Intelligent interactive system for comprehensive management of smart community

Abstract

This invention relates to the field of community management technology, and more particularly to an intelligent interactive system for comprehensive governance of smart communities. This system acquires basic information such as terminal hardware, functions, and spatial layout; monitors the interaction process in real time and collects response parameters, calculating collaborative interaction efficiency; constructs a database to uniformly store the above information and efficiency indicators; extracts key factors affecting efficiency from the database and establishes a correlation model between their relative strength and efficiency; trains a collaborative scheduling analysis model based on the database, inputs real-time terminal information and performance parameters, and dynamically outputs the optimal cross-terminal collaborative interaction scheme. This method accurately identifies system bottlenecks through data-driven approaches, achieving intelligent scheduling and global optimization of distributed terminal devices, effectively solving problems of response delays and inefficient service flow, significantly improving the response speed and governance closed-loop efficiency for residents' nearby reporting and interactive calls, and promoting the transformation of community governance towards intelligence and refinement.

Description

Technical Field

[0001] This invention relates to the field of community management technology, and in particular to an intelligent interactive system for comprehensive governance of smart communities. Background Technology

[0002] With the continuous advancement of smart city construction, smart communities, as an important component of modern urban governance, are increasingly improving their informatization and intelligence levels. These communities are widely equipped with various heterogeneous terminal devices, including security monitoring terminals, smart access control systems, environmental sensors, resident smart terminals, and property management systems. These devices generate a large amount of interactive data during daily operation and undertake key functions such as security and prevention, convenient services, and emergency response. In recent years, to enhance resident participation and governance response efficiency, some communities have begun to introduce smart terminal systems integrating voice interaction, one-click calling, and information reporting functions. These systems are deployed in high-frequency indoor and outdoor activity areas such as building entrances, elevator lobbies, public activity areas, and community entrances, achieving ubiquitous coverage of service touchpoints.

[0003] These smart terminal systems not only allow residents to easily call other residents or property management personnel via voice or button, facilitating convenient interpersonal interaction within the community, but also function as smart voice suggestion boxes. This allows residents to report needs for facility repairs, environmental remediation, and safety hazards in real time using natural language, significantly lowering the barrier to traditional reporting and maintenance and increasing residents' enthusiasm for participating in community co-governance. However, due to the dispersed deployment of terminals, varying hardware configurations, and complex network environments, coupled with a lack of unified optimization of scheduling strategies between different functional modules (such as calling, reporting, and broadcasting), cross-terminal interactions often experience problems such as response delays, lost commands, inaccurate voice recognition, or inefficient service flow.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide an intelligent interactive system for comprehensive governance of smart communities, aiming to solve the technical problems of existing community smart terminal systems, which lack a global collaborative optimization and dynamic scheduling mechanism for dispersed devices, resulting in response delays, poor service flow, and low resource allocation efficiency when supporting residents' local interaction and voice reporting.

[0006] To achieve the above objectives, the present invention provides an intelligent interactive system for comprehensive governance of smart communities, the system comprising: The information acquisition module is used to acquire basic terminal information of smart community access terminals, including terminal hardware information, terminal function information and terminal access layout information. The efficiency calculation module is used to monitor the cross-terminal collaborative interaction process, collect and obtain interaction response performance parameters, and calculate the collaborative interaction efficiency based on the interaction response performance parameters. The data construction module is used to build a community collaborative interaction database, which is used to store and manage the basic information of the terminal and the interaction response performance parameters, and corresponds to the collaborative interaction efficiency; The correlation analysis module is used to extract the factors affecting the efficiency of the collaborative interaction during the interaction process from the community collaborative interaction database, and to establish strong and weak correlations between each of the factors and the collaborative interaction efficiency. The solution output module is used to establish a collaborative scheduling analysis model based on the community collaborative interaction database, input the terminal basic information and interaction response performance parameters into the collaborative scheduling analysis model, and output the optimal cross-terminal collaborative interaction solution.

[0007] Optionally, the construction of the community collaborative interaction database, used to store and manage the terminal's basic information and interaction response performance parameters, and corresponding to the collaborative interaction efficiency, includes: The terminal's basic information and interactive response performance parameters are collected, and the collaborative interaction efficiency corresponding to the terminal's basic information and interactive response performance parameters is obtained, wherein the collaborative interaction efficiency includes interactive response speed and interactive accuracy. The terminal basic information and interactive response performance parameters are divided into several variable data blocks according to sub-data items. The collaborative interaction efficiency is integrated into the variable data blocks, and each variable data block and sub-data item corresponds one-to-one. Each of the independent variable data blocks is chained together with the dependent variable data blocks and integrated into a data chain, with each data chain corresponding to a single cross-terminal collaborative interaction; Historical data chains are collected and integrated into the community collaborative interaction database.

[0008] Optionally, a unique identifier is assigned to each component data of the community collaborative interaction database, including: Each of the independent and dependent data blocks is identified, wherein an independent block key is assigned to each of the independent data blocks, and a dependent block key is assigned to each of the dependent data blocks; The independent block key and the dependent block key are integrated in the data chain, and a chain key is assigned to each data chain, wherein the independent block key and the dependent block key are respectively used as foreign keys of the chain key; The community collaborative interaction database indexes and retrieves the terminal basic information, interaction response performance parameters, and collaborative interaction efficiency through the independent block key, dependent block key, and chain key.

[0009] Optionally, the step of extracting factors affecting the efficiency of collaborative interaction during the interaction process from the community collaborative interaction database, and establishing strong or weak correlations between each of the influencing factors and the collaborative interaction efficiency, includes: The collaborative interaction efficiency in the community collaborative interaction database is sorted, and sub-data items in several terminal basic information and interaction response performance parameters corresponding to the collaborative interaction efficiency are indexed according to the sorting results. Create an influencing factor table for each sub-data item, and arrange the sub-data items in the same order according to the sorting result in the influencing factor table; Deep learning is performed on several influencing factor tables arranged in the same order to filter the influencing factor tables corresponding to the impact of the sub-data items on the collaborative interaction efficiency, and a strong-weak effect correlation is established based on the magnitude of the impact.

[0010] Optionally, the step of filtering the table of influencing factors corresponding to the impact of the sub-data items on the collaborative interaction efficiency, and establishing a strong-weak relationship based on the magnitude of the impact, includes: The efficiency of the collaborative interaction is evaluated for each sub-data item through deep learning, and the table of influencing factors that produce the impact is selected as the strong and weak correlation analysis table. At least one of the strong and weak correlation analysis tables is selected and arranged in combination. Taking any sub-data item as the target, the results of the arrangement and combination are traversed, and the strong and weak correlation analysis tables containing the target item are selected to form several single-factor influence sets. The single-factor influence sets correspond one-to-one with the sub-data items obtained by the selection. Deep learning analysis is performed on several sets of single-factor influences to obtain the mapping relationship between the filtered sub-data items and the collaborative interaction efficiency, and a strong-weak effect association is established based on the mapping relationship.

[0011] Optionally, the step of establishing a collaborative scheduling analysis model based on the community collaborative interaction database, inputting the terminal basic information and interaction response performance parameters into the collaborative scheduling analysis model, and outputting the optimal cross-terminal collaborative interaction scheme includes: The system determines the sub-data items of the input terminal basic information and interactive response performance parameters, retrieves the community collaborative interaction database and matches the sub-data items that have an impact, and indexes the corresponding single-factor impact set. The strong and weak effect associations established by deep learning analysis for the single-factor influence set obtained from the index; A basic collaborative scheduling scheme is obtained by matching the community collaborative interaction database. The strong and weak effects are converted into correction coefficients. The basic collaborative scheduling scheme is corrected according to the correction coefficients, and the optimal cross-terminal collaborative interaction scheme is output.

[0012] Optionally, digital modeling is performed on the smart community access terminal based on the terminal's basic information, including: The input basic terminal information is arranged according to the different deployment locations of the smart community terminals and in hierarchical order to generate a terminal hierarchy sequence. The input terminal basic information is divided into several functional parameter units according to the different attributes of the sub-data items, and each functional parameter unit corresponds to a sub-data item in the terminal basic information. Construct a numbered network matrix, including a basic numbered matrix block and an attribute extended matrix block, and perform digital management on the terminal hierarchical sequence and functional parameter unit respectively; The terminal hierarchy sequence and functional parameter units are input into the numbered network matrix to generate a terminal interaction information network matrix, thereby digitally modeling the smart community access terminal.

[0013] Optionally, training and validation sets are constructed to train and validate the collaborative scheduling analysis model, including: Data information that is filtered and matched with terminal access layout information and interaction response performance parameters based on the community collaborative interaction database; The matched data information is used to construct n training sets, and n verifications are performed. In each verification, one of the training sets is selected as the verification set, and the verification results of the n verifications are obtained respectively. The verification results from n trials are averaged to obtain a comprehensive verification result, and the output of the collaborative scheduling analysis model is corrected based on the comprehensive verification result.

[0014] Optionally, hierarchical access permissions are configured for the generated terminal interaction information network matrix, including: Based on the different roles in smart community governance, the system is divided into three levels: community grid worker authority, property service authority, and community resident authority. Different levels of authority grant different degrees of access to terminal interactive data query and editing. Only community grid worker authority can adjust and update the core parameters of the collaborative scheduling and analysis model.

[0015] Optionally, during the execution of the cross-terminal optimal collaborative interaction scheme, the running status and interaction response data of each access terminal are collected in real time. When any terminal is detected to have a response timeout, data packet loss, or interaction abnormality, the backup collaborative mechanism is immediately triggered. The pre-stored suboptimal collaborative interaction scheme is retrieved from the community collaborative interaction database to complete the interaction. At the same time, the abnormal terminal information is recorded and pushed to the community grid member's authorized terminal for alarm notification.

[0016] In this invention, an intelligent interactive system for comprehensive smart community governance is constructed by comprehensively acquiring hardware, functional, and spatial layout information of community access terminals. Real-time monitoring of response performance parameters during cross-terminal interaction and quantitative calculation of collaborative interaction efficiency are performed, building a community collaborative interaction database integrating data storage, analysis, and optimization. This enables dynamic perception and unified management of terminal status and interaction efficiency. Based on this, by identifying key factors affecting collaborative efficiency and establishing their correlation with efficiency indicators, a collaborative scheduling analysis model is further constructed to intelligently output the optimal cross-terminal interaction solution. This method effectively solves problems such as response delays and poor service flow caused by heterogeneous terminals, dispersed layouts, and coarse scheduling in existing community systems. It significantly improves the response speed and service loop efficiency when residents use smart terminals to call property management, report needs, or engage in voice interaction. This realizes the transformation of smart community multi-terminal systems from passive response to proactive collaboration and from isolated operation to global optimization, enhancing the intelligence, refinement, and humanization of community governance. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the first embodiment of the intelligent interactive system for comprehensive governance of smart communities according to the present invention; Figure 2 This is a flowchart illustrating the specific steps involved in constructing a community collaborative interaction database within the intelligent interactive system for comprehensive smart community governance, as described in this invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] In one embodiment, such as Figure 1 As shown, an intelligent interactive system for comprehensive governance of smart communities is provided, the system comprising: The information acquisition module 10 is used to acquire the basic terminal information of the smart community access terminals. The basic terminal information includes terminal hardware information, terminal function information and terminal access layout information. The smart community access terminal can be an intelligent device deployed in the community's public areas or residential units, possessing voice interaction, calling, or information reporting capabilities. It can serve as a physical contact point for residents to request services and transmit information with property management or neighbors. For example, a smart community access terminal may include, but is not limited to, one or more of the following: a corridor voice call terminal, an elevator one-click repair panel, or an entrance / exit smart access control terminal. The terminal's basic information can be a set of metadata describing the basic attributes of the access terminal, which can be used to provide a basis for the system to identify the terminal's capability boundaries and applicable scenarios. The terminal's hardware information can be technical parameters reflecting the terminal's physical configuration and computing and communication capabilities, which can be used to assess the terminal's capacity to handle tasks such as voice recognition and network transmission. For example, the terminal's hardware information may include, but is not limited to, one or more of the following: processor model, memory capacity, and microphone array configuration. The terminal's functional information can be a list of functions identifying the types of services and interaction modes supported by the terminal, which can be used to determine the role the terminal can play in the collaborative network (such as a call initiator, voice recognition node, or broadcasting endpoint). Furthermore, the terminal's functional information may include, but is not limited to, one or more of the following: voice call function, one-click reporting function, and environmental broadcasting function. Terminal access layout information can be data recording the physical location of terminals in the community space and the network access topology. It can be used to support service proximity matching and path planning based on distance, coverage area, or network hop count. In one specific embodiment, terminal access layout information may include, but is not limited to, latitude and longitude coordinates, floor and room numbers, and relative adjacency relationships.

[0021] Obtaining basic terminal information for smart community access terminals can be achieved by collecting hardware, functional, and location metadata of the terminals through standardized interfaces or automatic discovery mechanisms. Furthermore, this basic terminal information can be extracted from the registration message when the terminal first goes online, or by periodically polling the terminal configuration API to obtain the latest status. This allows for the establishment of a unified description framework for heterogeneous terminals, eliminating management blind spots caused by device differences.

[0022] The efficiency calculation module 20 is used to monitor the cross-terminal collaborative interaction process, collect and obtain interaction response performance parameters, and calculate the collaborative interaction efficiency based on the interaction response performance parameters. The cross-terminal collaborative interaction process can be an interactive flow in which multiple terminals jointly complete a service request, reflecting the actual operational status of multiple devices jointly responding to residents' needs. For example, the cross-terminal collaborative interaction process may include, but is not limited to, one or more of the following: two-way calling (caller-called), reporting-dispatch-feedback closed loop, and broadcast-receive one-way notification. Interaction response performance parameters can be quantitative indicators measuring the timeliness and completeness of each stage in the cross-terminal interaction process, and can be used as raw input data for calculating collaborative interaction efficiency. Further, interaction response performance parameters may include, but are not limited to, one or more of the following: end-to-end response latency, voice command recognition accuracy, and service task completion rate. Collaborative interaction efficiency can be a normalized evaluation index that comprehensively reflects the quality of cross-terminal service flow, and can be used to horizontally compare the overall system performance under different scheduling strategies. In a specific embodiment, collaborative interaction efficiency may include, but is not limited to, timeliness efficiency, reliability efficiency, and resource utilization efficiency.

[0023] Monitoring cross-terminal collaborative interaction can be achieved by implanting probes at key nodes in the interaction link and recording the time series of event triggering and response. Furthermore, monitoring can be accomplished by recording the request sending time at the call initiator, recording the processing start time at the service receiver, and tracing the message forwarding path through intermediate proxy nodes, thus achieving observability of the entire service flow. Collecting interaction response performance parameters can involve extracting raw performance indicators such as latency, packet loss, and recognition errors from monitoring data. Further, collecting these parameters can be achieved by calculating end-to-end latency based on timestamp differences and calculating recognition accuracy by comparing speech-to-text results with the original commands, thus providing objective data support for efficiency quantification. Calculating collaborative interaction efficiency based on interaction response performance parameters can be achieved by weighted fusion or mapping of multi-dimensional performance parameters into a single efficiency score. In a specific embodiment, calculating collaborative interaction efficiency based on interaction response performance parameters can be achieved by using a linear weighting method to integrate latency and accuracy, and using a fuzzy comprehensive evaluation method to handle nonlinear relationships, thereby achieving comparability of service quality under different interaction scenarios.

[0024] The data construction module 30 is used to build a community collaborative interaction database, which is used to store and manage basic terminal information and interactive response performance parameters, as well as corresponding collaborative interaction efficiency. The community collaborative interaction database can be a centralized collection of data storing basic terminal information, interaction response performance parameters, and their corresponding collaborative interaction efficiencies. It can serve as a data foundation for dynamic system perception and unified management, supporting full lifecycle interaction optimization. Furthermore, the community collaborative interaction database can be written to by the data construction module and read by the correlation analysis module and solution output module, forming a data closed loop with each functional module. Building the community collaborative interaction database involves designing the data table structure and initializing storage instances, establishing the association between terminals and interaction events. Further, the database can be built using a relational database to create terminal tables and interaction log tables linked by foreign keys, and using a time-series database to store performance stream data by time window, thus forming a structured and traceable interaction performance knowledge base. Storing and managing basic terminal information and interaction response performance parameters involves writing the collected metadata and performance data into the corresponding fields of the database. In a specific embodiment, storing and managing basic terminal information and interaction response performance parameters can be achieved by periodically synchronizing terminal status through a batch insert interface and using a transaction mechanism to ensure atomic writing of interaction event data, thereby guaranteeing data integrity and consistency and supporting efficient retrieval. To assess collaborative interaction efficiency, the calculated efficiency value can be bound and stored with the corresponding terminal combination and interaction context. Furthermore, this efficiency can be achieved by adding an "efficiency" field to the interaction log and linking the efficiency calculation result to a unique interaction ID. This establishes a mapping between efficiency metrics and specific scenarios, facilitating subsequent analysis.

[0025] The correlation analysis module 40 is used to extract the factors that affect the efficiency of collaborative interaction during the interaction process from the community collaborative interaction database, and to establish strong and weak correlations between each factor and the efficiency of collaborative interaction. Among these, influencing factors can be variables that may affect the efficiency of collaborative interaction during the interaction process. They can be used as objects of correlation analysis to pinpoint the root cause of scheduling bottlenecks. For example, influencing factors may include, but are not limited to, one or more of the following: network bandwidth fluctuations, differences in terminal computing power, and spatial distance. Strong-weak correlations can be quantitative relationships describing the degree of influence of each influencing factor on collaborative interaction efficiency, and can be used to guide collaborative scheduling analysis models to prioritize variables with high influence. Furthermore, strong-weak correlations may include, but are not limited to, one or more of the following: Pearson correlation coefficient, feature importance score, and causal effect estimate.

[0026] Extracting factors influencing the efficiency of collaborative interactions from a community collaborative interaction database can be achieved by screening variables significantly related to efficiency as candidate influencing factors. In a specific embodiment, this can be done using analysis of variance to screen significant factors and a random forest model to rank feature importance, thereby narrowing the analysis scope and focusing on key variables. Establishing strong or weak relationships between each influencing factor and collaborative interaction efficiency can be done by calculating the statistical correlation strength between each factor and the efficiency index. Furthermore, establishing strong or weak relationships between each influencing factor and collaborative interaction efficiency can be achieved by calculating the Spearman rank correlation coefficient and training a linear regression model to obtain standardized regression coefficients, thereby quantifying the actual contribution of each factor to system performance.

[0027] The solution output module 50 is used to establish a collaborative scheduling analysis model based on the community collaborative interaction database. It inputs the terminal basic information and interaction response performance parameters into the collaborative scheduling analysis model and outputs the optimal cross-terminal collaborative interaction solution.

[0028] The collaborative scheduling analysis model can be an algorithmic model trained on historical data to predict and optimize cross-terminal interaction paths. It can be used to map input states to optimal scheduling schemes, improving service flow continuity. Furthermore, the collaborative scheduling analysis model can include, but is not limited to, one or more of rule-based expert systems, supervised learning-based regression models, and reinforcement learning-based policy networks. The optimal cross-terminal collaborative interaction scheme can be the best collaboration strategy generated under the constraints of the current terminal state and historical performance data. It can be used to directly adjust task allocation and communication paths between terminals, improving service experience. For example, the optimal cross-terminal collaborative interaction scheme can include, but is not limited to, one or more of the following: minimum latency routing scheme, highest accuracy terminal selection scheme, and balanced load distribution scheme.

[0029] Establishing a collaborative scheduling analysis model based on a community collaborative interaction database can involve training an algorithmic model capable of predicting optimal scheduling strategies using historical data. In one specific embodiment, this model can utilize a gradient boosting tree model to learn scheduling rules and construct a deep neural network for end-to-end strategy generation, thereby achieving intelligent mapping from state input to scheduling decisions. Inputting terminal basic information and interaction response performance parameters into the collaborative scheduling analysis model can extract the current terminal state as model input features when a new interaction request occurs. Furthermore, inputting terminal basic information and interaction response performance parameters into the model can construct feature vectors containing hardware capabilities, location, and current load, and concatenate historical average response latency as contextual features, enabling the model to make adaptive decisions based on the real-time environment. Outputting the optimal cross-terminal collaborative interaction scheme can involve generating specific terminal selection, task allocation, or communication path instructions after model inference. In an exemplary embodiment, outputting the optimal cross-terminal collaborative interaction scheme can involve outputting a list of target terminal IDs and their priority order, and generating instructions to offload speech recognition tasks to high-computing-power terminals, thereby directly guiding the system to perform efficient collaborative operations.

[0030] Taking a resident's voice report of a corridor lighting malfunction in the elevator as an example, the intelligent interactive system for smart community governance in this embodiment allows a resident to use a smart terminal in the elevator of Unit 2, Building 3 to say that the corridor light is broken and needs repair. The information acquisition module has pre-registered that the terminal has voice reporting and basic positioning capabilities. The efficiency calculation module monitors that this interaction took 8 seconds from voice input to the generation of a property work order and that the recognition was accurate. This data, along with the terminal hardware model and the floor information, is stored in the community collaborative interaction database. The correlation analysis module finds that similar reports are more accurate and less delayed when routed to entrance and exit terminals equipped with high-performance voice chips during nighttime hours. When a similar request occurs again, the solution output module calls the collaborative scheduling analysis model, combines the current load status of each terminal, and outputs a solution to dynamically allocate the voice recognition task to the nearest high-computing-power terminal, achieving a faster and more accurate service response.

[0031] In one embodiment, a community collaborative interaction database is constructed to store and manage basic terminal information and interaction response performance parameters, and to correspond to collaborative interaction efficiency, including: The system collects basic terminal information and interactive response performance parameters, and obtains the corresponding collaborative interaction efficiency, which includes interactive response speed and interactive accuracy.

[0032] The terminal basic information can be a dataset describing the static attributes of the terminal device, and the interaction response performance parameters can be indicators reflecting the dynamic performance of the terminal during service interaction. Collaborative interaction efficiency can be a composite indicator used to quantify the quality of cross-terminal service flow, including two core dimensions: interaction response speed and interaction accuracy. In an exemplary embodiment, collaborative interaction efficiency can be used to reflect the system's comprehensive performance in terms of timeliness and reliability. Further, collaborative interaction efficiency can include, but is not limited to, one or more of interaction response speed, interaction accuracy, and service loop integrity. Interaction response speed can be the time elapsed from when a user initiates an interaction request to when the system completes an initial response, and can be used to measure the system's ability to respond instantly to residents' needs. For example, interaction response speed can include speech recognition time, command routing time, and service feedback generation time. Interaction accuracy can be the degree to which the system's understanding and execution of user intent matches expectations, and can be used to reflect the correctness level of speech recognition, semantic parsing, and task assignment. In a specific embodiment, interaction accuracy can include speech transcription error rate, intent classification error rate, and service object matching error rate.

[0033] The terminal's basic information and interactive response performance parameters are divided into several independent data blocks according to sub-data items. The collaborative interaction efficiency is integrated into the dependent data blocks, and each independent data block and sub-data item corresponds one-to-one.

[0034] The sub-data items can be the smallest independently identifiable data unit from terminal basic information or interactive response performance parameters. They can be used as the basic granularity for dividing variable data blocks, supporting fine-grained data organization. Further, sub-data items can include, but are not limited to, processor model, number of microphones, building number, end-to-end latency value, etc. Variable data blocks can be independent data units formed by splitting terminal basic information and interactive response performance parameters into sub-data items, serving as input variable carriers affecting collaborative interaction efficiency. In an exemplary embodiment, variable data blocks can be used to achieve structural decoupling of multi-source heterogeneous data, facilitating subsequent feature extraction or attribution analysis by dimension. For example, variable data blocks can include hardware capability blocks, functional configuration blocks, spatial location blocks, network performance blocks, etc. Variable data blocks can be data units that integrate collaborative interaction efficiency indicators, serving as a unified output representation of a single interaction result. In a specific embodiment, variable data blocks can be used to centrally express service flow quality, providing target labels for efficiency evaluation and model training. Furthermore, the variable data blocks can include interaction response speed blocks, interaction accuracy blocks, task completion blocks, etc.

[0035] Dividing terminal basic information and interactive response performance parameters into several independent data blocks according to sub-data items can be achieved by deconstructing the raw collected data into multiple structurally consistent independent data blocks based on predefined sub-data item classification rules. Furthermore, this operation can be implemented by automatically segmenting fields to corresponding blocks according to the data dictionary and mapping the raw logs to preset block templates through an ETL process, thereby achieving standardization and modular organization of multi-dimensional heterogeneous input data, facilitating subsequent feature alignment and attribution analysis. Integrating collaborative interaction efficiency into dependent data blocks can be achieved by merging and encapsulating the calculated interactive response speed and interactive accuracy into a single dependent data block. Further, this operation can be achieved by concatenating speed and accuracy into a two-dimensional vector and storing it in the block, using a weighted fusion score as the block's principal value while retaining detailed sub-items, thereby unifying the output indicator expression and strengthening the mapping boundary between input and output. Ensuring a one-to-one correspondence between each independent data block and sub-data item can be achieved by ensuring that each independent data block carries only the complete value of one sub-data item. Furthermore, this operation can be achieved by strictly binding field names to block types and verifying the uniqueness of sub-items when writing data, thereby avoiding data mixing and ensuring the purity of dimensions in subsequent analysis.

[0036] Each independent data block is chained together with the dependent data block and integrated into a data chain. Each data chain corresponds to a single cross-terminal collaborative interaction.

[0037] In this context, a data chain can be a data unit composed of multiple self-variable data blockchains connected to a dependent data block, representing the complete input-output mapping of a single cross-terminal collaborative interaction. In an exemplary embodiment, the data chain can be used to preserve the semantic integrity of multi-dimensional heterogeneous data in a single interaction and explicitly establish the association structure between variables and performance. Further, the data chain can include call-type data chains, reporting-type data chains, broadcast-type data chains, etc. A single cross-terminal collaborative interaction can be a service event triggered by a single user and involving the collaboration of two or more terminals. In a specific embodiment, a single cross-terminal collaborative interaction can be used as the building unit of the data chain to ensure that the data corresponds one-to-one with the real business scenario. For example, a single cross-terminal collaborative interaction can include voice call-triggered interaction, key press reporting-triggered interaction, automatic alarm-triggered interaction, etc.

[0038] Linking each independent data block to a dependent data block can be achieved by establishing a reference relationship between independent and dependent data blocks at the logical or physical storage level. Further, this operation can be implemented by adding pointers to dependent block IDs in the block metadata and using a graph database to establish edge relationships between blocks, thus explicitly constructing a causal relationship structure of multiple inputs to a single output. Integrating into a data chain can be achieved by combining all independent data blocks and their corresponding dependent data blocks under the same interaction event into an indivisible data unit. Further, this operation can be achieved by packaging them into a JSON object and assigning a unique interaction ID, then associating all child blocks with the parent ID in the database, thus forming atomic interaction records that support backtracking or sampling at the event granularity. Enabling each data chain to correspond to a single cross-terminal collaborative interaction can be achieved by binding the data chain to the actual collaborative service instance through the interaction event ID. Further, this operation can be achieved by reusing the transaction ID generated by the interaction process engine as the chain primary key and triggering a chain solidification operation at the end of the interaction, thus ensuring strict alignment between data and business scenarios and improving the authenticity of training data.

[0039] Historical data chains are collected and integrated into a community collaborative interaction database.

[0040] The historical data chain can be a collection of completed and archived data chains representing the entire process of past cross-terminal collaborative interactions. In an exemplary embodiment, the historical data chain can be used to form the core asset of the community collaborative interaction database for training and validating scheduling models. Further, the historical data chain can include data chains from the past 24 hours, weekly summary data chains, monthly statistical data chains, etc. The community collaborative interaction database can be a structured data set that centrally stores terminal basic information, interaction response performance parameters, and their corresponding collaborative interaction efficiencies organized in the form of data chains. In a specific embodiment, the community collaborative interaction database can be used as a high-quality training data foundation with causal traceability to support subsequent correlation analysis and scheduling optimization. For example, the community collaborative interaction database can receive integrated input from historical data chains, providing a structured data source for the correlation analysis module and the solution output module.

[0041] Collecting historical data chains can be done periodically or based on event completion status by retrieving completed data chains from the runtime cache. Furthermore, this operation can be achieved by setting an interaction status listener to push data chains to the collection queue when the status changes to "complete," and by periodically scanning the temporary storage area to archive mature chains. This allows for a smooth transition from real-time streams to the historical database, ensuring data integrity. Integrating historical data chains into a community collaborative database can be achieved by persistently writing the collected historical data chains into a structured database. Further, this operation can be achieved by batch importing into a time-series database and storing it by time partition, or by incrementally synchronizing it to an analytical data warehouse via an API interface. This allows for the accumulation of high-quality training sets with causal structure, supporting continuous model iteration.

[0042] Taking voice interaction for reporting light outages in stairwells at night as an example, the intelligent interaction system for smart community governance in this embodiment allows residents to report light outages via voice terminal on the 3rd floor of Building 1. The system records the interaction involving terminal A (with basic voice recognition) and the backend property management terminal B. The data acquisition module obtains sub-data items such as the hardware model, location coordinates, and current network latency of terminal A, and encapsulates them into independent data blocks. Simultaneously, it calculates the interaction response speed as 6.2 seconds and the accurate recognition of the keyword "light outage" to determine that the interaction accuracy meets the standard, integrating it into the dependent data block. All blockchain-like connections form a complete data chain. This chain is then collected and integrated into the community collaborative interaction database. Subsequent analysis revealed that when the network latency in the independent block is >300ms and the terminal is a low-end model, the interaction accuracy in the dependent block significantly decreases. Based on this, the scheduling model optimizes its strategy, automatically offloading the voice recognition task to a high-computing-power terminal under similar conditions, improving the accuracy of subsequent similar interactions.

[0043] In one embodiment, a unique identifier is assigned to each component data of the community collaborative interaction database, including: Each independent and dependent data block is identified, wherein an independent block key is assigned to each independent data block and a dependent block key is assigned to each dependent data block.

[0044] The constituent data can be a collection of basic data units constituting the data chain in the community collaborative interaction database. This data can be used as a unique identifier for the scope of objects, covering independent data blocks, dependent data blocks, and the data chain itself. Assigning unique identifiers to each constituent data unit in the community collaborative interaction database can be achieved by generating unique key-value pairs for independent data blocks, dependent data blocks, and data chains during the data generation or storage phase. In an exemplary embodiment, this operation can be implemented by using a distributed ID generator to assign globally unique keys to data at each level and using content hash values ​​as block keys to ensure that identical content is not stored repeatedly. This establishes a three-level identifier system with relational semantics, supporting efficient indexing and causal tracing. Identifying each independent and dependent data block can be achieved by assigning a unique key-value pair to it immediately upon block creation and recording it in the metadata. For example, this operation can be implemented by adding a key field to each output block in the ETL process and automatically generating and populating key-value pairs through database triggers. This enables independent management and cross-chain reuse of fine-grained data units.

[0045] The independent block key and the dependent block key are integrated in the data chain, and a chain key is assigned to each data chain, wherein the independent block key and the dependent block key are respectively used as foreign keys of the chain key.

[0046] The independent block key can be a globally unique identifier assigned to each independent data block, which can be used to support independent indexing and precise retrieval of each sub-data item in the terminal basic information and interaction response performance parameters. In a specific embodiment, the independent block key can generate a unique string or value as the identity identifier of the block according to preset rules, so that any sub-item of the terminal basic information and interaction response performance parameters can be referenced individually. Further, the independent block key can include, but is not limited to, one or more of the following: a random key based on UUID, a content addressing key based on hash, and an incrementing key based on sequence number. The dependent block key can be a globally unique identifier assigned to each dependent data block, which can be used to achieve unique identification and efficient retrieval of collaborative interaction efficiency indicators (such as response speed and accuracy). In this embodiment, the dependent block key can assign a unique identifier to its result block after the collaborative interaction efficiency calculation is completed, ensuring that the results of each interaction performance evaluation can be independently tracked and compared. For example, the dependent block key can include, but is not limited to, a label key for model training, a feedback key for real-time evaluation, and a result key for archiving and auditing.

[0047] Assigning a unique block key to each independent data block can be achieved by generating a unique string or value as the block's identifier according to preset rules. Further, this operation can be implemented by using the snowflake algorithm to generate a 64-bit integer key with a timestamp and calculating a SHA-256 digest based on the block content as the key, allowing any sub-item of the terminal's basic information and interaction response performance parameters to be referenced independently. Assigning a dependent block key to each dependent data block can be achieved by assigning a unique identifier to the resulting block after the collaborative interaction efficiency calculation is completed. In a specific embodiment, this operation can be achieved by combining the interaction time and the terminal ID to generate a composite key and calling a centralized ID service to obtain an incrementing sequence number, thus ensuring that the results of each interaction performance evaluation can be independently tracked and compared. Integrating the independent and dependent block keys in the data chain can be achieved by summarizing the key values ​​of all blocks participating in this interaction into the metadata field of the data chain. For example, this operation can be achieved by embedding a block_keys array in the chain JSON structure and establishing many-to-one association records in the relationship table, thereby explicitly recording the ownership relationship between the chain and the constituent blocks, which facilitates reverse lookup.

[0048] A chain key can be a primary key-level unique identifier assigned to each data chain. It can be used as a global reference ID for a single cross-terminal collaborative interaction event, supporting event-level data aggregation and tracing. In this embodiment, the chain key can be generated as a globally unique primary key when the data chain is constructed, establishing the atomicity and referrability of a single cross-terminal collaborative interaction. Furthermore, the chain key can include, but is not limited to, temporary interaction keys, persistent archiving keys, and anonymized analysis keys. Assigning a chain key to each data chain can be achieved by generating a globally unique primary key when the data chain is constructed. In an exemplary embodiment, this operation can be implemented by using an auto-incrementing primary key in the database as the chain key and generating a business primary key based on the interaction initiation time and terminal combination, thereby establishing the atomicity and referrability of a single cross-terminal collaborative interaction.

[0049] Foreign keys, in relational data structures, are fields used to establish reference relationships between different data units. They can be used to point to the corresponding data chain through independent and dependent block keys, maintaining data integrity and hierarchical consistency. In this embodiment, a foreign key can be a declaration of the reference dependency relationship between a block key and a chain key in the database schema design, preventing the existence of isolated blocks. Exemplary examples include, but are not limited to, cascading delete foreign keys, read-only reference foreign keys, and nullable foreign keys. Using independent and dependent block keys as foreign keys for the chain key allows for the declaration of the reference dependency relationship between the block key and the chain key in the database schema design. Furthermore, this operation can be implemented by defining foreign key constraints in the relational database and validating the reference validity at the application layer in the document database, thereby maintaining logical consistency between data levels and preventing the existence of isolated blocks.

[0050] The community collaborative interaction database indexes and retrieves the terminal basic information, interaction response performance parameters, and collaborative interaction efficiency through the independent block key, dependent block key, and chain key.

[0051] By indexing and retrieving terminal basic information, interaction response performance parameters, and collaborative interaction efficiency using independent block keys, dependent block keys, and chain keys, a composite index can be constructed using a three-level key-value system, supporting efficient multi-dimensional queries. In a specific embodiment, this operation can be achieved by creating a joint index covering chain keys and block keys, and using an inverted index to accelerate filtering by terminal or efficiency indicators, thereby significantly improving the retrieval and aggregation efficiency of historical interaction data in high-concurrency scenarios.

[0052] Taking the property dispatch system's retrospective analysis of similar repair reports as an example, the intelligent interactive system for smart community governance in this embodiment can quickly search for historical records of similar issues reported by the same type of terminal under similar network conditions when a resident reports an elevator malfunction again via a building terminal. The chain key locates the complete interactive event, the self-variant block key accurately extracts the terminal's hardware configuration block and the network latency block at that time, and the dependent block key obtains the corresponding response speed and accuracy results. This three-level key-value system enables the system to accurately filter across hundreds of thousands of records within milliseconds, identifying previous misjudgments caused by speech recognition module overload. In this interaction, the system proactively routes the recognition task to an idle, high-computing-power terminal, avoiding repeated errors.

[0053] In one embodiment, factors influencing the efficiency of collaborative interaction during the interaction process are extracted from the community collaborative interaction database, and a strong or weak correlation is established between each influencing factor and the collaborative interaction efficiency, including: The collaborative interaction efficiency in the community collaborative interaction database is sorted, and the sub-data items in several terminal basic information and interaction response performance parameters corresponding to the collaborative interaction efficiency are indexed according to the sorting results.

[0054] The sorting result can be a sequence index formed by arranging all collaborative interaction efficiency values ​​in the community collaborative interaction database in descending order. This index can serve as a benchmark for constructing the mapping relationship between influencing factors and efficiency, ensuring that the arrangement of sub-data items is consistent with the efficiency change trend. In an exemplary embodiment, the sorting result can include one or more of the following: ascending order, descending order, quantile grouping, etc. Furthermore, sorting the collaborative interaction efficiency in the community collaborative interaction database can involve extracting the collaborative interaction efficiency values ​​of all historical interaction events, generating an ordered sequence based on numerical values, and retaining the original record index. For example, this operation can be achieved by using a quicksort algorithm to sort the efficiency value array and simultaneously adjusting the order of associated IDs, and directly generating a sorted view using the database ORDER BY statement. This achieves the technical effect of establishing a benchmark sequence for efficiency ranking and providing a sorting anchor point for backtracking associated variables.

[0055] Sub-data items can be the smallest data units that can be independently extracted and participate in correlation analysis from terminal basic information or interactive response performance parameters. They can be used as specific carriers of influencing factors to construct fine-grained performance attribution models. In one specific embodiment, sub-data items may include one or more of the following: device model identifier, voice recognition latency value, wireless signal strength reading, etc. Furthermore, the sub-data items in the terminal basic information and interactive response performance parameters corresponding to the collaborative interaction efficiency according to the sorted result index can be extracted from the original data table according to the sorted efficiency record ID, and various types of sub-data items of the corresponding terminal can be extracted. For example, this operation can be achieved by using a JOIN operation to associate the efficiency table with the terminal metadata table and by batch reading the sub-data item vector according to the sorted index, thereby achieving the technical effect of realizing a one-to-one correspondence between the efficiency sequence and multi-dimensional input variables, forming a structured analysis sample.

[0056] Create an influencing factor table for each sub-data item, and arrange the sub-data items in the same order according to the sorting result within the influencing factor table.

[0057] The shared sub-data items can be a set of sub-data items belonging to the same attribute category (such as all being CPU models or all being end-to-end delays) in multiple interaction records. These can be used to form the data foundation for a single influencing factor table, ensuring semantic consistency within the table. In one specific embodiment, shared sub-data items may include one or more of the following: all terminal memory capacity values, all call setup delay records, and all building number information. The influencing factor table can be a structured data table organized by sub-data item categories and aligned with the collaborative interaction efficiency ranking sequence. It can be used to transform raw heterogeneous parameters into homogeneous inputs that can be modeled and analyzed, facilitating the identification of the influence patterns of specific variables on efficiency. For example, the influencing factor table may include one or more of the following: a hardware capability influence table, a network performance influence table, and a spatial layout influence table. Furthermore, building an influencing factor table for each shared sub-data item can be achieved by grouping all extracted sub-data items by attribute name, with each group forming an independent data table. In one exemplary embodiment, this operation can be achieved by aggregating sub-data items by field name using a dictionary structure and generating sub-tables by grouping by column name using PandasDataFrame, thereby achieving the technical effect of variable decoupling and semantic alignment, and providing regularized input for subsequent modeling.

[0058] Arranging sub-data items in the same order according to the sorting result within the influencing factor table ensures that the row order in each influencing factor table is completely consistent with the sorting sequence of collaborative interaction efficiency. Furthermore, this operation can be achieved by performing a reindex operation on each sub-table using the sort ID as the key and inserting data in a unified index order during table creation. This achieves the technical effect of maintaining the time-series / performance alignment between efficiency change trends and the value sequences of each variable.

[0059] Deep learning is used to analyze several tables of influencing factors arranged in the same order to filter out the tables of influencing factors that affect the efficiency of collaborative interaction for sub-data items, and to establish strong-weak correlations based on the magnitude of the influence.

[0060] Deep learning can be a modeling method that automatically extracts nonlinear features and identifies key influencing factors from a structured table of influencing factors using multi-layer neural networks. It can be used to overcome the limitations of linear assumptions and uncover the composite influence mechanism of high-dimensional coupled variables on collaborative interaction efficiency. In a specific embodiment, deep learning can include one or more of attention mechanism neural networks, gradient boosting decision trees, and graph neural networks. Furthermore, deep learning on several influencing factor tables arranged in the same order can involve using multiple aligned influencing factor tables as input feature matrices and feeding them into a deep learning model for joint training. For example, this operation can be achieved by concatenating the tables into a wide-table input fully connected network and treating each table as a channel input convolutional neural network, thereby achieving the technical effect of automatically identifying which variable sequences have a significant nonlinear correlation with the efficiency sequence.

[0061] The magnitude of influence can be the degree of contribution of each sub-data item to the change in collaborative interaction efficiency, quantified by a deep learning model. This can be used as a numerical basis for establishing strong-weak correlations and guiding the weight allocation of subsequent scheduling strategies. In an exemplary embodiment, the magnitude of influence can include one or more of the following: feature importance score, biased dependency effect value, and SHAP explanatory value. Furthermore, the table of influencing factors corresponding to the impact of sub-data items on collaborative interaction efficiency can be based on the feature importance or attention weights output by the deep learning model, retaining the table of influencing factors corresponding to high-impact variables. For example, this operation can be achieved by setting an importance threshold to filter low-contribution tables and using a recursive feature elimination method to iteratively filter effective tables, thereby achieving the technical effect of focusing on key influence paths and eliminating noise or irrelevant variables. Establishing strong-weak correlations based on the magnitude of influence can be achieved by classifying the filtered influencing factors into strong, medium, and weak levels according to their quantified influence magnitude and recording the correlation direction. In a specific embodiment, this operation can be achieved by classifying strong and weak levels according to the quantile of influence magnitude and combining statistical significance tests to determine the validity of the correlation, thereby achieving the technical effect of forming an interpretable and configurable scheduling strategy knowledge base.

[0062] Taking the optimization of terminal routing strategy for nighttime voice repair requests as an example, the intelligent interaction system for smart community comprehensive governance in this embodiment can sort the collaborative interaction efficiency of 3,000 historical voice repair interactions in descending order, and extract sub-data items such as the CPU model, microphone signal-to-noise ratio, and distance between the building and the property management center of the corresponding terminal; construct hardware computing power table, audio quality table, and spatial distance table for the same type of sub-data items, and align them according to efficiency; discover through an attention mechanism deep learning model that in low-light environments, microphone signal-to-noise ratio is strongly positively correlated with efficiency, while the influence of building distance is weak; establish a strong-weak correlation based on this, and prioritize routing nighttime voice requests to terminals with high signal-to-noise ratio in subsequent scheduling, even if their physical distance is slightly far, thereby improving recognition accuracy and service loop rate.

[0063] In one embodiment, a table of influencing factors corresponding to the impact of sub-data items on collaborative interaction efficiency is selected, and a strong-weak correlation is established based on the magnitude of the impact, including: By using deep learning, the efficiency of collaborative interaction for each sub-data item is evaluated, and the resulting table of influencing factors is used as a strong-weak correlation analysis table.

[0064] The strong / weak association analysis table can be a structured table selected after deep learning evaluation, containing sub-data items that have a significant impact on collaborative interaction efficiency and their association strengths. It can be used as the basic input for subsequent single-factor modeling, carrying verified variable-efficiency relationships. In this embodiment, the strong / weak association analysis table can be obtained by retaining the original influencing factor table to which sub-data items with evaluation scores exceeding a threshold belong and labeling their influence strength levels. Furthermore, the strong / weak association analysis table can be a basic combining unit within [the model / framework].

[0065] The efficiency of collaborative interaction for each sub-data item is evaluated using deep learning. This can be achieved by using each sub-data item as an input feature and collaborative interaction efficiency as a label, training the model to assess its predictive contribution. Further, this operation can be implemented by using gradient boosting trees to calculate feature importance and employing a multilayer perceptron combined with L1 regularization for sparse filtering, thereby identifying the effective variables that truly drive efficiency changes and eliminating noise or redundant terms. The resulting table of influencing factors corresponding to the effects is a strong-weak association analysis table. This can be the original table of influencing factors to which sub-data items with evaluation scores exceeding a threshold belong, with their influence strength levels labeled. In an exemplary embodiment, this operation can be achieved by truncating the table of influencing factors by importance quantiles and retaining the table corresponding to statistically significant terms using p-value tests, thus transforming the preliminary screening results into standardized input units that can be used for combinatorial analysis.

[0066] At least one of the strong and weak association analysis tables is selected and arranged in combination. Taking any sub-data item as the target, the results of the arrangement and combination are traversed, and the strong and weak association analysis tables containing the target item are selected to form several single-factor influence sets. The single-factor influence sets correspond one-to-one with the selected sub-data items.

[0067] Permutations and combinations can be operations such as Cartesian product or subset enumeration on multiple strong and weak association analysis tables. They can be used to generate context combinations of co-occurring influencing factors, exploring the role patterns of target sub-data items in a multivariate environment. In one specific embodiment, permutations and combinations can be a combination generation mechanism. Selecting at least one strong and weak association analysis table for permutation and combination can be achieved by performing subset generation or Cartesian product operations on the filtered set of strong and weak association analysis tables. Furthermore, this operation can be implemented by generating all binary combinations for pairwise interaction analysis and limiting the combination size to no more than three items to control computational complexity. This allows the construction of a virtual context of multi-factor co-occurrence, simulating variable coupling scenarios in real interactions.

[0068] The target item can be a specific sub-data item designated as the focus of attention in a single analysis. It can serve as an anchor point for constructing a single-factor influence set, focusing on the performance of the variable in different collaborative scenarios. In this embodiment, the target item can be the focus of analysis. For example, the target item can be one or more of the following: hardware configuration target items, network status target items, spatial attribute target items, etc. Taking any sub-data item as the target, traversing the results of permutations and combinations can involve checking whether the specified target item is included in each combination. Furthermore, this operation can be achieved by quickly filtering combinations through set inclusion judgment and accelerating target item retrieval by building an inverted index, thereby locating all potential influence paths in which the target item participates.

[0069] A single-factor influence set can be a subset of all strong and weak association analysis tables containing the same target item. It reflects the influence pattern of the target item under various contextual conditions and can be used to isolate the multidimensional action paths of a single variable, supporting fine-grained mapping relationship modeling. In a specific embodiment, the single-factor influence set can be a contextualized aggregation unit within a dataset. Selecting strong and weak association analysis tables containing the target item to form several single-factor influence sets can be achieved by aggregating the original strong and weak association analysis tables corresponding to all combinations containing the target item into an independent set. Furthermore, this operation can be implemented by grouping and aggregating association tables by target item ID and dynamically constructing a subset view in memory, thereby forming a multidimensional influence view around a single variable and supporting contextualized modeling.

[0070] Deep learning analysis is performed on several sets of single-factor influences to obtain the mapping relationship between the filtered sub-data items and the collaborative interaction efficiency, and a strong-weak effect association is established based on the mapping relationship.

[0071] Deep learning analysis of several single-factor influence sets can be performed by using data from these sets as training samples to construct a sub-model specifically targeting the objective item. In an exemplary embodiment, this operation can be achieved by using an attention mechanism network to learn context weighting and employing a graph neural network to model the dependencies between variables, thereby capturing the nonlinear effects of the objective item under different covariate configurations. The mapping relationship can be a nonlinear functional relationship between sub-data items learned from the single-factor influence sets through deep learning and the collaborative interaction efficiency. This can be used to quantify the specific contribution of the objective item to efficiency under different conditions, supporting interpretable determination of the strength of its effects.

[0072] Obtaining the mapping relationship between the filtered sub-data items and the collaborative interaction efficiency can be achieved by extracting the output sensitivity or contribution function of the target item from the trained sub-model. Further, this operation can be realized by calculating the SHAP value sequence to reflect local influence and drawing a partial dependency graph to display the global trend, thereby obtaining an interpretable and quantifiable expression of the variable-efficiency relationship. Establishing strong and weak effect associations based on the mapping relationship can be achieved by classifying them into strong, medium, and weak levels of effect association based on the strength, stability, and directionality of the mapping relationship. In a specific embodiment, this operation can be achieved by classifying the strength and weakness levels according to the absolute value of the mapping slope and determining the reliability of the association by combining the confidence interval width, thereby generating structured knowledge rules that can be used for scheduling decisions.

[0073] Taking the optimization of the recognition performance of voice terminals in old buildings as an example, the intelligent interaction system for smart community comprehensive governance in this embodiment can be as follows: The system first evaluates all sub-data items and filters out terminal microphone models, background noise levels, network packet loss rates, etc. to form a strong and weak correlation analysis table; then, these tables are arranged and combined, and with the microphone model as the target item, all combinations are traversed and the combinations containing the model are extracted to form a single-factor influence set; deep learning analysis of this set reveals that under conditions of high background noise and network packet loss rate of less than 5%, a certain microphone model has a strong positive correlation with collaborative interaction efficiency, but the correlation disappears in a high packet loss environment; based on this, a context-aware strong and weak effect correlation is established, and during scheduling, the terminal model of this type is given priority in low packet loss areas to process voice reports, thereby improving the overall recognition success rate and service loop efficiency.

[0074] In one embodiment, a collaborative scheduling analysis model is established based on a community collaborative interaction database. Terminal basic information and interaction response performance parameters are input into the collaborative scheduling analysis model, which then outputs the optimal cross-terminal collaborative interaction scheme, including: Determine the sub-data items of the input terminal basic information and interactive response performance parameters.

[0075] The impactful sub-data items can be those identified as having a significant effect on collaborative interaction efficiency from the current input terminal basic information and interaction response performance parameters. These can serve as a bridge connecting real-time status and historical knowledge base, triggering the retrieval of corresponding single-factor impact sets. In an exemplary embodiment, the acquisition method of impactful sub-data items can be explained in context, i.e., extracting predefined sub-items through a field mapping table or extracting structured parameters from free text using natural language processing. Furthermore, impactful sub-data items can be key input variables used to drive subsequent database matching and impact analysis processes. Determining the input terminal basic information and interaction response performance parameters as sub-data items can involve parsing the metadata and real-time performance indicators of the terminal involved in the current interaction request to extract atomic data units that can participate in association analysis. Further, this operation can be achieved by extracting predefined sub-items through a field mapping table and extracting structured parameters from free text using natural language processing, thereby transforming the original input into structured features for easier alignment with the historical knowledge base.

[0076] Retrieve the community collaboration and interaction database and match the sub-data items that have an impact, and index the corresponding single-factor impact set.

[0077] The single-factor influence set corresponding to the index is retrieved from the database based on the matched sub-data items that have generated influence, and is used to obtain the historical effect pattern of the sub-item under multiple contextual conditions. In a specific embodiment, the method of obtaining the single-factor influence set corresponding to the index can be described in conjunction with the context, that is, directly querying the associated set by the sub-data item ID or using a hash index to accelerate set location. Retrieving the community collaborative interaction database and matching the sub-data items that have generated influence can be done by searching for historical effective variables that semantically or numerically match the current sub-data item in the influence factor index of the database. Furthermore, this operation can be achieved by matching based on precise field names and using fuzzy matching with similarity thresholds, thereby identifying the key factors that may dominate efficiency performance in the current scenario. The single-factor influence set corresponding to the index can be the data retrieval behavior triggered by the above matching results, which is to retrieve the unique single-factor influence set from the database based on the matched sub-data items that have generated influence. Furthermore, this operation can be achieved by directly querying the associated set by the sub-data item ID or using a hash index to accelerate set location, thereby obtaining the historical effect pattern of the sub-item under multiple contextual conditions.

[0078] We obtain the strong and weak effect associations established by deep learning analysis on the single-factor influence set obtained from the index.

[0079] To obtain the strength and weakness associations established by deep learning analysis for the single-factor influence set obtained from the index, one can read the association strength and direction information stored in the early training phase for that single-factor influence set. Furthermore, this operation can be achieved by loading SHAP value configurations from model metadata and reading pre-computed attention weight tables, thereby reusing validated causal knowledge and avoiding redundant modeling.

[0080] The basic collaborative scheduling scheme is obtained by matching the community collaborative interaction database. The strong and weak effects are converted into correction coefficients. The basic collaborative scheduling scheme is corrected according to the correction coefficients, and the optimal collaborative interaction scheme across terminals is output.

[0081] The basic collaborative scheduling scheme can be an initial scheduling strategy generated based on preset rules or historical high-frequency pattern matching in the community collaborative interaction database. It can serve as an optimization starting point, providing default collaborative paths suitable for common scenarios. In an exemplary embodiment, the acquisition method of the basic collaborative scheduling scheme can be explained in context: based on the current request type, initiating location, and target role, it searches the database for the closest historical successful scheduling cases or rule templates. Furthermore, the basic collaborative scheduling scheme can be the initial solution in the database, allowing for dynamic adjustment of the correction coefficient. Exemplary examples include, but are not limited to, schemes based on the nearest distance principle, schemes based on functional matching degree, and schemes based on historical success rate. The correction coefficient can be a numerical weighting factor derived from the strong / weak association, used to adjust the priority of each terminal or path in the basic collaborative scheduling scheme. It can be used to dynamically correct the scheduling strategy, adapting the scheme to key influencing factors in the current context. In a specific embodiment, the operating principle of the correction coefficient can be explained in context: based on the strength and direction of the association, it is mapped to specific numerical weights. Furthermore, the correction coefficient can be a regulating variable in the database, acting on the scoring or allocation logic of the basic collaborative scheduling scheme.

[0082] A basic collaborative scheduling scheme is obtained through community-based collaborative database matching. This can be achieved by searching the database for the closest historical successful scheduling cases or rule templates based on the current request type, initiating location, and target role. Furthermore, this operation can be implemented by matching similar contexts using the K-nearest neighbor algorithm and triggering preset scheduling logic through a rule engine, thereby providing a reasonable initial solution and reducing the online optimization search space. Transforming strong and weak associations into correction coefficients can be done by mapping the strength and direction of the association to specific numerical weights. Further, this operation can be achieved by using a predefined mapping table for lookup transformation and normalizing the association score into coefficients using the sigmoid function, thus realizing the transformation from qualitative knowledge to quantitative adjustment.

[0083] Correcting the basic collaborative scheduling scheme based on correction coefficients can involve applying these coefficients to terminal scores, path weights, or resource allocation ratios in the basic scheme, resulting in reordering or reallocation. Furthermore, this operation can be achieved by weighting and reordering the candidate terminal list according to coefficients and dynamically allocating the speech recognition task load according to the coefficient ratios, thereby generating an optimized scheme that adapts to dynamic changes in the current environment. Outputting the optimal cross-terminal collaborative interaction scheme can be achieved by formatting the corrected scheduling instructions into executable commands and sending them to relevant terminals or middleware. Further, this operation can be achieved by generating JSON-formatted scheduling instruction packages and asynchronously pushing them to the terminal control module via a message queue, thereby driving the system to execute efficient and robust collaborative service processes.

[0084] Taking a resident calling property management from a building in a weak signal area as an example, the intelligent interactive system for smart community governance in this embodiment can be as follows: The system receives a call request from a terminal on the 3rd floor of Building 5 and determines that its sub-data items include terminal model A, current network packet loss rate of 12%, and distance from the property management center of 80 meters; it searches the community collaborative interaction database and matches the sub-data items that both network packet loss rate and terminal model are influential; it indexes to obtain the corresponding single-factor influence set, where network packet loss rate > 10% is strongly negatively correlated with efficiency, while model A is moderately positively correlated under low load; it obtains the established strong and weak effect associations and converts them into correction coefficients: packet loss rate coefficient 0.6, model coefficient 1.1; at the same time, it matches the basic collaborative scheduling scheme from the database to route to the property management extension in this building; after comprehensive coefficient correction, it finds that the overall rating of the terminal in this building is lower than that of the terminal with edge computing capabilities in the neighboring Building 2, so it adjusts the route and outputs the optimal solution of offloading the call voice recognition task to the terminal in Building 2 to ensure accurate recognition and fast response.

[0085] In one embodiment, digital modeling of the smart community access terminal is performed based on the terminal's basic information, including: The input basic terminal information is arranged according to the different deployment locations of the smart community terminals and in hierarchical order to generate a terminal hierarchy sequence. The deployment location can be the specific installation location of the smart community access terminal in the physical space and its administrative or topological affiliation path. This can serve as the spatial basis for constructing the terminal hierarchy sequence, supporting location-based service matching based on proximity. In an exemplary embodiment, the deployment location can include, but is not limited to, one or more of the following: a four-level location (community-building-unit-floor), a three-level public area location (entrance-elevator-corridor), or a binary location (indoor-outdoor). The hierarchical order can be an ordered hierarchical rule based on community management or network topology, ensuring a consistent semantic structure and comparability of the terminal hierarchy sequence. For example, the hierarchical order can adopt administrative management hierarchy, network access hierarchy, or service coverage hierarchy. The terminal hierarchy sequence can be an ordered identifier sequence with spatial topological semantics, formed by arranging basic terminal information according to deployment location and hierarchical order. This can be used to encode the spatial affiliation of the terminal within the community, supporting hierarchical routing and regional aggregation analysis.

[0086] The input terminal basic information is arranged according to the different deployment locations of smart community terminals and in hierarchical order to generate a terminal hierarchical sequence. This can be achieved by parsing the location field in the terminal basic information, sorting according to preset hierarchical rules (such as community, building, unit, floor), and generating a sequence with paths. Furthermore, this operation can be implemented by recursively constructing hierarchical paths using a tree structure, extracting location fields using regular expressions, and concatenating them into a standard sequence. This allows for the establishment of a terminal organizational structure with spatial semantics, eliminating management blind spots caused by disordered deployment.

[0087] The input terminal basic information is divided into several functional parameter units according to the different attributes of the sub-data items. Each functional parameter unit corresponds to a sub-data item in the terminal basic information. The different attributes can be the functional or technical dimension categories to which the sub-data items belong, and can be used as the classification basis for splitting functional parameter units to achieve capability decoupling. In a specific embodiment, the different attributes may include hardware configuration attributes, communication capability attributes, interactive function attributes, etc. The functional parameter unit can be a structured capability description unit composed of sub-data items with the same attributes in the terminal's basic information, which can be used to modularly express the terminal's functions, facilitating dynamic combination and capability matching. For example, the functional parameter unit may include a voice processing parameter unit, a network transmission parameter unit, a positioning and recognition parameter unit, etc.

[0088] The input terminal basic information is broken down into several functional parameter units according to the different attributes of the sub-data items. This can be achieved by classifying sub-data items into corresponding functional parameter units based on a predefined attribute classification system. Furthermore, this operation can be implemented through mapping and grouping using an attribute tag dictionary and automatic classification of sub-items using an ontology knowledge graph, thereby achieving modular decoupling of terminal capabilities and supporting fine-grained function matching. Each functional parameter unit corresponds to a sub-data item in the terminal basic information, ensuring that the content of each functional parameter unit originates from one or more sub-data items with the same attributes in the terminal basic information. Further, this operation can be achieved by establishing a mapping table from sub-data items to functional units and using reference pointers to avoid data redundancy, thus maintaining data consistency and traceability in the modeling process.

[0089] Construct a numbered network matrix, including a basic numbered matrix block and an attribute extended matrix block, and perform numerical management on the terminal level sequence and functional parameter unit respectively; The numbering network matrix can be a two-dimensional matrix structure used for unified management of terminal hierarchical sequences and functional parameter units. It includes two logical blocks: a basic numbering matrix and attribute extensions, providing a digital management framework that combines stable identity identification with scalable functional descriptions. In an exemplary embodiment, the numbering network matrix can include sparse numbering matrices, dense attribute matrices, hybrid nested matrices, etc. The basic numbering matrix block can be the portion of the numbering network matrix used to store the terminal's unique identity and hierarchical path, ensuring the traceability and uniqueness of the terminal in the spatial topology. For example, the basic numbering matrix block can employ tree-structured path encoding blocks, hash ID mapping blocks, geographic coordinate index blocks, etc. The attribute extension matrix block can be an expandable area in the numbering network matrix used to mount functional parameter units, supporting dynamic updates and multi-dimensional queries of terminal capabilities. Further, the attribute extension matrix block can include column-appending extension blocks, key-value pair embedding blocks, graph structure association blocks, etc.

[0090] Constructing a numbered network matrix, including basic numbered matrix blocks and attribute extension matrix blocks, can be achieved by initializing a two-dimensional matrix structure, dividing it into a fixed basic numbered area and a variable attribute extension area. Furthermore, this operation can be implemented by allocating memory for the basic and extension blocks using a sparse matrix library and defining the boundaries between the two blocks through metadata headers, thus forming a unified management carrier that balances identity stability and capability scalability. Numerical management of terminal-level sequences and functional parameter units can be performed separately, by writing the terminal-level sequences into the basic numbered matrix blocks and the functional parameter units into the attribute extension matrix blocks. Further, this operation can be achieved by assigning consecutive row numbers to the basic blocks, creating column name indexes for the extension blocks, and using row and column intersections to store association relationships, thereby enabling separate storage and joint indexing of spatial and functional information.

[0091] By inputting the terminal hierarchy sequence and functional parameter units into the numbered network matrix, a terminal interaction information network matrix is ​​generated, and digital modeling of the smart community access terminals is performed.

[0092] The terminal hierarchy sequence and functional parameter units are input into the numbered network matrix. This can be done by aligning them by terminal ID, filling the hierarchy path into the basic numbered block, and filling each functional parameter unit into the corresponding extended column. Furthermore, this operation can be achieved by batch importing hierarchy and parameter data in CSV format, injecting it terminal-by-terminal via API, and automatically aligning the matrix positions, thus completing the conversion of structured data into a unified matrix model.

[0093] Generating a terminal interaction information network matrix can be achieved by integrating all data from a numbered network matrix to form a final digital model instance for the terminal. Further, this operation can be implemented by exporting the data as a NumPy array for model training and serializing it into Protobuf format for inter-system transmission, thus outputting a standardized input representation that can be used for collaborative scheduling analysis. The terminal interaction information network matrix can be a comprehensive digital representation matrix generated by fusing terminal hierarchical sequences and functional parameter units, which can be used as a standardized digital model for smart community access terminals, simultaneously carrying spatial relationships and functional attributes. For example, the terminal interaction information network matrix can include dedicated matrices for scheduling optimization, fault tracing, and resource prediction. Digital modeling of smart community access terminals can be achieved by abstracting the physical terminal into a matrix-based data entity containing spatial and functional information through the above steps. Further, this operation can be implemented by automatically triggering the modeling process during device registration and periodically updating the model instance based on the terminal status, thereby achieving a unified computable representation of heterogeneous terminals and supporting intelligent collaborative scheduling.

[0094] Taking intelligent terminal matching for cross-building voice repair requests as an example, the intelligent interaction system for smart community comprehensive governance in this embodiment can be as follows: The system receives a voice repair request from a terminal on the 3rd floor of Unit 2, Building 7. The basic information of the terminal includes sub-data items such as deployment location 7-2-3, hardware model X, and microphone signal-to-noise ratio 65dB. First, a terminal hierarchical sequence 7-2-3 is generated according to the hierarchical order. Then, the sub-data items are split into functional parameter units such as location unit, hardware unit, and audio unit according to attributes. The basic numbering block of the numbered network matrix records the 7-2-3 path, and the attribute extension block is attached with model X and the 65dB parameter. The final generated terminal interaction information network matrix shows that the terminal is located on a high floor and has medium audio capability. When the property dispatch engine queries, the model prioritizes recommending terminals with a signal-to-noise ratio >70dB in the same unit for assistance. If none are available, it extends to neighboring units to avoid direct routing to remote low-performance terminals, thereby improving recognition accuracy and response consistency.

[0095] In one embodiment, constructing training and validation sets to train and validate the collaborative scheduling analysis model includes: Data information is selected from the community collaborative interaction database and matched with terminal access layout information and interaction response performance parameters.

[0096] The construction of training and validation sets for training and validating the collaborative scheduling analysis model can be achieved by using a cross-validation strategy to partition the data and alternately training and evaluating the model. Furthermore, this operation can be implemented by executing an n-fold cross-validation process or using leave-one-out validation combined with incremental training, thereby improving the model's generalization ability and scheduling reliability in heterogeneous terminal environments. The matched data information can be valid records selected from the community collaborative interaction database that have spatiotemporal or logical consistency with terminal access layout information and interaction response performance parameters, ensuring that the data used for modeling reflects the real terminal deployment environment and actual interaction performance. In an exemplary embodiment, the matched data information can be obtained by filtering invalid or abnormal records from the database by setting spatial, functional, or temporal consistency conditions. Furthermore, the matched data information can be the original input that forms the basis for data partitioning with other objects such as the training and validation sets. The training set can be a subset of sample data used to train the collaborative scheduling analysis model, containing terminal access layout information, interaction response performance parameters, and their corresponding collaborative interaction efficiency or scheduling results, providing the basic data for the model to learn the mapping relationship between terminal states and optimal scheduling strategies. In one specific embodiment, the training set can be obtained by dividing the filtered data into multiple subsets according to preset rules. Furthermore, the training set can be a component of the data that, together with the validation set, forms the cross-validation structure.

[0097] Based on data information from the community collaborative interaction database that matches terminal access layout information and interaction response performance parameters, spatial, functional, or temporal consistency conditions can be set to filter invalid or abnormal records in the database. In an exemplary embodiment, this operation can be achieved by filtering interaction records based on terminal location adjacency relationships or removing abnormal samples with response latency exceeding a reasonable threshold, thereby improving the consistency between training data and real deployment scenarios and enhancing the model's practicality.

[0098] The matched data information is used to construct n training sets, and n verifications are performed. In each verification, one of the training sets is selected as the verification set, and the verification results of the n verifications are obtained respectively.

[0099] The n training sets can be n mutually exclusive or overlapping training subsets generated from matching data information through a specific partitioning strategy, which can be used to support the diversity and coverage of model training in n-fold cross-validation. In this embodiment, the n training sets can be constructed by dividing the data into n equal parts or dividing it into n groups after stratification by terminal type. Furthermore, the n training sets can be the data basis for rotating roles during the n-fold cross-validation process. The validation set can be an independent sample subset used to evaluate the generalization ability of the collaborative scheduling analysis model on unseen data, which can be used to test the model's adaptability to new terminal combinations or interaction scenarios and prevent overfitting. In an exemplary embodiment, the validation set can be obtained by dynamically specifying the role of a subset in n-fold cross-validation. Furthermore, the validation set can be a complementary data part that completes a single round of model evaluation together with the other training sets. The n-fold cross-validation can be a process of n rounds of model evaluation using different subsets as validation sets in sequence under the n-fold cross-validation framework, which can be used to comprehensively test the stability of the model under different data distributions. Furthermore, n validations can be achieved by rotating each subset as the validation set and the rest as the training set, thereby allowing for multi-faceted evaluation of model stability and avoiding random biases caused by single validation.

[0100] Constructing n training sets from the matched data can be achieved by dividing the filtered data into n subsets according to preset rules, each subset serving as either a training or validation set. Further, this operation can be implemented by dividing the data into n equal parts or by stratifying it into n groups based on terminal type, thus providing a structured data foundation for cross-validation. Performing n validation iterations can involve executing predictions on the corresponding validation set and recording the performance of the model after each round of training. In a specific embodiment, this operation can be achieved by sequentially rotating each subset as the validation set and the rest as the training set, allowing for multi-faceted evaluation of model stability and avoiding random biases introduced by single validation.

[0101] Each validation cycle selects one training set as the validation set. This can be achieved in n-fold cross-validation, where the i-th subset is used for validation in the i-th validation cycle, with the remainder used for training. In one specific embodiment, this operation can be implemented by cyclically specifying subset roles or dynamically switching training / validation identifiers using an index mask, ensuring all data participates in both training and validation, maximizing data utilization efficiency. The validation result can be a performance evaluation metric output by the model on the validation set during each validation cycle, reflecting the model's prediction accuracy or scheduling rationality on a specific data subset. In this embodiment, the validation result can be obtained by recording the model's output error or scheduling accuracy on the validation set in each validation cycle. For example, the validation result may include, but is not limited to, mean absolute error, scheduling success rate, and response latency reduction rate. Obtaining the validation results for each of the n validation cycles can be achieved by recording the model's output error or scheduling accuracy on the validation set in each validation cycle. Furthermore, this operation can be achieved by saving the loss function value for each validation cycle or recording the matching degree between the scheduling scheme and the actual optimal scheme, thereby accumulating multi-round evaluation data to support subsequent comprehensive judgment.

[0102] The verification results from n trials are averaged to obtain a comprehensive verification result, and the output of the collaborative scheduling analysis model is corrected based on the comprehensive verification result.

[0103] The comprehensive validation result can be a unified evaluation value obtained by statistically aggregating (e.g., averaging) the results of n validations. It can be used as a robust estimate of the overall generalization ability of the model and guide output correction. In an exemplary embodiment, the comprehensive validation result can be obtained by calculating the arithmetic mean or other statistical central value of the n validation results. Exemplarily, the comprehensive validation result can include, but is not limited to, the arithmetic mean validation result, the weighted average validation result, and the median validation result. Averaging the n validation results to obtain the comprehensive validation result can be achieved by calculating the arithmetic mean or other statistical central value of the n validation results. Further, this operation can be achieved by calculating the mean of the n validation errors or by using a moving average to smooth extreme values, thereby reducing the impact of fluctuations in a single validation and obtaining a more reliable model performance estimate. Correcting the output of the collaborative scheduling analysis model based on the comprehensive validation result can be achieved by adjusting the confidence level, threshold, or post-processing rules of the model output using the comprehensive validation result. In a specific embodiment, this operation can be achieved by compensating for prediction delays based on the average error or dynamically adjusting terminal selection priority based on the validation success rate, thereby making the model output closer to actual service needs and reducing the risk of misscheduling.

[0104] Taking the adaptive optimization of the model after adding new smart terminals as an example, the intelligent interaction system for comprehensive governance of smart communities in this embodiment can be a community adding a batch of voice terminals with high-definition microphones to Building 5. The system filters matching data information from the community collaborative interaction database, which includes the terminal access layout information and initial interaction response performance parameters of the building. After merging these data with historical data from other buildings, they are divided into 5 training sets, and 5-fold cross-validation is performed. Each time, a subset is rotated as the validation set, and the accuracy of the model in predicting the optimal call route is recorded. The average of the 5 validation results is used to obtain a comprehensive validation result of 89.2%. Based on this, the output of the collaborative scheduling analysis model is corrected. For example, when the predicted response delay is less than 2 seconds but the validation shows that the actual delay often exceeds 3 seconds, the delay prediction value of this type of terminal is automatically increased, so as to allocate tasks more reasonably in subsequent scheduling.

[0105] In one embodiment, configuring hierarchical access permissions for the generated terminal interaction information network matrix includes: Based on the different roles in smart community governance, the system is divided into three levels: community grid worker authority, property service authority, and community resident authority. Different levels of authority grant different degrees of access to terminal interactive data query and editing. Only community grid worker authority can adjust and update the core parameters of the collaborative scheduling and analysis model.

[0106] Smart community governance is a community governance model involving multiple stakeholders such as the government, property management, and residents. It relies on information technology to achieve collaborative management and services, and can be used as a business context for permission division, defining the responsibilities of different roles within the system. Role types can be identity categories defined based on the functions a user undertakes in smart community governance, and can be used as a basis for hierarchical access control, determining the scope of data they can operate and the system functions they can access. For example, role types can include, but are not limited to, community grid workers, property service personnel, and community residents. Permission levels can be system access control levels bound to role types, specifying data visibility and operational capabilities, and can be used to achieve differentiated authorization management of the terminal interaction information network matrix. Further, permission levels can include, but are not limited to, read-only query layers, restricted editing layers, and full configuration layers. Terminal interaction data can be structured data stored in the terminal interaction information network matrix, containing spatial location, functional parameters, and interaction performance information, and can be used as the object of permission control, with its access granularity dynamically adjusted according to the user role. In a specific embodiment, terminal interaction data can include, but is not limited to, public service status data, regional operation indicator data, and global topology configuration data.

[0107] Data query permissions can be the authorized ability to allow users to read specific fields or ranges in terminal interaction data, which can be used to ensure that users only obtain necessary information related to their responsibilities. In this embodiment, data query permissions may include, but are not limited to, querying personal interaction records, querying data within a jurisdiction, and querying aggregated data across the entire network. Data editing permissions can be the authorization to allow users to modify, mark, or annotate terminal interaction data, which can be used to restrict non-professionals from interfering with the system status and ensure data reliability. For example, data editing permissions may include, but are not limited to, status feedback marking, device fault reporting, and model parameter adjustment. The core parameters of the collaborative scheduling analysis model can be key configuration items that determine the logic for generating the optimal cross-terminal collaborative interaction scheme, such as efficiency weights, correction coefficient thresholds, and scheduling priority rules. These can directly affect the performance and adaptability of the system scheduling strategy and need to be maintained by a professional governance entity. Furthermore, the core parameters of the collaborative scheduling analysis model may include, but are not limited to, efficiency calculation weight vectors, strong and weak correlation thresholds, and terminal capability matching rule sets.

[0108] Adjustment and update operations can be management behaviors involving modifying, optimizing, or iterating the core parameters of the collaborative scheduling analysis model. These operations enable scheduling strategies to continuously evolve with changes in the community's operating environment. In one specific embodiment, adjustment and update operations may include, but are not limited to, parameter fine-tuning, rule replacement, and model hot updates. Configuring hierarchical access permissions for the generated terminal interaction information network matrix can be based on user role types, setting corresponding data views and operation whitelists in the system's access control module. Furthermore, configuring hierarchical access permissions for the generated terminal interaction information network matrix can be achieved through configuring permission policies using the RBAC (Role-Based Access Control) framework and implementing differentiated decryption authorization for different blocks of the matrix using attribute-based encryption, thereby achieving a synergistic balance between data security, privacy protection, and governance efficiency.

[0109] Based on different role types in smart community governance, the system is divided into three permission levels: community grid worker permission level, property service permission level, and community resident permission level. This can be achieved during user registration or identity authentication, mapping users to one of these three predefined permission levels. Furthermore, this division can be implemented by synchronizing organizational structure roles through a unified identity authentication system and tagging permission level labels in user profiles, thereby establishing a clear responsibility-permission correspondence and preventing unauthorized operations. Different permission levels correspond to different granularities of terminal interaction data query and editing permissions. This can be achieved by configuring the range of accessible matrix rows / columns and allowed operation types for each permission level. Further, this permission configuration can be implemented such that residents can only query the terminal status row corresponding to their initiated interaction; property management can read and write the operational indicator columns of the buildings under their jurisdiction; and grid workers have full matrix read and write permissions, thus ensuring minimal data exposure while meeting the business needs of each role. Only the community grid worker permission level can adjust and update the core parameters of the collaborative scheduling analysis model. This can be achieved by setting access control for the core parameter modification function in the system management interface, opening it only to the grid worker role. Furthermore, this restriction can be implemented by opening the parameter editing interface after dual verification of two-factor authentication and role verification, and by including changes to core parameters in the audit log and enforcing an approval process. This can ensure the professionalism and stability of the scheduling strategy and prevent unauthorized intervention.

[0110] Taking the adjustment of scheduling strategy after multiple terminals go offline due to a sudden rainstorm as an example, the intelligent interactive system for smart community comprehensive governance in this embodiment can be as follows: the rainstorm causes network interruption of multiple corridor terminals. After logging into the system, property service personnel can only view the offline status of terminals in Building 3 under their jurisdiction and mark it as an environmental fault, and cannot modify the scheduling logic; community residents can only see the status update of their previous repair orders; while the community grid members assigned by the street can log in and view the heat map of the online rate of terminals in the whole community. They find that terminals in low-lying areas are generally unavailable, so they enter the collaborative scheduling analysis model management interface, temporarily reduce the spatial distance weight, increase the hardware redundancy weight, and enable the backup broadcast terminal as a voice relay. After completing the adjustment of core parameters, the system automatically outputs a new cross-terminal optimal collaborative interaction scheme, giving priority to guiding residents to use the high waterproof level terminals in the elevator to report the danger, and ensuring the continuity of emergency response.

[0111] In one embodiment, during the execution of the cross-terminal optimal collaborative interaction scheme, the running status and interaction response data of each access terminal are collected in real time. When any terminal is detected to have a response timeout, data packet loss, or interaction abnormality, the backup collaborative mechanism is immediately triggered. The pre-stored suboptimal collaborative interaction scheme is retrieved from the community collaborative interaction database to complete the interaction. At the same time, the abnormal terminal information is recorded and pushed to the community grid member's authorized terminal for alarm notification.

[0112] Each access terminal can be a collection of smart community devices currently participating in the execution of the optimal cross-terminal collaborative interaction scheme. These terminals can serve as actual execution nodes for service flow, and their stability directly affects the continuity of interaction. In one exemplary embodiment, each access terminal can be one or more of, including but not limited to, a calling terminal, a relay processing terminal, and a target response terminal. The operating status can be a set of real-time indicators reflecting the current hardware and software working status of the terminal, which can be used to determine whether the terminal has normal service capabilities. Further, the operating status can include, but is not limited to, one or more of, CPU utilization, memory usage, and network connection status. Interaction response data can be timely and complete feedback information generated by the terminal during collaborative interaction, which can be used as a direct basis for detecting interaction anomalies. For example, interaction response data can include, but is not limited to, one or more of, request reception confirmation, voice recognition results, and service completion receipts.

[0113] During the execution of the optimal cross-terminal collaborative interaction scheme, the running status and interaction response data of each access terminal are collected in real time. This can be achieved by continuously monitoring the system indicators and interaction logs of the terminals through probes or agents. Furthermore, this operation can be implemented by a lightweight agent reporting CPU and network status and embedding interaction event tracking IDs in the message middleware, thereby establishing end-to-end observability of the service link. Response timeout refers to the state where a terminal fails to complete a specified interaction action within a preset time window, and can be used as one of the key fault types to trigger backup mechanisms. In a specific embodiment, response timeout may include, but is not limited to, call setup timeout, voice recognition timeout, and work order feedback timeout. Data packet loss refers to the phenomenon where some data packets fail to be transmitted successfully during the interaction due to network or terminal problems, and can be used as a typical anomaly leading to incomplete instructions or service interruptions. Interaction anomalies can be other unexpected interaction behaviors besides timeouts and packet loss, such as semantic recognition errors and service logic jump errors, and can be used to characterize deep compatibility or configuration problems in the terminal or link.

[0114] If any terminal experiences a response timeout, data packet loss, or interaction anomaly, the collected data can be compared with a preset threshold or normal mode to identify deviations. Furthermore, this operation can be implemented by detecting timeouts using a sliding time window, verifying data integrity through checksums, and identifying semantic anomalies using an NLP model, thereby promptly identifying single points of failure in the service chain. The backup coordination mechanism can be a fault-tolerant execution process automatically activated when the primary coordination path fails, ensuring uninterrupted service and achieving millisecond-level path switching. In a specific embodiment, the backup coordination mechanism may include, but is not limited to, terminal hot standby switching mechanisms, function degradation handling mechanisms, and local cache takeover mechanisms. Immediately triggering the backup coordination mechanism can involve initiating a predefined fault-tolerant process within milliseconds after an anomaly is detected. Furthermore, this operation can be achieved by interrupting the current link and activating the backup terminal route, switching to a local lightweight speech model for continued processing, thereby avoiding service interruption and maintaining interaction continuity.

[0115] Suboptimal collaborative interaction schemes can be pre-stored in the community collaborative interaction database as alternative scheduling strategies with slightly lower performance than the optimal scheme but high availability. These can be used as rapid alternatives in fault scenarios to maintain a basic service loop. For example, suboptimal collaborative interaction schemes can include, but are not limited to, one or more of the following: high robustness priority schemes, low-latency suboptimal schemes, and functional simplification schemes. Retrieving a pre-stored suboptimal collaborative interaction scheme from the community collaborative interaction database to complete the interaction can be achieved by matching the most suitable suboptimal scheme based on the current interaction context (such as initiation location and service type). Furthermore, this operation can be implemented by quickly retrieving pre-computed schemes using hash keys and matching historical successful suboptimal cases by similarity, thereby achieving seamless service takeover with minimal performance loss. Abnormal terminal information can be structured data recording the identity, exception type, timestamp, and context of the faulty terminal, which can be used to support subsequent root cause analysis and preventative maintenance. In an exemplary embodiment, abnormal terminal information can include, but is not limited to, terminal ID and location, exception code classification, and associated interaction event ID.

[0116] Recording abnormal terminal information can involve writing the faulty terminal's ID, anomaly type, time, and context into a structured log or database table. Furthermore, this operation can be achieved by writing to a dedicated abnormal event table and associating it with the terminal hierarchy sequence, generating a JSON-formatted abnormal snapshot archive, thus creating traceable and analyzable operational knowledge assets. The community grid worker's authorized terminal can be a high-privilege management terminal or operating interface specifically configured for the community grid worker role, serving as an authoritative entry point for receiving system alarms and performing operational interventions. For example, the community grid worker's authorized terminal can include, but is not limited to, one or more of mobile governance apps, command center large screens, and desktop management backends. Alarm notifications can be structured abnormal notification messages pushed to the community grid worker's authorized terminal, enabling proactive fault exposure and rapid response. Pushing abnormal terminal information to the community grid worker's authorized terminal for alarm notification can be done by sending alarm content to the grid worker's authorized terminal via message queues or push services. Furthermore, this operation can be achieved by pushing to the command center large screen in real time via WebSocket or sending alarm cards with location information via government affairs apps, thereby enabling proactive fault exposure and professional intervention.

[0117] Taking a sudden network interruption of the main terminal during a resident's nighttime voice repair request as an example, the intelligent interaction system for smart community comprehensive governance in this embodiment can be as follows: A resident reports a water pipe leak through a terminal in the elevator of Building 8. The system executes the optimal solution to route the request to a high-performance terminal in the same building for voice recognition. During the execution, real-time monitoring detects that the terminal has no network heartbeat for 3 consecutive seconds and the voice stream is interrupted, which is determined to be a data packet loss type of interaction anomaly. The backup collaboration mechanism is immediately triggered, and the pre-stored suboptimal solution is retrieved from the community collaborative interaction database: the request is switched to a backup terminal with offline recognition capability in the same floor corridor. At the same time, the abnormal terminal ID, packet loss time and context are recorded, and the alarm is pushed to the street grid member's mobile governance APP. The resident does not perceive the interruption, the repair voice is successfully recognized and a work order is generated, and the property management arrives to handle it within 10 minutes.

[0118] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An intelligent interactive system for comprehensive governance of smart communities, characterized in that, The system includes: The information acquisition module is used to acquire basic terminal information of smart community access terminals, including terminal hardware information, terminal function information and terminal access layout information. The efficiency calculation module is used to monitor the cross-terminal collaborative interaction process, collect and obtain interaction response performance parameters, and calculate the collaborative interaction efficiency based on the interaction response performance parameters. The data construction module is used to build a community collaborative interaction database, which is used to store and manage the basic information of the terminal and the interaction response performance parameters, and corresponds to the collaborative interaction efficiency; The correlation analysis module is used to extract the factors affecting the efficiency of the collaborative interaction during the interaction process from the community collaborative interaction database, and to establish strong and weak correlations between each of the factors and the collaborative interaction efficiency. The solution output module is used to establish a collaborative scheduling analysis model based on the community collaborative interaction database, input the terminal basic information and interaction response performance parameters into the collaborative scheduling analysis model, and output the optimal cross-terminal collaborative interaction solution.

2. The intelligent interactive system for comprehensive governance of smart communities as described in claim 1, characterized in that, The construction of a community collaborative interaction database, used to store and manage the terminal's basic information and interaction response performance parameters, and corresponding to the collaborative interaction efficiency, includes: The terminal's basic information and interactive response performance parameters are collected, and the collaborative interaction efficiency corresponding to the terminal's basic information and interactive response performance parameters is obtained, wherein the collaborative interaction efficiency includes interactive response speed and interactive accuracy. The terminal basic information and interactive response performance parameters are divided into several variable data blocks according to sub-data items. The collaborative interaction efficiency is integrated into the variable data blocks, and each variable data block and sub-data item corresponds one-to-one. Each of the independent variable data blocks is chained together with the dependent variable data blocks and integrated into a data chain, with each data chain corresponding to a single cross-terminal collaborative interaction; Historical data chains are collected and integrated into the community collaborative interaction database.

3. The intelligent interactive system for comprehensive governance of smart communities as described in claim 2, characterized in that, Assign a unique identifier to each component data of the community collaborative interaction database, including: Each of the independent and dependent data blocks is identified, wherein an independent block key is assigned to each of the independent data blocks, and a dependent block key is assigned to each of the dependent data blocks; The independent block key and the dependent block key are integrated in the data chain, and a chain key is assigned to each data chain, wherein the independent block key and the dependent block key are respectively used as foreign keys of the chain key; The community collaborative interaction database indexes and retrieves the terminal basic information, interaction response performance parameters, and collaborative interaction efficiency through the independent block key, dependent block key, and chain key.

4. The intelligent interactive system for comprehensive governance of smart communities as described in claim 1, characterized in that, The step of extracting factors affecting the efficiency of collaborative interaction during the interaction process from the community collaborative interaction database, and establishing strong or weak correlations between each of the influencing factors and the collaborative interaction efficiency, includes: The collaborative interaction efficiency in the community collaborative interaction database is sorted, and sub-data items in several terminal basic information and interaction response performance parameters corresponding to the collaborative interaction efficiency are indexed according to the sorting results. Create an influencing factor table for each sub-data item, and arrange the sub-data items in the same order according to the sorting result in the influencing factor table; Deep learning is performed on several influencing factor tables arranged in the same order to filter the influencing factor tables corresponding to the impact of the sub-data items on the collaborative interaction efficiency, and a strong-weak effect correlation is established based on the magnitude of the impact.

5. The intelligent interactive system for comprehensive governance of smart communities as described in claim 4, characterized in that, The step of filtering the sub-data items and identifying the influencing factors that affect the collaborative interaction efficiency, and establishing strong-weak relationships based on the magnitude of the influence, includes: The efficiency of the collaborative interaction is evaluated for each sub-data item through deep learning, and the table of influencing factors that produce the impact is selected as the strong and weak correlation analysis table. At least one of the strong and weak correlation analysis tables is selected and arranged in combination. Taking any sub-data item as the target, the results of the arrangement and combination are traversed, and the strong and weak correlation analysis tables containing the target item are selected to form several single-factor influence sets. The single-factor influence sets correspond one-to-one with the sub-data items obtained by the selection. Deep learning analysis is performed on several sets of single-factor influences to obtain the mapping relationship between the filtered sub-data items and the collaborative interaction efficiency, and a strong-weak effect association is established based on the mapping relationship.

6. The intelligent interactive system for comprehensive governance of smart communities as described in claim 5, characterized in that, The step of establishing a collaborative scheduling analysis model based on the community collaborative interaction database involves inputting the terminal basic information and interaction response performance parameters into the collaborative scheduling analysis model, and outputting the optimal cross-terminal collaborative interaction scheme, including: The system determines the sub-data items of the input terminal basic information and interactive response performance parameters, retrieves the community collaborative interaction database and matches the sub-data items that have an impact, and indexes the corresponding single-factor impact set. The strong and weak effect associations established by deep learning analysis for the single-factor influence set obtained from the index; A basic collaborative scheduling scheme is obtained by matching the community collaborative interaction database. The strong and weak effects are converted into correction coefficients. The basic collaborative scheduling scheme is corrected according to the correction coefficients, and the optimal cross-terminal collaborative interaction scheme is output.

7. The intelligent interactive system for comprehensive governance of smart communities as described in claim 6, characterized in that, Digital modeling of the smart community access terminal based on the aforementioned basic terminal information includes: The input basic terminal information is arranged according to the different deployment locations of the smart community terminals and in hierarchical order to generate a terminal hierarchy sequence. The input terminal basic information is divided into several functional parameter units according to the different attributes of the sub-data items, and each functional parameter unit corresponds to a sub-data item in the terminal basic information. Construct a numbered network matrix, including a basic numbered matrix block and an attribute extended matrix block, and perform digital management on the terminal hierarchical sequence and functional parameter unit respectively; The terminal hierarchy sequence and functional parameter units are input into the numbered network matrix to generate a terminal interaction information network matrix, thereby digitally modeling the smart community access terminal.

8. The intelligent interactive system for comprehensive governance of smart communities as described in claim 1, characterized in that, The collaborative scheduling analysis model is trained and validated by constructing training and validation sets, including: Data information that is filtered and matched with terminal access layout information and interaction response performance parameters based on the community collaborative interaction database; The matched data information is used to construct n training sets, and n verifications are performed. In each verification, one of the training sets is selected as the verification set, and the verification results of the n verifications are obtained respectively. The verification results from n trials are averaged to obtain a comprehensive verification result, and the output of the collaborative scheduling analysis model is corrected based on the comprehensive verification result.

9. The intelligent interactive system for comprehensive governance of smart communities as described in claim 7, characterized in that, Configure hierarchical access permissions for the generated terminal interaction information network matrix, including: Based on the different roles in smart community governance, the system is divided into three levels: community grid worker authority, property service authority, and community resident authority. Different levels of authority grant different degrees of access to terminal interactive data query and editing. Only community grid worker authority can adjust and update the core parameters of the collaborative scheduling and analysis model.

10. The intelligent interactive system for comprehensive governance of smart communities as described in claim 9, characterized in that, During the execution of the cross-terminal optimal collaborative interaction scheme, the running status and interaction response data of each access terminal are collected in real time. When any terminal is detected to have a response timeout, data packet loss, or interaction abnormality, the backup collaborative mechanism is immediately triggered. The pre-stored suboptimal collaborative interaction scheme is retrieved from the community collaborative interaction database to complete the interaction. At the same time, the abnormal terminal information is recorded and pushed to the community grid member's authorized terminal for alarm notification.

Images