Medical service provider intelligent risk control SaaS platform based on micro-service architecture

Abstract

The invention discloses a medical service provider intelligent risk control SaaS platform based on a micro-service architecture, and relates to the technical field of information. Comprising a privacy computing gateway layer which is used for realizing compliance sharing, joint modeling, ciphertext reasoning and data right confirmation tracing of cross-mechanism data through a trusted execution environment, multi-party security computing, homomorphic encryption, zero-knowledge proof and block chain fingerprint anchoring, and a hyper-temporal-spatial dynamic risk portrait engine which is used for constructing a time-space-causal hypergraph. And performing continuous time modeling based on a neural differential equation, and generating a causal stability voucher in combination with cross-environment causal discovery and zero-knowledge proof. In the invention, through the privacy computing gateway layer, by utilizing advanced technologies such as a trusted execution environment, multi-party security computing, homomorphic encryption and zero-knowledge proof, security sharing and conjoint analysis of cross-mechanism data are realized on the premise of not leaking original data, and the problems of data islands and privacy compliance are effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and particularly relates to a medical service provider intelligent risk control SaaS platform based on a micro-service architecture. BACKGROUND

[0002] With the rapid development and digital transformation of the pharmaceutical industry, medical service providers face increasingly complex risk challenges in daily operations, including but not limited to fraud, compliance risks, supply chain disruption risks, and data security and privacy protection issues. Traditional risk control methods often rely on manual experience, static rules or single data sources, which have limitations such as response lag, low recognition rate, difficulty in adapting to new risk patterns, serious data silos and lack of cross-agency collaboration capabilities.

[0003] The existing technology has the following defects in the field of medical risk control: the medical industry data is scattered in different institutions, due to data privacy and compliance requirements, it is difficult to effectively integrate and share, leading to lack of comprehensiveness in risk control analysis, traditional risk control models are mostly based on historical data and fixed rules, which are difficult to capture real-time dynamic risk changes and have insufficient recognition ability for new and variant fraud patterns, many risk control decision models are "black box" type, which makes it difficult to provide clear risk explanation and causal chain, leading to difficulty in understanding, trusting and tracing the decision, the medical supply chain is complex and fragile, and there is a lack of effective simulation and optimization tools to cope with the impact of unexpected events, leading to high risk of supply chain disruption. SUMMARY

[0004] The purpose of the present application is to solve the problems existing in the prior art, and to provide a medical service provider intelligent risk control SaaS platform based on a micro-service architecture.

[0005] In order to achieve the above object, the application adopts the following technical scheme: A medical service provider intelligent risk control SaaS platform based on a micro-service architecture comprises: a privacy computing gateway layer for realizing compliance sharing, joint modeling, ciphertext reasoning and data right tracing of cross-institution data through a trusted execution environment, multi-party secure computing, homomorphic encryption, zero-knowledge proof and blockchain fingerprint anchoring; a time-space-dynamic risk portrait engine for constructing a time-space-cause hypergraph, performing continuous time modeling based on a neural differential equation, generating a cause stability certificate combining cross-environment causal discovery and zero-knowledge proof, performing risk propagation analysis based on a graph partial differential equation, and realizing active feature collection under a privacy budget constraint, and outputting a cause chain, a risk heat map and an on-chain explanation card; an adaptive evolutionary decision engine for making shunt decisions between a guardrail track and a deep reasoning track, updating a rule library combining diffusion-generated adversarial samples, realizing cold start adaptation and personalized migration using cross-tenant Bayesian meta-learning, and guaranteeing compliance through an LTL / CTL formalized security shield and a CEGIS mechanism, and realizing risk-sensitive control based on CVaR and ChanceConstraint optimization; a metaverse supply chain sandbox for generating a multi-domain co-simulation scene based on a hybrid differentiable twin model fusion mechanism equation and a neural residual network, performing collaborative optimization combining multi-agent reinforcement learning and contract incentive mechanism, and realizing simulation result auditing through zero-knowledge proof and cross-chain anchoring, and outputting a distribution robust resilience index and a sensitivity curve; a blockchain risk evidence layer for chaining the risk control interception action, the cause chain and the explanation certificate on the whole link, adopting a DAO governance mechanism to jointly participate in strategy updating by pharmaceutical enterprises, hospitals and regulatory parties, and realizing result mutual recognition between a medical insurance chain and a drug supervision chain through a cross-chain verification protocol; and a multi-tenant SaaS management layer for realizing tenant-level isolation through a Kubernetes namespace, realizing elastic scaling combining container orchestration and resource scheduling strategies, realizing self-evolution defense through cross-tenant fraud sample sharing and adversarial experience propagation, and providing a unified visual dashboard and audit interface to tenants and regulatory parties.

[0006] As a further description of the above technical scheme: The time-space-dynamic risk portrait engine models enterprises, doctors, patients, drugs, warehouse points and transportation hubs as a node set through a time-space-cause hypergraph, models prescription, payment, drug delivery, distribution and inventory change as a hyperedge set, each hyperedge carries a time interval, a geographical division, a privacy identifier and an evidence weight, and supports event-driven dynamic incremental update.

[0007] As a further description of the above technical scheme: The adaptive evolutionary decision engine generates three types of counter samples of medical insurance card cashing, group purchase prescription and ghost drugstore by using a FraudGen-Diffusion diffusion model, and constructs a privacy playback cache by combining differential privacy noise and cluster center abstract to prevent the real data from being leaked by the counter generator.

[0008] As a further description of the above technical solution: The adaptive evolutionary decision engine realizes cross-tenant Bayesian meta-learning through hierarchical prior and tenant posterior distribution, and quickly adapts through a small amount of target domain samples when a new tenant is cold started.

[0009] As a further description of the above technical solution: The meta-universe type supply chain sandbox generates superimposed scenes containing epidemic disturbance, typhoon disaster and policy tightening by constraining diffusion scene devices, and each scene is bound with parameter commitment information to support zero-knowledge proof.

[0010] As a further description of the above technical solution: The meta-universe type supply chain sandbox drives the multi-agent of upstream suppliers, warehouse nodes, trunk transportation and terminal distribution vehicles to form a stable Nash equilibrium strategy by designing ladder rebate contracts and service level penalty contracts.

[0011] As a further description of the above technical solution: The blockchain risk evidence layer stores the risk interception action, causal stability proof and on-chain explanation card on the chain, and all governance proposals and voting results are written into the on-chain ledger to form an audit record.

[0012] As a further description of the above technical solution: The multi-tenant SaaS management layer realizes cross-tenant propagation and defense strategy update of new fraud modes within 24 hours through a shared fraud sample pool and an adversarial experience propagation mechanism.

[0013] As a further description of the above technical solution: The visual dashboard of the multi-tenant SaaS management layer displays the causal chain and risk heat map generated by the risk portrait engine, the distribution robustness index and simulation prediction result output by the supply chain sandbox, and the on-chain evidence information recorded by the blockchain risk evidence layer.

[0014] The present application has the following advantages: 1、In the present application, the core of the risk control data is the behavior, time and space of the business personnel. Through the privacy computing gateway layer, advanced technologies such as trusted execution environment, multi-party secure computation, homomorphic encryption and zero-knowledge proof are used to realize the safe sharing and joint analysis of cross-institutional data without revealing the original data, effectively solving the problems of data silos and privacy compliance, and supporting data right tracing. The space-time-dynamic risk portrait engine can capture the dynamic evolution and propagation path of risks by constructing a time-space-cause supergraph and a neural differential equation model, and can generate clear causal chains and risk explanation cards by combining verifiable causal discovery, greatly improving the real-time accuracy of risk identification and the transparency and credibility of decision-making. The adaptive evolutionary decision engine can quickly adapt to new risk patterns through the shunt mechanism of guardrail track and deep reasoning track, combined with the evolution rule library of adversarial generated samples. At the same time, cross-tenant Bayesian meta-learning is used to realize fast cold start and personalized adaptation, and the formal security shield and risk-sensitive optimization are used to significantly improve the robustness and attack resistance of the risk control strategy.

[0015] 2、In the present application, the meta-universe supply chain sandbox can simulate complex supply chain scenarios through hybrid differentiable twin models and multi-domain simulation, and can realize the collaborative optimization of supply chains through multi-agent contract mechanisms, and can output distribution robustness indicators through zero-knowledge proof, effectively improving the ability of the pharmaceutical supply chain to cope with uncertainty. The blockchain risk evidence layer chains the risk control actions, causal chains and explanation certificates, ensuring the non-tamperability and traceability of the risk control process. By supporting cross-chain mutual recognition and decentralized governance, a more transparent, credible and collaborative risk control ecosystem is built for the pharmaceutical industry. The multi-tenant SaaS management layer realizes tenant-level resource isolation, ensuring the data security and performance of each tenant. At the same time, through the self-evolution experience sharing mechanism, new fraud patterns and adversarial experiences identified by different tenants can be quickly shared and learned, significantly improving the risk identification and defense capabilities of the entire platform, and realizing fast cross-tenant identification. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 The system architecture of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0018] REFERENCE Figure 1In one embodiment, the application provides a medical service provider intelligent risk control SaaS platform based on a micro-service architecture, comprising: a privacy computing gateway layer for realizing compliance sharing, joint modeling, ciphertext reasoning and data right tracing of cross-institution data through a trusted execution environment, multi-party secure computing, homomorphic encryption, zero-knowledge proof and blockchain fingerprint anchoring; a time-space-dynamic risk portrait engine for constructing a time-space-cause hypergraph, performing continuous time modeling based on a neural differential equation, generating a cause stability certificate based on cross-environment causal discovery and zero-knowledge proof, performing risk propagation analysis based on a graph partial differential equation, and realizing active feature collection under a privacy budget constraint, and outputting a causal chain, a risk heat map and an on-chain explanation card; an adaptive evolutionary decision engine for making shunt decisions between a guardrail track and a deep reasoning track, updating a rule library combined with diffusion generated adversarial samples, realizing cold start adaptation and personalized migration using cross-tenant Bayesian meta-learning, and guaranteeing compliance through an LTL / CTL formalized security shield and a CEGIS mechanism, and realizing risk sensitive control based on CVaR and Chance Constraint optimization; a metaverse supply chain sandbox for generating a multi-domain co-simulation scene based on a hybrid differentiable twin model fusion mechanism equation and a neural residual network, performing collaborative optimization combined with multi-agent reinforcement learning and contract incentive mechanism, and realizing simulation result auditing through zero-knowledge proof and cross-chain anchoring, and outputting a distribution robust resilience index and a sensitivity curve; a blockchain risk evidence layer for on-chain linking of risk control interception actions, causal chains and explanation certificates, adopting a DAO governance mechanism for joint participation in strategy updating by pharmaceutical enterprises, hospitals and regulatory parties, and realizing result mutual recognition between a medical insurance chain and a drug supervision chain through a cross-chain verification protocol; and a multi-tenant SaaS management layer for realizing tenant-level isolation through Kubernetes namespaces, realizing elastic scaling combined with container orchestration and resource scheduling strategies, realizing self-evolution defense through cross-tenant fraud sample sharing and adversarial experience propagation, and providing a unified visual dashboard and audit interface to tenants and regulatory parties.

[0019] The time-space-dynamic risk portrait engine models enterprises, doctors, patients, drugs, warehouse sites and transportation hubs as a node set through a time-space-cause hypergraph, models prescription writing, payment, drug dispensing, distribution and inventory changes as a hyperedge set, each hyperedge carrying a time interval, a geographical division, a privacy identifier and an evidence weight, and supports event-driven dynamic incremental updating.

[0020] The adaptive evolutionary decision engine generates three types of adversarial samples, namely medical insurance card cashing, group purchase prescription and ghost drugstore, using the FraudGen-Diffusion diffusion model, and constructs a privacy playback cache combining differential privacy noise and cluster center abstract to prevent the adversarial generator from leaking real data. The adaptive evolutionary decision engine realizes cross-tenant Bayesian meta-learning through hierarchical prior and tenant posterior distribution, and quickly adapts to a small amount of target domain samples when a new tenant is cold started.

[0021] The metaverse supply chain sandbox generates superimposed scenes containing epidemic disturbance, typhoon disaster and policy tightening by constraining the diffusion scene instrument, and each scene is bound with parameter commitment information to support zero-knowledge proof. The metaverse supply chain sandbox drives the multi-agent of upstream suppliers, warehouse nodes, trunk transportation and terminal distribution fleets to form a stable Nash equilibrium strategy by designing a ladder rebate contract and a service level penalty contract. The blockchain risk evidence layer stores on-chain evidence of risk interception actions, causal stability proof and on-chain explanation cards, and all governance proposals and voting results are written into the on-chain ledger to form an audit record.

[0022] The multi-tenant SaaS management layer realizes cross-tenant propagation and defense strategy update of new fraud modes within 24 hours through a shared fraud sample pool and an adversarial experience propagation mechanism. The visual dashboard of the multi-tenant SaaS management layer displays the causal chain and risk heat map generated by the risk portrait engine, the distribution robustness index and simulation prediction results output by the supply chain sandbox, and the on-chain evidence information recorded by the blockchain risk evidence layer.

[0023] In an embodiment of the present application, the privacy computing gateway layer is the core module of the privacy security layer, mainly used to ensure data compliance sharing and safe cooperation in cross-institution and cross-border scenarios, so as to realize joint modeling and trusted computing without leaking original data.

[0024] First, in terms of trusted execution, the privacy computing gateway layer provides independent secure computing areas for each institution using a trusted execution environment. During model training and inference, the original data is always confined to the secure domain of the institution, and external entities cannot directly access the data content, ensuring the isolation of data during transmission and computation. Through this mechanism, even when cross-institutional collaboration modeling is performed, participants cannot spy on each other's original data, thus meeting the basic requirements of compliance and privacy protection.

[0025] Secondly, in terms of joint calculation, the privacy computing gateway layer introduces multi-party secure computing mechanism, each participating institution completes the encryption operation locally, and then uploads the encrypted gradient information or local model parameter to the cooperative node, and the platform aggregates these local results through the security protocol to obtain the global fraud detection model or risk reasoning model. In the whole process, the participants only share the ciphertext calculation results instead of plaintext data, realizing the joint modeling and joint reasoning of data without domain outflow.

[0026] Further, in terms of ciphertext operation, the privacy computing gateway layer uses homomorphic encryption technology to support direct encryption calculation of sensitive feature vectors and business parameters. The platform can complete addition and multiplication operations without decryption, realizing the secure processing of prescription feature vectors, transaction amount parameters and inventory indicators. This mechanism avoids the intermediate link of data decryption in the process of risk analysis and model reasoning, effectively preventing the leakage of private data in the calculation process.

[0027] In terms of verifiability of calculation results, the privacy computing gateway layer combines the zero-knowledge proof mechanism to prove the correctness of joint modeling and ciphertext reasoning. The supervisory institution or third-party auditing party can verify the compliance of model calculation process and results through zero-knowledge proof without accessing the underlying sensitive data, thereby realizing the dual protection of data privacy and result credibility.

[0028] Finally, in terms of data right and traceability, the privacy computing gateway layer records the data blood relationship of all participating modeling and reasoning through the block chain fingerprint anchoring mechanism. Each data call, feature collection and model update generates a unique block chain fingerprint and is written into the distributed ledger, realizing the non-tamperable evidence of the whole process. This mechanism not only guarantees the compliance of data use, but also provides a credible evidence chain for subsequent audit and dispute arbitration.

[0029] Through the above implementation mode, the privacy computing gateway layer can realize the compliant sharing of cross-institution data, ciphertext reasoning and traceable right through the synergistic effect of trusted execution environment, multi-party secure computing, homomorphic encryption, zero-knowledge proof and block chain fingerprint anchoring, thereby providing safe and reliable privacy computing capability for the intelligent risk control platform of medical service providers.

[0030] In an embodiment of the present application, the super-time-space dynamic risk portrait engine, as an important part of the intelligent decision layer, is mainly used for time-space-cause modeling of multi-source heterogeneous data in the business scene of medical service providers, and generates an interpretable, verifiable and traceable risk portrait result under the premise of compliance and privacy protection.

[0031] First, in terms of portrait data structure construction, the hyper-time-space dynamic risk portrait engine takes enterprises, doctors, patients, drugs, warehouses, and transportation hubs as a node set, and five types of events, including prescribing, payment, drug dispensing, distribution, and inventory changes, as a hyper-edge set, forming a time-space-cause hypergraph. In each hyper-edge, four types of attributes are bound, including time interval, geographic partition, privacy identification, and evidence weight, which is consistent with the on-chain fingerprint and used for subsequent audit and traceability. Through the event-driven mechanism, the engine can dynamically update the hypergraph incrementally when new business events occur, thereby maintaining the integrity and timeliness of the portrait structure.

[0032] Second, in terms of time series modeling, the hyper-time-space dynamic risk portrait engine uses neural differential equations to model non-uniform prescription flow, payment flow, and inventory flow in continuous time. This method can handle irregular intervals caused by sudden events and abnormal stagnation, and explicitly introduces time interval and geographic partition information in the embedding evolution process, forming a time-space-sensitive risk representation. At the same time, the engine aligns the continuous-time state with the topology of the hypergraph, ensuring the consistency of risk propagation and causal structure reasoning. Continuous-time neural control differential equations are used to absorb irregular arrivals and sudden events, forming a continuous-time representation that aligns with the file "G-NCDE Continuous-Time Flow Modeling, Formula: ; : Continuous-time hidden state : Time : Neural vector field determined by parameters : Geographical embedding that maps geographical partition identification : Time interval embedding : Control signal derivative driven by five types of events, including prescribing, payment, drug dispensing, distribution, and inventory changes : Initial state : Readout function : Time-space-sensitive risk representation output In terms of causal structure learning, the hyper-time-space dynamic risk portrait engine obtains a stable causal parent set in different tenants, regions, and regulatory periods through cross-environment causal estimation in a federated environment. This process follows the principle of invariant risk minimization and outputs a causal stability certificate through zero-knowledge proof, thereby achieving the goal of non-data leakage and verifiable causality. This verifiable causal discovery mechanism ensures the robustness and credibility of the portrait model in cross-institution, cross-region, and cross-regulatory scenarios.

[0033] In terms of causal structure learning, the hyper-time-space dynamic risk portrait engine obtains a stable causal parent set in different tenants, regions, and regulatory periods through cross-environment causal estimation in a federated environment. This process follows the principle of invariant risk minimization and outputs a causal stability certificate through zero-knowledge proof, thereby achieving the goal of non-data leakage and verifiable causality. This verifiable causal discovery mechanism ensures the robustness and credibility of the portrait model in cross-institution, cross-region, and cross-regulatory scenarios; ​Cross-environment verifiable causal discovery follows the principle of invariant risk minimization, seeking a cross-environment stable parent set in a federated environment; then generating ZKP for audit in accordance with the file cross-environment invariant risk + ZKP formula:

[0034] : denotes learning mapping; : linear classification head; : environment set (divided by tenant, region, regulatory period); : environment on experience risk; : invariance regularization coefficient; : gradient of ; : unit weight vector.

[0035] In terms of risk wave propagation calculation, the super-spatial dynamic risk portrait engine calculates the conduction path and conduction strength of risk between the supply chain and the prescription behavior based on the graph partial differential equation. For any target node, the engine can extract the shortest causal path and identify the contribution degree of the key edge combined with the evidence weight. At the same time, exogenous shock variables are introduced in the risk propagation process to absorb and model the interference effects of macro price fluctuations, extreme weather and policy changes, so as to ensure that the risk conduction result has sensitivity and explanatory power to external disturbances; graph risk wave propagation, formula: ; : risk intensity vector of each node at time ; : self-maintaining coefficient; : adjacency diffusion coefficient; : adjacency matrix weighted by time and evidence weight (including time interval, geographical division, and evidence weight); : shock injection matrix; : exogenous shock term (raw material price fluctuation, extreme weather, policy change).

[0036] In terms of active feature collection mechanism under privacy budget, the super-spatial dynamic risk portrait engine calculates the minimum necessary feature set through the active feature collection method under privacy budget. This method takes information gain and false positive reduction as utility standards, prioritizes features with the highest causal distinction and risk propagation contribution in prescription features, transaction features, inventory features, and logistics features, and completes compliant collection within the privacy budget. The collection process is secure through trusted execution environment and homomorphic encryption, and only uses declared features through zero-knowledge proof to ensure the compliance and transparency of feature request; active feature collection under privacy budget, formula: ; : candidate feature universe : selected feature subset : risk label : observation of feature subset : information gain : false positive rate reduction : utility weighting coefficient : compliance cost or privacy budget consumption of single feature : total privacy budget upper limit

[0037] Finally, in terms of result output, the super-spatiotemporal dynamic risk portrait engine generates three types of standardized outputs, namely causal chain, risk heat map and on-chain explanation card, wherein the causal chain is used to describe the causal parent set and causal path of the risk, the risk heat map is used to show the risk distribution in the time dimension and the space dimension, and the on-chain explanation card is bound to the evidence weight through the block chain fingerprint, providing verifiable compliance credentials for regulatory agencies and auditors. These results all have auditability and traceability, and are provided as input to the subsequent decision execution module and risk control storage module.

[0038] Through the above implementation, the super-spatiotemporal dynamic risk portrait engine can realize full-chain risk modeling of enterprises, doctors, patients, drugs, warehouses, transportation hubs and other aspects under the constraints of compliance and privacy protection, and stably output risk portrait results including causal chain, risk heat map and on-chain explanation card, thereby providing the medical and pharmaceutical risk control platform with accurate, verifiable and explainable portrait capabilities.

[0039] In an embodiment of the present application, the adaptive evolutionary decision engine is the core module of the intelligent decision layer, mainly used for realizing dynamic shunting of low-delay guardianship and high-precision deep reasoning in different risk control scenarios, and capable of self-evolving adaptation to new fraud patterns at an hourly level, so as to ensure the real-time, robustness and compliance of the medical and pharmaceutical risk control system.

[0040] Firstly, regarding the dual-track decision-making system, the adaptive evolutionary decision engine simultaneously constructs a guardrail track and a deep inference track. The guardrail track performs millisecond-level screening through rule constraints and a lightweight graph neural network, used in high-frequency prescription issuance, payment settlement, and drug dispensing processes to achieve immediate risk interception. The deep inference track employs causal reasoning and counterfactual evaluation to perform sub-second-level analysis of potential high-complexity risk events, thereby obtaining refined judgment results. To ensure that the dynamic switching between different tracks conforms to the upper bound of the false positive rate, the engine uses the inconsistency score of the calibration set to set a conformal shunting threshold, and performs real-time correction through an online recalibration mechanism in distribution drift scenarios to maintain coverage and stability. The conformal shunting threshold and the upper bound of the false positive rate are defined by the following formula: ; :time The shunt threshold; Inconsistent scores quantiles; :sample Inconsistent scores; :time Online calibration set; : Upper bound of target false positive rate.

[0041] Secondly, regarding the adversarial evolution mechanism, the adaptive evolutionary decision engine synthesizes three representative adversarial examples through the FraudGen-Diffusion diffusion generation model: medical insurance card cash-out samples, group-buying prescription samples, and ghost pharmacy samples. The generation of synthesized samples is controlled by business constraints and is used to drive the synchronous evolution of the rule base and decision model, thereby achieving rapid response to new fraud patterns. Simultaneously, the engine introduces differential privacy noise and cluster core summaries during adversarial example generation, constructing a privacy replay caching mechanism to prevent the adversarial generator from leaking real prescription data, transaction data, or inventory data through reverse reasoning. The adversarial example training formula is: ; : Generator parameters; Noise time step; Standard Gaussian noise; Network prediction noise; Samples at the noise scale; Business constraints (three types of models: cashing out medical insurance cards, group-buying prescriptions, and ghost pharmacies); Business constraint weights; Penalties for violating the business feasibility domain.

[0042] In cross-tenant learning, the adaptive evolutionary decision engine employs a cross-tenant Bayesian meta-learning mechanism. This mechanism first captures the shared risk structure across tenants through hierarchical priors, and then establishes a personalized tenant posterior distribution within each tenant to preserve the differences between different institutions. When a new tenant joins, cold start adaptation can be completed with a small number of target domain samples, ensuring that the model can reach a usable state in a short time. During long-term operation, the cross-tenant shared structure and the tenant posterior distribution are continuously migrated and updated simultaneously, ensuring that the risk strategy has both global sharing and local personalization. The cross-tenant hierarchical Bayesian meta-learning formula is as follows: ; Prior parameters shared across tenants; : Number of tenants; Tenant Personalized strategy parameters; Approximate posterior; Hierarchical priors; Tenant Decision-making losses; : KL regularization coefficient.

[0043] Regarding compliance assurance, the adaptive evolutionary decision engine transforms compliance clauses into formalized temporal logic formulas in LTL and CTL. It also employs a runtime shield mechanism to prune candidate actions before policy execution, shielding against risky actions that violate regulatory requirements at the source. If a violation is detected during policy execution, the engine uses the CEGIS mechanism to generate counterexamples and iteratively corrects them within a privacy-preserving computing environment, ensuring that the final policy meets verifiability and compliance requirements. The policy synthesis under formalized security shield constraints is expressed in the formula: ; : Strategies behind the security shield; :state; :action; : Action value; Action space; : Satisfies the sequential logic formula A set of safety actions; System model; : Compliance formulas encoded by LTL and CTL.

[0044] Finally, at the optimization strategy level, the adaptive evolutionary decision engine employs a risk-sensitive optimization method based on CVaR and ChanceConstraint. It controls the strategy's performance at the worst-case quantile through conditional value-at-risk constraints, reducing systemic losses caused by extreme risks. ChanceConstraint sets the minimum success probability of the decision-making scheme, ensuring stable prevention and control effects even in highly uncertain environments. Based on this, the engine comprehensively considers three objectives: limiting false positive rates, penalizing violations, and maximizing profits, achieving a dynamic balance between robustness and efficiency. The risk-sensitive optimization formula is as follows: ; Decision-making strategies; Strategy losses (including penalties for false positives and penalties for violations); : CVaR tradeoff coefficient; Confidence level Conditional Value at Risk (VaR); : Constraint functions (compliance or operational constraints); : Upper bound of the probability of default.

[0045] Through the above implementation methods, the adaptive evolutionary decision engine can flexibly switch between low-latency protection and deep inference, and achieve hourly adversarial evolution and cross-tenant adaptation in the case of rapid mutation of fraud patterns. At the same time, combined with formal security shield and risk-sensitive optimization mechanism, it ensures that the decision results meet compliance requirements and have high robustness, thereby providing medical service providers with reliable, explainable and evolvable intelligent decision-making capabilities.

[0046] In one embodiment of the present invention, the metaverse-style supply chain sandbox serves as the core module of the supply chain simulation layer. It is mainly used to perform multi-domain disturbance simulation and robustness assessment of the pharmaceutical supply chain in a virtual environment, thereby providing pharmaceutical service providers with provable, auditable, and optimizable preventive risk control measures.

[0047] Firstly, regarding twin construction, the metaverse-style supply chain sandbox employs a hybrid differentiable twin model. This model embeds the inventory flow equation, cold chain thermal equation, and transportation delay equation as mechanistic layers within a differentiable computational framework. Then, a neural residual network learns the residual terms that the mechanistic model fails to fit. During historical perturbation replay, this module uses simulation-based inference methods to jointly calibrate the mechanistic and residual layers, ensuring that the hybrid twin provides reliable extrapolation predictions even in unobserved scenarios. The hybrid differentiable twin formula is as follows: ; The predicted state of the supply chain in the next moment (inventory temperature field, in-transit quantity, and delivery delay). Differentiable realization of the mechanistic layer (inventory flow equation, cold chain thermodynamic equation, transportation time delay equation); Mechanism layer parameters; Neural residual networks; Current supply chain status; Decision-making and external control (replenishment, allocation, and transportation instructions); Noise or unmodeled disturbance.

[0048] Secondly, regarding simulation scenario generation, the metaverse-style supply chain sandbox introduces a multi-domain co-simulation mechanism. This mechanism simultaneously considers policy constraints, weather changes, energy consumption, logistics conditions, and human resource limitations, coupling all elements of the pharmaceutical supply chain. Based on this, a constraint diffusion scenario generator is used to generate combined scenarios encompassing three types of macroeconomic shocks: pandemic disruptions, typhoon disasters, and policy tightening. The generated results are guaranteed to meet macroeconomic statistical characteristics and regulatory constraints. Each generated simulation scenario is bound to parameter commitment information to support subsequent compliance verification and auditing. The formula for generating constraint diffusion scenarios under multi-domain co-simulation is as follows: ; : Diffusion generation parameters; : Scene state at the diffusion time step; Standard noise; Noise prediction network; , , , , These are five categories of constraints: policy, weather, energy consumption, logistics, and human resources. Constraint set; Constraint weights; Violation of Article Penalties for regulatory or physically feasible domains.

[0049] In terms of collaborative optimization, the metaverse-style supply chain sandbox is based on multi-agent reinforcement learning modeling, which sets upstream suppliers, warehousing nodes, trunk transportation and last-mile delivery fleets as multi-agents respectively, and designs a contract incentive mechanism on this basis. This mechanism includes tiered rebate contracts and service level penalty contracts, which are used to drive all participants to form a stable strategy Nash equilibrium under incentive constraints. In the optimization process, conditional risk value constraints and opportunity constraints are introduced to enable the supply chain scheduling scheme to maintain robustness in terms of fill rate and delivery window achievement rate, so as to ensure the timely supply of medical supplies even under high-risk disturbance conditions.

[0050] In terms of auditing and verification, the metaverse-style supply chain sandbox generates zero-knowledge proofs for each simulation scenario to demonstrate that resilience indicators and compliance parameters are indeed generated by the specified model and inputs. This mechanism enables regulatory agencies or customers to complete verification without knowing the underlying private data. Simultaneously, this module employs a cross-chain anchoring mechanism, writing the parameter commitments and result summaries of the simulation scenarios into the medical insurance chain and the drug regulatory chain, achieving mutual recognition of audits and result sharing between different consortium chains. The contract-incentivized multi-agent collaborative optimization formula is as follows: ; : A collection of participating entities (upstream suppliers, warehousing nodes, trunk transportation, and last-mile delivery fleets); :main body Strategies; :main body Operating returns; Contractual incentive rewards; , Rebate and penalty coefficients; : based on fill rate A ladder reciprocal function based on ; Based on delivery window achievement rate Penalty functions based on .

[0051] Finally, regarding robustness measurement, the metaverse-style supply chain sandbox establishes a sub-Bruker robust optimization model under uncertain sets to evaluate supply chain performance under different disturbance conditions. Based on this, it calculates the lower bound of the supply chain fill rate under the worst-case distribution condition, obtaining the sub-Bruker robustness index, and outputs the corresponding sensitivity curve and minimum inventory increment. These indicators provide quantitative basis for pharmaceutical service providers, enabling them to formulate replenishment plans and allocation strategies in advance, reducing the risk of supply chain disruptions. The sub-Bruker robustness index and the lower bound formula for the worst-case distribution are as follows: ; In the uncertain set The lower limit of the filling rate (the toughness index of the Brussels rod). : by radius Characterized by an uncertain distribution set; : Disturbance distribution; In strategy Supply chain fill rate.

[0052] Through the above implementation methods, the metaverse-style supply chain sandbox can combine mechanistic modeling with neural residual networks to complete the construction of hybrid differentiable twins; construct complex perturbation environments through multi-domain co-simulation and constraint diffusion scenario generation; achieve supply chain collaborative optimization based on multi-agent reinforcement learning and contract incentives; achieve encrypted auditing and mutual recognition with the help of zero-knowledge proofs and cross-chain anchoring; and quantify the worst-case performance and sensitivity of the supply chain through the deblog bar resilience index, thereby providing the pharmaceutical industry with verifiable, traceable and preventive supply chain risk management capabilities.

[0053] In one embodiment of the present invention, the blockchain risk storage layer serves as the core module of the compliance traceability layer. It is mainly used to perform trusted storage, governance management, and cross-chain mutual recognition of the entire process of pharmaceutical risk control in a consortium blockchain environment, so as to ensure the compliance, transparency, and traceability of risk control behavior.

[0054] Firstly, regarding the evidence storage mechanism, the blockchain risk evidence storage layer achieves full-chain on-chain operation. This module writes the risk interception actions generated during the risk control execution process into the consortium blockchain in the form of blockchain transaction records, ensuring that each risk blocking has an immutable certificate. At the same time, the causal chain from the hyperspatial dynamic risk profiling engine is completely recorded on the chain, enabling the causal basis of the risk judgment process to be traced in the long term. For the risk interpretation stage, this module writes the interpretation certificate (including causal stability proof and on-chain interpretation card) into the consortium blockchain, thereby ensuring that the risk control results are transparent and verifiable.

[0055] Secondly, in terms of governance mechanisms, the blockchain risk storage layer introduces a DAO-style governance framework. Pharmaceutical companies, hospitals, and regulators, as core participants in the consortium blockchain, jointly participate in the governance of risk control strategies. The governance process completes the blacklist update and strategy optimization adjustment through a voting mechanism, ensuring the transparency, fairness, and multi-party consensus of blacklist changes and strategy evolution. All governance proposals and voting results are written into the on-chain ledger, forming a traceable and auditable governance record.

[0056] Furthermore, in terms of cross-chain interoperability, the blockchain risk storage layer establishes an interoperability mechanism between the medical insurance chain and the drug regulatory chain to achieve cross-chain sharing of risk control audit information. Through the cross-chain verification protocol, this module ensures that risk control storage data can be recognized by both medical insurance regulatory agencies and drug regulatory agencies, avoiding information silos. At the same time, this module supports mutual recognition of results and joint auditing between multiple consortium chains, enabling different institutions to complete compliance verification and collaborative supervision without sharing original data.

[0057] Through the above implementation methods, the blockchain risk evidence storage layer can form a complete risk evidence storage closed loop through the synergistic effect of full-chain on-chain, DAO governance and cross-chain mutual recognition. This module ensures that risk interception actions, causal chains and explanatory certificates are traceable and tamper-proof, thereby providing a compliant, trustworthy and transparent operating foundation for the intelligent risk control platform of pharmaceutical service providers.

[0058] In one embodiment of the present invention, the multi-tenant SaaS management layer serves as a key support module for platform delivery and operation. It is mainly used to achieve isolated deployment of data and computing in a multi-organization, multi-tenant environment. At the same time, it enhances the overall risk control capability through a self-evolution mechanism and cross-tenant experience sharing, and provides a unified visualization and auditing interface to tenants and regulators.

[0059] Firstly, regarding tenant isolation, the multi-tenant SaaS management layer uses Kubernetes Namespace to partition resources for different tenants. This mechanism ensures that tenant-level data storage, computing resources, and network traffic are completely independent, so that model training, inference tasks, and data calls between different tenants do not interfere with each other. In the same physical cluster environment, this module provides tenants with elastic scaling capabilities through container orchestration and resource scheduling strategies, enabling resources to automatically expand during peak periods and be reclaimed on demand during low-load periods, thereby achieving a balance between security and efficiency.

[0060] Secondly, in terms of the self-evolving ecosystem, the multi-tenant SaaS management layer supports a cross-tenant fraud sample sharing mechanism. New fraud samples identified by different tenants during operation will be standardized and written into a shared sample pool, enabling the platform to quickly respond to new fraud patterns across the entire network. At the same time, this module enables cross-tenant propagation of new fraud patterns and updates of defense strategies within 24 hours through an adversarial experience sharing mechanism, thereby shortening response time. In response to the differentiated needs of different tenants, the platform adopts an adaptive strategy distribution mechanism to personalize the shared rules and evolutionary experience according to tenant characteristics, achieving a balance between rapid global response and personalized adaptation.

[0061] Furthermore, in terms of visualization and auditing, the multi-tenant SaaS management layer provides tenants with a unified visualization dashboard. This dashboard centrally displays risk profiling results from the hyper-temporal dynamic risk profiling engine, simulation prediction results from the meta-universe-style supply chain sandbox, and on-chain evidence information from the blockchain risk evidence storage layer. Tenants can view causal chains, risk heatmaps, resilience indicators, and risk control interception records in real time through the dashboard, thereby grasping the platform's operational status. At the same time, this module also provides a unified auditing interface, enabling tenants and regulators to conduct compliance checks on the risk profiling, simulation prediction, and on-chain evidence storage processes, ensuring that the platform operates in a transparent and verifiable environment.

[0062] Through the above implementation methods, the multi-tenant SaaS management layer works collaboratively in three aspects: tenant isolation, self-evolving ecosystem, and visual auditing. This not only ensures the independence and security of different tenants, but also improves the overall defense efficiency through cross-tenant fraud sample and anti-fraud experience sharing. Furthermore, it provides transparent, compliant, and reliable operational guarantees for pharmaceutical service providers and regulatory agencies through visualization and auditing interfaces, thereby supporting the efficient delivery and continuous evolution of the entire platform.

[0063] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart risk control SaaS platform for pharmaceutical service providers based on a microservice architecture, characterized in that: include: The privacy computing gateway layer is used to achieve compliant sharing, joint modeling, encrypted reasoning, and data ownership verification and traceability of cross-institutional data through trusted execution environments, multi-party secure computation, homomorphic encryption, zero-knowledge proofs, and blockchain fingerprint anchoring. The hyperspace-time dynamic risk profiling engine is used to construct a spatiotemporal-causal hypergraph. It performs continuous-time modeling based on neural differential equations, generates causal stability credentials by combining cross-environmental causal discovery and zero-knowledge proof, performs risk propagation analysis based on graph partial differential equations, and achieves proactive feature collection under privacy budget constraints, outputting causal chains, risk heatmaps, and on-chain explanation cards. An adaptive evolutionary decision engine is used to make triage decisions between guardrail tracks and deep inference tracks. It combines adversarial examples generated by diffusion to update the rule base, uses cross-tenant Bayesian meta-learning to achieve cold start adaptation and personalized migration, and ensures compliance through LTL / CTL formal security shield and CEGIS mechanism. It adopts CVaR and Chance Constraint-based optimization to achieve risk-sensitive control. The meta-universe-style supply chain sandbox is used to generate multi-domain co-simulation scenarios based on the fusion of mechanism equations and neural residual networks using a hybrid differentiable twin model. It combines multi-agent reinforcement learning and contract incentive mechanisms for collaborative optimization, and achieves simulation result auditing through zero-knowledge proofs and cross-chain anchoring. It outputs the resilience index and sensitivity curve of the sub-Bruker bar. The blockchain risk storage layer is used to record risk control interception actions, causal chains and explanatory certificates on the chain. It adopts the DAO governance mechanism to allow pharmaceutical companies, hospitals and regulators to jointly participate in policy updates, and realizes mutual recognition of results between the medical insurance chain and the drug regulatory chain through cross-chain verification protocol. The multi-tenant SaaS management layer is used to achieve tenant-level isolation through Kubernetes namespaces, achieve elastic scaling by combining container orchestration and resource scheduling strategies, achieve self-evolving defense through cross-tenant fraud sample sharing and adversarial experience propagation, and provide tenants and regulators with a unified visual dashboard and audit interface.

2. The intelligent risk control SaaS platform for pharmaceutical service providers based on a microservice architecture as described in claim 1, characterized in that: The hyper-temporal dynamic risk profiling engine models enterprises, doctors, patients, pharmaceuticals, warehouses, and transportation hubs as a set of nodes through a temporal-spatial-causal hypergraph, and models prescription, payment, dispensing, delivery, and inventory changes as a set of hyperedges. Each hyperedge carries a time interval, geographical partition, privacy identifier, and evidence weight, and supports event-driven dynamic incremental updates.

3. The intelligent risk control SaaS platform for pharmaceutical service providers based on a microservice architecture as described in claim 1, characterized in that: The adaptive evolutionary decision engine uses the FraudGen-Diffusion model to generate three types of adversarial samples: cash-out of medical insurance cards, group-buying prescriptions, and ghost pharmacies. It also combines differential privacy noise and cluster core summaries to construct a privacy replay cache to prevent the adversarial generator from leaking real data.

4. The intelligent risk control SaaS platform for pharmaceutical service providers based on a microservice architecture as described in claim 1, characterized in that: The adaptive evolutionary decision engine achieves cross-tenant Bayesian meta-learning through hierarchical prior and tenant posterior distributions, enabling rapid adaptation with a small number of target domain samples during cold start of new tenants.

5. The intelligent risk control SaaS platform for pharmaceutical service providers based on a microservice architecture as described in claim 1, characterized in that: The metaverse-style supply chain sandbox generates superimposed scenarios including pandemic disturbances, typhoon disasters, and policy tightening through a constraint diffusion scenario generator. Each scenario is bound with parameter commitment information to support zero-knowledge proofs.

6. The intelligent risk control SaaS platform for pharmaceutical service providers based on a microservice architecture as described in claim 1, characterized in that: The metaverse-style supply chain sandbox drives a stable Nash equilibrium strategy among multiple agents, including upstream suppliers, warehousing nodes, trunk transportation, and last-mile delivery fleets, through the design of tiered rebate contracts and service level penalty contracts.

7. The intelligent risk control SaaS platform for pharmaceutical service providers based on a microservice architecture as described in claim 1, characterized in that: The blockchain risk storage layer stores risk interception actions, causal stability proofs, and on-chain explanatory cards on the blockchain. All governance proposals and voting results are written into the on-chain ledger to form audit records.

8. The intelligent risk control SaaS platform for pharmaceutical service providers based on a microservice architecture as described in claim 1, characterized in that: The multi-tenant SaaS management layer enables cross-tenant propagation of new fraud patterns and updates of defense strategies within 24 hours through a shared fraud sample pool and anti-fraud experience propagation mechanism.

9. The intelligent risk control SaaS platform for pharmaceutical service providers based on a microservice architecture as described in claim 1, characterized in that: The visualization dashboard of the multi-tenant SaaS management layer displays the causal chain and risk heat map generated by the risk profiling engine, the resilience index and simulation prediction results output by the supply chain sandbox, and the on-chain evidence information recorded by the blockchain risk evidence storage layer.

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