Future community service intelligent management method and system based on big data

Abstract

The invention relates to the field of intelligent management, data science and community services, in particular to a future community service intelligent management method and system based on big data, and the method comprises the core processes of data collection, feature extraction, intelligent agent task scheduling, resource optimization and the like. Feature data which can be used for analysis are generated through data fusion; analyzing a resource use state in real time by using a deep learning technology, and generating resource recommendation data; three-dimensional visual display is carried out through the holographic data management platform, and a decision maker can quickly identify problems and make adjustments; according to the invention, the efficiency and quality of community service are improved through intelligent regulation and control, and scientific basis and technical support are provided for community management.

Description

Technical Field

[0001] The present invention relates to the fields of intelligent management, data science and community services, and in particular to a future community service intelligent management method and system based on big data. Background Art

[0002] With the acceleration of urbanization and the improvement of residents' living standards, the demand for community services is becoming increasingly diverse and complex. Intelligent management of future community services has emerged, aiming to improve the utilization efficiency of community resources and the quality of life of residents through the collection, analysis and application of real-time data. This management model integrates various types of data (such as environmental monitoring, personnel flow, resource use, etc.) to achieve intelligent resource allocation and service optimization, thereby effectively meeting the needs of residents' daily lives.

[0003] However, the existing technology (Chinese invention patent, publication number: CN117875718B, name: A community intelligent management service method and system based on big data) has exposed many shortcomings in its application in this field. Many traditional community management systems rely on rule-based decision-making algorithms and simple statistical analysis. These methods often have lags in data processing and cannot reflect community dynamics in a timely manner. For example, experience-based resource allocation strategies cannot be adjusted quickly in the face of emergencies (such as public events, weather changes, etc.), resulting in waste or shortage of resources. In addition, the lack of in-depth analysis of complex data makes it difficult for many potential problems to be discovered and solved in a timely manner. This information island phenomenon seriously affects the efficiency and effectiveness of community management.

[0004] In addition, the existing system does not have sufficient requirements for the real-time and adaptability of data, and cannot flexibly respond to the changing needs of different user groups in the community. This limitation often makes it difficult for community managers to make accurate decisions when faced with changing service needs, reducing the overall service quality. Therefore, there is an urgent need for an intelligent management solution that can comprehensively improve the efficiency of community services, so as to achieve precise regulation and optimize resource allocation, thereby better meeting the diverse needs of residents. Summary of the invention

[0005] In view of the many problems existing in the above-mentioned prior art, the present invention provides a future community service intelligent management method and system based on big data. The present invention realizes intelligent prediction and regulation of community resources by integrating multiple advanced data processing technologies (such as generative adversarial networks and deep reinforcement learning). By integrating global control data and execution feedback data, and extracting multi-dimensional features using recurrent neural convolutional networks, accurate resource allocation and optimization can be achieved, significantly improving the efficiency and responsiveness of community management.

[0006] A future community service intelligent management method based on big data, comprising the following steps: Deploy multiple types of sensor nodes to collect community environmental parameters and personnel characteristic data, perform denoising and feature extraction on the collected data, and generate environmental characteristic data and personnel characteristic data; Based on environmental and personnel feature data, cellular automata are used to deduce the local state of community resources, and causal relationship analysis is performed on the local state in combination with a multivariate causal inference network to generate a causal chain between resources. Multi-path deduction is performed through a Markov decision process to generate future state prediction data. Based on the future state prediction data, the task states of multiple agents are initialized, and the states between agents are shared through the graph convolutional network. The strategies of each agent are dynamically adjusted using the symbiotic reinforcement learning network. Each agent performs tasks according to the adjusted strategy, generates execution feedback data and forms global control data. Integrate global control data with execution feedback data, extract multi-dimensional features from the integrated data through a recurrent neural convolutional network, generate enhanced training data through an adversarial knowledge transfer model, and use deep reinforcement learning to optimize and iterate the model to generate reinforcement learning strategy data; The global control data and future status prediction data are visualized through the holographic data management platform, and resource recommendation data is generated for the optimization and regulation of community resources.

[0007] Preferably, the deployed multi-type sensor nodes utilize a sensor fusion algorithm to perform multi-source data fusion on the collected environmental parameter data and personnel characteristic data, and process the environmental parameter data and personnel characteristic data of multiple sensors through a weighted average method to generate consistent environmental characteristic data and personnel characteristic data.

[0008] Preferably, the local state deduction of community resources includes deduction based on a rule set of cellular automata and state changes of neighboring cells, each cell represents a resource point in the community, the state of the resource point is dynamically updated according to the influence weight of the neighboring cells and the time step change, and the transfer rule of the cell state is adjusted according to the current state and historical state data of the neighboring cells.

[0009] Preferably, the multivariate causal inference network utilizes an autoregressive neural network model to identify the causal chain between resource points through time series analysis and calculation of causal weights, and generates a causal weight matrix. The causal weight matrix determines the strength of the causal relationship between resource points according to the Granger causality test method.

[0010] Preferably, the task state initialization of the agent includes setting the initial task goal, strategy parameters and constraints for each agent, and aggregating the state features of multiple agents through a graph convolutional network so that each agent can share the global state feature information of neighboring agents.

[0011] Preferably, the symbiotic reinforcement learning network uses symbiotic factors to adjust the strategy functions of the agents through strategic interactions and game relationships between the agents. The symbiotic factors are calculated by evaluating the sensitivity of the strategy functions of neighboring agents based on the strategy changes of each agent, thereby forming an adaptive strategy adjustment mechanism.

[0012] Preferably, after the global control data and the execution feedback data are integrated, the time series features and spatial features are extracted through a recurrent neural convolutional network. The recurrent neural convolutional network includes a combination of multiple convolutional layers and recurrent layers. The convolutional layers are used to extract the spatial features of the input data, and the recurrent layers are used to extract the time series features of the input data.

[0013] Preferably, the adversarial knowledge transfer model generates pseudo data by generating an adversarial network in the alternating training process of the generator and the discriminator, and the objective function of the generator is:

[0014] in, represents the loss function of the generator; Indicates expected value; represents the discriminator; Represents a generator; Represents the input noise vector.

[0015] Preferably, the holographic data management platform uses augmented reality technology to visualize the global control data and future status prediction data in three dimensions, and displays the real-time status and resource control strategies of community resources through a virtual interface. Augmented reality technology includes real-time three-dimensional model rendering and dynamic annotation.

[0016] A system for implementing the future community service intelligent management method based on big data, the system comprising: Multi-type sensor node modules are used to collect environmental parameter data and personnel characteristic data of the community, and perform denoising and feature extraction on the collected data to generate environmental characteristic data and personnel characteristic data; The central control system module is used to receive environmental feature data and personnel feature data from multiple types of sensor node modules, and use cellular automata to deduce the local state of community resources, combine multivariate causal reasoning networks to perform causal relationship analysis on local states, generate causal relationship chains between resources, perform multi-path deduction based on Markov decision processes, and generate future state prediction data; The agent control module is used to initialize the task states of multiple agents based on future state prediction data, realize state sharing between agents through graph convolutional networks, dynamically adjust the strategies of each agent using symbiotic reinforcement learning networks, and generate execution feedback data and global control data; Holographic data management platform module, which is used to integrate global control data with execution feedback data, extract multi-dimensional features of the integrated data through a recurrent neural convolutional network, generate enhanced training data through an adversarial knowledge transfer model, and optimize and iterate the model using deep reinforcement learning to generate reinforcement learning strategy data; The visualization and recommendation module is used to visualize the global control data and future status prediction data through the holographic data management platform, and generate resource recommendation data to achieve optimal control of community resources.

[0017] Compared with the prior art, the advantages and beneficial effects of the present invention are: The present invention realizes intelligent regulation and optimal allocation of community resources by adopting advanced machine learning and deep learning technologies.

[0018] By using methods such as generative adversarial networks, graph convolutional networks and recurrent neural networks, the present invention can analyze and predict the usage status of community resources in real time, provide accurate resource recommendations, and improve management efficiency.

[0019] Through the combination of holographic data management platform and augmented reality technology, the present invention provides intuitive data visualization to help decision makers quickly identify problems and make timely adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 A schematic diagram of task initialization and state sharing of an intelligent agent in the present invention; Figure 3 It is a schematic diagram of the adversarial knowledge transfer model in the present invention; Figure 4 It is a schematic diagram of visual display of the holographic data management platform in the present invention; Figure 5 It is a structural block diagram of the system of the present invention. DETAILED DESCRIPTION

[0021] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0022] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0023] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0024] like Figure 1 As shown, a future community service intelligent management method based on big data includes the following steps: Deploy multiple types of sensor nodes to collect community environmental parameters and personnel characteristic data, perform denoising and feature extraction on the collected data, and generate environmental characteristic data and personnel characteristic data; Preferably, the deployed multi-type sensor nodes utilize a sensor fusion algorithm to perform multi-source data fusion on the collected environmental parameter data and personnel characteristic data, and process the environmental parameter data and personnel characteristic data of multiple sensors through a weighted average method to generate consistent environmental characteristic data and personnel characteristic data.

[0025] In the present invention, the deployment of sensor nodes covers multiple data collection points, and the sensor nodes include ultrasonic sensors, laser radars, infrared thermal imaging sensors and gas sensors. Ultrasonic sensors are mainly used for ranging and obstacle detection, laser radars are mainly used for accurate spatial scanning and positioning, infrared thermal imaging sensors are used to monitor human activities and identify human heat characteristics, and gas sensors are used to detect changes in the concentration of harmful gases in the ambient air.

[0026] The data types collected by these multi-type sensors cover environmental parameter data (such as temperature, humidity, gas concentration) and personnel characteristic data (such as personnel movement trajectory, body surface temperature characteristics, etc.). In order to ensure the accuracy and effectiveness of the data, edge computing is used to denoise the raw data during the data collection process of the sensor node. The core of denoising is to use a combination of Gaussian filters and median filters to eliminate sensor noise and retain the detailed features of the data to generate denoised environmental characteristic data and personnel characteristic data. This process can effectively reduce the impact of sensor noise on subsequent data processing, thereby improving the quality of feature data.

[0027] After multiple types of sensor nodes are deployed and start collecting data, it is necessary to perform multi-source data fusion on different types of sensor data. The sensor fusion algorithm uses a weighted average method to comprehensively process the data of multiple sensors to solve problems such as large measurement errors and low accuracy of a single sensor.

[0028] Specifically, the core of the sensor fusion algorithm is to assign a weight to the data collected by each sensor. , the fusion result is calculated by the weighted average formula. The formula is as follows:

[0029] in, Represents the fused data result, Indicates The data collected by the sensors, Indicates The weight of each sensor, Indicates the number of sensors. The sensor weight setting depends on the accuracy and reliability of the sensor. The system will dynamically adjust the weight based on historical data analysis results, sensor status, and current environmental conditions.

[0030] For example, the data from the LiDAR may have a higher weight in clear weather, but in foggy conditions, the accuracy of the LiDAR data decreases, and the system automatically reduces its weight while increasing the weight of the infrared thermal imaging sensor data. This adaptive weighting mechanism can maintain high data consistency in complex environments.

[0031] By deploying multiple types of sensor nodes and adopting sensor fusion algorithms, the present invention can achieve multi-dimensional real-time perception of the community environment. De-noising and feature extraction make environmental feature data and personnel feature data more accurate and reliable, and the application of sensor fusion algorithms can effectively reduce data deviations caused by single sensor measurement errors. The present invention integrates sensor data from different sources by weighted averaging, improves data consistency and accuracy, and provides a more solid foundation for subsequent data analysis and decision-making.

[0032] Taking the detection of community population density as an example, multi-type sensor nodes first collect environmental data and personnel thermal imaging data at different points. The edge computing platform denoises and extracts features from the data at each point to extract population density data and personnel temperature feature data. After that, through the sensor fusion algorithm, weights are set according to the historical accuracy of infrared thermal imaging sensors and lidars, and weighted average calculations are performed to generate population density information at the current moment. This information can be used to determine the congestion in certain areas of the community in real time, and send early warning signals to community managers through the data analysis platform.

[0033] Based on environmental and personnel feature data, cellular automata are used to deduce the local state of community resources, and causal relationship analysis is performed on the local state in combination with a multivariate causal inference network to generate a causal chain between resources. Multi-path deduction is performed through a Markov decision process to generate future state prediction data. Cellular Automaton (CA) is a discrete dynamic model based on a lattice structure, which is used to simulate the local change state of resources in a community. Each cell represents a community resource unit (such as the population density or environmental changes in a specific area), and its state changes with the state of neighboring cells in a discrete time step. The state update of a cell is carried out according to a certain transfer rule, which reflects the influence of neighboring cells on the target cell.

[0034] In this invention, cellular automata are used to simulate the local state of community resources (such as population density, environmental pollution level, etc.). represents an area in the community, and the state variable is , indicating the Line Column cells at time The transition rule for state update can be expressed by the following formula:

[0035] in, Representation Cell The set of neighbor cells of Neighbor cells For the target cell The influence weight of the state update function Dynamically set according to the actual application scenarios of the community. For example, in the scenario of monitoring population density, the impact weight can reflect the tendency of personnel flow between different areas.

[0036] In order to further identify the complex causal relationship between community resources, the present invention adopts a multivariate causal inference network (Causal Inference Network), which analyzes the causal relationship based on environmental feature data and personnel feature data, thereby generating a causal relationship chain between resources.

[0037] In the multivariate causal inference network, an autoregressive neural network is used for time series modeling, and the causal weight matrix is ​​constructed by calculating the significance level of Granger causality. The specific causal weight calculation is as follows:

[0038] in, Indicates resource point and The causal weight between them, the judgment condition of Granger relationship is when the resource point The probability distribution of Through the causal weight matrix, we can build a causal chain between resources, so as to analyze and understand the impact mechanism of community resource status.

[0039] After identifying the causal chain between resources, the present invention further uses the Markov Decision Process (MDP) to predict the future state of community resources. MDP is a mathematical framework for modeling and optimizing sequential decision problems. It mainly consists of state space, action space, state transition probability and reward function.

[0040] In this invention, the state space consists of the states of each resource point in the community, and the action space reflects the possible resource allocation or regulation strategies. The core of MDP is to calculate the state transition probability ,in Indicates time The state of the moment, Represents the currently executed action. MDP uses the policy function (i.e., the policy function selects the best action) and the reward function Optimize the path to the future state.

[0041] In the specific implementation process, in order to determine the optimal solution of multi-path deduction, a value iteration algorithm based on dynamic programming is adopted, and its main formula is:

[0042] in, Indicates in status The value function under is the discount factor. By solving the optimal value function, we can get the optimal strategy , and generate future state prediction data.

[0043] Through the above process, the present invention can realize the simulation of community resource status, the reasoning analysis of causal relationships, and the multi-path deduction of future status. Cellular automata provide dynamic simulation of local changes in the community, multivariate causal reasoning networks generate causal chains by analyzing time series data, and the introduction of Markov decision processes enables the present invention to predict and optimize future regulation strategies for community resources. Combined with multi-dimensional data and algorithms, this process can improve the accuracy and intelligence of community resource management.

[0044] In the application of community air quality management, environmental sensors in multiple areas continuously collect air quality parameters. Cellular automata are used to simulate the local diffusion effect of air pollution in each area. Through the cellular state update formula, the air quality of the current area is updated according to the change of pollution state in the neighboring area. At the same time, the multivariate causal reasoning network analyzes the propagation path of pollution sources between different areas and generates a causal chain of pollution diffusion. Finally, based on the predicted pollution trend, the Markov decision process determines the possible path of future pollution diffusion and formulates the best air governance strategy.

[0045] Preferably, the local state deduction of community resources includes deduction based on a rule set of cellular automata and state changes of neighboring cells, each cell represents a resource point in the community, the state of the resource point is dynamically updated according to the influence weight of the neighboring cells and the time step change, and the transfer rule of the cell state is adjusted according to the current state and historical state data of the neighboring cells.

[0046] The basic unit of cellular automaton is the cell. In the present invention, each cell represents a community resource point. The state of the cell Indicates at time Moment, Community Line The state of the resource point is listed. This state can be a characteristic value such as air quality index and personnel density. The state change of a cell is affected by the state change of its adjacent cells.

[0047] The dynamic update of the state is carried out according to the rule set of cellular automata, which is formulated according to the actual needs of community resource management and usually includes three main elements: the influence of neighboring cells, the change of time step, and historical state data.

[0048] The influence of the state of neighboring cells and the calculation of weights: In the process of updating the cell state, the state of the neighboring cells has a direct impact on the target cell. In the present invention, the degree of influence is represented by the weight of the neighboring cells. Assume that the cell Status Affected by its neighboring cells The basic formula for state update is:

[0049] in, Indicates time Time Cell status, Representation Cell The set of neighbor cells of Represents neighbor cells Influence weight on target cell, state transition function Represents the set of rules for a cellular automaton that determines the transition to a new state.

[0050] Influence weights between cells It can be set according to the distance and type of neighbor cells. For example, if the change of population density is taken into account, the weight of the influence of the neighbor cells on the target cell is greater than that of the distant neighbor cells. The weight calculation formula can be:

[0051] in, The target cell and the neighbor cell This distance-based weight calculation can more truly reflect the local correlation of community resources.

[0052] The cell state update depends not only on the state of the current neighbor cells, but also on the impact of the time step change on the state. In this invention, in order to enhance the dynamics of the model, the time step The selection of is related to the rate of change of community resources. If the resources change rapidly (such as a sudden change in population density), a smaller time step is selected to improve the accuracy of the deduction.

[0053] In addition, the rule set of the cellular automaton will be adjusted according to the historical state data in certain situations. For example, when the historical data shows that the population density in a certain area has periodic fluctuations, the model will adaptively modify the transition rules. This modification can be done by using a recurrent neural network (RNN) to analyze the periodic characteristics of the historical data and adjust the state transition function. to more accurately reflect the actual changes.

[0054] Through local state deduction based on cellular automata, the present invention can achieve fine simulation of community resource points and capture the local correlation between different resource points. In particular, through the weight influence of neighbor cells and the dynamic adjustment rule set, the model can flexibly respond to the uncertainty and suddenness of community resource changes. The final result is to generate the dynamic evolution trend of community resources and provide a scientific basis for community resource management.

[0055] Taking the population density monitoring in the community as an example, the cellular automaton divides the community into multiple small areas (i.e., cells), and the population density in each small area is determined by the state value of the cell. According to the flow of people in adjacent areas, the state of neighbor cells affects the change of population density in the target cell. The influence weight is dynamically calculated based on the distance between neighboring areas and historical personnel flow data.

[0056] Assume that the cell The population density is , its neighbor cells are , then the density update formula of the target cell is:

[0057] in, Represents the influence coefficient of the target cell's own state on the density change, The target cell and the neighbor cell This formula can dynamically adjust the changes in population density by integrating the influence of neighbor cells and the historical status of the target cell.

[0058] Preferably, the multivariate causal inference network utilizes an autoregressive neural network model to identify the causal chain between resource points through time series analysis and calculation of causal weights, and generates a causal weight matrix. The causal weight matrix determines the strength of the causal relationship between resource points according to the Granger causality test method.

[0059] Autoregressive neural network (ARNN) is a model that combines autoregressive features with neural network capabilities, and is mainly used to analyze time series data. In the present invention, each resource point (such as air quality, traffic flow, personnel flow in a certain area, etc.) is modeled through a series of data sequences at a series of time points to capture the trend of resource point status over time. Specifically, the AR neural network predicts the future status of the resource point based on the time series of historical data. The basic formula of the autoregressive model is as follows:

[0060] in, Indicates the resource point at time status, is the autoregressive order, is the autoregressive coefficient, is the error term.

[0061] In the present invention, the AR neural network introduces a nonlinear activation function of the neural network, so that the model can better capture the trend changes of complex time series. The calculation of the neural network layer consists of multiple hidden layers, and the activation function This is achieved through functions such as ReLU or Sigmoid.

[0062] In order to further identify the causal relationship between resource points, the present invention introduces the Granger causality test method to calculate the causal relationship weights between resource points. The core idea of ​​the Granger causality test is: if the resource point The historical data can significantly improve the resource points The predictive ability of the state is considered right There is a causal relationship.

[0063] The specific causal relationship test formula is:

[0064] in, Indicates resource point Resource Points The causal weight of and denote the residual sum of squares of the restricted model and the unrestricted model respectively. If the significance level exceeds a certain threshold, it is considered right There is a significant causal effect.

[0065] Through the above calculations, a causal weight matrix can be generated , whose elements Indicates resource point Resource Points The generation of the causal weight matrix provides basic data support for subsequent community resource optimization.

[0066] Through the multivariate causal inference network, the present invention can effectively identify the causal relationship between various resource points in the community and quantify the strength of this relationship. This allows community managers to better understand the mutual influence between different resource points and provides data support for future community resource optimization and intelligent regulation. In particular, in the process of formulating resource regulation strategies, it is possible to prioritize resource points with greater causal influence based on the size of the causal relationship weight, thereby achieving more accurate resource management.

[0067] Suppose that in community management, it is necessary to analyze the causal relationship between the traffic flow of a main road and the air quality of the surrounding area. First, the two types of data are collected by sensors deployed at traffic nodes and air monitoring stations to form two time series. Then, the autoregressive neural network is used to model the two time series to predict future traffic flow and air quality status. Then, the causal weight of traffic flow on air quality is calculated through the Granger causality test, and a causal weight matrix is ​​generated. Finally, by analyzing the causal weight matrix, it can be determined whether the change in traffic flow has a significant causal impact on air quality, and how strong the impact is.

[0068] like Figure 2 As shown in the figure, based on the future state prediction data, the task states of multiple agents are initialized, the state sharing between agents is achieved through the graph convolutional network, and the strategy of each agent is dynamically adjusted using the symbiotic reinforcement learning network. Each agent performs the task according to the adjusted strategy, generates execution feedback data and forms global control data; Before each agent performs a task, it is first initialized based on the predicted future state data. The task state may include information such as the target location, target resource quantity, and task priority. This initialization process ensures that the agent can participate in resource management at the right time and in the right way. For example, an agent may be initialized to allocate more resources in high-density areas and reduce resource allocation in low-density areas.

[0069] State sharing between agents is achieved through a graph convolutional network (GCN). GCN models the relationship between agents through a graph structure, allowing each agent to effectively aggregate information from its neighboring agents to better understand the overall state of the environment. The formula for state sharing can be expressed as:

[0070] in, Representing an Agent In the The state characteristics of the layer, It is an intelligent agent The neighbor set of is the standardized coefficient, It is The weight matrix of the layer, is the activation function. This process enables each agent to integrate the state characteristics of multiple neighboring agents and dynamically adjust its task strategy.

[0071] The strategy adjustment of the agent is carried out through the Cooperative Reinforcement Learning Network. The network dynamically adjusts the strategy of each agent through the cooperation and game mechanism between agents. The core of symbiotic reinforcement learning is to improve the efficiency of the overall system by sharing information and updating strategies. The specific strategy adjustment formula is:

[0072] in, Indicates in status Take action The expected return, Is to perform an action Instant rewards, is the state transition probability, is the discount factor. This strategy update process ensures that the agent can adjust its execution strategy in real time in a dynamic environment to complete the task more effectively.

[0073] Each agent performs specific tasks according to the adjusted strategy. The execution process of the task will generate execution feedback data, which includes execution results, resource usage and state changes. These feedback data are aggregated to form global control data for further optimization of the entire system. Global control data will be used to evaluate the effectiveness of current resource allocation and provide support for future decision-making.

[0074] Through the above process, the present invention can achieve efficient collaboration of intelligent agents in complex community environments. Future state prediction data provides a clear basis for intelligent agents to operate, while graph convolutional networks enhance the information sharing capabilities between intelligent agents, and symbiotic reinforcement learning networks ensure that intelligent agents can optimize decisions in dynamic environments. Finally, the generated execution feedback data provides valuable feedback information for the real-time regulation of community resources, helping managers make more accurate resource allocation decisions.

[0075] Taking community service resource scheduling as an example, assuming that the community faces a high population density, multiple agents are initialized to meet this challenge. The agent first obtains future state prediction data and identifies high-density areas. With the support of the graph convolutional network, each agent shares the current personnel distribution information and dynamically adjusts its scheduling strategy. The symbiotic reinforcement learning network optimizes the resource allocation strategy of each agent in real time based on the feedback of the current task. After execution, the feedback data generated by the agent shows the effectiveness of resource scheduling and changes in regional personnel flow, providing data support for subsequent decision-making.

[0076] Preferably, the task state initialization of the agent includes setting the initial task goal, strategy parameters and constraints for each agent, and aggregating the state features of multiple agents through a graph convolutional network so that each agent can share the global state feature information of neighboring agents.

[0077] In the specific implementation, the task goal can be defined by mathematical expression as , indicating the The task goal of each agent. Strategy parameters It may include learning rate, exploration rate, etc., and the constraints This may include resource availability, maximum processing capacity, etc. For example:

[0078] in, is the amount of resources, is the demand, which means that the agent's goal is to dynamically adjust based on the relationship between available resources and demand.

[0079] The agents aggregate state features through the Graph Convolutional Network (GCN). GCN allows agents to represent their relationships through graph structures, thereby sharing state features. By aggregating the state characteristics of its neighboring intelligent agents, information interconnection and intercommunication are achieved.

[0080] Specifically, the agent The state feature aggregation formula is:

[0081] in, Representing an Agent In the The state characteristics of the layer, It is an intelligent agent The neighbor set of is the standardized coefficient, It is The weight matrix of the layer, is the activation function. Through this aggregation process, the agent Ability to obtain status information from neighboring agents, thereby achieving effective understanding and feedback of the global state.

[0082] By initializing the task objectives, strategy parameters and constraints for each agent, and using graph convolutional networks to aggregate state features, the present invention can ensure that agents can achieve efficient collaboration in complex community management tasks. Each agent can not only clearly define its own goals, but also dynamically adjust them with the help of the state information of neighboring agents, thereby improving its response speed and decision-making quality.

[0083] Suppose that in a community, multiple agents are assigned to be responsible for resource scheduling in different areas. Each agent initializes its own task goal based on the current resource status and expected demand. Through the graph convolutional network, the agent can receive and aggregate the status information of the agents in the surrounding area to form an overall understanding of the resource status of the entire community.

[0084] For example, the agent The goal is to optimize the area water resource allocation, while the intelligent agent Responsible for monitoring and deploying areas Through graph convolutional networks, the agent Can obtain intelligent agent The water resource allocation strategy can be adjusted based on the status of electricity use to avoid waste of resources. This dynamic adjustment mechanism based on information sharing greatly improves the efficiency and flexibility of community resource management.

[0085] Preferably, the symbiotic reinforcement learning network uses symbiotic factors to adjust the strategy functions of the agents through strategic interactions and game relationships between the agents. The symbiotic factors are calculated by evaluating the sensitivity of the strategy functions of neighboring agents based on the strategy changes of each agent, thereby forming an adaptive strategy adjustment mechanism.

[0086] When performing tasks, agents face multiple resource configuration and scheduling options. The symbiotic reinforcement learning network is based on a game theory model that simulates the mutual influence and collaboration between agents. In this framework, agents not only focus on their own benefits, but also need to consider the strategies and state changes of neighboring agents. This strategic interaction enables agents to adapt to changes in the overall environment when making decisions, improving the flexibility and effectiveness of decision-making.

[0087] The symbiosis factor is a key parameter for evaluating the impact of changes in the strategy of an agent on neighboring agents. The calculation method of the symbiosis factor can be expressed as:

[0088] in, Representing an Agent For Agents The symbiotic factors of strategy, It is an intelligent agent In Status Take action The expected return, It is an intelligent agent The strategy function.

[0089] Through this calculation, the symbiosis factor can quantify the sensitivity between agents and adjust the agent's policy function. The change of strategy has an impact on the agent When the effect of It can adjust its strategy accordingly to better adapt to changes. This interdependent relationship forms an adaptive strategy adjustment mechanism, which enables each agent to make more effective decisions when facing a dynamic environment.

[0090] In the specific implementation, the intelligent body will adjust its strategy parameters according to the calculated symbiotic factors to form a new strategy The process of policy updating can be expressed as:

[0091] in, is the learning rate, Representing an Agent In Status Through such strategy updates, the agent can adapt to changes in the environment and improve the efficiency of overall resource management.

[0092] Through the application of symbiotic reinforcement learning networks, the present invention can achieve efficient collaboration and resource optimization management between intelligent agents. The introduction of symbiotic factors enables intelligent agents to take into account the status and strategy changes of neighboring intelligent agents when making decisions, thereby achieving adaptive adjustments. This dynamic strategy adjustment mechanism enhances the adaptability of intelligent agents in complex community environments and improves the accuracy and timeliness of resource allocation.

[0093] Suppose that in a community, multiple agents are assigned to dispatch different service resources, such as cleaning, security, and maintenance. The initial strategy of each agent is set based on historical data, but in actual operation, the environment and demand are constantly changing. Through the symbiotic reinforcement learning network, each agent can share each other's status and strategy information in real time.

[0094] For example, the agent Responsible for cleaning services, intelligent body Responsible for security. At a certain point in time, the agent It is found that there is a lot of garbage in a certain area, so it is decided to send more people to clean it up. Knowing that the increase in cleaning services may lead to an increase in security in the area, it decides to moderately reduce its security efforts and deploy resources to other areas in need. The strategy update is based on The sensitivity to policy changes improves overall service efficiency.

[0095] Integrate global control data with execution feedback data, extract multi-dimensional features from the integrated data through a recurrent neural convolutional network, generate enhanced training data through an adversarial knowledge transfer model, and use deep reinforcement learning to optimize and iterate the model to generate reinforcement learning strategy data; In the present invention, the process of integrating global control data and execution feedback data is a key step in achieving efficient community service management. Multi-dimensional features are extracted from the integrated data through the Recurrent Convolutional Neural Network (RCNN), and then enhanced training data is generated by combining the adversarial knowledge transfer model. Finally, deep reinforcement learning is used to optimize and iterate the model to generate reinforcement learning strategy data.

[0096] The global control data mainly comes from the collaborative process of the intelligent agents and the resource allocation of the community, while the execution feedback data is the actual effect feedback collected by the intelligent agents after executing the tasks. After these data are integrated, they lay the foundation for further data processing and analysis. The integration formula can be expressed as:

[0097] in, represents the integrated data set, is the global control data, is the execution feedback data, Represents a data integration operation.

[0098] The integrated data will be input into the recurrent neural convolutional network for processing. RCNN combines the advantages of convolutional layers and recurrent layers, and can effectively capture the spatial and temporal features in the data. In the feature extraction process, the convolutional layer is responsible for extracting spatial features from the integrated data, while the recurrent layer is responsible for processing time series features. The formula for feature extraction is:

[0099] in, Represents the time step The following feature representation: and are the weight matrices of the current and previous time steps, respectively, is the activation function. In this way, RCNN is able to extract rich multi-dimensional features from the integrated data, providing a basis for further analysis.

[0100] like Figure 3 As shown in Figure 1, after feature extraction, enhanced training data is generated through the adversarial knowledge transfer model. This model uses the idea of ​​generative adversarial network (GAN) to construct the adversarial process between the generator and the discriminator. The generator is responsible for generating new data samples, while the discriminator judges the authenticity of these samples. The generation of enhanced training data can be expressed as:

[0101] in, is the sample generated by the generator, is random noise sampled from a standard normal distribution. Through adversarial training, the generator will continuously improve the quality of generated samples to enhance the generalization ability of the model.

[0102] The generated enhanced training data is then used to train and optimize the deep reinforcement learning model. The process aims to maximize the cumulative reward by continuously adjusting the policy function through repeated iterations. The optimization process uses the following value update formula:

[0103] in, Indicates in status The value of It’s an instant reward. is the discount factor, is the next state. In this way, the model can continuously adjust its strategy based on environmental feedback and improve the effectiveness of its decision-making.

[0104] By integrating global control data and execution feedback data, using recurrent neural convolutional networks for feature extraction and adversarial knowledge transfer models to generate enhanced training data, the present invention can effectively improve the intelligence and response speed of community resource management systems. The introduction of deep reinforcement learning ensures the self-optimization ability of the model in a dynamic environment, making community management more flexible and accurate.

[0105] Taking community water resource management as an example, assume that the water resource usage and feedback data collected by smart sensors are integrated and input into a recurrent neural convolutional network for feature extraction. By analyzing historical usage data, the network can identify water usage patterns and trends in different regions. After generating enhanced training data, the deep reinforcement learning model can learn and optimize based on the decision results in the simulation environment, so that in actual applications, the scheduling of water resources can be adjusted in time to cope with sudden demands, thereby improving resource utilization efficiency.

[0106] Preferably, after the global control data and the execution feedback data are integrated, the time series features and spatial features are extracted through a recurrent neural convolutional network. The recurrent neural convolutional network includes a combination of multiple convolutional layers and recurrent layers. The convolutional layers are used to extract the spatial features of the input data, and the recurrent layers are used to extract the time series features of the input data.

[0107] In the present invention, after integrating the global control data and the execution feedback data, the recurrent convolutional neural network (RCNN) is used to analyze the data and extract the time series features and spatial features, thereby supporting the decision-making of intelligent management of future community services. RCNN combines the advantages of convolutional neural network (CNN) and recurrent neural network (RNN), can process spatial and temporal information in the data at the same time, and is suitable for processing complex multi-dimensional data structures.

[0108] Global control data includes resource allocation information from different agents, while execution feedback data reflects the actual execution results. The integration of these two types of data provides a comprehensive perspective to facilitate the identification of problems and opportunities in community resource management. After integration, the data can be expressed as:

[0109] in, represents the integrated data set, is the global control data, is the execution feedback data, Represents a data integration operation.

[0110] In RCNN, the convolution layer is used to extract spatial features and identify local features of the integrated data through a sliding window. The specific convolution operation can be expressed as:

[0111] in, It is The output of the layer, is the convolution kernel, is the bias term, is the activation function, is the data of the previous layer. This process enables the network to learn useful spatial features from the input data, such as the geographical distribution pattern of resource allocation.

[0112] After feature extraction, the recurrent layer receives the features extracted by the convolutional layer and analyzes the dynamic changes of the time series. The state update formula of the recurrent layer is as follows:

[0113] in, Represents the time step The following feature representation: and are the weight matrices of the current and previous time steps, respectively, is the activation function. In this way, RCNN can capture the dependencies in time series and analyze the trend changes in resource usage.

[0114] By combining feature extraction from convolutional layers and recurrent layers, RCNN can effectively identify key patterns in community resource management, including the spatial distribution and time series changes of resource allocation. This information provides solid data support for subsequent decision-making processes, making community management more intelligent and efficient.

[0115] Taking community water resource management as an example, suppose that water resource usage and feedback data in various regions are collected through sensors. These data are first integrated into Through RCNN, the convolutional layer extracts the spatial characteristics of water resource use, identifies areas with high water usage, and analyzes the time period of peak water usage through the recurrent layer of the time series.

[0116] Specifically, if the convolutional layer identifies a region The frequency of water usage during peak hours is , and the circulation layer analysis shows that in a certain season, the usage shows a fluctuating pattern (such as peak and trough periods), and these characteristics will be used to adjust the water resource allocation strategy within the community. Based on the extracted features, managers can take timely measures, such as increasing water supply before the peak of water use, or reasonably allocating resources during the off-peak period to improve the efficiency of water resource use.

[0117] Preferably, the adversarial knowledge transfer model generates pseudo data by generating an adversarial network in the alternating training process of the generator and the discriminator, and the objective function of the generator is:

[0118] in, represents the loss function of the generator; Indicates expected value; represents the discriminator; Represents a generator; Represents the input noise vector.

[0119] In the present invention, the adversarial knowledge transfer model generates pseudo data through a generative adversarial network (GAN) to enhance the diversity and effectiveness of training data, thereby improving the intelligence level of the community service management system. GAN consists of two parts, a generator and a discriminator, which compete with each other during alternating training, allowing the generator to generate more and more realistic data samples.

[0120] The goal of the generator is to minimize the loss function , that is, to make the discriminator's judgment on the generated data closer to the probability of the real data as much as possible.

[0121] In the specific implementation, the generator The random noise The data is converted into pseudo data, mapped and generated through a multi-layer neural network. This process optimizes the parameters through back propagation and gradient descent, so that the generated pseudo data is close to the real data in structure and distribution.

[0122] The generated pseudo data can be used in a variety of application scenarios. For example, in community resource scheduling, by generating representative scenario data, the model training can be enhanced and its adaptability to dynamic environments can be improved. The diversity of pseudo data enables the model to have better generalization ability in practical applications.

[0123] Discriminator training and adversarial mechanism: The task of the discriminator is to distinguish between real data and generated data as accurately as possible, and its loss function is:

[0124] in, Represents real data. By maximizing the discriminator’s loss function, the discriminator can learn better features to identify the difference between fake data and real data.

[0125] During the training process, the generator and the discriminator are optimized alternately: first, the discriminator is trained to improve its ability to identify real data, and then the generator is trained to make the data it generates more and more real. Through this adversarial mechanism, the generator and the discriminator promote each other, forming a dynamic optimization process.

[0126] By generating pseudo data through the adversarial knowledge transfer model, the present invention can introduce more training samples into the community service management system and enhance the robustness and adaptability of the model. This process improves the performance of the model in complex and dynamic environments and improves the accuracy of resource allocation and demand forecasting.

[0127] Taking community traffic flow prediction as an example, it is assumed that the traffic flow data collected by sensors has certain omissions or noise. In this context, using the adversarial knowledge transfer model, the generator can generate pseudo data based on the existing traffic flow pattern to make up for the deficiencies in the real data. The discriminator is used to verify the authenticity of the generated data.

[0128] For example, the generator input is random noise , output a set of pseudo traffic flow data The discriminator determines whether the data is true and feeds it back to the generator for optimization. Through multiple alternating trainings, the generated traffic flow data can not only simulate the traffic flow under normal circumstances, but also reflect the impact of specific events (such as activities, construction, etc.) on traffic flow, thereby improving the accuracy of the prediction model.

[0129] like Figure 4 As shown, the global control data and future state prediction data are visualized through the holographic data management platform, and resource recommendation data is generated for the optimization and regulation of community resources.

[0130] Preferably, the holographic data management platform uses augmented reality technology to visualize the global control data and future status prediction data in three dimensions, and displays the real-time status and resource control strategies of community resources through a virtual interface. Augmented reality technology includes real-time three-dimensional model rendering and dynamic annotation.

[0131] In the present invention, the process of visualizing global control data and future state prediction data through the holographic data management platform aims to improve the intelligent level of community resource management. The holographic data management platform uses augmented reality (AR) technology to present complex data in three dimensions, making it easier for decision makers to understand and apply resource conditions and control strategies.

[0132] The basics of data visualization: Global control data includes resource allocation, usage, and management strategies, while future state prediction data describes the dynamic changes of community resources based on historical data and model prediction results. After the integration of these two types of data, a rich information foundation is provided for visualization. The integrated data can be expressed as:

[0133] in, Represents the data required for visualization. is the global control data, It is the future state prediction data.

[0134] The holographic data management platform uses augmented reality technology to achieve three-dimensional visualization of data. Augmented reality technology superimposes virtual information on real-world scenes, allowing users to interact with data in real environments. For example, the resource distribution, usage status and control strategies within the community can be displayed through 3D models. The core technologies to achieve this goal include real-time 3D model rendering and dynamic annotation.

[0135] Real-time 3D model rendering: The integrated data is converted into a visual 3D model through a 3D rendering engine. The rendering engine generates corresponding geometric shapes based on the data features and uses lighting, material and other effects to enhance the realism of visualization. For example, OpenGL or WebGL and other technologies are used for efficient graphics rendering.

[0136] Dynamic annotation: Add dynamic information annotations to the model, such as the current status of resources, usage efficiency, recommended control measures, etc. Dynamic annotations can be updated according to real-time data changes to ensure that the displayed information always reflects the current status of community resources.

[0137] By analyzing the visualized data, the holographic data management platform can generate resource recommendation data to help managers make optimization decisions. The resource recommendation data is based on the current resource usage and forecast results, and uses decision support algorithms, such as multi-objective optimization algorithms, to propose the best resource allocation strategy. The generation of recommendation data can be expressed as:

[0138] in, Represents the generated resource recommendation data, It is real-time environmental context information, such as weather changes, event arrangements, etc.

[0139] Through the visualization of the holographic data management platform, decision makers can intuitively understand the distribution and status of community resources, quickly identify potential problems and take corresponding measures. At the same time, the generated resource recommendation data provides a scientific basis for the optimization and regulation of community resources, and improves the efficiency and effectiveness of management.

[0140] Suppose that in a community, the use of public facilities needs to be monitored and managed. Global control data includes information such as the frequency of use and maintenance status of each facility, and future status prediction data predicts future facility needs based on historical usage data. The integrated data is visualized in three dimensions through the holographic data management platform and displayed on the screen of the community management center, so that community managers can clearly see the real-time status of each facility.

[0141] For example, the frequency of use of recreational facilities in a park increases significantly on weekends. AR technology displays this information in three dimensions, allowing managers to intuitively see which facilities require additional maintenance staff during peak hours. Based on visual analysis, the resource recommendation data generated by the platform may suggest increasing the frequency of cleaning and deployment of maintenance staff in the park before the weekend, thereby improving the quality of community services.

[0142] like Figure 5 As shown, a system for implementing the future community service intelligent management method based on big data, the system includes: Multi-type sensor node modules are used to collect environmental parameter data and personnel characteristic data of the community, and perform denoising and feature extraction on the collected data to generate environmental characteristic data and personnel characteristic data; by deploying various sensors (such as ultrasonic sensors, infrared thermal imaging sensors, gas sensors and lidar), environmental parameters and personnel characteristic data in the community are collected in real time. These sensors can monitor environmental indicators such as temperature, humidity, gas concentration, and personnel characteristics such as number of people and behavior patterns. The collected data is denoised, such as using low-pass filtering or Kalman filtering to ensure data quality, and then feature extraction is performed to generate high-quality environmental characteristic data and personnel characteristic data, providing an accurate information basis for subsequent data analysis and decision-making.

[0143] The central control system module is used to receive environmental feature data and personnel feature data from multi-type sensor node modules, and use cellular automata to deduce the local state of community resources, combine the multivariate causal reasoning network to perform causal analysis on the local state, generate a causal chain between resources, and perform multi-path deduction based on the Markov decision process to generate future state prediction data; the central control system module is the core of the present invention, responsible for receiving environmental feature data and personnel feature data from multi-type sensor node modules. By using cellular automata to deduce the local state of community resources and combining the multivariate causal reasoning network to perform causal analysis on the local state, the system can generate a causal chain between resources. Subsequently, based on the Markov decision process, the module performs multi-path deduction to generate future state prediction data. This process enables the system to proactively identify resource needs and potential problems, providing important support for managers' decision-making.

[0144] The agent control module is used to initialize the task states of multiple agents based on future state prediction data, realize state sharing between agents through graph convolutional networks, dynamically adjust the strategies of each agent using symbiotic reinforcement learning networks, and generate execution feedback data and global control data; In the agent control module, the system uses future state prediction data to initialize the task states of multiple agents, and realizes state sharing between agents through graph convolutional networks, so that each agent can obtain global information about the surrounding environment. The symbiotic reinforcement learning network is used to dynamically adjust the strategies of each agent so that it can flexibly respond to environmental changes, optimize resource usage, and generate execution feedback data and global control data. This intelligent scheduling capability significantly improves the efficiency of resource allocation and achieves more accurate community resource management.

[0145] The holographic data management platform module is used to integrate global control data with execution feedback data, extract multi-dimensional features of the integrated data through a recurrent neural convolutional network, generate enhanced training data through an adversarial knowledge transfer model, and optimize and iterate the model using deep reinforcement learning to generate reinforcement learning strategy data; The holographic data management platform module integrates global control data with execution feedback data, and uses a recurrent neural convolutional network to extract multi-dimensional features, thereby extracting time series features and spatial features. Through the adversarial knowledge transfer model, the module can generate enhanced training data and optimize and iterate the model using deep reinforcement learning, thereby improving the accuracy and robustness of the model. This integration and optimization capability ensures the efficient operation of the management system in complex environments, making community management decisions more scientific.

[0146] The visualization and recommendation module is used to visualize the global control data and future state prediction data through the holographic data management platform, and generate resource recommendation data to achieve the optimization and regulation of community resources. The visualization and recommendation module visualizes the global control data and future state prediction data through the holographic data management platform to help managers intuitively understand the real-time status and control strategies of community resources. The resource recommendation data generated by this module provides managers with suggestions for optimization and regulation, and enhances the intelligence level and responsiveness of community services. Through these technical means, the present invention not only improves the utilization efficiency of community resources, but also greatly enhances the responsiveness to residents' needs, and realizes the intelligent management of community services.

[0147] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0148] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A future community service intelligent management method based on big data, characterized in that: The following steps are involved: Deploy multiple types of sensor nodes to collect community environmental parameters and personnel characteristic data, perform denoising and feature extraction on the collected data, and generate environmental characteristic data and personnel characteristic data; Based on environmental and personnel feature data, cellular automata are used to deduce the local state of community resources, and causal relationship analysis is performed on the local state in combination with a multivariate causal inference network to generate a causal chain between resources. Multi-path deduction is performed through a Markov decision process to generate future state prediction data. Based on the future state prediction data, the task states of multiple agents are initialized, and the states between agents are shared through the graph convolutional network. The strategies of each agent are dynamically adjusted using the symbiotic reinforcement learning network. Each agent performs tasks according to the adjusted strategy, generates execution feedback data and forms global control data. Integrate global control data with execution feedback data, extract multi-dimensional features from the integrated data through a recurrent neural convolutional network, generate enhanced training data through an adversarial knowledge transfer model, and use deep reinforcement learning to optimize and iterate the model to generate reinforcement learning strategy data; The global control data and future status prediction data are visualized through the holographic data management platform, and resource recommendation data is generated for the optimization and regulation of community resources.

2. The intelligent management method for future community services based on big data according to claim 1 is characterized in that: The deployed multi-type sensor nodes use sensor fusion algorithms to fuse the collected environmental parameter data and personnel characteristic data from multiple sources, and process the environmental parameter data and personnel characteristic data from multiple sensors through the weighted average method to generate consistent environmental characteristic data and personnel characteristic data.

3. The intelligent management method for future community services based on big data according to claim 1 is characterized in that: The local state deduction of community resources includes deduction based on the rule set of cellular automata and the state changes of neighboring cells. Each cell represents a resource point in the community. The state of the resource point is dynamically updated according to the influence weight and time step changes of the adjacent cells. The transfer rules of the cell state are adjusted according to the current state and historical state data of the adjacent cells.

4. The intelligent management method for future community services based on big data according to claim 3 is characterized in that: The multivariate causal inference network uses an autoregressive neural network model to identify the causal chain between resource points through time series analysis and causal weight calculation, and generates a causal weight matrix. The causal weight matrix determines the causal relationship strength between resource points according to the Granger causality test method.

5. The intelligent management method for future community services based on big data according to claim 1 is characterized in that: The task state initialization of the agent includes setting the initial task objectives, strategy parameters and constraints for each agent, and aggregating the state features of multiple agents through a graph convolutional network, so that each agent can share the global state feature information of neighboring agents.

6. The intelligent management method for future community services based on big data according to claim 5 is characterized in that: The symbiotic reinforcement learning network uses symbiotic factors to adjust the strategy function of the agents through strategic interactions and game relationships between agents. The calculation method of the symbiotic factors is to evaluate the sensitivity of the strategy function of the neighboring agents according to the strategy changes of each agent, thereby forming an adaptive strategy adjustment mechanism.

7. The intelligent management method for future community services based on big data according to claim 1 is characterized in that: After integrating the global control data with the execution feedback data, the time series features and spatial features are extracted through a recurrent neural convolutional network. The recurrent neural convolutional network includes a combination of multiple convolutional layers and recurrent layers. The convolutional layers are used to extract the spatial features of the input data, and the recurrent layers are used to extract the time series features of the input data.

8. The intelligent management method for future community services based on big data according to claim 1 is characterized in that: The adversarial knowledge transfer model generates pseudo data through the generation of adversarial networks in the alternating training process of the generator and the discriminator. The objective function of the generator is: in, represents the loss function of the generator; Indicates expected value; represents the discriminator; Represents a generator; Represents the input noise vector.

9. The intelligent management method for future community services based on big data according to claim 1 is characterized in that: The holographic data management platform uses augmented reality technology to visualize global control data and future status prediction data in three dimensions, and displays the real-time status and resource control strategies of community resources through a virtual interface. Augmented reality technology includes real-time three-dimensional model rendering and dynamic annotation.

10. A system for implementing the future community service intelligent management method based on big data as described in any one of claims 1 to 9, characterized in that: The system comprises: Multi-type sensor node modules are used to collect environmental parameter data and personnel characteristic data of the community, and perform denoising and feature extraction on the collected data to generate environmental characteristic data and personnel characteristic data; The central control system module is used to receive environmental feature data and personnel feature data from multiple types of sensor node modules, and use cellular automata to deduce the local state of community resources, combine multivariate causal reasoning networks to perform causal relationship analysis on local states, generate causal relationship chains between resources, perform multi-path deduction based on Markov decision processes, and generate future state prediction data; The agent control module is used to initialize the task states of multiple agents based on future state prediction data, realize state sharing between agents through graph convolutional networks, dynamically adjust the strategies of each agent using symbiotic reinforcement learning networks, and generate execution feedback data and global control data; Holographic data management platform module, which is used to integrate global control data with execution feedback data, extract multi-dimensional features of the integrated data through a recurrent neural convolutional network, generate enhanced training data through an adversarial knowledge transfer model, and optimize and iterate the model using deep reinforcement learning to generate reinforcement learning strategy data; The visualization and recommendation module is used to visualize the global control data and future status prediction data through the holographic data management platform, and generate resource recommendation data to achieve optimal control of community resources.

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