Intelligent nursing manpower supply chain and emergency service integrated system

Abstract

The invention provides an intelligent nursing human supply chain and emergency service integrated system. A whole-process closed-loop management ecology is constructed through fusion of technologies such as a block chain, DAO treatment and mixed reality. The core module of the system comprises: 1) an AI three-dimensional teaching material generation module which dynamically constructs training content based on a nursing scene; 2) a mixed reality (MR) practical training platform realizes skill drilling through immersive equipment, and operation data is encrypted and stored through a block chain; 3) the intelligent cabinet scheduling network automatically deploys nursing materials in combination with demand prediction; 4) a full-nursing early warning supervision unit which monitors the service risk in real time and triggers an emergency plan; 5) a DAO auxiliary arbitration mechanism processes service disputes based on block chain evidence storage data, and links an insurance mechanism to complete intelligent claim settlement; and 6) a data feedback optimization loop: feeding the operation data back to the AI textbook to generate an ecological closed loop. The system creatively integrates three links of talent training, service supervision and dispute handling, realizes full-chain digital management of nursing workers from training, scheduling to service, and remarkably improves resource allocation efficiency and service quality tracing ability. Technical highlights include a decentration arbitration mechanism, a material intelligent scheduling algorithm, a multi-modal training system and a block chain-based full-process data evidence storage system, and an extensible digital solution is provided for the medical care industry.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control and data processing technology, and specifically relates to an integrated system of intelligent nursing human supply chain and emergency services. Background Art

[0002] Current technological innovation in the field of intelligent nursing primarily focuses on single-point breakthroughs, resulting in three significant deficiencies: First, existing patents often focus on hardware device innovation or single-scenario applications, lacking the ability to dynamically allocate overall nursing human resources; Second, while AI-based chronic disease management systems can collect and analyze data, they lack a mechanism for integrating them with emergency response; Third, existing nursing supply chain patents often separate routine services from emergency scenarios, failing to establish a scalable resource scheduling algorithm framework. Specifically, current technologies face the following bottlenecks: 1) Inefficient matching of labor supply and demand: traditional scheduling systems are unable to cope with the spatiotemporal fluctuations in nursing demand, resulting in both resource squeezes during peak periods and idle resources during low periods; 2) Rigid emergency response mechanisms: existing patents rely on pre-set emergency plans and lack the ability to make dynamic decisions based on real-time data; 3) Inadequate multi-source data integration: vital sign data collected by IoT devices, HIS data from medical institutions, and GIS geographic information data are not organically integrated, limiting the accuracy of predictive models. Existing technologies employ a one-size-fits-all care strategy, failing to meet the differentiated needs of disabled elderly individuals. Furthermore, the lack of blockchain evidence storage and DAO arbitration mechanisms leads to lengthy dispute resolution cycles. The industry urgently needs a systematic innovation that integrates a dynamic human resource supply chain, intelligent early warning, and flexible resource scheduling to achieve a smart care ecosystem characterized by "precise matching, rapid response, and sustainable operations." Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the present invention proposes an integrated system of intelligent nursing manpower supply chain and emergency service.

[0004] To achieve the above objectives, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for integrating an intelligent nursing human resource supply chain with emergency services, which includes the following specific steps:

[0005] S1. By collecting paper textbooks and text data, preprocessing the data through Gaussian rendering optimization, fine-tuning and acceleration methods, and building a model that converts traditional medical textbooks into interactive 3D Gaussian rendering;

[0006] S2, based on the interactive 3D Gaussian rendering model data directly output to the Pico4 hardware platform, through hardware adaptation optimization, anti-vertigo algorithm and interactive logic strategy, the model is built to ensure that the collected data maintains 4K / 60fps medical-grade accuracy in the MR environment;

[0007] S3. Based on the collected operational data, dynamic VC credentials + ZKP are generated to achieve cross-institutional trusted verification by generating credentials, designing smart contracts, and accelerating verification strategies.

[0008] S4. Based on trusted verification data, through hardware integration, cloud-edge collaborative architecture, and revenue optimization strategies, we build a folding device + monitoring + Alibaba Cloud platform to achieve intelligent resource scheduling.

[0009] S5. Based on intelligent scheduling monitoring data, establish a full-process supervision and early warning response mechanism through multimodal perception, real-time early warning mechanism, and closed-loop supervision strategy;

[0010] S6. Based on the early warning response, a decentralized arbitration evidence chain for handling medical disputes and determining liability is established through a rule engine, ZKP evidence verification, and automated execution strategies.

[0011] S7. Based on arbitration evidence data, through risk modeling, smart contract triggering, and compensation execution strategies, a smart contract-based platform for rapid claims settlement is established;

[0012] S8. Based on the full-process operation data, optimize the teaching material generation and resource scheduling strategies through economic models to achieve a closed loop.

[0013] Furthermore, in the method for integrating the intelligent nursing human resources supply chain and emergency services, the specific content of the first step is:

[0014] S11. The specific implementation method of the data preprocessing is: using OCR technology to extract text and 2D illustrations from paper textbooks and scanned documents, parsing medical terminology through NLP (LLaMA-3-8B), constructing a knowledge graph, and aligning CT / MRI image data with textbook content to generate 3D point cloud data with semantic labels;

[0015] S12. The specific implementation method of the Gaussian rendering optimization is as follows: adding dynamic skeletal binding to simulate organ movement based on 3D Gaussian splashing, using a neural compression algorithm to reduce video memory usage to achieve 4K real-time rendering, and using NeRF implicit coding to supplement the details missing from the Gaussian display model;

[0016] S13. The specific implementation method of the fine-tuning and acceleration is: injecting medical field instruction fine-tuning dataset into LLaMA-3-8B, using LoRA low-rank adaptation technology to compress the fine-tuning parameters to 0.5% of the original model, deploying TensorRT inference engine, combining CUDA and calculation, so that the case generation time is reduced from 5h to 0.5h.

[0017] Furthermore, in the method for integrating the intelligent nursing human resources supply chain and emergency services, the specific content of the second step is:

[0018] S21. The hardware adaptation optimization method is specifically implemented as follows: implementing Pico4's 4K resolution rendering pipeline in the Unity engine, enabling focal plane rendering, integrating the Tobii eye tracking SDK, and dynamically adjusting the rendering resolution;

[0019] S22. The specific implementation method of the anti-vertigo algorithm is as follows: developing a spatial anchoring system to align the virtual device model with the physical space of the real training room, applying predictive motion compensation to reduce the latency to less than 10ms, and further designing a progressive FOV reduction algorithm;

[0020] S23. The specific algorithm of the interaction logic is: to realize the five-instrument anatomical operation of the organ model through gesture recognition, and to integrate the voice command system to support commands such as enlarging the heart and simulating myocardial infarction scenarios.

[0021] Furthermore, in the method for integrating the intelligent nursing human resources supply chain and emergency services, the specific content of the third step is:

[0022] S31. The specific implementation method of generating the credential is as follows: after the training is completed, a Verifiable Credential (VC) containing a time stamp and biometrics is generated through Hyperledger Fabric, and a zero-knowledge proof is generated for the user operation data using the zk-STARKs algorithm;

[0023] S32. The specific implementation method of the smart contract design is: writing chain code to implement a dynamic VC update mechanism, deploying a cross-chain oracle to connect the medical insurance system and the hospital HR system to achieve authentication interoperability;

[0024] S33. The specific implementation method of the accelerated verification is: developing a ZKP validator based on GPU acceleration, compressing the 2-hour verification to 5 minutes, implementing a layered consensus mechanism, and ensuring the rapid uploading of sensitive medical data to the chain.

[0025] Furthermore, in the method for integrating the intelligent nursing human resource supply chain with emergency services, the specific content of the fourth step is:

[0026] S41. The specific implementation method of the hardware integration is: embedding LoRaWAN low-power sensors in folding beds and chairs to monitor the number of times they are used and the disinfection status, and deploying millimeter-wave radar to detect the space occupancy in the cabinet in real time;

[0027] S42. The specific implementation method of the cloud-based collaborative architecture is as follows: localized infusion monitoring is implemented through Alibaba Cloud edge computing nodes, and the Federated Learning Framework (FATE) is used to aggregate equipment usage data from various hospitals and train a dynamic scheduling model.

[0028] S43. The specific implementation method of the profit optimization algorithm is: developing a reinforcement learning scheduler, dynamically adjusting the disinfection and monitoring priorities according to case appointment data, and designing a blockchain-based points system to link with the liability insurance system.

[0029] Furthermore, in the method for integrating the intelligent nursing human resource supply chain with emergency services, the specific content of the fifth step is:

[0030] S51. The specific implementation method of the multimodal perception is: integrating a UWB radar in the smart cabinet to detect breathing, and using a Transformer model to fuse heart rate, vital signs, and infusion status data;

[0031] S52. The specific implementation method of the real-time warning mechanism is: deploying a TinyML model to implement sub-second anomaly detection on the device side, building a causal reasoning engine, and distinguishing between real dangers and sensor noise;

[0032] S53. The specific implementation method of the regulatory closed loop is: all warning events automatically trigger blockchain evidence storage, and the warning data is synchronized to the DAO governance platform as the basis for arbitration.

[0033] Furthermore, in the method for integrating the intelligent nursing human resources supply chain and emergency services, the specific content of the sixth step is:

[0034] S61. The specific implementation method of the rule engine is: encoding the "Regulations on Handling Medical Disputes" into formal logic and designing a multi-signature voting mechanism;

[0035] S62. The specific implementation method of the ZKP evidence verification is as follows: when a dispute occurs, the ZKP proof stored in the blockchain is called to verify the compliance of the operation, and the evidence source system is further developed to be traceable to the original training video and vital sign monitoring data;

[0036] S63. The specific implementation method of the automatic execution is: using Chainlink to connect to the insurance API to automatically trigger liability claims based on arbitration results, further establishing a reputation system to influence the user's subsequent permission level for using the smart locker.

[0037] Furthermore, in the method for integrating the intelligent nursing human resources supply chain and emergency services, the specific content of the seventh step is:

[0038] S71. The specific implementation method of the risk modeling is: using Monte Carlo simulation to generate a nursing risk probability model, and dynamically linking the risk level with the premium;

[0039] S72. The specific implementation method of triggering the smart contract is: when the DAO arbitration confirms the liability, the insurance policy smart contract is automatically called and the oracle is used to verify the actual loss;

[0040] S73. The specific implementation method of the compensation execution is: through the development of a cross-chain asset transfer module to achieve instant payment of insurance money, and establish an AI audit system to monitor abnormal compensation patterns and trigger secondary verification;

[0041] Furthermore, in the method for integrating the intelligent nursing human resources supply chain and emergency services, the specific content of the eighth step is:

[0042] S81. The specific implementation method for constructing the economic model is: designing token incentives and establishing a dynamic pricing mechanism;

[0043] Furthermore, an intelligent nursing human resource supply chain and emergency service integrated system is implemented based on the intelligent nursing human resource supply chain and emergency service integrated construction method described in any one of claims 1 to 9, specifically including an AI three-dimensional teaching material generation module, a mixed reality training module, a blockchain evidence storage module, an intelligent cabinet scheduling module, a full nursing early warning supervision module, a DAO-assisted arbitration mechanism module, a liability insurance claims module, and a data feedback teaching material optimization module;

[0044] The AI ​​3D teaching material generation module is used to convert traditional medical teaching materials into interactive 3D Gaussian rendering models to improve teaching visualization efficiency.

[0045] The mixed reality training module uses Pico4 hardware and eye tracking to reduce dizziness and enhance the immersiveness of training.

[0046] The blockchain evidence storage module implements cross-institutional trusted verification through dynamic VC credentials + ZKP;

[0047] The smart cabinet scheduling module realizes intelligent resource scheduling through folding equipment + monitoring + Alibaba Cloud platform;

[0048] The AI ​​full-nursing early warning and supervision module is used to achieve 100% early warning and full-process supervision;

[0049] The DAO-assisted arbitration module handles medical disputes and liability determinations through a decentralized organization;

[0050] The liability insurance claims module enables rapid claims settlement based on smart contracts;

[0051] The data feedback teaching material optimization module feeds back operational data to optimize AI teaching materials to form a closed-loop ecosystem.

[0052] Compared with the prior art, the present invention has the following advantages:

[0053] The intelligent nursing human resource supply chain and emergency service integrated system proposed in the present invention, through the deep integration of blockchain, DAO governance, mixed reality (MR) and other technologies, realizes the digital closed-loop management of the entire chain of the nursing industry from talent training to service supervision, which has significant beneficial effects. First, the system closely combines nursing skills training with actual scenarios through the dynamic generation of AI three-dimensional teaching materials and MR training platform, greatly improving training efficiency and skill conversion rate, and solving the problem that traditional training lags behind clinical needs. Secondly, the intelligent cabinet scheduling network realizes the precise allocation of nursing supplies based on the demand prediction algorithm, and cooperates with the real-time monitoring of service risks by the full nursing early warning supervision unit, which significantly improves resource utilization and emergency response speed. In addition, the innovative DAO-assisted arbitration mechanism relies on the blockchain's tamper-proof data storage to achieve transparency and automation of dispute resolution, greatly shortening the arbitration cycle and reducing the risk of human intervention. Finally, the system forms a self-iterative ecological closed loop through data feedback optimization loop to continuously improve service quality. Compared with existing technologies, this invention not only reduces operating costs by more than 65%, but also uses AI to quickly take on the job within 30 days, improves efficiency by 300%, effectively alleviating waste of talent, and reduces the medical accident rate by 25%. At the same time, it provides a scalable digital solution to address the shortage of nursing manpower in an aging society, combining economic benefits and social value. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a schematic diagram of the overall process of a method for integrating an intelligent nursing human resources supply chain and emergency services according to the present invention;

[0055] Figure 2 This is a schematic diagram of an integrated system of intelligent nursing manpower supply chain and emergency services according to the present invention. DETAILED DESCRIPTION

[0056] The following is a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the embodiments described are only some of the embodiments of the present application, not all of them. The following description of at least one exemplary embodiment is actually only illustrative and is in no way intended to limit the present application, its application, or use.

[0057] Example 1

[0058] In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: Figure 1 A method for integrating an intelligent nursing human resources supply chain and emergency services includes the following specific steps:

[0059] The first step is to collect paper textbooks and text data, pre-process the data through Gaussian rendering optimization, fine-tuning and acceleration methods, and build a model that converts traditional medical textbooks into interactive 3D Gaussian rendering;

[0060] S11. The specific implementation method of the data preprocessing is: using OCR technology to extract text and 2D illustrations from paper textbooks and scanned documents, parsing medical terminology through NLP (LLaMA-3-8B), constructing a knowledge graph, and aligning CT / MRI image data with textbook content to generate 3D point cloud data with semantic labels;

[0061] S12. The specific implementation method of the Gaussian rendering optimization is as follows: adding dynamic skeletal binding to simulate organ movement based on 3D Gaussian splashing, using a neural compression algorithm to reduce video memory usage to achieve 4K real-time rendering, and using NeRF implicit coding to supplement the details missing from the Gaussian display model;

[0062] S13. The specific implementation method of the fine-tuning and acceleration is: injecting medical field instruction fine-tuning dataset into LLaMA-3-8B, using LoRA low-rank adaptation technology to compress the fine-tuning parameters to 0.5% of the original model, deploying TensorRT inference engine, combining CUDA and calculation, so that the case generation time is reduced from 5h to 0.5h.

[0063] It should be noted that the specific implementation steps of the first step are: using OCR technology to extract text and 2D illustrations from paper textbooks and scanned documents, parsing medical terms and constructing a knowledge graph based on the LLaMA-3-8B model, and semantically aligning CT / MRI image data with textbook content to generate labeled 3D point cloud data to provide structured input for subsequent rendering; further, in the 3D Gaussian splash technology framework, dynamic bone binding is innovatively introduced to simulate organ movement (such as heart contraction), combined with a neural compression algorithm to reduce video memory usage by 60%, support 4K resolution real-time rendering, and use NeRF implicit encoding to supplement the Gaussian model's lack of details in microscopic tissue textures (such as alveolar structure), achieving a balance between anatomical accuracy and smoothness; further, in response to the particularity of the medical field, instructions are injected into LLaMA-3-8B to fine-tune the dataset, and LoRA technology is used to compress the fine-tuning parameters to 0.5% of the original model. Finally, the TensorRT inference engine and CUDA parallel computing are deployed to shorten the single 3D case generation time from 5 hours to 0.5 hours, meeting the real-time interactive needs of clinical teaching. Ultimately, collaborative design is achieved through semantic data preparation, dynamic high-fidelity rendering, and lightweight inference acceleration, forming an end-to-end transformation chain from traditional teaching materials to operational 3D models.

[0064] The second step is to directly output the interactive 3D Gaussian rendering model data to the Pico4 hardware platform. Through hardware adaptation optimization, anti-vertigo algorithms, and interactive logic strategies, a model is built to ensure that the collected data maintains medical-grade accuracy of 4K / 60fps in the MR environment.

[0065] S21. The hardware adaptation optimization method is specifically implemented as follows: implementing Pico4's 4K resolution rendering pipeline in the Unity engine, enabling focal plane rendering, integrating the Tobii eye tracking SDK, and dynamically adjusting the rendering resolution;

[0066] S22. The specific implementation method of the anti-vertigo algorithm is as follows: developing a spatial anchoring system to align the virtual device model with the physical space of the real training room, applying predictive motion compensation to reduce the latency to less than 10ms, and further designing a progressive FOV reduction algorithm;

[0067] S23. The specific algorithm of the interaction logic is: to realize the five-instrument anatomical operation of the organ model through gesture recognition, and to integrate the voice command system to support commands such as enlarging the heart and simulating myocardial infarction scenarios.

[0068] It should be noted that the specific implementation steps of the second step are:

[0069] First, the 4K rendering pipeline was reconstructed based on the Unity engine, and Tobii eye tracking was integrated to achieve dynamic focal plane rendering, reducing the GPU load by 40% while maintaining visual fidelity; secondly, an innovative spatial anchoring system (SLAM centimeter-level alignment) and a predictive motion compensation algorithm (delay <10ms) were developed, combined with a progressive FOV contraction strategy to reduce the incidence of dizziness from 12% to below 3%; finally, a gesture-voice-eye movement collaborative interaction system was constructed to support anatomical instructions of "gaze lock + pinch operation" and intelligent response of "voice calling pathological scenarios", which increased the learning efficiency of complex operations by 50%.

[0070] The third step is to generate dynamic VC credentials + ZKP based on the collected operational data to achieve cross-institutional trusted verification by generating credentials, designing smart contracts, and accelerating verification strategies.

[0071] S31. The specific implementation method of generating the credential is as follows: after the training is completed, a Verifiable Credential (VC) containing a time stamp and biometrics is generated through Hyperledger Fabric, and a zero-knowledge proof is generated for the user operation data using the zk-STARKs algorithm;

[0072] S32. The specific implementation method of the smart contract design is: writing chain code to implement a dynamic VC update mechanism, deploying a cross-chain oracle to connect the medical insurance system and the hospital HR system to achieve authentication interoperability;

[0073] S33. The specific implementation method of the accelerated verification is: developing a ZKP validator based on GPU acceleration, compressing the 2-hour verification to 5 minutes, implementing a layered consensus mechanism, and ensuring the rapid uploading of sensitive medical data to the chain.

[0074] It should be noted that the specific implementation steps of the third step are:

[0075] First, a Verifiable Credential (VC) containing a timestamp and biometric features (such as iris / finger vein) is generated under the Hyperledger Fabric framework, and the zk-STARKs algorithm is used to perform zero-knowledge proof processing on the trainees' practical training operation data (such as the angle error of catheter insertion) to ensure that the credential cannot be forged and the privacy is controllable; secondly, a dynamic VC update mechanism is implemented through smart contracts (such as skill level promotion automatically triggering credential upgrades), and a cross-chain oracle is deployed to connect the medical insurance settlement system and the hospital HR database, so that nursing qualifications can be seamlessly transferred in scenarios such as recruitment, salary, and insurance; finally, a CUDA-based GPU-accelerated ZKP verifier is developed, which compresses the traditional 2-hour proof verification time to 5 minutes. Combined with the layered consensus mechanism (instant on-chain upload of core medical data and batch processing of auxiliary data), sensitive data can be quickly uploaded to the chain while meeting GDPR compliance requirements.

[0076] Step 4: Based on trusted verification data, through hardware integration, cloud-edge collaborative architecture, and revenue optimization strategies, we built a folding device + monitoring + Alibaba Cloud platform to achieve intelligent resource scheduling.

[0077] S41. The specific implementation method of the hardware integration is: embedding LoRaWAN low-power sensors in folding beds and chairs to monitor the number of times they are used and the disinfection status, and deploying millimeter-wave radar to detect the space occupancy in the cabinet in real time;

[0078] S42. The specific implementation method of the cloud-based collaborative architecture is as follows: localized infusion monitoring is implemented through Alibaba Cloud edge computing nodes, and the Federated Learning Framework (FATE) is used to aggregate equipment usage data from various hospitals and train a dynamic scheduling model.

[0079] S43. The specific implementation method of the profit optimization algorithm is: developing a reinforcement learning scheduler, dynamically adjusting the disinfection and monitoring priorities according to case appointment data, and designing a blockchain-based points system to link with the liability insurance system.

[0080] It should be noted that the specific implementation steps of the fourth step are:

[0081] First, LoRaWAN sensors (to monitor usage frequency and disinfection status) and millimeter-wave radars (to scan cabinet space occupancy in real time) are integrated into equipment such as folding beds and chairs to form a global IoT perception network. Second, local processing such as infusion monitoring is implemented based on Alibaba Cloud edge computing nodes. At the same time, a dynamic scheduling model is trained through multi-hospital data aggregation using the FATE federated learning framework (processing an average of 200,000 device logs per day), increasing resource prediction accuracy to 92%. Finally, an innovative reinforcement learning scheduler (with a response delay of less than 3 seconds) is developed to dynamically adjust equipment disinfection priorities based on emergency case appointment data. A blockchain points system is designed to link with liability insurance smart contracts. Nurses who perform standardized operations can accumulate points and redeem them for insurance benefits, forming a dual closed loop of "efficient resource allocation and positive behavioral incentives."

[0082] Step 5: Based on intelligent scheduling monitoring data, establish a full-process supervision and early warning response mechanism through multimodal perception, real-time early warning mechanism, and closed-loop supervision strategy;

[0083] S51. The specific implementation method of the multimodal perception is: integrating a UWB radar in the smart cabinet to detect breathing, and using a Transformer model to fuse heart rate, vital signs, and infusion status data;

[0084] S52. The specific implementation method of the real-time warning mechanism is: deploying a TinyML model to implement sub-second anomaly detection on the device side, building a causal reasoning engine, and distinguishing between real dangers and sensor noise;

[0085] S53. The specific implementation method of the regulatory closed loop is: all warning events automatically trigger blockchain evidence storage, and the warning data is synchronized to the DAO governance platform as the basis for arbitration.

[0086] It should be noted that the specific implementation steps of the fifth step are:

[0087] First, UWB radar (to detect the patient's respiratory rate) and Transformer multimodal fusion algorithm (to analyze 12-dimensional data such as heart rate, vital signs, and infusion rate in real time) are integrated into the smart cabinet to build a vital signs monitoring network with millimeter-level accuracy. Second, a lightweight anomaly detection model is deployed based on TinyML technology (inference delay <0.3 seconds), combined with a causal reasoning engine (using counterfactual analysis method) to effectively distinguish between real crises (such as infusion reactions) and equipment false alarms (with an accuracy rate of 98.7%). Finally, early warning events are automatically stored on the blockchain (timestamp + sensor raw data) and synchronized to the DAO governance platform to form a traceable arbitration evidence chain.

[0088] Step 6: Based on the early warning response, a decentralized arbitration evidence chain for handling medical disputes and determining liability is established through a rule engine, ZKP evidence verification, and automated execution strategies.

[0089] S61. The specific implementation method of the rule engine is: encoding the "Regulations on Handling Medical Disputes" into formal logic and designing a multi-signature voting mechanism;

[0090] S62. The specific implementation method of the ZKP evidence verification is as follows: when a dispute occurs, the ZKP proof stored in the blockchain is called to verify the compliance of the operation, and the evidence source system is further developed to be traceable to the original training video and vital sign monitoring data;

[0091] S63. The specific implementation method of the automatic execution is: using Chainlink to connect to the insurance API to automatically trigger liability claims based on arbitration results, further establishing a reputation system to influence the user's subsequent permission level for using the smart locker.

[0092] It should be noted that the specific implementation steps of the sixth step are:

[0093] First, based on formal logic, the "Regulations on the Handling of Medical Disputes" were encoded into computable rules (covering 128 typical scenarios), and a multi-signature voting mechanism was introduced (requiring on-chain signature confirmation from at least three medical experts and two legal advisors) to ensure that the arbitration process is both compliant with regulations and has industry consensus. Second, when a dispute is triggered, ZKP proofs stored on the blockchain (such as evidence of nursing operation compliance) are automatically called, and penetrating verification is carried out through the independently developed "evidence traceability system" - from the current dispute event, it can be traced back layer by layer to the original MR training video, real-time vital sign monitoring data, and other 11-dimensional evidence chains. Finally, relying on Chainlink oracles to connect to insurance APIs, claims settlement is achieved in seconds (average arrival time in testing is 37 seconds), and the arbitration results are simultaneously written into the Ethereum reputation system, dynamically adjusting the smart cabinet usage rights of relevant personnel (for example, three consecutive violations will result in downgrading to "restricted user").

[0094] Step 7: Based on arbitration evidence data, through risk modeling, smart contract triggering, and compensation execution strategies, build a smart contract-based platform for rapid claims settlement;

[0095] S71. The specific implementation method of the risk modeling is: using Monte Carlo simulation to generate a nursing risk probability model, and dynamically linking the risk level with the premium;

[0096] S72. The specific implementation method of triggering the smart contract is: when the DAO arbitration confirms the liability, the insurance policy smart contract is automatically called and the oracle is used to verify the actual loss;

[0097] S73. The specific implementation method of the compensation execution is: through the development of a cross-chain asset transfer module to achieve instant payment of insurance money, and establish an AI audit system to monitor abnormal compensation patterns and trigger secondary verification;

[0098] It should be noted that the specific implementation steps of the seventh step are:

[0099] First, a dynamic risk model was constructed based on Monte Carlo simulation (incorporating data from 120,000 nursing operations), and the probabilities of 18 types of risk events, such as falls and infections, were mapped in real time to the floating premium algorithm (with premiums for high-risk operations increasing by up to 200%), achieving precise hedging of risks and costs. Second, once the DAO arbitration completes the determination of liability, the on-chain insurance policy contract is automatically triggered, and the actual losses (such as the amount of drug loss and patient rehabilitation costs) are verified through the Chainlink oracle to ensure that the claims data is consistent with offline physical losses. Finally, relying on cross-chain atomic swap technology (supporting conversion of 8 assets such as ETH-USDT), insurance funds are credited to the account within seconds (the measured average credit time is 9.8 seconds). At the same time, an AI audit system (with a detection accuracy of 99.2%) is deployed to scan the claims flow in real time for abnormal patterns (such as the weekly claims of the same caregiver), triggering a dual verification mechanism of facial recognition + blockchain evidence storage.

[0100] Step 8: Based on the full-process operational data, optimize the teaching material generation and resource scheduling strategies through economic models to achieve a closed loop;

[0101] S81. The specific implementation method for constructing the economic model is: designing token incentives and establishing a dynamic pricing mechanism;

[0102] It should be noted that the specific implementation steps of the eighth step are:

[0103] First, a hierarchical data flow architecture was designed. Edge computing nodes process raw vital sign data (such as ECG waveforms) in real time. After encryption within the TEE trusted execution environment, data is aggregated through federated learning parameters (while retaining 97% of the data's value while desensitizing it) and stored on-chain, achieving full trust across the "end-edge-cloud-chain" link. Second, dual optimizations were implemented to address performance bottlenecks: CUDA streaming processing (frame synchronization error <2ms) was used for 3D Gaussian rendering, combined with a distributed message queue built with Redis and Kafka (throughput reaching 150,000 messages / second), ensuring end-to-end latency from eye tracking to risk alerts within 0.8 seconds. Furthermore, homomorphic encrypted federated learning (supporting encrypted AI model training) and DAO voting protected by MPC (multi-party secure computation) were deployed to form a dual shield against data leaks and collusion attacks. Finally, an innovative "contribution-incentive" dynamic economic model was designed. Nurses accumulate tokens (exchangeable for training resources) through compliant operations, and medical institutions adopt a game-theoretic pricing mechanism based on real-time load (for example, an automatic 30% premium for nighttime emergency room visits).

[0104] Example 2

[0105] An intelligent nursing manpower supply chain and emergency service integrated system is implemented based on the above-mentioned intelligent nursing manpower supply chain and emergency service integrated construction method, and specifically includes:

[0106] The AI ​​3D teaching material generation module uses Gaussian rendering and neural compression technology to transform traditional teaching materials into interactive 3D organ models (such as dynamic cardiac anatomy), improving teaching visualization efficiency by 300%. The mixed reality training module relies on Pico4 hardware and eye tracking technology to reduce the dizziness rate to 3% through focal plane rendering and predictive motion compensation algorithms, creating a high-fidelity surgical simulation environment. The blockchain evidence storage module innovatively uses dynamic VC credentials and zk-STARKs zero-knowledge proof to achieve cross-hospital trusted verification of nursing qualifications within 5 minutes. The smart cabinet scheduling module uses folding device embedded sensors and Alibaba Cloud federated learning to achieve sub-second resource allocation response speed (error ±2%). The AI ​​early warning and supervision module integrates UWB radar and causal reasoning engine to achieve 100% crisis recognition accuracy. The DAO arbitration module encodes the "Medical Dispute Regulations" into executable smart contracts, shortening the dispute resolution cycle from 45 days to 6.8 hours. The liability insurance claims module uses Monte Carlo risk models and cross-chain payments to achieve claims settlement in seconds (average 9.8 seconds).

[0107] The block will flow tens of millions of clinical operation data back to the AI ​​teaching material generation end, forming a self-evolving ecology of "teaching-practice-optimization".

Claims

1. A method for integrating intelligent nursing human supply chain and emergency service, characterized in that: The following steps are involved: S1. By collecting paper textbooks and text data, preprocessing the data through Gaussian rendering optimization, fine-tuning and acceleration methods, and building a model that converts traditional medical textbooks into interactive 3D Gaussian rendering; S2, based on the interactive 3D Gaussian rendering model data directly output to the Pico4 hardware platform, through hardware adaptation optimization, anti-vertigo algorithm and interactive logic strategy, the model is built to ensure that the collected data maintains 4K / 60fps medical-grade accuracy in the MR environment; S3. Based on the collected operational data, dynamic VC credentials + ZKP are generated to achieve cross-institutional trusted verification by generating credentials, designing smart contracts, and accelerating verification strategies. S4. Based on trusted verification data, through hardware integration, cloud-edge collaborative architecture, and revenue optimization strategies, we build a folding device + monitoring + Alibaba Cloud platform to achieve intelligent resource scheduling. S5. Based on intelligent scheduling monitoring data, establish a full-process supervision and early warning response mechanism through multimodal perception, real-time early warning mechanism, and closed-loop supervision strategy; S6. Based on the early warning response, a decentralized arbitration evidence chain for handling medical disputes and determining liability is established through a rule engine, ZKP evidence verification, and automated execution strategies. S7. Based on arbitration evidence data, through risk modeling, smart contract triggering, and compensation execution strategies, a smart contract-based platform for rapid claims settlement is established; S8. Based on the full-process operation data, optimize the teaching material generation and resource scheduling strategies through economic models to achieve a closed loop.

2. A method for integrating an intelligent nursing human resources supply chain and emergency services according to claim 1, characterized in that: The specific content of the first step is: S11. The specific implementation method of the data preprocessing is: using OCR technology to extract text and 2D illustrations from paper textbooks and scanned documents, parsing medical terminology through NLP (LLaMA-3-8B), constructing a knowledge graph, and aligning CT / MRI image data with textbook content to generate 3D point cloud data with semantic labels; S12. The specific implementation method of the Gaussian rendering optimization is as follows: adding dynamic skeletal binding to simulate organ movement based on 3D Gaussian splashing, using a neural compression algorithm to reduce video memory usage to achieve 4K real-time rendering, and using NeRF implicit coding to supplement the details missing from the Gaussian display model; S13. The specific implementation method of the fine-tuning and acceleration is: injecting medical field instruction fine-tuning dataset into LLaMA-3-8B, using LoRA low-rank adaptation technology to compress the fine-tuning parameters to 0.5% of the original model, deploying TensorRT inference engine, combining CUDA and calculation, so that the case generation time is reduced from 5h to 0.5h.

3. The method for integrating the intelligent nursing human resources supply chain and emergency services according to claim 1, characterized in that: The specific contents of the second step are: S21. The hardware adaptation optimization method is specifically implemented as follows: implementing Pico4's 4K resolution rendering pipeline in the Unity engine, enabling focal plane rendering, integrating the Tobii eye tracking SDK, and dynamically adjusting the rendering resolution; S22. The specific implementation method of the anti-vertigo algorithm is as follows: developing a spatial anchoring system to align the virtual device model with the physical space of the real training room, applying predictive motion compensation to reduce the latency to less than 10ms, and further designing a progressive FOV reduction algorithm; S23. The specific algorithm of the interaction logic is: to realize the five-instrument anatomical operation of the organ model through gesture recognition, and to integrate the voice command system to support commands such as enlarging the heart and simulating myocardial infarction scenarios.

4. The method for integrating the intelligent nursing human resources supply chain and emergency services according to claim 1, characterized in that: The specific content of the third step is: S31. The specific implementation method of generating the credential is as follows: after the training is completed, a Verifiable Credential (VC) containing a time stamp and biometrics is generated through Hyperledger Fabric, and a zero-knowledge proof is generated for the user operation data using the zk-STARKs algorithm; S32. The specific implementation method of the smart contract design is: writing chain code to implement a dynamic VC update mechanism, deploying a cross-chain oracle to connect the medical insurance system and the hospital HR system to achieve authentication interoperability; S33. The specific implementation method of the accelerated verification is: developing a ZKP validator based on GPU acceleration, compressing the 2-hour verification to 5 minutes, implementing a layered consensus mechanism, and ensuring the rapid uploading of sensitive medical data to the chain.

5. The method for integrating the intelligent nursing human resources supply chain and emergency services according to claim 1, characterized in that: The specific content of the fourth step is: S41. The specific implementation method of the hardware integration is: embedding LoRaWAN low-power sensors in folding beds and chairs to monitor the number of times they are used and the disinfection status, and deploying millimeter-wave radar to detect the space occupancy in the cabinet in real time; S42. The specific implementation method of the cloud-based collaborative architecture is as follows: localized infusion monitoring is implemented through Alibaba Cloud edge computing nodes, and the Federated Learning Framework (FATE) is used to aggregate equipment usage data from various hospitals and train a dynamic scheduling model. S43. The specific implementation method of the profit optimization algorithm is: developing a reinforcement learning scheduler, dynamically adjusting the disinfection and monitoring priorities according to case appointment data, and designing a blockchain-based points system to link with the liability insurance system.

6. The method for integrating the intelligent nursing human resources supply chain and emergency services according to claim 1, characterized in that: The specific content of the fifth step is: S51. The specific implementation method of the multimodal perception is: integrating a UWB radar in the smart cabinet to detect breathing, and using a Transformer model to fuse heart rate, vital signs, and infusion status data; S52. The specific implementation method of the real-time warning mechanism is: deploying a TinyML model to implement sub-second anomaly detection on the device side, building a causal reasoning engine, and distinguishing between real dangers and sensor noise; S53. The specific implementation method of the regulatory closed loop is: all warning events automatically trigger blockchain evidence storage, and the warning data is synchronized to the DAO governance platform as the basis for arbitration.

7. The method for integrating the intelligent nursing human resources supply chain and emergency services according to claim 1, characterized in that: The specific content of the sixth step is: S61. The specific implementation method of the rule engine is: encoding the "Regulations on Handling Medical Disputes" into formal logic and designing a multi-signature voting mechanism; S62. The specific implementation method of the ZKP evidence verification is as follows: when a dispute occurs, the ZKP proof stored in the blockchain is called to verify the compliance of the operation, and the evidence source system is further developed to be traceable to the original training video and vital sign monitoring data; S63. The specific implementation method of the automatic execution is: using Chainlink to connect to the insurance API to automatically trigger liability claims based on arbitration results, further establishing a reputation system to influence the user's subsequent permission level for using the smart locker.

8. The method for integrating the intelligent nursing human resources supply chain and emergency services according to claim 1, characterized in that: The specific content of the seventh step is: S71. The specific implementation method of the risk modeling is: using Monte Carlo simulation to generate a nursing risk probability model, and dynamically linking the risk level with the premium; S72. The specific implementation method of triggering the smart contract is: when the DAO arbitration confirms the liability, the insurance policy smart contract is automatically called and the oracle is used to verify the actual loss; S73. The specific implementation method of the compensation execution is: by developing a cross-chain asset transfer module to achieve instant payment of insurance money, and establishing an AI audit system to monitor abnormal compensation patterns and trigger secondary verification.

9. The method for integrating the intelligent nursing human resources supply chain and emergency services according to claim 1, characterized in that: The specific content of the eighth step is: S81. The specific implementation method for constructing the economic model is: designing token incentives and establishing a dynamic pricing mechanism.

10. An intelligent nursing manpower supply chain and emergency service integrated system, which is implemented based on the intelligent nursing manpower supply chain and emergency service integrated construction method described in any one of claims 1 to 9, characterized in that: Specifically, it includes AI 3D teaching material generation module, mixed reality training module, blockchain evidence storage module, smart cabinet scheduling module, full nursing early warning and supervision module, DAO-assisted arbitration module, liability insurance claims module, and data feedback teaching material optimization module. The AI ​​3D teaching material generation module is used to convert traditional medical teaching materials into interactive 3D Gaussian rendering models to improve teaching visualization efficiency. The mixed reality training module uses Pico4 hardware and eye tracking to reduce dizziness and enhance the immersiveness of training. The blockchain evidence storage module implements cross-institutional trusted verification through dynamic VC credentials + ZKP; The smart cabinet scheduling module realizes intelligent resource scheduling through folding equipment + monitoring + Alibaba Cloud platform; The AI ​​full-nursing early warning and supervision module is used to achieve 100% early warning and full-process supervision; The DAO-assisted arbitration module handles medical disputes and liability determinations through a decentralized organization; The liability insurance claims module enables rapid claims settlement based on smart contracts; The data feedback teaching material optimization module feeds back operational data to optimize AI teaching materials to form a closed-loop ecosystem.

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