2001 results about "Community setting" patented technology

The invention discloses a community intelligent monitoring and emergency linkage method and system fusing BIM spatial semantics, and the method comprises the steps: constructing a BIM scene map, and obtaining the attributes and mutual relationships of components and spatial regions in a BIM model; mapping a dynamic target detected in video monitoring into the BIM model, and obtaining spatial semantic information of the dynamic target; based on BIM spatial semantic information of a dynamic target, a target-environment interaction graph is constructed, a graph neural network model is used for training and reasoning, and specific complex events related to spatial contexts are recognized; taking the BIM model as a space-time reference, fusing multi-source heterogeneous data, and reconstructing by adopting a graph-based event association algorithm to form a complete event chain containing an atomic event sequence and an association relationship; and when an emergency event or an event chain is detected to indicate an emergency state, combining BIM preset information and real-time sensor data, dynamically generating an optimal emergency plan, and performing visual commanding and dispatching through a BIM three-dimensional scene and augmented reality.

The invention, which relates to the technical field of community safety supervision, discloses an artificial intelligence-based community safety environment supervision system comprising a data acquisition module, a data processing and analysis module, an intelligent decision module, an early warning response module and a system management module. The data acquisition module is used as a sensing layer of the system; the data processing and analysis module specifically comprises a feature extraction unit and a behavior recognition unit; the intelligent decision module is used for receiving the risk assessment result output by the data processing and analysis module; and the early warning response module generates the scheme according to the intelligent decision module. According to the community safety environment supervision system based on artificial intelligence, intelligent supervision of a community safety environment is realized through a complete closed loop of data acquisition, data processing, intelligent decision making, early warning response and system management; all the modules are in close cooperation, full-process automation from data collection to emergency response is ensured, and the efficiency and accuracy of community safety management are greatly improved.

The invention provides a community management method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of community management. The method comprises the steps of obtaining an initial inspection task of an unmanned aerial vehicle cluster and planning an inspection route, and controlling the unmanned aerial vehicle cluster to execute inspection operation to obtain multi-source inspection data. And constructing a community portrait based on the inspection data, identifying community management hidden dangers through a classification reasoning model, and generating an early warning event and a processing task. A processing task is decomposed into hierarchical sub-tasks by utilizing an ontology reasoning framework, a task execution main body is selected, and a task set is generated. And a multi-Agent reinforcement learning framework is adopted to carry out space-time collaborative arrangement on the task set to form an overall collaborative scheme, the overall collaborative scheme is converted into an interpretable scheduling decision instruction, and finally the unmanned aerial vehicle is controlled to execute the task. According to the technical scheme, the emergency response speed of security and protection and the capability of coping with complex conditions are improved.

The invention relates to the technical field of smart communities, and discloses a smart community resident health management method based on machine learning, and the method comprises the steps: continuously collecting the health feedback data of residents, such as the physical condition change after outdoor activities, carrying out the correlation analysis of the health feedback data with the previous health data and environment data, dynamically updating and optimizing a correlation model, and obtaining the health feedback data of the residents; the prediction precision of the model and the effectiveness of suggestions are improved, the relationship between the use condition of the community public space and the resident health indexes is analyzed, key public facilities and areas influencing resident health are identified, data support and decision basis are provided for community planning and facility reconstruction, and the method is suitable for popularization and application. Community resident health and environment condition analysis reports are generated regularly, and resident health index change trends are visually displayed for reference of community managers and residents. According to the invention, intelligent analysis and application of environment and health data are realized, and technical support is provided for building a healthy community.

Embodiments of the disclosure provide systems and methods for detecting malicious software packages. Detecting malicious software packages can include collecting information identifying one or more known malicious software component classifiers, collecting information identifying one or more known suspicious community behavior classifiers associated with the one or more known malicious software component classifiers and receiving a software package including software components. The method also includes identifying one or more software components of the software package as malicious based on a comparison between the software components of the software package and each of the collected one or more known malicious software component classifiers and the collected one or more known suspicious community behavior classifiers, generating a malicious probability for each of the identified one or more software components and evaluating whether the software package is malicious based on the generated malicious probability for each of the identified one or more software components.

The invention discloses a smart community security management method and system based on the Internet of Things. The method comprises the following steps: acquiring environmental data in real time through Internet of Things sensing equipment deployed in a community; generating a dynamic risk thermodynamic diagram through a risk assessment model based on historical security event data and environment data collected in real time; generating an optimal patrol path by using a reinforcement learning algorithm according to the dynamic risk thermodynamic diagram and a preset patrol constraint condition; issuing the optimal patrol path to a patrol terminal for execution, and dynamically adjusting the path in the execution process according to the environment data updated in real time; when a high-risk event is detected, an emergency response mechanism is triggered, and the unmanned aerial vehicle, the patrol robot and the mobile terminal are automatically dispatched for cooperative disposal. Through the method, the intelligent degree of community safety monitoring can be enhanced.

The invention relates to the technical field of smart communities, and discloses a smart community target identification tracking method based on big data, which comprises the following steps: constructing an omnibearing monitoring network in a community, collecting real-time video and sensor data in the community, obtaining feature data of a target through an edge computing technology, and carrying out target identification tracking on the feature data; transmitting the target feature data to a cloud server through a network, identifying and classifying the target feature data, judging the type and identity of the target, analyzing the current behavior of the target according to a preset target historical behavior pattern library, and determining whether the behavior is abnormal or not; and if the behavior of the target is not matched with the normal mode in the historical behavior mode library, judging that the behavior is an abnormal behavior, and triggering an early warning mechanism. According to the invention, intelligent identification, dynamic tracking and timely early warning of the abnormal behavior of the smart community are realized, and the intelligent level and response efficiency of community safety management are improved.

A disclosed data platform may be configured to access a custom policy developed by a particular client entity that uses the data platform. The custom policy may invoke a query that targets a target dataset ingested from a cloud environment and stored in a data store. The data platform may determine that the custom policy is likely to be of value to one or more other client entities, besides the particular client entity, that also use the data platform. In response to the determining that the custom policy is likely to be of value to the one or more other client entities, the data platform may generate a community policy based on the custom policy. The community policy may be available for use by the one or more other client entities. Corresponding methods, systems, and products are also disclosed.

The invention relates to the technical field of environment intelligent management, and discloses a multi-community-oriented AI intelligent household garbage control processing system which comprises a multi-mode sensing module, a data processing and edge calculation module, a garbage quantity prediction and scheduling optimization module, an intelligent decision and multi-source data fusion module and a self-adaptive adjustment and feedback mechanism module. The multi-community-oriented AI intelligent household garbage control processing method comprises the following steps that S1, a multi-modal sensing module collects garbage data; s2, an edge calculation module performs preprocessing, classification and encrypted transmission; s3, optimizing transportation and scheduling; s4, generating an optimal processing decision; and S5, adaptively adjusting the strategy. Through the technical scheme of combining multi-modal perception and edge calculation, accurate garbage data acquisition and efficient processing in a multi-community environment are realized, the technical effects of reducing data transmission burden and improving garbage classification data quality are achieved, and garbage classification is more accurate and reliable.

The invention relates to the technical field of artificial intelligence graph neural networks (GNN), in particular to a graph neural network pre-training method and system based on community structure generality. The pre-training method comprises the following steps: preprocessing an input graph to form a graph G (V, A) comprising a node set V and an edge set A; community structure detection is carried out on the graph G (V, A) to identify the community structure of the graph G (V, A), and a community is highly identified by adopting a modularity-based Louvain method or a label propagation LPA method; the invention provides a graph neural network pre-training method based on community structure generality. The problems that in the prior art, large-scale graph data processing efficiency is low, and dependence on labeled data is high can be effectively solved. Through preprocessing and community structure detection on an input graph, a modularity optimized Louvain method or a label propagation LPA method is adopted, and communities can be efficiently identified and divided, so that local and global structure information of the graph is reserved.

The invention relates to the technical field of smart community data management, in particular to a smart community metadata interaction method and system based on an edge computing framework, and the method comprises the steps: carrying out the dynamic storage of multi-dimensional data through a hierarchical rolling cache; generating a uniform resource vector through sequential progressive mapping; community semantic tags are added for the uniform resource vectors through extensible tag slots, connection is established according to association rules, and a resource semantic graph oriented to community services is constructed; receiving a resident task request, constructing a multi-factor weighted scoring engine based on the resource semantic graph, and generating a task data packet with a priority label; a two-channel hybrid scheduling network is constructed, a first channel outputs resource availability and node association degree, and a second channel outputs time sequence characteristics and load prediction parameters; and fusing dual-channel output to obtain task-node correlation and generate an optimal matching strategy. According to the method, task collaboration and resource adaptive management are realized through semantic resource modeling and hybrid scheduling.

The invention relates to the technical field of community video monitoring, and discloses a community personnel abnormal behavior video monitoring method based on human factor engineering, and the method comprises the steps: dividing community monitoring video data based on a preset activity region, and obtaining the monitoring data of each sub-region, including a personnel track, environment interaction and the like; determining the abnormal level of each sub-region according to the movement frequency of the personnel trajectory data and the interaction strength of the environment interaction data; key monitoring nodes are calculated in combination with abnormal levels and the like, and the scanning sequence and the scanning duration are adjusted and determined according to a dynamic path; then generating observation anchor points and redundant anchor points at the boundaries of the sub-regions; and finally, associating and integrating to generate a monitoring path. According to the method, through multi-dimensional data processing and dynamic optimization, the accuracy, flexibility and environmental adaptability of community abnormal behavior monitoring are improved, and the method is suitable for community safety management.

The invention relates to the technical field of smart communities, and discloses a smart community intelligent security scheduling management method based on deep learning, which comprises the following steps: acquiring video monitoring data and access control record data of a plurality of communities, cleaning the data, unifying the format and content of the data by adopting a data standardization technology, and storing the unified data into a database; the method comprises the steps of obtaining standardized community security and protection data, mining a personnel flow mode and a vehicle track mode among different communities by adopting a graph algorithm analysis technology on the basis of a community relation network to obtain a cross-community personnel flow mode and a vehicle track mode, and obtaining a cross-community security and protection result according to analysis results of the cross-community personnel flow mode and the vehicle track mode. And an anomaly detection algorithm is adopted to identify potential safety hazards, and if an abnormal condition is detected, early warning information is generated. According to the invention, deep correlation analysis of cross-community security data is realized, and the intelligent level of community security management is effectively improved.

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

The invention discloses a community fire-fighting toughness evaluation method and system based on an association rule and a complex network, and the method comprises the steps: analyzing the disaster cause of an accident case based on a community fire accident case, and determining a community fire-fighting toughness evaluation index system from four dimensions: defending, rescuing, recovering and adapting; an Apriori algorithm is adopted to mine a coupling relationship between indexes, an association rule is generated, and a community fire-fighting toughness index complex network is constructed; key factors influencing the community fire-fighting toughness are determined by calculating the degree of a community fire-fighting toughness node, the intermediary centrality, the clustering coefficient and the like; and finally, making a fire-fighting toughness improvement scheme according to the key factors. According to the method, the invisible relation between the toughness indexes can be mined, the key factors influencing the community fire-fighting toughness can be determined, the community fire-fighting toughness improvement scheme can be formulated, the problem that community fire-fighting toughness evaluation mainly depends on subjective experience at present is effectively solved, and improvement of the overall fire-fighting toughness of the community is facilitated.

A system for assessing the ecological sensitivity and development potential of areas within ecologically sensitive mountain towns, consisting of: a data acquisition module configured to extract various data types, including multi-source geospatial data, environmental data, built environment data, socio-economic data and community participation data, with the extracted data being validated and stored in a database; a central processing unit comprising one or more processors connected to a memory, the processor communicatively connected to the database in which the extracted data is stored, the memory storing instructions that, when executed, configure the processor to implement the following: a data input module configured to receive data corresponding to multiple indicators from different assessment dimensions, including ecology and environment, built environment, economy, heritage and culture, mobility, location, community participation, visual aspects and safety from hazards; an indicator evaluation module configured to evaluate each indicator according to predefined sensitivity categories and assign corresponding sensitivity index values in the range of 0.1 to 1; a dimension weighting module configured to apply dimension-specific weights to calculate weighted scores for each indicator; a development potential calculation module that aggregates the weighted assessments to determine an overall sensitivity assessment and assigns this overall sensitivity assessment to a development potential rating scale; and a visualization module configured to generate visual representations and reports of the assessment results; and an output module having a user interface operatively connected to the visualization module of the central processing unit, the output module configuring the user interface to display the visualized results and the final index value obtained from the central processing unit.

The invention discloses a smart community comprehensive service method, system, device and product, and relates to the field of smart community services, and the method comprises the steps: obtaining sensor data in a current community; the sensor data in the current community comprises behavior data, equipment data and environment data of residents; performing data mining and predictive analysis on the sensor data in the current community by using a user behavior analysis model to obtain a future behavior trend; the user behavior analysis model is a deep learning network or a machine learning algorithm; analyzing the future behavior trend by using a machine learning model to obtain a resident demand; the machine learning model is a model which is obtained by training a cloud database and is deployed in an edge computing node. According to the invention, efficient operation of the system and quick response of services can be ensured, intelligent and personalized service recommendation is provided, all-around demands of residents are met, and user experience is improved.

The invention relates to the technical field of smart communities, and discloses a smart community intelligent security monitoring management method based on data interconnection, which comprises the following steps: acquiring real-time monitoring data, reading a preprocessed monitoring data set from a database, extracting feature parameters related to community security patrol, and determining the real-time monitoring data; the characteristic parameters comprise road length, road width, building height and building density, and constructing a community security patrol environment model based on the characteristic parameters; and aiming at the community security patrol route optimization model, solving by adopting an ant colony algorithm to obtain a plurality of candidate optimal patrol routes, evaluating the candidate optimal patrol routes according to historical patrol data, and selecting the patrol route with the optimal comprehensive evaluation index as the optimal patrol route. According to the invention, intelligent and fine management of community security patrol is realized, the patrol efficiency and the safety prevention and control level are improved, and powerful decision support is provided for community security management.

The invention provides a method and a system for issuing tasks in a collaborative community and computing equipment, users of the collaborative community have user label information, and the method comprises the following steps: determining a first collaborative template for completing a first task; determining task setting information according to the first cooperation template; according to the task setting information, task planning information of the first task is generated, the first task corresponds to the first cooperation template, and the first task comprises at least one subtask; issuing the first task according to the task planning information, including: determining a task scheduling scheme for the first task according to the task planning information and the user tag information, the task scheduling scheme being used for setting a user executing the subtask; according to the task scheduling scheme, the corresponding subtasks are sent to corresponding users set by the task scheduling scheme in a pushing mode and / or a dispatching mode. By means of the method, a more friendly interaction mode can be provided.

The invention belongs to the technical field of urban low-carbon planning, and relates to a low-carbon community evaluation index library dynamic construction method and system. The method comprises the steps of knowledge graph mode layer construction, knowledge extraction, semantic relation enhancement and fusion and graph database storage. Defining the types of the entities and the semantic relationship between the entities to obtain a mode layer with a hierarchical topological structure; knowledge extraction: mapping the entity relationship in the mode layer into a knowledge extraction template; after an extraction result is subjected to subgraph structured organization, vector space mapping is carried out, semantic association strength among indexes is calculated, and entity alignment is carried out by adopting a knowledge fusion mechanism driven by a large language model; and importing the knowledge graph subjected to semantic relationship enhancement into a graph database through a query language. According to the method, efficient construction and expansion of the cross-domain index library can be realized; the adaptability of the index system is enhanced; and in combination with a knowledge fusion feedback mechanism, the mode layer is dynamically adjusted, so that the data processing efficiency and accuracy are improved.

The invention discloses a large language model question and answer method based on a retrieval enhancement generation scheme. The method comprises the following steps: segmenting a source document into a plurality of text blocks; extracting element instances from the text blocks; converting the element instance into a readable element abstract; constructing the element abstract into a graph community based on a graph theory scheme; generating a community-level abstract for the graph community; generating community answers according to the digests of the community levels and reducing the community answers to obtain global answers, and completing large language model questions and answers based on the retrieval enhancement generation scheme. The invention further discloses a system for realizing the retrieval enhancement generation scheme-based large language model question and answer method. According to the large language model question and answer method and system based on the retrieval enhancement generation scheme, entity knowledge graph and community abstract generation technologies are combined, question and answer of the large language model are achieved based on the retrieval enhancement generation scheme, reliability is higher, and accuracy is better.

The invention discloses a service database dialogue method and device, electronic equipment and a medium, and the method comprises the steps: carrying out the cleaning and partitioning of original knowledge data, and obtaining text blocks; carrying out data extraction and construction by utilizing a large language model to obtain graph structure data; detecting and dividing the graph structure data through a community detection algorithm to obtain a plurality of communities; generating a corresponding community abstract for each community through a large language model; performing embedding processing on the community abstract to obtain a first vector; in response to an input question of the target object, performing similarity matching on the first vector to obtain candidate communities, generating an intermediate answer for each candidate community, and obtaining a correlation score of each intermediate answer and the input question; taking the candidate community with the highest correlation score as a target community; and based on the node attribute information corresponding to the target community, generating a target answer by using a large language model. Business database dialogues can be efficiently achieved, and the method can be widely applied to the technical field of data processing.

The invention provides a smart community Internet of Things access control operation system, and relates to the technical field of data processing, the system comprises: a data acquisition module, used for obtaining a standard identity data set; the preliminary verification module is used for matching the standard identity data set with a preset resident database to generate preliminary verification result data; the distributed computing node module is used for performing deep verification on the preliminary verification result data through computing nodes to obtain final verification result data; the task distribution module is used for generating node distribution data; the load balancing module is used for adjusting node distribution data; the fault-tolerant processing module is used for redistributing the verification task of the fault node to other computing nodes; the dynamic authority management module is used for generating access control authority information for the user according to the final verification result data; the access control module is used for controlling target access control equipment to execute door opening operation; according to the invention, the data processing efficiency of the system is improved.

The invention relates to the technical field of urban geography, in particular to a crowd movement flow-based multi-scale community structure importance identification method, which comprises the following steps of: constructing a crowd movement space-time network according to a preset geographic space unit and a time interval on the basis of crowd movement starting place and destination data; identifying a dynamic community structure based on a crowd mobile space-time network; performing multi-scale community structure object role and importance identification; and performing pattern classification by using the time sequence importance of the multi-scale community structure object. According to the method, the problems of inaccurate dynamic community structure recognition and lack of multi-scale object contribution decoupling mechanisms are solved.

The invention relates to the technical field of intelligent monitoring and maintenance, in particular to an urban community embedded service facility operation management method and system based on deep learning, and the method comprises the steps: dynamically collecting equipment state data, analyzing the state data through a deep learning algorithm, and generating an equipment health index and a fault risk assessment value. And finally outputting fault early warning data. Through the intelligent monitoring and prediction capability, real-time monitoring and fault early warning can be realized, the maintenance decision can be optimized, the fault occurrence rate can be reduced, the efficient and safe operation of equipment can be ensured, and the intelligent development of urban communities can be promoted.

The invention discloses a big data energy management method for intelligent monitoring of a community, and relates to the technical field of energy management, and the method is characterized in that through the hierarchical processing of community energy consumption data, the comprehensive analysis of a time dimension and a periodic rule is combined, and the energy use characteristics can be deeply mined. According to the processing mode, data of residential areas, public areas and commercial areas are layered, and detailed statistics is carried out according to categories of electric power, water resources, natural gas and the like, so that extraction of energy consumption characteristics is more targeted and systematic. Through time interval segmentation and clustering analysis, the behavior pattern of energy consumption is identified, and abnormal fluctuation characteristics are found in combination with entropy calculation, so that an abnormal source lower than a threshold value can be accurately positioned, and the efficiency and accuracy of troubleshooting are remarkably improved. Meanwhile, based on the dynamic prediction of the future energy consumption trend, in combination with the correlation analysis of the fluctuation region and the risk equipment, the potential problems in the energy consumption can be effectively pre-judged.

Systems and methods of intelligent reporting within online communities are provided. A current communication session associated with a plurality of user device may be monitored. The current communication session may include a stream of audio-visual content generated in real-time based on interactions between the user devices. A recording trigger may be detected within the current communication session. A recording of a portion of the stream of audio-visual content may be recorded in response to the detected trigger event. The recording may be analyzed to attribute one or more sub-portions within the recording to one or more of the user devices. At least one of the sub-portions attributed to an identified one of the user devices may be determined to meet a moderation event. A report regarding the identified user device may be generated that includes the at least one sub-portion that meets the moderation event.

The invention relates to the field of community old-age care, and discloses a community old-age care-oriented medical-care-and-care-combined data intercommunication and service coordination system, which comprises a sensing module used for acquiring physiological data of old people, old-age care service data, environment data and service demand information to form multi-modal information; the network module is used for carrying out multi-modal information transmission, protocol conversion and edge calculation preprocessing to obtain processed data; and the data module is used for constructing a standardized data lake according to the processed data by adopting a unified data standard and a main data management technology, and carrying out fusion storage, treatment and safety protection to obtain multi-source information. Multi-modal information is collected to provide a data basis for cross-mechanism cooperation, data transmission and preprocessing are performed, and a standardized data lake is constructed through a unified data standard and main data management, so that medical institutions, pension institutions and community grids can share global data such as health states, service records and the like of old people in real time.

The invention relates to the field of data management, in particular to a community data management system for an intelligent old-age care community, and the system comprises a video monitoring unit which comprises a plurality of community monitoring devices and is used for collecting monitoring videos of all monitoring areas; the video analysis unit is used for periodically determining the video frame category of each basic video frame according to the personnel change reference value and the personnel influence reference value of the basic video frame corresponding to the community monitoring equipment; the video optimization unit is used for determining the state of the monitoring equipment according to the first-class video frame proportion and the first-class video frame density corresponding to the community monitoring equipment, and determining a corresponding video frame optimization mode as compensation optimization or activity feature optimization according to the state of the monitoring equipment; the key frame optimization unit is used for determining a processing mode to supplement or generate image description for the associated frame according to the effective state; and the monitoring data processing efficiency is improved.

The invention discloses an intelligent comprehensive management method and system for a safe community, and the method comprises the steps: carrying out the data aggregation and storage through a distributed big data platform for massive community data resources accessed to a unified management platform, extracting key community management indexes and business elements through the operation of data cleaning, correlation analysis and the like, and carrying out the data collection and storage. A command and dispatch-oriented multi-dimensional data model is constructed, and flexible data query and analysis are supported; on the basis of a multi-dimensional data model, a rule-based expert system technology is adopted, experience knowledge and decision logic of managers are subjected to formalized expression, a community management knowledge base and a rule base are constructed, automatic execution of business logic is achieved through a rule engine, and a command and dispatch scheme is dynamically generated according to the real-time state of a community. The problems of information isolated island, low data utilization rate, low commanding and dispatching efficiency and the like in community management are effectively solved, and intelligence and refinement of community management are realized.