802 results about "Security validation" patented technology

The invention relates to the technical field of factory management. The intelligent factory management method based on digital twinning comprises the following steps: performing dynamic identification and analysis processing on a multi-source heterogeneous communication protocol of factory equipment to obtain a standardized data stream; constructing a three-dimensional digital twinborn model comprising an equipment state and a technological process; generating a virtual control instruction according to an analog control strategy in the three-dimensional digital twin model; sandbox security verification processing is carried out on the virtual control instruction, and a compliance instruction set is screened out; converting the compliance instruction set into a protocol format supported by the target equipment, generating a reverse control instruction and issuing the reverse control instruction to the physical equipment; performing closed-loop feedback optimization processing according to the difference between the execution result of the physical equipment and the prediction result of the three-dimensional digital twinborn model, so as to solve the problem that the digital twinborn model lacks real-time sensor data injection and dynamic behavior constraint embedding, ensure that the model can reflect the real state of the equipment, and improve the real-time performance of the equipment. And the reliability and the execution security of the virtual control instruction are improved.

The invention discloses a network security verification method and system for a security system, and belongs to the technical field of network security, and the method comprises the steps: obtaining a biological feature recognition request triggered by a user on terminal equipment, synchronously collecting dynamic behavior track data, and generating a behavior feature code; inputting physiological feature data and behavior feature codes in the biological feature recognition request into a feature verification model, when an output biological behavior correlation index is lower than a first preset threshold value, triggering a dynamic verification strategy adjustment instruction, generating a multi-dimensional verification superposition strategy, and executing at least two verification operations in the multi-dimensional verification superposition strategy, the verification result is input into the distributed time synchronization node to generate an encryption token; and when the synchronous matching degree of the encryption token among different nodes exceeds a second preset threshold value, outputting a security verification passing instruction. According to the method, multi-modal feature fusion, dynamic hierarchical verification and time-space encryption technologies are adopted, replay attacks are prevented, an adaptive defense system is constructed, and reliable authentication of a high-security network scene is ensured.

The invention discloses a CAPEC vulnerability management system based on a large language model, and belongs to the technical field of network security. The system comprises three modules: a vulnerability-CAPEC dynamic mapping module jointly encodes vulnerability description and code context through a bimodal large language model, accurately associates vulnerability logic with a CAPEC attack mode in combination with comparative learning and knowledge graph construction, and breaks through semantic limitation of traditional rule matching; the adversarial repair code generation module is used for generating high-robustness repair codes through adversarial training and syntax tree verification by fusing CAPEC relieving suggestions and code features on the basis of the association result, so that the secondary vulnerability risk is remarkably reduced; and the full-process automatic verification and DevOps integration module performs multi-dimensional security verification such as symbolic execution, fuzzy testing and the like on the generated repair code, and deeply integrates a development tool chain to realize real-time pushing and closed-loop management of a repair scheme.

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

The invention discloses an edge computing assisted wireless communication system, which relates to the technical field of edge computing, and comprises an edge computing node cluster which is deployed at a base station and an access point and provides distributed computing and storage capability; the resource sensing module is used for monitoring computing resources, communication resources and task queue states of the edge nodes in real time; the dynamic scheduling controller is used for dynamically deciding a task unloading path based on reinforcement learning and game theory algorithms, wherein the task unloading path comprises local processing, edge node cooperative processing, cloud unloading and global time delay and energy consumption optimization; the cross-layer communication interface supports multi-protocol fusion of 5G NR and Wi-Fi 6, and realizes low-delay data interaction between edge nodes; and the security verification module is used for ensuring credibility and integrity of data between edge nodes through a lightweight block chain and a TEE (Trusted Execution Environment). According to the invention, through a hybrid decision framework of reinforcement learning and game theory, the problem that a single algorithm in a traditional scheme cannot give consideration to dynamic environment adaptability and multi-node benefit coordination is solved.

The invention provides an interactive integration method and system for an AI large model and a low-code platform, and relates to the technical field of interaction of the AI large model and the low-code platform, and the method comprises the steps: obtaining a task demand input by a user, analyzing the task demand into a structured task instruction, automatically matching an optimal AI model, and dynamically generating an adaptive API call parameter, according to the method, the AI large model and the low-code platform can be deeply integrated, the technical problems of integration complexity, interaction mode solidification, updating lag and the like are solved, and the method has the advantages that the method is simple and convenient to operate, and the method is suitable for popularization and application. According to the method, a user can call a complex model without AI professional knowledge, the low-code development efficiency is improved by more than 50%, and the security in a data processing process is enhanced by introducing a security verification mechanism, so that the integration efficiency of a low-code platform and an AI large model is greatly improved.

The invention belongs to the field of artificial intelligence, and discloses a scheduling strategy selection large model training method based on reinforcement learning, and the method comprises the steps: constructing a virtualized cluster environment; generating training data; carrying out large model base pre-training; learning environment interaction is reinforced; performing priority experience playback training; performing near-end strategy optimization; carrying out multi-expert strategy distillation; policy security verification is carried out; carrying out progressive online deployment; and feedback driving continuous optimization is carried out. According to the method, the resource topology, the task portrait and the dynamic load feature are decomposed to the special agent, and structured semantic tags such as a high-bandwidth demand task and a critical overheat node are generated, so that the heterogeneous resource state recognition accuracy is improved by 40%, and the multi-dimensional feature fusion efficiency is improved by 35%.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent decision-making method, device, equipment and medium based on security boundary constraint. Limiting a security boundary according to a macroscopic decision direction, retrieving updated knowledge from the dynamic knowledge graph and generating a multi-modal decision priority analysis result fused with the updated knowledge, determining a target decision in the security boundary based on the multi-modal decision priority analysis result, and performing security verification by comparing the target decision with the security boundary. And generating a target decision that the security verification is passed. According to the method, multi-modal decision priority analysis is carried out by combining unified feature representation and updated knowledge, and target decisions are screened and verified in a security boundary, so that effective fusion and dynamic knowledge enhancement of multi-source information are realized, and the accuracy and security of decisions in a complex environment are improved.

The invention provides a security verification method for a large model service operation environment, a medium, equipment and a product, and relates to the technical field of computers, large model services are deployed in a container group created in a virtual machine, and the virtual machine is deployed in a hardware-based trusted execution environment, so that the security verification of the large model service operation environment is realized. The method comprises the following steps: acquiring environment measurement corresponding to a running environment of a large model service through a remote attestation service deployed in a hardware-based trusted execution environment, the environment measurement at least comprising a first measurement corresponding to the trusted execution environment in which a virtual machine is located and a container measurement under a container stack dimension corresponding to a container group; the remote attestation service generates a remote attestation report according to the environmental metrics to enable a user to validate the security of the operating environment through the remote attestation report. According to the invention, not only can the attack from the IaaS layer be shielded, but also the credibility verification under the dimension of the virtual machine and the credibility verification under the dimension of the container stack can be provided, so that the credibility of the user to the service provided by the service provider is improved.

The invention relates to the technical field of quantum key distribution, and discloses a communication protocol security verification method based on quantum key distribution, which comprises the following steps of: establishing a quantum communication channel framework, loading protocol parameters to generate an initial security verification model, configuring a security state transition rule, and performing cross-layer data fusion in combination with quantum entity parameters; and obtaining a whole-process safety tracking model. And executing security path deduction through the model, generating a key exchange trajectory chain, constructing security trajectory simulation data, establishing a quantum conflict resolution mechanism, training and generating a security policy optimization model, and outputting security governance parameters. Security increment simulation information is generated based on security governance parameters and the like, a multi-target decision model is constructed in combination with protocol evolution constraint conditions, optimal security path parameters are adjusted and generated, a security verification strategy is verified and updated through a real-time quantum mapping network, and a whole-process security matching target is achieved. According to the method, the comprehensiveness, the accuracy and the dynamic adaptability of quantum communication protocol security verification are effectively improved.

The present disclosure describes apparatuses and methods for implementing secure verification of a physical unclonable function (PUF). In various aspects, a PUF verifier generates a PUF reagent value by obtaining a key from a PUF and a message value useful for PUF verification. The PUF verifier computes a digest value of the PUF key and the message value and selects a portion of the hash digest as a PUF reagent value. The PUF verifier writes the PUF reagent value to a non-volatile memory to enable subsequent verification of the PUF. The PUF verifier may also generate error-correction code information for the PUF reagent value and write this information to the non-volatile memory to enable error correction. Upon device hardware reset, the PUF verifier can securely verify PUF operation by generating a PUF key hash digest and comparing the hash digest with the PUF reagent value without exposing the PUF key.

The invention discloses a streaming knowledge injection and adversarial self-optimization large language model training method and system, and the method comprises the steps: collecting the newest knowledge of an authoritative information source in real time, converting the newest knowledge into a structured constraint rule through a semantic analyzer, and dynamically updating a knowledge base; based on the updated knowledge base, adopting a PPO algorithm to optimize a generator, actively constructing a high-risk adversarial sample, and forcing the model to expose security vulnerabilities; after user input and adversarial samples are input into the model, total loss is calculated through constraint detection, gradient updating is blocked if the total loss exceeds a threshold value, and otherwise, a multi-level safety verification stage is started; and finally, fusing a security verification result and the adversarial loss, updating a total loss function and cooperatively adjusting model parameters to form a continuous self-optimization training cycle. According to the method, compliance is guaranteed through streaming knowledge injection, vulnerabilities are actively mined in combination with adversarial training, verification precision is improved by means of multi-level detection and dynamic threshold adjustment, and safety and reliability of a large language model in a complex scene are enhanced.

The invention relates to the technical field of network security, in particular to a non-cooperative network attack tracing method based on multiple stages. The method comprises the following steps: providing security verification for network equipment and a terminal in a traceability process based on a collaborative authentication code, and ensuring credibility and integrity of the traceability process; converting the network attack into behavior feature data based on the feature extraction of the attack behavior; extracting event-level features, revealing a sequential relationship and a dependency relationship of attack behaviors, and identifying a potential attack mode; performing time alignment on the events by using causal analysis to form a standardized causal chain; performing multi-dimensional feature fusion in combination with a Transform network and an NLP pre-training model, and identifying an attack mode; an IP address database, a self-built organization library and a code author library are combined, and a multi-cascade CNN structure is adopted to carry out attack source positioning and attacker tracing. According to the invention, through multi-level feature analysis, accurate identification and traceability of complex network attacks are realized.

The invention provides a distributed energy regulation and control method and system based on 5G network and block chain collaboration, and the method comprises the steps: building an energy block chain-based IES power transaction architecture which comprises a block chain node; docking the regulation and control object with a block chain node in an IES power transaction architecture, wherein the block chain node is used for storing the smart contract subjected to security verification; constructing a distributed energy optimization scheduling model considering the 5G network and a mathematical model of a cooperative scheduling mechanism; and the intelligent contract generates a scheduling instruction according to the distributed energy optimization scheduling model and the mathematical model of the cooperative scheduling mechanism, and the scheduling instruction value is issued to the power generation and utilization unit after passing constraint condition check.

The invention relates to the technical field of cloud security verification, in particular to a cloud security verification method and system, electronic equipment and a computer readable storage medium. The method comprises the steps of generating and isolating an AKSK which is an access key pair for identity verification, simulating an attack behavior in combination with the AKSK, and performing security capability verification based on the simulated attack behavior. According to the method, the AKSK is generated to simulate the attack in the real cloud environment, it is ensured that the test scene highly restores the real attack path, and the technical problem that the traditional security test mainly depends on static policy analysis (such as IAM policy grammar check) and artificial penetration test and cannot simulate the complete attack path after AKSK leakage in the real attack chain is solved.

The invention discloses an automatic driving safety key simulation scene generation method based on adversarial generation and co-evolution. The method comprises the following steps: receiving a basic traffic scene described by a natural language, generating an antagonistic element scene containing security threats by using a large language model in combination with a traffic safety knowledge base, and analyzing the antagonistic element scene into an executable scene script; constructing a multi-agent confrontation collaboration diagram based on the meta-scene, and recognizing a key background vehicle through a cross-timing attention mechanism in combination with a time mask and time decay mechanism; and performing disturbance optimization on the key background vehicle trajectory to generate an automatic driving test scene. According to the method, a scientific and systematic solution with engineering operability is provided for safety verification of the automatic driving system when the automatic driving system faces real traffic challenges such as multi-source intervention and dynamic collaborative threat, and the method has wide adaptation capability and important industrial popularization value.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

The invention relates to the technical field of data disaster recovery, and provides a disaster recovery drill and disaster switching method and system, and the method comprises the steps: automatically recognizing multi-source heterogeneous database assets through a rule engine and a machine learning algorithm, and constructing a disaster recovery topological graph; a disaster recovery strategy is generated and optimized by adopting an intelligent algorithm based on the disaster recovery topological graph, and the disaster recovery strategy is dynamically adjusted in combination with real-time service requirements; fusing the historical disaster recovery data and the real-time monitoring data to reconstruct a disaster recovery strategy; executing data are collected in real time in the drilling process, and a disaster recovery strategy is optimized through a closed-loop feedback mechanism; monitoring a whole process state through a visual interface and displaying key indexes; based on the multi-dimensional evaluation model, quantitatively analyzing the drilling result, and determining a target disaster recovery strategy; and a security verification mechanism is adopted to authorize the key operation, a disaster recovery scene scheme is matched according to the fault type, and an automatic execution unit is controlled to complete disaster switching based on the target disaster recovery strategy. According to the invention, the disaster recovery switching accuracy in a complex scene is improved.

The invention discloses a zero-trust architecture network security protection method and system, a medium and a program product, and the method comprises the steps: obtaining an access request; extracting credit characteristics, and calculating a user trust index according to the credit characteristics; constructing a directed trust transfer graph; calculating a trust attenuation coefficient of each trust transfer edge; according to the trust attenuation coefficient, calculating along the directed trust transfer graph to obtain a node trust value; constructing a relation similarity matrix; clustering according to the relationship similarity matrix to obtain a plurality of trust groups; calculating a group trust threshold according to the node trust value in each trust group; judging whether the node trust value of each trust node in the trust group in which the target resource is located is greater than a group trust threshold value corresponding to the trust group in which the target resource is located; if yes, safety verification is passed; and if not, updating the transmission path information and returning to execute the step of constructing the directed trust transfer graph according to the transmission path information. According to the invention, the security of the network is improved, and the network security protection effect is further improved.

The invention provides a data access security verification method, which comprises the following steps of: acquiring multi-dimensional feature data of an access request, and cleaning and formatting the feature data to obtain a standardized feature data set; if the abnormal mode is detected, marking the abnormal mode as a suspected counterfeit request record; for the suspected counterfeit request record, collecting state information of the suspected counterfeit request record in each node of a propagation path to obtain a state information set; according to the state information set, analyzing the state consistency of the suspected counterfeiting request record in each node, and determining whether a counterfeiting behavior suspicion exists or not; if it is determined that the forgery behavior is suspected, generating a corresponding exception report; according to the exception report, determining a specific node position where an exception occurs, acquiring propagation trajectory data of related nodes, and generating a trajectory log file; and according to the final detection report, updating a feature database and optimizing the classification model.

The invention provides a unified management method, device and equipment for confidential containers, and the method comprises the steps: on the basis of a container cluster management system k8s and a virtualization container technology kata, based on sev, scv, tdx, tpm, tpcm, vtpm, vtpcm and the like, calling a security container kata run to create a VM on a k8s platform; the secret container is pulled up by the kata runtime through the kata-agent; the attestation-agent of the kata virtual machine generates a remote proof Quote with a hardware executable environment, and the remote proof Quote is sent to the host-attestation-agent; the host-attestation-agent is used for sending a remote certificate (Quote) with a hardware executable environment to the CA (Certificate Authority); after the CA receives the remote proof Quote with the hardware executable environment, trusted remote verification type judgment is carried out; and the CA performs container security verification according to the judgment result of the credible remote verification type judgment, so that a processor micro-architecture level memory access control and isolation mechanism can be provided, encryption and integrity protection can be provided at the memory level, and sensitive data and codes are prevented from being leaked or tampered in the calculation process.

The invention provides a personnel safety dynamic early warning method and system for distribution network line operation, and relates to the technical field of intelligent power grid management, and the method comprises the steps: calling an operation work ticket which comprises a work task, operation personnel information, safety measure requirements, grounding wire installation information, and a regulation and control instruction; text semantic structuring preprocessing is executed, and three-dimensional security features are extracted; security statement extraction is carried out, and a structured task template is established; when the positioning information of the target operating personnel is identified to accord with the area range of the target area, activating the target area; establishing a time sequence working data set based on ground wire intelligent sensing and operation tool intelligent sensing; and performing security verification, and generating a security dynamic early warning. According to the invention, the technical problem that the response speed and accuracy of safety early warning are reduced because the safety assessment of the operation task is not accurate enough due to the lack of an intelligent information extraction and task verification mechanism in the distribution network line operation in the prior art is solved.

A security verification method, apparatus, medium, device and product for a large model service relate to the technical field of computers, receive an encryption service request, acquire a private key of a client from a key management service running in a trusted execution environment through an encryption and decryption service running in the trusted execution environment, decrypt the encryption service request based on the private key, and verify the security of the encryption service request. The method comprises the following steps: decrypting a service request of a user, sending the decrypted service request to a large model service to obtain a reasoning result of the large model service, encrypting the reasoning result through a public key of a client, and returning the encrypted reasoning result to the client, so that the service request of the user can be visible in a plaintext in a trusted execution environment; the security of the user data is greatly ensured, and the problem of user data leakage can be avoided through the public and private key pair of the user dimension. In addition, the key management service and the encryption and decryption service both run in the trusted execution environment, so that attacks from an IaaS layer can be shielded.

The invention discloses a game controller dynamic configuration system, and relates to the technical field of computers. The system comprises a controller end and a host end. The controller end adopts a heterogeneous equipment hybrid communication protocol stack to realize bidirectional data synchronization between the host end and the controller end; the controller end comprises a trigger, a selector, an interceptor, a switch assembly, a dynamic resource configuration unit and a firmware upgrading interface; the host end comprises an equipment dynamic identification and registration module, a game semantic analysis module, a dynamic mapping strategy generation model, a user adaptive learning unit, a multi-modal feedback fusion unit and a security verification and exception handling unit. According to the method, the mapping relation library of the controller hardware features and the game scene parameters is established, the physical state data of the controller is collected in real time through the sensor group, and the dynamic matching rule is generated in combination with the role action threshold in the game scene, so that the controller configuration difficulty is reduced, and the controller configuration efficiency is improved.

The invention discloses a manufacturing task autonomous negotiation and execution method based on a large language model agent, and the method comprises the steps: constructing a production scheduling agent, an equipment management agent, a material distribution agent and a quality control agent, analyzing a natural language task instruction through the production scheduling agent, and decomposing the natural language task instruction into subtasks; each agent calculates a utility value based on the load rate, the resource matching degree, the estimated completion time and the historical success rate, and performs structured negotiation to achieve a task allocation consensus; a prediction-check-rollback architecture is adopted to generate an action instruction sequence, the sequence is compiled into a time Petri network transition sequence, and reachability verification is carried out based on hard security constraints; production environment data is collected in real time to trigger anomaly detection and re-negotiation, and a formalized security verification and causal anti-factual reasoning parameter updating mechanism is introduced. According to the method, unstructured instruction understanding, autonomous task planning, multi-agent collaborative decision and closed-loop optimization are realized, and the problems of real-time performance, safety and interpretability of a large language model in manufacturing control are solved.

The invention provides a power network data security encryption method and system based on deep learning, and the method comprises the steps: obtaining an encryption demand data set of a target power network, carrying out the multi-level feature extraction processing of the encryption demand data set, and obtaining the spatial and temporal distribution features and correlation features of the encryption demand data; and based on a preset deep learning strategy network, performing dynamic encryption strategy matching processing on the spatial-temporal distribution characteristics and the associated characteristics, generating a dynamic encryption strategy of the encrypted data sequence, performing encryption operation on the encrypted data sequence according to the dynamic encryption strategy, generating an encrypted power network data set, and sending the encrypted power network data set to a server. And encrypting the power network data set, transmitting the encrypted power network data set to a data storage node of a target power network, performing security verification processing on the encrypted power network data set, generating an encryption strategy optimization instruction, and triggering encryption strategy adaptive updating. According to the invention, the real-time security of power data transmission is ensured.

The invention discloses a security verification method based on an ACME protocol, a security verification system based on the ACME protocol, a computer program product, a CA server, ACME client equipment and a computer readable storage medium. The method comprises the steps that a to-be-verified ACME client sends a certificate request to the CA server based on a target ACME account; if the CA server does not query an effective client authorization record of the to-be-verified ACME client based on the to-be-verified identification information, selecting at least one target domain name from the at least one verified domain name, and re-verifying the ownership of the to-be-verified ACME client to the at least one target domain name; and if the verification of the ownership is passed, the CA server generates an effective client authorization record for the to-be-verified ACME client based on the to-be-verified identification information, and issues a certificate for the to-be-verified ACME client based on the generated effective client authorization record and the effective domain name authorization record of the at least one verified domain name. The security of certificate issuing can be improved.

A method of protecting a model, which relates to a field of computer, a field of artificial intelligence, and may be applied to an AI model protection scenarios. The method includes: generating a WASM file for providing a runtime environment for a target model, the WASM file containing a corresponding model inference algorithm and security verification algorithm, wherein the security verification algorithm is configured to perform at least one security verification operation to protect the target model, the at least one security verification operation is selected from: a verification of a host environment; a verification of an integrity of the WASM file; a verification of an integrity of the model file generated corresponding to an original model file of the target model; a timeout verification of a specified inference process during a model inference process; or a timeout verification of an entire inference process during the model inference process.

The invention relates to the technical field of electric power communication security, in particular to a power grid data secure transmission system and method based on quantum cryptography and a medium, comprising: a power grid data access module extracts identities of a power grid data request end and a target response end; the quantum key distribution module obtains an original shared key pre-stored by the two communication parties, and generates a quantum key according to the one-time session password and the original shared key; the communication security management module analyzes the shared key information from the quantum key received by the target response end to obtain analyzed shared key information, and performs communication security verification on the original shared key and the analyzed shared key information; and the power grid security communication module encrypts the to-be-transmitted power grid data when the communication security verification result is that the verification is passed, and transmits the power grid encrypted data to the target response end. Through cooperation mechanisms such as quantum key dynamic distribution and security verification, security protection of power grid data transmission is realized, and power grid data communication security is improved.

The invention belongs to the technical field of power network inspection, and particularly relates to a power network security inspection system which comprises a multi-source sensing module, an edge calculation module, a zero-trust security verification module, a core analysis module, an execution module and a feedback optimization module. Through an edge-cloud collaborative architecture, sensors, network flow and video data are fused in real time, the detection efficiency is improved, a physical equipment state and zero-trust dynamic authority control are introduced, 'physical-digital 'dual verification is realized, real threats and false alarms are marked, algorithm parameter adjustment is guided, and a feedback optimization module continuously reduces the false alarm rate through a closed loop mechanism; full-dimension safety protection of the power network is realized through modular design, dynamic branch processing and a closed-loop optimization mechanism; the threat level and the risk assessment result are dynamically adjusted through the environmental risk correction factor, the risk misjudgment rate is reduced, and the fault prediction accuracy in extreme weather is improved.