493 results about "Central node" patented technology

The invention discloses a multi-level governance method for real-time data of new energy station power generation equipment, and particularly relates to the field of power data governance, and the method comprises the steps: cooperatively generating an edge governance rule and a central processing logic according to a global data governance strategy, and transmitting the edge rule to an edge node; and meanwhile, the data quality is monitored in real time, and a result is fed back to dynamically adjust a global data governance strategy, so that closed-loop optimization of data governance is realized. According to the multi-level treatment method for the real-time data of the new energy station power generation equipment, an edge treatment rule set and a central processing logic set are generated through driving of a global data treatment strategy, and the strategy is dynamically adjusted based on third treatment data; the problem that the edge node processing rule and the central data processing logic lack collaboration and dynamic adaptation is effectively solved; by implementing cross-level timestamp synchronous calibration, secondary alignment processing of the center node on the multi-source heterogeneous data is avoided, and the equipment state evaluation precision is effectively improved.

The invention relates to the technical field of micro-grid energy optimization, in particular to a distributed federal reinforcement learning-based hydrogen-containing micro-grid energy scheduling method, which comprises the steps of constructing a multi-region hydrogen-containing micro-grid system model, designing a state space, an action space and a reward function of an intelligent agent, constructing an Actor-Critic network and an experience pool, and completing environment initialization. The intelligent agent inputs the operation state of the equipment into the Actor network, updates the state of the equipment according to the output action, verifies the constraint and outputs a reward value; tuples are extracted from the experience pool to update local network parameters, and the exploration rate is updated regularly; when a federation interaction period is reached, exchanging Critic network parameters and updating federation parameters; and when the training round arrives, outputting an equipment operation plan, deploying the model to the local hydrogen-containing micro-grid in the island mode, and outputting an equipment output value. According to the scheme, strategy sharing and learning collaboration are realized through neighborhood collaboration and local communication among the regional intelligent agents, so that dependence on a central node is avoided.

The embodiment of the invention provides an incomplete multi-mode emotion recognition method based on a diffusion model and a federal model, and belongs to the technical field of data recognition. The method specifically comprises the steps that a client downloads a global model to the local; the client extracts incomplete multi-modal features through a shallow feature extractor, the missing modal features are input as random noise, the extracted original features are input into a global model for reverse diffusion denoising, and finally the missing modal features are complemented; sending the complemented full-modal features into an emotion classifier to perform an emotion classification task; the central node receives the model parameters uploaded by the clients, evaluates the contribution degree of each client, calculates the reputation of the clients, preferentially determines the final weight of each client according to the reputation of the clients, and carries out aggregation according to the final weight; and the central node updates the global model according to the local model set and the aggregated weight. Through the scheme disclosed by the invention, the recognition accuracy and safety are improved.

Disclosed in the present application is a blockchain-based distributed ensemble learning method. On the basis of a designed three-layer blockchain structure, which consists of a mini block, an ensemble block and a key block, by means of a consensus protocol, a miner in a network is enabled to train a base model on a training set after performing sampling with replacement, and aggregates models from other miners; and finally, information of the base model and information of an ensemble model are recorded in a blockchain, such that the whole process of model training, model ensemble and model evaluation is fused into a blockchain consensus, and the whole ensemble learning process is automatically executed in a blockchain network. In this way, the present application can increase the utilization rate of the computing power in a blockchain network by means of a proof-of-useful-work mechanism based on machine learning, and a center node is prevented from being introduced during a model aggregation process, thereby maximizing the degree of decentralization of a blockchain.

The invention discloses a multi-chain collaborative center node switching method for ensuring authentication efficiency and data reliability, which is applied to a system comprising an identity chain, an authentication chain, an edge computing center, a key generation center, a plurality of unmanned aerial vehicle clusters and at least two aerial directors, and is characterized in that the identity chain is used for identity management of all the unmanned aerial vehicle clusters; the authentication chain is jointly maintained by all the unmanned aerial vehicle clusters and all the air directors. According to the multi-chain collaborative central node switching method for ensuring the authentication efficiency and the data reliability provided by the invention, the secure communication between the clusters can be quickly established in the authentication process of the unmanned aerial vehicle clusters, and the switching of the central nodes can be quickly carried out when the central nodes are threatened and attacked in a multi-chain manner; the new central node continues to execute the flight task, and the forward and backward security is ensured in the switching process, so that the problems of low authentication efficiency of the unmanned aerial vehicle cluster, strong dependency of the central node and insufficient security in the switching process of the central node are solved.

The invention discloses a cluster server management method based on edge computing. The cluster server management method comprises the following steps: S1, deploying a center node and a plurality of edge nodes in a cluster server architecture; s2, the central node collects global task information, edge node resource state information and communication link state information of the cluster server; s3, generating a plurality of task allocation schemes and selecting the scheme with the highest fitness as an initial scheme; s4, monitoring the task execution state and the resource use condition in real time; s5, migrating part of tasks to other available edge nodes; s6, when detecting that the edge node or the communication link has a fault, triggering a fault-tolerant mechanism by the center node; s7, adjusting a resource allocation strategy in advance, and optimizing a task allocation scheme; s8, when the task load is increased, part of nodes are restarted to meet resource requirements; and S9, after task execution is completed, forming a collaborative management closed loop of the edge node and the center node. The operation efficiency, the reliability and the energy-saving effect of the system are remarkably improved.

In an implementation, a tunable traveling wave parametric amplifier (TWPA) includes a T-stage that includes a first DC-SQUID and a first interface inductively communicatively coupled to the first DC SQUID operable to apply a first bias to the first DC SQUID. The T-stage also includes a second DC-SQUID electrically communicatively coupled to the first DC-SQUID in series via a center node, and a second interface inductively communicatively coupled to the second DC-SQUID operable to apply a second bias to the second DC-SQUID. The TWPA also includes a shunting resonator communicatively coupled to the center node via a coupling capacitance. The shunting resonator includes a third DC-SQUID, and a third interface inductively communicatively coupled to the third DC SQUID operable to apply a third bias to the third DC SQUID. The first, second, and third biases are adjustable to improve a bandwidth of the tunable TWPA.

The invention discloses a data hierarchical aggregation and query method and system for privacy protection in a 6G network, and the method comprises six stages: a system initialization stage, a data uploading and edge aggregation stage, an edge data preprocessing stage, an edge classification query request stage, a central data preprocessing stage, and a central node classification query request stage. The system initialization stage, the data uploading and edge aggregation stage, the edge data preprocessing stage and the center data preprocessing stage only need to be executed once, and then the edge classification query request stage and the center node classification query request stage can be responded unlimitedly. According to the method, the privacy protection problem in the data hierarchical aggregation and query process of the AI service in the 6G network is effectively solved.

The invention provides a task risk early warning method and system based on project management data, and relates to the technical field of project management early warning. According to the method, the task risk index weight is automatically adjusted to adapt to process states and external environment changes in different stages of a project, the accuracy and flexibility of risk assessment are remarkably improved, and good dynamic adaptability is achieved; performing grading evaluation on tasks in each stage of the project by means of a quantitative comprehensive risk value, determining a management priority, and assisting a project team to focus on key risks; a task cooperation relationship is visually presented by constructing a task cooperation network, a center node and a fragile link are accurately identified, and a risk propagation path is effectively grasped; and real-time monitoring and graded early warning are realized in combination with a dynamic risk early warning rule, so that the risk can be found and dealt with in time.

The invention relates to the technical field of cloud computing, and discloses an integrated disaster recovery method, system, device and equipment based on a cloud side end technology. The method comprises the steps of obtaining real-time power grid data of a power distribution network; transmitting the real-time power grid data to an edge node, so that the edge node performs short-term load prediction by using a low-order prediction model to obtain a preliminary prediction result; when the preliminary prediction result is abnormal, performing preliminary fault response by using the edge node, and generating a preliminary response result; transmitting the real-time power grid data and the preliminary response result to a cloud center node, so that the cloud center node performs long-term load prediction by using a high-order prediction model and analyzes a fault reason; and generating a recovery execution instruction according to the fault reason, and sending the execution instruction to the fault terminal. Through cooperative work of the edge node and the cloud center node, prediction accuracy is effectively enhanced, rapid positioning and recovery of faults are realized, and flexibility and comprehensiveness of power grid management are improved.

The invention provides a heavy-load unmanned aerial vehicle cooperative hoisting method, device and equipment and a medium, and relates to the technical field of edge calculation, and the method comprises the steps that a virtual pilot unmanned aerial vehicle in an unmanned aerial vehicle cluster calculates total load data of loads hoisted by a plurality of edge nodes based on load distribution data of each edge node; based on the total load data and the node position, determining an overall motion track of the unmanned aerial vehicle cluster, and sending the overall motion track to the edge node, so that the edge node finds a reference tension value needing to be provided and a node motion track based on an ant colony algorithm; if it is determined that the lifting rope of the first unmanned aerial vehicle in the multiple unmanned aerial vehicles is broken in the flight process, according to a preset load redistribution scheme, synchronous tension is distributed to each second unmanned aerial vehicle; and setting the first unmanned aerial vehicle as a proxy center node, and receiving an updated movement track of the proxy center node for the unmanned aerial vehicle cluster. According to the invention, the sudden fault in the cooperative lifting process of the multiple unmanned aerial vehicles can be quickly processed.

The invention discloses an agent collaborative generation method based on dynamic sharing of bureau-school interconnection teaching resources, which comprises the following steps: S1, constructing a bureau-school interconnection network system, and deploying a resource management agent which comprises a data acquisition module, a semantic analysis module, a decision module and a communication module; s2, collecting school teaching data, and sending the school teaching data to a central node of an education management department; s3, performing semantic annotation and classification on the received data, and constructing a dynamic teaching resource semantic database; s4, constructing an intelligent agent collaborative decision-making model, and dynamically adjusting a sharing strategy of teaching resources; s5, generating an optimal resource sharing scheme through the dynamic teaching resource semantic database and the intelligent agent collaborative decision-making model, and sending the optimal resource sharing scheme to the school child node; and S6, after sharing is completed, a resource sharing scheme is fed back to the center node, and the agent collaborative decision model is optimized. According to the invention, intellectualization, dynamics and precision of teaching resource sharing are realized, and a brand new technology is provided for balanced configuration of education resources.

The invention relates to the technical field of federated learning, and discloses a pricing and incentive method for a federated learning calculation task, based on a central node, a server and a plurality of clients, the method comprises the following steps: initializing a system environment; the client locally constructs and trains a pricing model and uploads parameters to the server; the server obtains a global model based on a federal aggregation algorithm and issues the global model to the client; the client uses the updated global model to carry out test evaluation and uploads an evaluation value to the server; and the server distributes corresponding rewards to the client based on the incentive mechanism. According to the invention, the limitation of single subject pricing in traditional data resource pricing and the problem of low collaborative efficiency in a collaborative pricing process are solved, the accuracy of data resource pricing is improved, and more clients are indirectly attracted to join in training to obtain a better pricing model.

The invention discloses a distributed privacy protection method and system based on federated learning, and relates to the related field of privacy protection, and the method comprises the steps: a hospital end carries out the homomorphic encryption of patient data, calls an initial model through the encrypted data, and carries out the incremental training, and generates a first model parameter set; sending to a central node, aggregating by using a secure multi-party computing protocol, and generating a first aggregation parameter group; after differential privacy noise is added, the data is transmitted back to a hospital end, local patient data is continuously called to carry out incremental learning after homomorphic encryption, and so on until model prediction precision meets constraints, and a target model parameter set is generated; and after differential privacy noise is added, the data is transmitted back to the hospital end for target model generation. The technical problems that existing privacy protection is large in calculation overhead and high in communication cost, and accurate aggregation of model parameters is difficult to achieve while privacy is guaranteed are solved, and the technical effects of small calculation overhead, low communication cost, privacy protection and accurate aggregation of the model parameters are achieved.

The invention discloses an equipment broadcast message distribution method and system based on dynamic polling grouping, and belongs to the technical field of equipment broadcast, and the method specifically comprises the following steps: setting an initial query interval, sending a state query request to all equipment at every query interval by a central node, and sending a state message to all equipment after any equipment receives the query request; sending self monitoring data to a center instruction; obtaining a quantitative index in the monitoring data, generating a quantitative index sequence of any equipment every n query intervals, calculating an initial coefficient according to the quantitative index sequence, and converting the initial coefficient into an adjustment coefficient of the query intervals; calculating the duration of the next n query intervals according to the adjustment coefficient; a plurality of intervals are set, the devices with the adjustment coefficients in the same interval are divided into the same group, the query priorities of the devices in each group are ranked from large to small according to the numerical values of the adjustment coefficients, and polling is carried out in sequence according to the priorities.

The invention belongs to the technical field of agent safety control, and relates to a distributed cooperative safety control method applied to a multi-agent system, and the method comprises the steps: S1, building a kinematic model of an omnidirectional mobile agent, and generating a nominal speed instruction according to a preset track; s2, determining position information of each agent and broadcasting the position information to other agents; s3, determining a candidate zero-crossing obstacle function; s4, generating a security constraint by using the candidate zero-crossing obstacle function; s5, determining a loss function based on the nominal speed, and solving a control quantity which enables the loss function to be minimum on the premise of meeting security constraints as a security control instruction; and S6, controlling the intelligent body to move according to the safety control instruction. The method has the beneficial effects that in a large-scale agent cluster, even if global information and a central node are lacked, collision among the agents can be effectively avoided, meanwhile, falling into a local optimal solution is avoided, and it is ensured that the agents are safely navigated to a specified target point.

The invention provides a construction method of a central architecture diagram and an architecture diagram construction system. The method can be applied to an architecture graph construction system, so that a central architecture graph with a better visualization effect is generated. Specifically, the architecture graph construction system obtains a plurality of pieces of entity information and a relationship among the plurality of pieces of entity information, and then determines first entity information, where the first entity information is one of the plurality of pieces of entity information. Afterwards, the architecture graph construction system constructs a center architecture graph according to the first entity information and the relation between the multiple pieces of entity information and displays the center architecture graph, a center node in the center architecture graph is used for indicating the first entity information, and the center architecture graph further comprises multiple annular layers with the center node as the center; each annular layer comprises at least one node, and the nodes of different annular layers have different relationships with the central node.

The invention is suitable for the technical field of unmanned aerial vehicle cluster cooperative control, and provides an edge calculation distribution system and method for unmanned aerial vehicle cluster cooperative tasks, and the system comprises a multi-heterogeneous neural network module, a local fusion module, a global fusion module, a conflict resolution module, a task distribution module, and a self-adaptive optimization module. According to the invention, by constructing a multi-level semantic feature extraction and fusion architecture and combining an intelligent conflict resolution mechanism and a dynamic task allocation algorithm, efficient intelligent collaborative operation of an unmanned aerial vehicle cluster in a complex and changeable environment is realized, all core algorithms can be executed in a distributed manner in the local of the unmanned aerial vehicle through a distributed edge computing architecture, and the distributed edge computing architecture can be applied to the unmanned aerial vehicle cluster. Dependence on a central node is remarkably reduced, communication overhead and delay are greatly reduced, cooperative work can still be maintained even if part of nodes fail, and perfect balance between robustness and performance is achieved.

The invention relates to a distance perception-based graph division retrieval method and system and a readable storage medium. The method comprises the following steps of: constructing original data into an initial graph structure; distance measurement is selected, a weight is allocated to each edge, and the nearest neighbor node pairs are combined based on the weights to obtain a coarsened graph structure; performing hierarchical segmentation on the coarsened image structure, and projecting the coarsened image structure back to the initial image structure based on the segmentation condition to obtain a plurality of initial cut edge sets; finding a corresponding node according to each initial cut edge in the initial cut edge set, randomly putting the nodes into different partitions, and optimizing each initial cut edge set by taking the sum of minimum cut edge weights as a target function to obtain a graph index structure; and inputting a query, calling the graph index structure, respectively calculating the distance between the query and the central node of each partition of the graph index structure, and selecting the nearest partition for retrieval. According to the method, the number of times of cross-partition query is reduced, and the query efficiency is improved.

The invention belongs to the technical field of data processing, and provides a data management optimization method and system. The method comprises the following steps: based on an acquisition strategy, acquiring log and business data of each child node, and calculating a data acquisition success rate and a data acquisition delay evaluation value to measure an acquisition condition; transmitting the collected logs and service data of each child node to a central node, carrying out multi-class data processing, and calculating and determining a quantitative index of a data processing delay condition so as to optimize a data processing process; monitoring the data after processing the multiple types of data in real time, identifying a risk event by using a self-constructed risk event identification model, and calculating a risk score of the risk event, including analyzing and calculating a risk monitoring result so as to update a monitoring rule and a feature library of the risk event identification model; and when the calculated risk score exceeds a preset threshold value, an alarm mechanism is triggered, and alarm information is pushed to safety management personnel of the related child nodes. According to the invention, the multi-node data acquisition and data processing process is effectively optimized.

The invention discloses a power distribution network short-circuit parameter real-time monitoring method, system and equipment based on edge calculation and a medium, and belongs to the technical field of power system monitoring and protection, and the method comprises the steps: collecting data information in real time at each monitoring node of a power distribution network, generating a state vector reflecting a node operation state, and inputting the state vector to an edge calculation unit, processing through an internal first calculation model to obtain a power grid fault evaluation result of a corresponding node, and compressing and uploading the power grid fault evaluation result to a cloud; based on the uploaded data and the system operation state, a system performance evaluation result is generated in real time, and an adaptive optimization algorithm is used for dynamically adjusting internal model parameters of the edge calculation unit. According to the method, the composite algorithm model fusing feature extraction and parameter calculation is deployed at the edge node, the problems that a traditional centralized processing mode is large in data transmission delay and central node calculation pressure is concentrated are solved, and the technical effect of reducing bandwidth occupation of a backbone communication network is achieved.

The invention discloses a hybrid federal learning modeling method and device for a neurocognitive impairment evolution trajectory, and the method comprises the steps that a child node receives an expert global model distributed by a central node, and the expert global model is determined by the central node from an expert global model group according to metadata pre-registered by the child node; taking the expert global model as an initial model, and performing localization training by using the local multi-modal non-invasive physiological signal data to obtain a trained local model; extracting global sharing parameters in the trained local model, and uploading the global sharing parameters to a central node; and for the corresponding expert global model, the central node aggregates global shared parameters uploaded by all the child nodes training the expert global model to obtain an updated corresponding expert global model, issues the updated expert global model to the child nodes, and circulates until a termination condition is met. According to the method, the heterogeneous data of the child nodes are adapted, privacy is protected, and the adaptability and accuracy of trajectory prediction are improved.

The invention discloses an access control method and system for cross-platform video resource sharing, and belongs to the technical field of video sharing. Video platforms are divided into a plurality of groups, each group randomly selects one video platform to be marked as a national standard node, and each national standard node serves as a bridge between a center node and other video platforms and is responsible for seamless cascading and equipment access of video resources of a superior platform and other platforms; the system implements a unified access control strategy, and ensures that a user accesses resources according to authority through authority management; an organization tree configuration function is constructed, a video resource management system is constructed, and sharing and integration of lower-level platform resources are achieved; searching and browsing organization tree information and an equipment list through a resource retrieval function; sharing of organizations and devices is supported, and a superior platform is allowed to retrieve a shared directory and a device list; according to the invention, a safe, efficient and easy-to-manage cross-platform video resource sharing solution is realized.

The invention provides an intelligent question answering method and system based on a traditional Chinese medicine classical meta-term engine. The method comprises the following steps: identifying and extracting core traditional Chinese medicine terms and a user query intention in a user question; the core traditional Chinese medicine terms are input into a pre-constructed traditional Chinese medicine classical meta-term engine to be processed, normalized mapping and semantic association extension of the terms are completed, and the engine is constructed based on a classification-term-word list-semantic CTVS association framework. Comprising a traditional Chinese medicine classical book term ancient and modern double-classification system C layer, a meta-term extraction and standardization T layer, a dynamic word list V layer and a semantic association network S layer. Carrying out graph pattern matching and reasoning in the S layer, traversing and integrating a plurality of deep association paths by taking classical element terms and corresponding modern clinical terms as double center nodes, and retrieving answer information associated with the query intention; and carrying out integration sorting and structured assembly on the answer information to generate a structured answer. Intelligent questions and answers can be performed on traditional Chinese medicine related questions of the user.

The invention provides a power system node equivalent inertia determination method and system, and belongs to the technical field of power systems. The method comprises the steps of determining the equivalent inertia of a power system; applying load power disturbance to each node in the power system, and determining one node as an inertia center node in the power system by using the frequency of each node after disturbance; fitting the frequency of each node after the load power disturbance is applied to the power system into a curve of the frequency changing along with time so as to determine the frequency change rate of each node after the disturbance occurs; and calculating the equivalent inertia of the target node by using the equivalent inertia of the power system according to the relationship between the frequency change rate of the target node after the disturbance and the frequency change rate of the inertia center node after the disturbance. According to the method, the equivalent inertia of each node in the power system can be accurately calculated, and the spatial distribution characteristic of the inertia can be better reflected.

The invention relates to a digital twinning method for a data center optical network. The method is applied to an optical network, a plurality of edge computing nodes and a center node are deployed in the optical network, corresponding digital twin models are deployed on the edge computing nodes, and the method comprises the following steps: obtaining edge computing node data, the edge computing node data comprising optical signal state information and equipment state information; inputting the edge computing node data into a corresponding digital twinborn model to obtain a node prediction result; and transmitting the edge computing node data and the node prediction result to a central node, performing optimization calculation based on the edge computing node data and the node prediction result to obtain an optimization result, and using the optimization result to indicate an operation of coordinating the edge computing node. Compared with traditional centralized processing, the distributed digital twinborn model has the advantages that due to the fact that the edge computing nodes are located at the key positions of the optical network, data can be directly acquired and processed, delay of data transmission is reduced, and the overall computing capacity is enhanced.

The invention discloses a hybrid cloud-oriented multi-cluster scheduling method and device and a medium, and relates to the technical field of distributed system resource management. The method comprises the following steps: continuously collecting multi-dimensional state data of a cluster to be scheduled, and encrypting and transmitting the multi-dimensional state data to a central node to obtain a global state view; if the multi-dimensional state data meets a preset triggering condition, analyzing a service strategy associated with the global state view to obtain a structured constraint condition and weight configuration; removing clusters contained in the global state view based on a structured constraint condition to obtain a candidate cluster list; calculating a cost factor, a performance factor, a utilization rate factor and a stability factor of each candidate cluster in the candidate cluster list, and performing weighted calculation with the weight configuration to obtain a comprehensive score of each candidate cluster; and extracting the cluster with the highest comprehensive score as a target cluster, and calling the integrated scheduling executor to perform flow scheduling and resource scheduling on the target cluster.

The invention belongs to the field of Internet of Things equipment identification, and provides an Internet of Things equipment identification method and system based on a lightweight knowledge graph, and the method comprises the steps: carrying out the preliminary scanning to obtain the data information of multiple dimensions of Internet of Things equipment and the equipment type, and obtaining a comprehensive feature; constructing a knowledge graph by taking the equipment type as a central node, taking each piece of data of each dimension as a node and taking a corresponding relationship between each node and the central node as an edge; continuously identifying the Internet of Things equipment to determine the corresponding latest comprehensive characteristics, and calculating the similarity between the latest comprehensive characteristics and the comprehensive characteristics in the knowledge graph to generate a candidate equipment type list; verifying the device types in the candidate device type list by using the context information, if the verification is successful, determining the final matched device type, and updating the latest comprehensive characteristics to the device type corresponding to the knowledge graph; otherwise, updating the knowledge graph according to the newest comprehensive features as new nodes.

The invention provides an equipment networking method and system based on a distributed soft bus, and the method comprises the steps: starting a distributed soft bus module and carrying out the discovery of adjacent equipment, and obtaining a trusted equipment set after identity verification; a point-to-point communication link between the devices is established according to the trusted device set, and a distributed mesh interconnection topology is formed; performing routing calculation and data forwarding on data transmission in the mesh topology to realize end-to-end communication; meanwhile, dynamic changes of equipment in the network are monitored, topology updating is carried out, and the network stability is maintained. The scheme does not depend on the dependence of the traditional star network on the central node, realizes direct interconnection between devices through the distributed soft bus technology, improves the fault-tolerant capability and transmission efficiency of the network, and solves the problems of poor network reliability and limited expansibility in the prior art.

The invention belongs to the field of self-organizing networks, and relates to a self-organizing node space reference construction method, which comprises the following steps of: constructing a local space reference topology comprising a distance and an orientation angle by utilizing mutual measurement among distributed self-organizing nodes, and realizing global space reference construction through distributed fusion. And the self-organizing multi-node network distributed space reference construction requirement is met. According to the method, external information support such as satellites is not needed, and only distance and angle measurement between self-organizing nodes is needed; the space reference parameters comprise position and orientation angle information at the same time, and more applications can be supported; a distributed fusion processing method is adopted, a center node is not needed, and the complexity is low.