss as the independent variable and the actual duration of business interruption as the dependent variable. If the regression coefficient k is greater than 0, it indicates that the company has a weaker risk resistance under that business type, and the system will automatically amplify the severity score.

[0038] The fifth step involves implementing complex event handling and dynamic iterative optimization. The complex event handling engine uses Coordinated Universal Time (UTC) for spatiotemporal alignment. When the system simultaneously receives two heterogeneous events—a "Typhoon Blue Warning" and "Accelerated Foundation Pit Settlement"—within a 3-minute time window, the triggering engine determines whether they meet the reassessment conditions based on a preset logic rule tree. The rule tree employs a nested logic gate structure, supporting multiple conditions through AND, OR, and NOT combinations. Once a reassessment is triggered, the system calls the latest dynamic weights and Bayesian parameters, updating the comprehensive risk value within 500 milliseconds. The assessment results are pushed to the management terminal in real-time via a message queue and marked as red hotspots on the geographic information system map. After the handling is completed, the system automatically records the response time and actual losses, feeding this data back to the historical database and triggering the recalculation of the Pearson correlation coefficient, completing a full model self-evolution.

[0039] Example 2 Based on Example 1, this example further refines the specific configuration and deep computing logic of the system when dealing with the recycling and disposal of construction waste, especially in the application of unstructured data processing and cross-industry transfer learning.

[0040] In terms of system architecture, this embodiment adds industrial-grade visual perception units to the crushing production line, sorting workshop, and finished product warehouse of the treatment plant. The visual units employ infrared night vision cameras with 4K resolution and incorporate a high-performance embedded neural network processor. This processor is configured to run the YOLOv8 object detection algorithm to identify in real time whether workers are wearing safety helmets and whether foreign objects have intruded into the high-speed conveyor belt. The visual perception units transmit structured recognition results (such as personnel violation markers, object coordinates, and confidence scores) to the central server via a gigabit Ethernet interface.

[0041] At the data layer, this embodiment designs a cold start module based on transfer learning for newly integrated business scenarios. When the system connects to a brand-new construction waste treatment plant that has no historical accident data, the cold start module retrieves the existing node with the highest business similarity from the graph database. By calculating the cosine of the angle between the business feature vectors (including dimensions such as annual processing volume, equipment automation level, and average length of service of employees), the system automatically transfers the prior probability distribution of similar business types. This means that on the first day of the new system's launch, it can make risk assessments based on the "knowledge" accumulated in the same industry, solving the problem that traditional assessment models cannot work during periods of data scarcity.

[0042] This embodiment further expands the methodology by detailing the micro-processing of feature vector construction and the weighted nearest neighbor algorithm in step 4. Feature vector construction not only incorporates sensor values ​​but also introduces one-hot encoding technology to handle classification features. For example, equipment types are categorized as "mobile crusher," "stationary screening machine," and "magnetic separator," each corresponding to a different binary encoding sequence. For text-based maintenance logs, the system employs a word frequency inverse document frequency algorithm from natural language processing to extract high-weight keywords such as "bearing wear," "motor overheating," and "lubrication failure."

[0043] The weighted nearest neighbor algorithm performs similarity matching through the following logic: The system calculates the weighted Euclidean distance between the current risk feature vector and tens of thousands of case vectors in the historical database. In the distance calculation, for the construction waste disposal industry, the system artificially increases the weight of the "cumulative equipment operating time" dimension while decreasing the weight of "ambient humidity." The algorithm selects K nearest neighbor cases, and their physical loss scores (1 to 5) are weighted and voted on based on the reciprocal of the distance. The voting result not only provides a final score but also outputs a confidence interval.

[0044] This embodiment also details the optimization process of the Bayesian network model under multi-distribution fitting. Considering the data distribution characteristics of different business sectors, the system no longer uses a single Gaussian distribution. In engineering construction scenarios, due to the obvious continuity and trend of settlement data, the system uses a Gaussian distribution to fit its probability density function; while in equipment failure scenarios, due to the discreteness and suddenness of failure occurrence, the system automatically switches to a Poisson distribution model. This adaptive probability distribution fitting method calculates the posterior probability of the current comprehensive risk index at each risk level.

[0045] The mathematical expression for calculating the posterior probability is:

[0046] in, Let be the probability density function of the corresponding distribution. This represents the mean and standard deviation of the risk index for level i in historical data. By comparing the posterior probabilities corresponding to scores 1 to 5, the system selects the one with the highest probability as the final likelihood score. If two levels have similar probabilities, the system will use the business sensitivity parameter for a secondary judgment, prioritizing the level with the greater impact on the business to reflect the principle of "strict control".

[0047] Furthermore, this embodiment incorporates a parameter verification mechanism in the dynamic iteration phase of step 5. At the end of each quarter, the system automatically compares the predicted risk values ​​with the actual risk losses incurred over the past three months. If the deviation rate (i.e., the average absolute value of the difference between the predicted and actual risk levels) exceeds 15%, the system will automatically trigger a parameter recalibration procedure. This procedure re-executes linear regression analysis, updates the business type sensitivity parameters, and uses reinforcement learning algorithms to adjust the risk level classification thresholds. This closed-loop optimization ensures that the system can dynamically adjust to changes in the external economic environment, policy guidance, and the company's internal management level, maintaining the accuracy of its assessments at all times.

[0048] To more clearly illustrate the workflow and effects of this invention, and to apply the system architecture, algorithm model, and dynamic iteration mechanism proposed above to a specific scenario, the following uses a simulated "City Investment Group" as an example to demonstrate how the system conducts dynamic risk assessments on its subordinate "Deep Foundation Pit Project in City A" (engineering construction business) and "Construction Waste Resource Utilization Plant in City B" (construction waste disposal business). The focus is on demonstrating algorithm improvement, data alignment, and feature adaptation in multi-business scenarios, verifying the feasibility and effectiveness of this invention.

[0049] 1. System initialization and data loading Scenario Setting: The City Investment Group connects to this system and enters its business attributes (two business types: engineering construction and construction waste disposal). The system automatically identifies the business type through a business attribute recognition algorithm and loads the corresponding risk list, feature dimensions, and initial business type sensitivity parameters from the "model knowledge base" (graph database). For example, for the "Deep Foundation Pit Project in City A," risk types such as "foundation pit collapse" and "fall from height" are loaded, with corresponding feature dimensions of "geological conditions, support type, and settlement rate," and an initial business type sensitivity parameter of 1.2. For the "Treatment Plant in City B," risk types such as "equipment mechanical injury" and "dust explosion" are loaded, with corresponding feature dimensions of "equipment vibration, dust concentration, and operating time," and an initial business type sensitivity parameter of 0.99. Since the Treatment Plant in City B is a new business type with no historical data, the system adopts a transfer learning strategy to transfer prior probability parameters from similar construction waste disposal companies to initialize the digital twin model.

[0050] Digital Twin Construction: The system creates digital twin models for these two projects, each model containing multi-dimensional features such as equipment, scenarios, and personnel, and synchronizes with the actual scenarios in real time. The system begins to continuously receive real-time data streams from its deployed sensors (such as settlement monitoring instruments in the foundation pit, with a sampling frequency of 1 time / minute; vibration sensors in the crusher of the processing plant, with a sampling frequency of 10 times / minute), as well as early warning information from the meteorological bureaus of cities A and B subscribed to via API (updated once per hour). After the sensor data is preprocessed by the module (dimensional normalization and outlier removal), it is cached in the data layer.

[0051] 2. Initial risk assessment under normal circumstances After the system completes initialization and data loading, it enters the normal assessment mode. For the two projects with different business formats, the corresponding algorithm models and parameters are called to complete the initial risk quantification assessment. The specific calculation process is as follows: Calculate the comprehensive risk index (comprehensive). The system calculates the risk of "foundation pit collapse" for the "Deep Foundation Pit Project in City A": Real-time: The current settlement rate at the monitoring point on the east side of the foundation pit is 0.5 mm / day, which is lower than the warning threshold of 2 mm / day, so real-time = 0.

[0052] History: According to the group's historical database, the frequency of foundation pit accidents under similar geological conditions is f=0.05%, therefore history=0.3.

[0053] Industry: Compared with the industry database, the average safety period for similar foundation pits is 180 days. This project has been under construction for 150 days. Industry = min(1.0, 150 / 180) ≈ 0.83.

[0054] Dynamic weight calculation: The system calls the enterprise's historical data and uses the improved Pearson correlation coefficient to calculate the correlation coefficient of the risk of "engineering construction - foundation pit collapse": r real-time = 0.6, r historical = 0.3, r industry = 0.1.

[0055] Calculate the total: Total = 0.60 + 0.30.3 + 0.1 × 0.83 = 0.173.

[0056] Calculate the risk probability score: Initially, the system fixed the threshold according to Table 1, and the overall score was 0.173 < 0.2, with a probability score of 1 (extremely unlikely).

[0057] As data accumulates, the system activates a Bayesian network. Assuming historical data shows that when the overall probability is approximately 0.173, the posterior probability P(rank=1|overall) = 85% and P(rank=2) = 15%. The system still classifies the probability as 1, awarding 1 point.

[0058] Calculate the severity score: Physical loss score: The system extracts the feature vector of the current scene (geology: soft soil; support: pile anchor; depth: 15 meters, etc.). By matching in the historical case database using the weighted KNN algorithm and through weighted voting, the most likely consequence is predicted to be "local collapse, causing 1 serious injury and economic loss of about 800,000 yuan", corresponding to Table 2, with a physical loss score of 4 points.

[0059] Business Format Sensitivity Parameter: The system retrieves the corrected baseline parameter for "Engineering Construction - Foundation Pit Collapse" from the "Impact Level Dimension Library," which is assumed to be 1.2 (indicating that the company's sensitivity to foundation pit accident losses is 20% higher than the industry average).

[0060] Comparison method calculation process (example): Taking "City Investment Group" as an example, the system has been running for some time and has accumulated some historical data. Now it is necessary to calculate the business sensitivity parameter λ for its "Engineering Construction" business (taking "foundation pit collapse" risk as an example) and "Construction Waste Disposal" business (taking "equipment failure" risk as an example).

[0061] Step 1: Determine the industry benchmark weight (k0) This data comes from authoritative industry statistics and is loaded from the "Influence Level Dimension Library" during system initialization.

[0062] Regarding “Engineering Construction - Foundation Pit Collapse”: According to industry white paper statistics, losses in this business are prone to triggering chain reactions (such as construction delays, claims, and social attention), therefore, its industry benchmark weight k0 construction is set at 1.1.

[0063] For “Construction Waste Disposal - Equipment Failure”: According to industry reports, the operation process of this business is relatively independent and the scope of the failure is relatively controllable. Therefore, its industry benchmark weight k0 disposal is set to 0.9.

[0064] ("Based on the business characteristics of different business formats, a business format weight adjustment term k0 is introduced (engineering construction k0=1.1, construction waste disposal k0=0.9)") Step 2: Calculate the enterprise-specific adjustment factor (k) This coefficient is derived by analyzing the company's own historical data. Assume the urban investment group's risk case database contains the following records: Historical data on engineering construction business (used to calculate k construction): We extracted data pairs from three historical "foundation pit collapse" risk events (physical loss score X, actual business impact Y). Here, the business impact Y is quantified as the number of weeks of project delay.

[0065] The data pairs are: (X1=4, Y1=5), (X2=3, Y2=3), (X3=5, Y3=8). Among them, X=4 represents "1 person seriously injured, 800,000 in losses" (see Table 2 in the document).

[0066] Calculation process: Mean: Xˉ=(4+3+5) / 3=4.0, Yˉ=(5+3+8) / 3=5.33 Covariance: cov(X,Y)=[(4) 4) (5 5.33)+(3 4) (3 5.33)+(5 4) (8 5.33)] / (3 1) = 4.33 / 2 = 2.165 Variance of X: σX² = [(4 4)2+(3 4)2+(5 4)2] / (3 1) = 2 / 2 = 1.0 Correction factor: kconstruction = cov(X,Y) / σX2 = 2.165 / 1.0 = 2.165 Interpretation of results: kconstruction = 2.165 > 0, indicating that in the engineering construction business, the actual business impact (delay) of the city investment group is exacerbated by physical losses much faster than the industry average, and its ability to withstand such losses is relatively weak.

[0067] Historical data on construction waste disposal businesses (used to calculate k-disposal): Extract three historical "equipment failure" risk event data pairs, and quantify the business impact Y as the direct revenue loss (ten thousand yuan) caused by production interruption.

[0068] The data pairs are: (X1=2, Y1=1), (X2=3, Y2=1.5), (X3=1, Y3=0.5).

[0069] Calculation process: Mean: Xˉ=2.0, Yˉ=1.0 Covariance: cov(X,Y)=[(2 2) (1 1)+(3 2) (1.5 1)+(1 2) (0.5 1)] / 2=1.0 / 2=0.5 Variance of X: σX² = [(2 2)2+(3 2)2+(1 2)2] / 2=2 / 2=1.0 Correction factor: ktreatment = 0.5 / 1.0 = 0.5 Interpretation of results: k_disposal = 0.5 > 0, but the value is relatively small, indicating that in the construction waste disposal industry, the business impact increases as the loss expands, but the trend is more gradual than that of engineering construction.

[0070] ("A linear regression model for physical loss X and actual business impact Y based on the impact hierarchy dimension library: Y=kX+b" and the formula for calculating the correlation coefficient) Step 3: Synthesize the final business type sensitivity parameter (λ) and apply it. Substituting the above calculation results into the formula λ=(1+k)×k0: Regarding "construction project - foundation pit collapse": λconstruction = (1 + 2.165) × 1.1 = 3.165 × 1.1 = 3.48 The system stores this value 3.48 in the "Impact Level Dimension Library", with the key being Business Type: Engineering Construction and Risk: Foundation Pit Collapse.

[0071] Regarding "Construction waste disposal - equipment failure": λ_disposal = (1 + 0.5) × 0.9 = 1.5 × 0.9 = 1.35 The system stores this value 1.35 in the database, with the key "Business Type: Construction Waste Disposal" and the risk "Equipment Failure".

[0072] Step 4: Present differentiated effects in risk assessment Suppose that at a certain moment, the system predicts a certain risk scenario for two business types, and their physical loss scores are both 4 points after being matched by the KNN algorithm (corresponding to "1 person seriously injured, economic loss of 800,000 yuan").

[0073] Before calibration: If business type differences are not considered, the severity score for both is 4.

[0074] After calibration: The system calls its respective λ value for calculation.

[0075] The severity score for the construction industry is 4 × 3.48 = 13.92 points. Severity score for construction waste disposal business = 4 × 1.35 = 5.4 points Final risk composite score (assuming all probability scores are 3): The risk value of the project construction is 3 × 13.92 = 41.76 points, which is far beyond the high-risk threshold (assuming the threshold is 16 points).

[0076] Construction waste disposal risk value = 3 × 5.4 = 16.2 points → slightly higher than the high risk threshold.

[0077]

[0078] Severity score = 4 points × 1.2 = 4.8 points.

[0079] Risk composite score = probability score (1) × severity score (4.8) = 4.8 points.

[0080] Application layer response: For example, the risk level determination rules are: low risk: 1-5 points, medium risk: 6-15 points, high risk: >16 points. The system determines a score of 4.8 as "low risk," and this item is displayed as "green" on the group's risk dashboard. This item is located low in the risk ranking list.

[0081] 3. Dynamic Triggering and Reassessment After the routine assessment is completed, the system continuously monitors changes in multi-source data. When an anomaly occurs that may affect the risk level, the engine is dynamically triggered to start the reassessment process. The following example, using a deep foundation pit project in City A that encountered a typhoon warning, illustrates in detail the CEP spatiotemporal alignment mechanism and the reassessment process: Triggering scenario: The Meteorological Bureau of City A issues a blue typhoon warning (timestamp t1=UTC10:00), the settlement monitoring sensor of the deep foundation pit project in City A detects a sudden increase in the settlement rate to 3.2mm / day (timestamp t2=UTC10:02), the project management platform reports a work stoppage on site (timestamp t3=UTC10:04), and the three sets of data arrive at the system asynchronously.

[0082] Specific example of asynchronous data processing flow: Background: A deep foundation pit project in City A is facing typhoon weather, and data from three different sources arrives at the system asynchronously.

[0083] Step 1: Asynchronous data arrival from multiple sources Suppose data from three independent sources arrives at the system's data access layer at nearly the same time, but not simultaneously or sequentially, with different formats, frequencies, and network latency: Data A (External API, low frequency): Source: Early Warning Release Platform of City A Meteorological Bureau.

[0084] Content: {"event_id":"20240428001","type":"Typhoon Blue Warning","location":"City A, City","utc_timestamp":"2026-04-28T10:00:00Z","effective_radius_km":50} Features: Pushes data in JSON format via a RESTful API, with an update frequency of approximately once per hour. System timestamp: 10:00:05.

[0085] Data B (IoT sensor, high frequency): Source: Settlement monitoring sensors deployed on the east side of the foundation pit.

[0086] Content: Device ID: SENSOR_001, Measurement value: 3.2 mm / day, Status: Normal, Acquisition time: 2026-04-28T10:02:00Z Features: Continuous reporting via IoT protocol, sampling frequency is 1 time / minute. System timestamp: 10:02:30 (slight delay due to network jitter).

[0087] Data C (Business System, Event-Driven): Source: Group Project Management Platform.

[0088] Content: {"project_id":"PROJ_A","event":"Work has been suspended on site","report_time":"2026-04-28T10:04:15Z","reason":"Weather reasons"} Features: Triggered manually within the system, with asynchronous notification via an internal message queue. System arrival timestamp: 10:04:20.

[0089] Initial state: Three data points exist in isolation in the stream, all pointing to the same risk event, but the system has not yet associated them.

[0090] Step 2: CEP engine starts the space-time alignment mechanism The dynamic triggering engine (based on Complex Event Processing (CEP) technology) immediately processes these three data points: Time alignment (setting a sliding window): The engine reads the event timestamps (utc_timestamp, collection time, report_time) from the three data entries and converts them into a unified UTC timeline.

[0091] Based on the preset sliding time window (Δt=3 minutes) of the "Engineering Construction" business type, the engine determines an alignment window. The earliest event time 10:00:00 (typhoon warning time) is taken as the starting point of the window, and the window range is [10:00:00, 10:03:00].

[0092] Alignment result: Data A (10:00:00) is in the window.

[0093] Data B (10:02:00) is in the window.

[0094] Data C (10:04:15) is not in the window.

[0095] Handling out-of-order and delayed data: Data C's report_time (10:04:15) is more than 3 minutes later than the window end time. The engine startup delay compensation algorithm is used to determine this.

[0096] Upon verification, the report_time of data C is very close to the system arrival timestamp (10:04:20), which is a reasonable business delay and not old network data.

[0097] Since "work stoppage" is a direct consequence of the typhoon warning, and the delay time (1 minute and 15 seconds) is within an acceptable range of business latency (usually configurable, such as 5 minutes), the engine determines that this data is valid and calibrates its event time to around the window end boundary of 10:03:00 for correlation calculation, instead of discarding it directly. This ensures that critical consequence information is not lost.

[0098] Frequency adaptive sampling: Data B (sensor) is a high-frequency stream (1 data point / minute), while data A is updated at a low frequency (1 data point / hour). To perform pattern judgment within a unified logic, the engine downsamples data B.

[0099] Within this window, the engine only uses the reading of data B at 10:02:00 (3.2 mm / day) as the representative state within the window, ignoring the high-frequency fluctuations in the seconds before and after, so that its frequency matches the decision-making rhythm of weather warnings.

[0100] At this point, the three data points that arrived asynchronously in physical time and at different frequencies were logically aligned into the same decision time window, and the key feature extraction was completed.

[0101] Step 3: Consistency Verification and Trigger Decision The engine will match the aligned data features with the "complex trigger condition rule tree" preset in the rule base: Entity association: The engine successfully associated three data entries with the same risk unit: "Deep foundation pit project in City A - Foundation pit collapse risk" through fields such as project_id and location.

[0102] Rule tree evaluation: An example of the trigger condition rule tree structure is as follows: Root node (triggers re-evaluation) = Node 1 AND Node 2 Node 1 (External Warning) = Weather Warning Type IN("Typhoon Red Warning", "Typhoon Orange Warning", "Typhoon Blue Warning") Node 2 (Site Anomaly) = Real-time Settlement Rate >= Warning Threshold W (3mm / day) Condition matching: In data A, the "type": "Typhoon Blue Warning" satisfies node 1.

[0103] The measured value in data B is 3.2 mm / day, which satisfies node 2 (3.2 ≥ 3).

[0104] Although data C (on-site work stoppage) does not directly participate in rule judgment, it serves as strong corroboration, increasing the confidence of the triggered decision and may be used for subsequent log recording and result interpretation.

[0105] Trigger decision: The root node condition of the rule tree (node ​​1 AND node 2) is evaluated as True.

[0106] Step 4: Triggering Actions and Asynchronous Re-evaluation Once the triggering condition is met, the engine immediately performs the following actions: Sending asynchronous trigger signals: The engine sends an asynchronous message (such as through an internal message queue) to the "Risk Quantification Engine" in the computing layer. The message body contains: {"Instruction": "Immediate Reassessment", "target": "Deep Foundation Pit Project in City A - Foundation Pit Collapse", "trigger_event": "Typhoon Blue Warning + Settlement Exceeds Threshold", "window_data": {...}}.

[0107] Parallel Reassessment: Upon receiving the message, the computation layer does not need to wait for the next assessment cycle; it immediately schedules computational resources to initiate an emergency risk assessment of the specified target in parallel. The computation process will use the latest data (I is updated to 0.5 in real time) and the urgently updated weights.

[0108] Non-blocking operation: The entire triggering and evaluation process is asynchronous. During engine processing and computation, tasks such as system data access and routine evaluation of other projects will not be blocked and will continue to operate normally.

[0109] Step 5: Result Feedback and Closed-Loop Optimization (Asynchronous Backend Tasks) The assessment results are written asynchronously: After the reassessment is completed, the new risk level ("medium risk") and warning information are asynchronously pushed to the message queue of the application layer, which is consumed by the visualization module and the warning module to realize the dashboard refresh and warning push.

[0110] Asynchronous data feedback: All raw data, alignment logs, trigger judgments, evaluation results, and subsequent manual handling feedback for this triggered event are treated as a complete "event case," asynchronously packaged and structured by the background task, and stored in the historical case library and model training library.

[0111] Asynchronous model optimization: When such cases accumulate to a certain number, the system will automatically start a background incremental training task during off-peak business hours (such as at night), recalculate the Pearson correlation coefficient with new data, update the weights (α, β, γ), and may fine-tune the parameters of the Bayesian network to complete the self-iterative optimization of the model.

[0112] CEP spatiotemporal alignment processing: The dynamic trigger engine starts the spatiotemporal alignment mechanism, with UTC time as the main time axis, and sets a sliding time window Δt=3 minutes for the engineering construction industry, incorporating the three sets of data into the same time window; through the timestamp sorting and caching mechanism, it is confirmed that the data time sequence is not disordered and the delay time is ≤3 minutes, and the delay compensation algorithm is used to complete the time calibration; for heterogeneous frequency data (weather warning 1 hour / time, sensor 1 time / minute), the sensor data is downsampled and aligned with the frequency of the weather warning data to complete the data feature alignment.

[0113] Consistency verification and reassessment triggering: The engine confirms that all three sets of data point to the risk unit "City A Deep Foundation Pit Project - Foundation Pit Collapse" by matching the risk unit ID; it checks the rule tree triggering conditions (typhoon warning ≥ blue AND settlement rate ≥ warning threshold W = 3mm / day), and if the root node conditions are met, it immediately sends a reassessment trigger signal to the computing layer. The reassessment adopts a parallel computing mechanism.

[0114] Reassessment: The engine immediately sends a reassessment instruction to the computing layer regarding the risk of "foundation pit collapse" for the project.

[0115] Real-time update: Settlement rate is 3.2 mm / day, which is between the warning threshold W=3 mm / day and the high-risk threshold H=4 mm / day, so real-time = 0.5.

[0116] History: The improved KNN algorithm was used to match foundation pit cases in the same industry during typhoon weather. The accident frequency f=0.8%, so history=0.6.

[0117] Industry: During typhoon weather, the average settlement rate of similar foundation pits is 2.5 mm / day, and the current settlement rate is 3.2 mm / day. Industry = min(1.0, 3.2 / 2.5) = 1.0.

[0118] Dynamic weights: Due to the special event of a typhoon warning, the system triggers an emergency weight update, recalculating the weights using an improved Pearson correlation coefficient. r_real-time = 0.7, r_historical = 0.2, r_industry = 0.1, and the normalized weights are α = 0.7, β = 0.2, and γ = 0.1.

[0119] Total = 0.7 × 0.5 + 0.2 × 0.6 + 0.1 × 1.0 = 0.35 + 0.12 + 0.1 = 0.57.

[0120] Probability score: After using the improved Bayesian network and fitting the Gaussian distribution, P(rank=3|overall)=72%, and the probability score is determined to be 3 points.

[0121] Severity score: The physical loss score is combined with the impact of typhoon weather. After matching similar cases, it is 3 points. The business type sensitivity parameter is still 1.2. The severity score = 3 × 1.2 = 3.6, which is rounded to 4 points.

[0122] The overall risk score is 3 × 4 = 12 points, which corresponds to a medium risk level (6-15 points). The system triggers a yellow alert and pushes the alert information to the project management personnel's PC and mobile terminals through the message queue (MQ). At the same time, the project is marked as yellow on the visual heat map.

[0123] 4. Application of Results and Closed-Loop Optimization After the reassessment is completed and an alert is triggered, management personnel take appropriate measures. The results and related data are fed back to the system, initiating the model iterative optimization process to enable the system to learn itself. The specific process is as follows: Feedback data entry: Project management personnel took measures such as reinforcement and suspension of work, and the settlement rate gradually decreased to 1.0 mm / day. No actual accidents occurred. Feedback information such as the handling results, settlement change data, and early warning response status were entered into the system through the interactive module and stored in the historical case library to provide data support for model optimization.

[0124] Model optimization: The system incorporates feedback data into the algorithm training, recalculates the improved Pearson correlation coefficient, and updates the weight coefficients of the "engineering construction - foundation pit collapse" risk to α=0.75, β=0.15, γ=0.1; at the same time, it optimizes the prior probability of the Bayesian network, adjusts the threshold parameters under typhoon weather, and improves the evaluation accuracy of similar scenarios in the future.

[0125] Long-term optimization: As the B City processing plant accumulates its own historical data (more than 100 cases), the system stops migrating data of similar business types, uses its own data to train the model, dynamically updates the weight coefficients, Bayesian network parameters and business type sensitivity parameters, and gradually improves the evaluation accuracy of the new business type, forming a complete closed loop of "data collection-evaluation-feedback-optimization", which fully demonstrates the self-learning ability of the invention.

[0126] Feedback data entry: Project management personnel took measures such as reinforcement and support and suspension of work, and the settlement rate gradually decreased to 1.0 mm / day. No actual accidents occurred. Feedback information such as the handling results, settlement change data, and early warning response status were entered into the system through the interactive module and stored in the historical case library.

[0127] Model optimization: The system incorporates feedback data into the algorithm training, recalculates the improved Pearson correlation coefficient, and updates the weight coefficients of the "engineering construction - foundation pit collapse" risk to α=0.75, β=0.15, γ=0.1; at the same time, it optimizes the prior probability of the Bayesian network, adjusts the threshold parameters under typhoon weather, and improves the evaluation accuracy of similar scenarios in the future.

[0128] Long-term optimization: As the B City processing plant accumulates its own historical data (more than 100 cases), the system can use its own data to train the model, dynamically update the weight coefficients, Bayesian network parameters and business type sensitivity parameters, and gradually improve the evaluation accuracy of the new business type, forming a complete closed loop of "data collection-evaluation-feedback-optimization".

[0129] Example 3 This embodiment focuses on the data flow guarantee mechanism of the system in a complex multi-business concurrent environment, the deep logic of the complex event processing engine, and the specific implementation method of application layer visualization decision support.

[0130] In terms of system architecture connectivity, this embodiment adopts a microservice architecture based on remote procedure calls. The perception layer, data layer, computing layer, and application layer communicate asynchronously via a high-performance message bus. To ensure system stability under concurrent massive data from multiple business scenarios, a load balancer is introduced in the computing layer to dynamically distribute evaluation tasks to different computing nodes. Each computing node has an internal data buffer, processing real-time signal streams using a first-in, first-out (FIFO) principle. 256-bit Advanced Encryption Standard (AES-256) is implemented throughout the data transmission process to prevent sensitive and risky enterprise data from being intercepted or tampered with during network transmission.

[0131] In constructing the complex event processing engine, this embodiment designs a timestamp-based sorting and caching mechanism to address the issues of asynchronous and out-of-order data arrival in multi-business environments. Since meteorological data is accessed via a wide area network (WAN), the latency can reach several seconds or even minutes, while on-site sensor data is accessed via a local area network (LAN), with a latency of only milliseconds. The CEP engine uses Coordinated Universal Time (UTC) as its primary timeline, assigning a high-precision timestamp to each event entering the system. The engine sets a 5-minute sliding time window, and all events falling within this window are reordered according to their timestamps.

[0132] For asynchronous delayed data, the system implements a delay compensation algorithm. If a critical event (such as a power outage alarm in a certain business sector) has not arrived before the time window closes, but its preceding related events have already triggered the warning logic, the engine will estimate the possible value of the missing data based on historical probability distribution and perform a "soft trigger," correcting the data once the actual data arrives. This mechanism greatly reduces the false negative rate caused by network fluctuations.

[0133] At the application layer, this embodiment details the generation logic of the dynamic risk heatmap and risk priority list. The visualization module maps the coordinates of various business points of the enterprise (such as construction sites, factories, and warehouses) to an electronic map by calling the geographic information system interface. The comprehensive risk value is converted into a color gradient: 1 to 5 points are dark green, representing extremely low risk; 6 to 10 points are light green, representing low risk; 11 to 15 points are yellow, representing medium risk; 16 to 20 points are orange, representing high risk; and 21 to 25 points are red, representing extremely high risk.

[0134] The heatmap not only displays the current static risk value but also supports trend prediction. Based on the risk value fluctuations over the past 24 hours, the system uses simple linear regression to predict the risk trend for the next 4 hours. If the predicted value shows an upward trend, the points on the heatmap will be accompanied by an outward-spreading ripple effect. The risk priority list sorts all assessed objects in real time using a fast sorting algorithm, allowing managers to easily filter out "high-risk and highly sensitive" key monitoring objects.

[0135] This embodiment also introduces an interactive feedback mechanism. When managers receive an early warning push on their mobile devices and take on-site measures, they can provide feedback on the measures taken (e.g., reinforcing supports, replacing parts, evacuating personnel) through the interactive interface. Upon receiving the feedback signal, the system immediately marks the risk unit as "under handling" and temporarily reduces its weight in the global risk ranking until the sensor data returns to normal. This feedback data is then structured and stored in the historical case library, becoming a training sample for the model's next iteration.

[0136] Regarding multi-industry adaptability, this embodiment adds an extended configuration for the chemical manufacturing industry. In this configuration, the system's perception layer additionally connects to an explosion-proof gas detector and an infrared thermal imaging thermometer to monitor the concentration of flammable and explosive gases and the surface temperature of the reaction vessel. The calculation layer automatically loads safety benchmark parameters for the chemical industry, such as flash point thresholds and pressure limits. Through this modular expansion approach, this system can easily achieve cross-industry horizontal expansion, providing a unified, standardized, and dynamic risk management platform for multi-industry integrated enterprises.

[0137] Example 4 This embodiment further explores the robustness of the present invention in extreme data environments, as well as the resource scheduling strategy for large-scale concurrent computing tasks, aiming to demonstrate the technical advantages of the system in handling risk assessment of ultra-large multi-business enterprises.

[0138] At the system hardware level, to support the concurrent access of tens of thousands of sensing terminals, this embodiment introduces a multi-level caching architecture at the edge. Each edge computing node at each business site is configured with non-volatile memory to temporarily store sensing data during network outages. Once the network is restored, the edge nodes use a breakpoint resumption protocol to re-transmit the backed-up data to the cloud server in timestamp order. The cloud computing cluster adopts a containerized deployment scheme, dynamically adjusting the number of container replicas based on the computational intensity of risk assessment tasks for each business sector using an auto-scaling strategy. For example, during extreme weather events such as typhoons, when assessment tasks for the engineering construction sector surge, the system automatically allocates more CPU cores to the risk quantification engine for that sector.

[0139] In terms of the micro-decomposition of the methodology, this embodiment significantly expands upon the unstructured text processing in step 1. The system introduces a deep learning-based natural language understanding module to parse government-issued policy documents. This module first performs word segmentation and part-of-speech tagging on the text, then extracts the "industry sectors," "restrictive indicators," and "effective date" related to the policy using named entity recognition technology. This information is converted into numerical risk impact coefficients, directly affecting the industry benchmark comparison index in step 3. For example, if a new policy significantly tightens emission standards for construction waste, the system will automatically increase the benchmark index for "environmental compliance risk" in that industry, thereby guiding companies to upgrade their equipment in advance.

[0140] In this three-dimensional risk assessment model, the calculation details of the historical risk statistical index are described in detail. To avoid the influence of outdated data on the current assessment, the system introduces a time decay factor. The further back in time a historical incident case is from the present, the lower its weight is in calculating the frequency and similarity. The decay function adopts an exponential form, with a half-life set to 2 years. This means that a risk event that occurred two years ago contributes only half as much to the current probability as a recent event. This approach makes the quantitative results more reflective of the company's recent safety management level.

[0141] For the physical loss score calculation in step 4, this embodiment refines the automatic mapping logic for the "business interruption" dimension. The system reads data from the enterprise resource planning system and production scheduling system within the enterprise in real time through a standardized open database connection interface. When a risk unit is determined to have a high probability of occurrence, the system automatically retrieves the business processes associated with that unit. If the unit is on a critical path that generates core profits for the enterprise (e.g., a unique automated sorting line), its business interruption score will be automatically calculated based on the product of the estimated downtime and the average daily output. This loss prediction based on business logic is more objective and accurate than traditional manual estimation.

[0142] Finally, this embodiment illustrates the system's self-diagnosis and integrity verification logic. An independent monitoring process is internally responsible for verifying data consistency between different levels. If there is a discrepancy between the amount of raw data collected by the perception layer and the amount of data entering the computation layer, or if the risk value output by the computation layer exceeds the range allowed by physical logic (e.g., a negative value), the monitoring process will immediately issue a system anomaly alarm and mark the assessment as "invalid," preventing erroneous risk information from guiding management to make incorrect decisions. Through this comprehensive, multi-level technical safeguard, this invention ensures the dynamic nature of multi-business risk assessment while significantly improving the system's reliability and practicality.

[0143] Example 5 This embodiment focuses on the innovative applications of the present invention in cross-business-related risk analysis and dynamic threshold adjustment mechanism based on reinforcement learning, so as to further enhance the system's early warning capability in complex business chains.

[0144] At the system architecture level, this embodiment introduces a correlation risk analysis module. This module runs on a graph database and uses graph traversal algorithms to identify potential risk transmission paths between different business sectors. For example, when a foundation pit project in the engineering construction sector experiences severe cost overruns, the system automatically identifies, through the correlation of the capital chain, the potential negative impact on equipment maintenance investment in the construction waste disposal sector, thereby increasing the expected "equipment failure risk" for the disposal plant. This cross-sectoral linkage analysis breaks the "siloed" isolation of traditional risk management, achieving a holistic understanding of enterprise-wide risks.

[0145] Regarding the dynamic optimization of the methodology, this embodiment details the threshold calibration logic based on the reinforcement learning Q-learning algorithm. In the initial stage of system operation, the risk level classification threshold (e.g., 15 points for high risk) is a static value set based on industry experience. As the system accumulates a large number of early warning records and actual handling feedback, the reinforcement learning module begins to intervene. The system defines "early warning accuracy" and "management response efficiency" as reward functions.

[0146] If the system determines a situation to be high-risk but no incident actually occurs and the monitoring data quickly returns to normal, the system will consider the current threshold too low, leading to a false alarm, and thus receive a negative reward. Conversely, if a false alarm occurs, a significant negative reward will be given. Through thousands of iterations, the Q-learning algorithm automatically finds an optimal set of dynamic thresholds. This set of thresholds not only varies depending on the type of business but also automatically adjusts with changes in environmental factors such as seasons and day / night cycles. For example, at night when visibility is poor, the system will automatically lower the threshold for judging violations to improve safety.

[0147] In the severity assessment, this embodiment further refines the dynamic adjustment logic of the business format sensitivity parameter. The system establishes a correction model based on multivariate linear regression, introducing "social public opinion heat" and "regulatory pressure index" as covariates in addition to physical losses. By using web crawling technology to obtain real-time discussion volume on social media and news platforms regarding the company's relevant business formats, the system automatically increases the business format sensitivity parameter when public opinion heat abnormally rises. This means that during sensitive periods (such as during major industry inspections), the same physical losses will be mapped to a higher severity score, prompting the company to take more advanced countermeasures.

[0148] The application layer's presentation logic has also been enhanced in this embodiment. The system supports a "risk tracing" function. When a manager clicks on a red high-risk point on the heatmap, the application layer will query the intermediate results of the calculation layer through the data interface, intuitively displaying the contribution factors that caused the high risk (e.g., real-time monitoring contributes 60%, historical frequency contributes 30%, and industry deviation contributes 10%). This transparent assessment process greatly enhances the manager's trust in the system's results and can guide them in targeted risk management.

[0149] In summary, by introducing graph association analysis, reinforcement learning optimization, and multivariate sensitivity correction, this embodiment enables the invention not only to quantify isolated risk points but also to gain in-depth insights into complex enterprise risk ecosystems, providing solid technical support for scientific decision-making in multi-business enterprises.

[0150] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A dynamic quantitative assessment method for the risks of multi-business enterprises, characterized in that, Includes the following steps: Step 1: Construct a multi-source heterogeneous data sensing and preprocessing system. By deploying multimodal IoT sensors in physical scenarios of different business formats, internal sensing data containing physical state signals is collected in real time. External sensing data including weather warnings, policy and regulatory changes, and industry safety benchmarks are accessed through the application programming interface of the expressive state transition architecture. Standardized algorithms are used to perform filtering, noise reduction, and format standardization processing on the raw signals, and the heterogeneous data of multiple business formats is uniformly transformed into a standardized digital signal stream. Step 2: Establish a multi-dimensional hybrid storage and association mapping knowledge base. Adopt a hybrid architecture that combines relational databases and graph databases. Structure the storage of historical risk event case library, industry security standard library, risk type dimension library and impact level dimension library. By constructing entity relationship mapping between business type, risk type, feature dimension and benchmark parameters, realize the association retrieval and digital mapping of risk features of multiple business types. Step 3: Perform dynamic quantitative calculation of three-dimensional risk probability. Construct a three-dimensional assessment model based on real-time status monitoring index, historical risk statistical index and industry benchmark comparison index. Use an improved correlation coefficient algorithm to dynamically generate real-time weight coefficients based on the data accumulated by the enterprise itself. Introduce a probabilistic network model and map the comprehensive risk index to the probability score by calculating the posterior probability to achieve autonomous optimization of the assessment logic. For new business formats without historical data, use a transfer learning strategy to transfer similar feature parameters from similar business formats and initialize the digital twin model and Bayesian network prior probability. Step 4: Perform risk severity and business type sensitivity assessment. Construct risk scenario feature vectors using feature extraction algorithms and coding techniques. Match similar cases in the historical case library using a weighted neighborhood algorithm to predict physical loss scores. At the same time, calculate business type sensitivity parameters based on regression models. Multiply the physical loss score by the business type sensitivity parameters to obtain the severity score. Step 5: Implement complex event processing and dynamic iterative optimization. Utilize complex event processing technology to establish a spatiotemporal alignment mechanism. Perform time alignment, consistency verification, and frequency adaptive processing on asynchronously arriving multi-source heterogeneous data. Determine whether to trigger risk reassessment based on the preset rule tree structure, and feed the assessment results and handling feedback back to the historical database to drive the continuous iteration of the algorithm model.

2. The dynamic quantitative assessment method for the risk of multi-business enterprises according to claim 1, characterized in that, The internal sensing data collection process in step 1 implements differentiated deployment strategies for different business formats: in the engineering construction business scenario, settlement sensors and tilt sensors are deployed to monitor structural stability; In the construction waste recycling industry, vibration sensors and dust concentration sensors are deployed to monitor equipment operation status; sensor data from different industries are processed to ensure uniform accuracy through a sensor calibration module; the external sensing data access interface has a built-in timeout reconnection mechanism to ensure the continuity of data flow, and data transmission adopts an object representation format; The specific logic of data preprocessing in step 1 includes: removing outliers from continuous sensor data using preset statistical criteria, and identifying and filtering extreme sensor false alarm data. For unstructured text data, the word frequency inverse document frequency algorithm in natural language processing is used to extract keywords and convert them into structured feature vectors. For data from the same source in different business formats, the dimensional differences in cross-business format comparisons are eliminated through dimensional normalization, so that physical quantities are converted into signals in a unified format for use by upper-level computing units.

3. The dynamic quantitative assessment method for the risk of multi-business enterprises according to claim 1, characterized in that, In step 2, the relational database uses a structured query language database, and its historical risk event case library contains fields such as case identifier, business type, risk type, feature vector, physical loss, occurrence time, and processing result. The graph database adopts a native graph storage architecture. Its risk type dimension library defines the risk classification system for various business formats and associates each risk type with a specific feature dimension. The impact hierarchy dimension library stores the risk and consequence mapping relationship and business sensitivity benchmark parameters under different business formats, and supports multi-business data association query through graph traversal algorithm; In step 2, the rule base adopts a combination of key-value pair in-memory database and disk database for persistent storage. The in-memory database is used to store the real-time weight coefficients and trigger thresholds of high-frequency access, while the persistent layer is used to store all rule data and support dynamic updates and version management of rules.

4. The dynamic quantitative assessment method for the risk of multi-business enterprises according to claim 1, characterized in that, The calculation logic of the real-time status monitoring index in step 3 is as follows: compare the real-time reading of the sensor with the preset safety threshold, warning threshold and high-risk threshold, and map it to the predetermined numerical range through a piecewise function; The system automatically matches corresponding threshold parameters for different business types from the impact level dimension library; the historical risk statistics index is calculated based on the frequency of occurrence of the assessed object or similar objects in historical risk events, and the improved weighted nearest neighbor algorithm is called to find similar cases in the historical case library; When calculating case similarity, a business type similarity weight is introduced. Different preset weight values ​​are set according to the correlation between the business type of the case and the target business type. The accuracy of the matching results is ensured by calculating feature distance. The industry benchmark comparison index is used to compare the current object status with the industry average level or life cycle model. For equipment assets, in the engineering construction industry, the running time is compared with the industry average mean time between failures (MTBF), while in the construction waste recycling industry, the equipment failure rate is compared with the industry average failure rate.

5. The dynamic quantitative assessment method for the risk of multi-business enterprises according to claim 1, characterized in that, The dynamic generation algorithm for weight coefficients in step 3 is activated after the enterprise's historical data accumulation reaches a preset sample size threshold: The system uses a correlation coefficient algorithm to calculate the correlation between real-time data fluctuations, historical fault frequencies, industry benchmark deviations, and accident rate sequences. A dimensional correction term is introduced during the calculation process to eliminate the influence of heterogeneous data. The correlation coefficients of each dimension are obtained by calculating the covariance and standard deviation. After absolute value conversion and normalization, weight coefficients are generated. The system maintains an independent weight system for each business format and each specific risk type, and performs automatic updates according to a predetermined time period. If a major risk event occurs, an emergency update mechanism is triggered. The probabilistic network model employs a two-stage scoring strategy: in the stage without historical data, evaluation is performed based on an industry default weight and fixed threshold mapping table; in the data accumulation stage, the system learns prior probabilities from historical data and statistically analyzes the distribution of the comprehensive risk index across different score intervals; for multi-business scenarios, prior probabilities are learned separately for each business; when a new comprehensive risk index is generated, the system uses a preset probability distribution model to fit historical data, calculates the posterior probability of the current value at each risk level, and selects the level with the highest posterior probability as the final probability score.

6. The dynamic quantitative assessment method for the risks of multi-business enterprises according to claim 1, characterized in that, The feature vector construction process in step 4 includes: numerical conversion of classification features of equipment type and working environment using one-hot encoding; normalization of continuous features such as personnel density and equipment operating time; keyword extraction and conversion of text features such as work logs and accident descriptions into high-dimensional vectors; when assessing specific risks, the feature vectors cover geological type, support type, depth, personnel density, and historical similar events; the weighted neighborhood algorithm determines scene similarity by calculating weighted Euclidean distance, introduces a business type feature weight penalty term, and automatically assigns feature weights according to business type; the algorithm selects the shortest predetermined distance. For each case, voting weights are assigned based on the reciprocal of the distance. Physical loss scores within a pre-defined scoring range are weighted and voted on, with the highest total weight used as the prediction result. Physical loss scores are mapped across multiple dimensions: casualties, economic losses, social impact, and business interruption. Casualties are determined using personnel density sensors and video analysis combined with a pre-defined target detection algorithm. Economic losses are calculated using an asset ledger interface and a loss accounting model. Social impact is determined by obtaining media exposure data through a public opinion monitoring interface. Business interruption is determined by obtaining the duration of core system downtime through a business system interface.

7. The dynamic quantitative assessment method for the risk of multi-business enterprises according to claim 1, characterized in that, The calculation of the business format sensitivity parameter in step 4 is divided into two stages: industry benchmark generation and enterprise dynamic correction. The industry benchmark is extracted from the authoritative industry database and outliers are removed by using preset statistical criteria and the isolated forest algorithm. The probability distribution model is fitted by the maximum likelihood estimation method, and a specific statistical value is taken as the benchmark value. In the enterprise dynamic correction phase, a linear regression model is used to establish the correlation between physical losses and actual business impacts, and regression coefficients are calculated. The final business type sensitivity parameter is determined by the ratio of the corrected baseline parameter to the original baseline parameter, and a business type weight correction term is introduced for adaptation and optimization. In step 5, the spatiotemporal alignment mechanism of the complex event processing technology uses Coordinated Universal Time (UTC) as the main time axis, sets a configurable sliding time window, and sets a preset window duration suitable for different business types. For out-of-order data, a timestamp sorting and caching mechanism is used, and sorting is completed in a temporary queue. For asynchronous delayed data, a delay compensation algorithm is used for calibration. For asynchronous delayed data, if the delay time is less than the preset sliding time window, a delay compensation algorithm is used for time calibration. For high- and low-frequency heterogeneous data, a frequency adaptive sampling mechanism is used to achieve data alignment. The trigger engine uses a rule tree structure for consistency verification, and rule tree nodes are associated through logical operators. When an associated event meets the trigger condition, a reassessment signal is sent to the computing layer. The assessment results are pushed to the management terminal in real time through a message queue, driving the application layer to generate a dynamic risk heatmap.

8. A dynamic quantitative assessment system for the risks of multi-business enterprises, characterized in that, include: The physical sensing layer is used to deploy multimodal IoT sensors in physical scenarios of different business formats, collect internal sensing data containing physical state signals in real time, and access external sensing data through the application programming interface of the expressive state transition architecture. It uses standardized algorithms to perform filtering, noise reduction and format standardization processing on the original signals, and transforms heterogeneous data from multiple business formats into a standardized digital signal stream. The data storage layer is used to establish a multi-dimensional hybrid storage and association mapping knowledge base. It adopts a hybrid architecture that combines relational databases and graph databases to store historical risk event case libraries, industry safety standard libraries, risk type dimension libraries, and impact level dimension libraries. By constructing entity relationship mappings between business formats, risk types, feature dimensions, and benchmark parameters, it realizes the association retrieval and digital mapping of risk features of multiple business formats. The risk quantification calculation layer is used to perform dynamic quantification calculation of three-dimensional risk probability. It constructs a three-dimensional evaluation model based on real-time status monitoring index, historical risk statistical index and industry benchmark comparison index. It uses an improved correlation coefficient algorithm to dynamically generate real-time weight coefficients based on the data accumulated by the enterprise itself, and introduces a probability network model to map the comprehensive risk index into a probability score by calculating the posterior probability. The severity assessment layer is used to perform risk severity and business type sensitivity assessment. It uses feature extraction algorithms and coding techniques to construct risk scenario feature vectors, uses a weighted neighborhood algorithm to match similar cases in the historical case library to predict physical loss scores, and calculates business type sensitivity parameters based on regression models. The severity score is obtained by multiplying the physical loss score by the business type sensitivity parameters. The dynamic optimization iteration layer is used to implement complex event processing and dynamic iterative optimization. It uses complex event processing technology to establish a spatiotemporal alignment mechanism, performs time alignment, consistency verification and frequency adaptive processing on asynchronously arriving multi-source heterogeneous data, determines whether to trigger risk reassessment based on the preset rule tree structure, and feeds back the assessment results and handling feedback to the historical database.

9. The dynamic quantitative assessment system for multi-business enterprise risk according to claim 8, characterized in that, The system hardware architecture adopts a layered distributed deployment mode: the physical sensing layer includes high-precision capacitive settlement sensors and microelectromechanical system tilt sensors deployed in engineering construction scenarios, and triaxial piezoelectric vibration sensors and laser scattering dust concentration sensors deployed in construction waste recycling scenarios; the sensing terminals are connected to the local area network through industrial-grade wireless gateways, with analog-to-digital conversion chips integrated inside the gateways; the computing layer is deployed on a high-performance server cluster, with multi-core processors and a predetermined amount of random access memory; the computing layer integrates a graphics processor with tensor processing units for running target detection algorithms; in the data storage layer, a relational database cluster is responsible for storing structured historical risk cases, a native graph database is used to construct a risk knowledge graph, and an in-memory database is used to cache frequently changing real-time weight coefficients and trigger thresholds; The application layer communicates with the computing layer and data layer through a remote procedure call interface, and the data transmission process is encrypted using a high-level encryption standard with a preset bit width.

10. The dynamic quantitative assessment system for multi-business enterprise risk according to claim 8, characterized in that, The system has a built-in parameter verification mechanism that automatically verifies the accuracy of business type sensitivity parameters according to a predetermined cycle. If the deviation exceeds a preset deviation threshold, a parameter recalculation process is triggered. The dynamic optimization iteration layer includes a correlation risk analysis module that runs on a graph database and uses a graph traversal algorithm to identify potential risk transmission paths between different business types. The risk quantification calculation layer introduces a threshold calibration logic based on a reinforcement learning algorithm, defining the early warning accuracy and management response efficiency as reward functions, and finding the optimal dynamic threshold sequence through iterative evolution. The severity assessment layer establishes a modified model based on multivariate linear regression, introducing social media sentiment and regulatory pressure index as covariates, and dynamically adjusting the business type sensitivity parameters by obtaining social media discussion volume in real time through web crawling technology. The application layer maps the coordinates of enterprise business points to an electronic map by calling a geographic information system interface, converts the comprehensive risk value into a color gradient display, and supports risk trend prediction display based on simple linear regression.

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