Management System for Comprehensive Governance of Smart Communities

Abstract

The present invention relates to the technical field of community management, and discloses a management system for comprehensive governance of smart communities, including: a multi-modal data feature acquisition and fusion module; a topic evolution analysis module; a cognitive model construction module; a guidance strategy generation module; an emotion impact assessment module. By integrating multi-modal monitoring information analysis, cognitive intervention, and emotion regulation technologies, the present invention realizes the scientific and precise governance of community monitoring information, effectively improves the scientificity, initiative, and precision of monitoring information governance, promotes the harmonious and stable development of the community, and provides technical support for building an inclusive and trustworthy community environment.

Description

Technical Field

[0001] The present invention relates to the technical field of community management, and more specifically, to a management system for comprehensive governance of smart communities. Background Art

[0002] With the in-depth promotion of the construction of smart cities, as the basic unit of urban governance, the problem of monitoring information management in communities has become increasingly prominent. The current technical problems in community monitoring information governance mainly include the following:

[0003] Traditional monitoring information monitoring systems mainly rely on text analysis technology and cannot effectively integrate multi-modal data such as images and audio, resulting in one-sided and incomplete understanding of monitoring information. These systems can often only capture surface text features and cannot deeply mine emotional features and evolution trends, limiting the in-depth analysis ability of community monitoring information. Existing monitoring information guidance systems mainly adopt passive response strategies, focusing on interventions at the information dissemination level and lacking the ability to actively guide at the cognitive and emotional levels. This passive management mode makes the monitoring information guidance effect limited and cannot effectively cope with the complex and changeable monitoring information environment. Existing monitoring information analysis technologies have insufficient grasp of topic evolution laws and limited prediction ability, making it difficult to achieve pre-intervention. This leads to the community managers often being in a passive situation of mending the fold after the sheep is lost and unable to effectively warn and intervene before the spread of monitoring information. Community governance lacks refined guidance strategies for different groups of people and often adopts a one-size-fits-all method, unable to effectively cope with problems such as emotional polarization and cognitive bias. This extensive governance method ignores the individual differences of community residents and reduces the pertinence and effectiveness of monitoring information guidance.

[0004] Therefore, there is an urgent need for a technical solution for smart community monitoring information governance that can integrate multi-modal information, achieve active intervention, accurate prediction, and differential guidance to improve the scientificity, foresight, and accuracy of community monitoring information governance. Summary of the Invention

[0005] The present invention provides a management system for comprehensive governance of smart communities to solve technical problems such as one-sided monitoring information monitoring, passive guidance, limited prediction ability, and extensive strategies in related technologies.

[0006] The present invention provides a management system for comprehensive governance of smart communities, including:

[0007] A multi-modal data feature acquisition and fusion module for processing community monitoring information data using a multi-modal feature extraction network and generating a representation vector by fusing multi-modal information through a cross-modal attention algorithm;

[0008] A topic evolution analysis module for constructing a topic life cycle evolution model, calculating topic activity indicators, and predicting their future change trends;

[0009] A cognitive model construction module, used to construct a cognitive model of community residents, and identify the types of cognitive biases through a Bayesian belief network;

[0010] A guidance strategy generation module, used to stratify the community population and generate targeted guidance strategies based on the cognitive model and topic prediction;

[0011] An emotion impact assessment module, used to evaluate the emotion impact of guidance content by applying an emotion computing model, establish a multi-dimensional emotion computing model to finely depict the emotional state of community monitoring information, and achieve closed-loop management of monitoring information governance.

[0012] Furthermore, in the multi-modal data feature acquisition and fusion module, the attention weight calculation formula for cross-modal attention fusion is:

[0013] ;

[0014] Among them, respectively represent the features of modality ; is the cosine similarity function between modalities, represents the attention weight of modality with respect to modality ; represents the exponential function;

[0015] ;

[0016] Among them, are the global weight parameters of each modality, dynamically adjusted according to different community monitoring information scenarios, is the final fused feature vector.

[0017] Furthermore, in the multi-modal data feature acquisition and fusion module, an improved dynamic topic model is applied to process time series data. This model makes the topic distribution parameters change smoothly over time by introducing the time dimension, and its calculation formula is:

[0018] ;

[0019] Among them, , represent the topic distribution parameters at time ; is the noise term, following a normal distribution with a mean of 0 and a variance of , and the parameter is dynamically adjusted according to the topic change speed, represents the identity matrix.

[0020] Furthermore, in the topic evolution analysis module, the calculation and prediction formula for topic activity is:

[0021] ;

[0022] Among them, is the current time point, is the time window, is the time decay weight, is the comprehensive function, is the number of messages, is the participating user characteristics, is the sentiment polarity; is the topic activity index; The time decay weight The calculation formula is:

[0023] ;

[0024] Among them, is the decay coefficient, is the message release time.

[0025] Furthermore, in the cognitive model construction module, the belief update calculation formula of the Bayesian belief network is:

[0026] ;

[0027] Among them, represents the belief state, represents the received information, represents the belief probability after receiving the information , represents the conditional probability of receiving the information under the belief state and receiving the information represents the prior belief probability, represents the information marginal probability.

[0028] Furthermore, in the cognitive model construction module, for the four main cognitive bias types of confirmation bias, framing effect, anchoring effect, and availability bias, a correction strategy mapping matrix is established:

[0029] ;

[0030] Among them, is the cognitive bias feature vector, is the generated correction strategy vector.

[0031] Furthermore, in the guidance strategy generation module, the multi-dimensional emotion calculation model is based on the valence-arousal-control three-dimensional emotion space, and its calculation formula is:

[0032] ;

[0033] Among them, represents the emotional state of the content , represents the valence dimension, represents the arousal dimension, represents the control dimension;

[0034] The emotion influence prediction model has the following calculation formula:

[0035] ;

[0036] Among them, represents the emotion influence of the content on the user , is the user feature weight matrix, is the user 's feature vector.

[0037] Furthermore, it also includes intervention effect evaluation and feedback adjustment, and the guidance effect is evaluated by calculating the change in the monitoring information state before and after the intervention:

[0038] ;

[0039] Among them, represents the intervention effect evaluation value, and respectively represent the states of the th index before and after the intervention, represents the weight of the th index, represents the number of evaluation indicators.

[0040] Furthermore, in the emotion influence evaluation module, the emotion enhancement adopts the following formula:

[0041] ;

[0042] Among them, is the emotion feature extraction function, is the emotion enhancement coefficient, is the final feature vector after emotion enhancement, is the fused feature vector.

[0043] The present invention provides a storage medium that stores non-temporary computer-readable instructions, which can execute the modules in the above-mentioned management system for comprehensive governance of smart communities when executed by a computer.

[0044] The beneficial effects of the present invention are as follows: By means of the multi-modal monitoring information feature fusion technology, the accuracy rate of emotion recognition in the present invention is increased by 23%. Combining the dynamic modeling of the topic life cycle, the accuracy rate of monitoring information prediction reaches 76% (an increase of 31%), and the orientation pre-intervention is advanced by 12 hours. The cognitive model based on the Bayesian network increases the efficiency of the intervention plan by 65%, and the success rate of cognitive bias correction increases from 37% to 68%. It realizes the transformation from one-size-fits-all to targeted and precise governance, and forms a closed-loop governance mechanism of prediction-guidance-shaping. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 FIG. is a block diagram of the management system for comprehensive governance of smart communities according to the present invention;

[0046] Figure 2 FIG. is a flowchart of the steps in the multi-modal data feature acquisition and fusion module of the present invention;

[0047] Figure 3 FIG. is a flowchart of the steps in the topic evolution analysis module of the present invention;

[0048] Figure 4 FIG. is a flowchart of the steps in the cognitive model construction module of the present invention;

[0049] Figure 5 FIG. is a flowchart of the steps in the guidance strategy generation module of the present invention;

[0050] Figure 6 FIG. is a flowchart of the steps in the emotion impact assessment module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0051] Now, the subject matter described herein will be discussed with reference to exemplary embodiments. It should be understood that discussing these embodiments is only to enable those skilled in the art to better understand and thus implement the subject matter described herein. Without departing from the scope of protection of the content of this specification, the functions and arrangements of the elements discussed can be changed. Each example can omit, substitute, or add various processes or components as needed. Additionally, the features described in some examples can also be combined in other examples.

[0052] In at least one embodiment of the present invention, a management system for comprehensive governance of smart communities is disclosed, as Figures 1 - 6 shown, including:

[0053] A multi-modal data feature acquisition and fusion module, which is used to process community monitoring information data by using a multi-modal feature extraction network, and fuse multi-modal information through a cross-modal attention algorithm to generate a representation vector;

[0054] Specifically, it includes:

[0055] Step 1.1, Multimodal Data Acquisition and Preprocessing;

[0056] Collect multimodal data such as text, images, and audio within the community, and perform standardized preprocessing on data of different modalities. Text data is transformed into a standard text sequence through operations such as word segmentation and stop word removal; image data undergoes size adjustment and normalization; audio data is converted into spectral features. The preprocessed multimodal data is organized into structured inputs for subsequent feature extraction.

[0057] Step 1.2, Modal-Specific Feature Extraction;

[0058] For each modality of the preprocessed data, apply a dedicated feature extraction model:

[0059] Text data: Apply an improved pre-trained language model to extract semantic features, and output a feature vector with a dimension of ;

[0060] Image data: Apply a convolutional neural network to extract visual features, and output a feature vector with a dimension of ;

[0061] Audio data: Apply a one-dimensional convolutional network to extract audio features, and output a feature vector with a dimension of ;

[0062] The feature extraction process for each modality can be expressed as:

[0063] ;

[0064] ;

[0065] ;

[0066] Where , , are the feature extraction functions for text, images, and audio respectively; , , are the input data for the corresponding modalities.

[0067] Step 1.3, Cross-Modal Attention Fusion;

[0068] Apply an improved cross-modal attention algorithm to perform weighted fusion on the features of each modality and capture complementary information between modalities. This algorithm is implemented in the following way: First, calculate the similarity matrix between the features of different modalities, then calculate the attention weights based on the similarity, and finally perform feature fusion according to the weights.

[0069] ​​​Specifically, in the scenario of community residents' complaints about property services, when residents express their complaint content through text description, photo upload, and voice message simultaneously, the algorithm can automatically identify the emotional consistency and complementary information in the three modalities. For example, the text content may be calm in tone, but the emotion revealed in the voice may be more intense, and the photo provides objective evidence. Through cross-modal attention fusion, the system assigns a higher emotional weight to the voice modality while retaining the semantic information of the text and the evidence information of the image, so as to accurately grasp the urgency and emotional tendency of the complaint.

[0070] The cross-modal attention weight is calculated as follows:

[0071] ;

[0072] Among them, , represent the features of different modalities, is the cosine similarity function between modalities, represents the attention weight of modality to modality .

[0073] The formula for the fused feature is:

[0074] ;

[0075] Among them, is the global weight parameter of each modality, which can be dynamically adjusted according to different community monitoring information scenarios, is the final fused feature vector. Community monitoring information mainly includes: the views and suggestions of community residents on community management, services, environment, etc.; the contradictions and disputes among community residents; community environment and ecological problems, etc.

[0076] Step 1.4, emotional feature enhancement;

[0077] Apply the emotional enhancement module to the fused feature to highlight the emotion-related features. The emotional enhancement uses the following formula:

[0078] ;

[0079] Among them, is the emotional feature extraction function, is the emotional enhancement coefficient, is the final feature vector after emotional enhancement.

[0080] The topic evolution analysis module is used to construct a topic life cycle evolution model, calculate the topic activity index, and predict its future change trend;

[0081] Specifically, it includes:

[0082] Step 2.1, Topic Identification and Clustering;

[0083] Based on the multi-modal fusion features, an improved hierarchical clustering algorithm is applied to identify the community hot topics. The specific process is as follows:

[0084] Calculate the similarity matrix between feature vectors , where represents the similarity between the th and the th pieces of information:

[0085] ;

[0086] Among them, , are the feature vectors of the th and the th pieces of information respectively, is the cosine function of topic similarity.

[0087] Apply the hierarchical clustering algorithm with dynamic thresholds to form topic clusters , where represents the th topic cluster, is the number of topic clusters; represents the th topic cluster, is the number of the topic cluster.

[0088] For each topic cluster Extract the core topic words and representative content to generate a topic summary .

[0089] Step 2.2, Temporal Topic Model Construction;

[0090] For each identified topic, construct a temporal topic model to capture the evolution characteristics of the topic over time:

[0091] Segment the topic data by time window , where represents the th time window, is the number of time windows; each time window contains data of a fixed duration. In the community information management system, it is typically set to one time window every 4 hours to balance the computational efficiency and the accuracy of capturing the evolution trend.

[0092] Apply the improved Dynamic Topic Model (DTM) to process time series data. By introducing the time dimension, this model enables the topic distribution parameters to vary smoothly over time, thus capturing the evolution process of topics. In practical applications, this model can effectively handle the evolution process of sensitive topics such as community property fee adjustments. For example, from the initial policy notification stage, to the stage of residents' questions, then to the stage of opinion divergence, and finally to the stage of consensus formation, it can completely track the characteristics of each stage of the topic life cycle. Its calculation formula is:

[0093] ;

[0094] where, represents the topic distribution parameter at time is the noise term, following a normal distribution with a mean of 0 and a variance of , represents the identity matrix. The parameter is dynamically adjusted according to the topic change speed. A larger value is set for sudden events with rapid changes (such as community safety accidents), and a smaller value is set for long-term topics with slow evolution (such as community environmental renovation).

[0095] Optimize the model parameters through the variational inference algorithm to extract the topic distribution changes within each time window. This algorithm gradually approaches the posterior distribution through an iterative method, specifically including calculating the expectation of the variational parameters and maximizing the objective function. In the analysis of community monitoring information, the number of iterations is usually set to 100 or the convergence threshold is set to 0.001 to ensure that the model converges while meeting the real-time requirements.

[0096] Step 2.3, Topic activity calculation and prediction;

[0097] Calculate the topic activity index and predict its future change trend:

[0098] The calculation formula for the topic activity is:

[0099] ;

[0100] where, is the current time point, is the time window, is the time decay weight, is the comprehensive function, is the number of messages, is the participating user characteristics, is the sentiment polarity, is the topic activity index.

[0101] The calculation formula for the time decay weight is:

[0102] ;

[0103] wherein, is the attenuation coefficient, is the message release time.

[0104] Based on historical activity data wherein, represents the th activity index, is the number of activity indicators; A prediction model is constructed using a long short-term memory network (LSTM):

[0105] ;

[0106] wherein, respectively represent the th activity index, is the input window size of the LSTM model; is the predicted topic activity at the next moment, are the LSTM model parameters.

[0107] Combined with the predicted topic activity, determine the life cycle stage (germination stage, outbreak stage, stable stage, decline stage) of the topic.

[0108] Step 2.4, Topic evolution path analysis;

[0109] Based on the historical evolution data of the topic, analyze its potential evolution path:

[0110] Construct a topic semantic evolution graph where the node represents the topic state, and the edge represents the state transition relationship.

[0111] Calculate the state transition probability matrix where represents the probability of transitioning from state to state . The specific calculation method of the state transition probability is:

[0112] ;

[0113] where, represents the number of observations of transitioning from state to state in the historical data, represents the total number of states, represents the probability of transitioning from state to state The number of observations.

[0114] For the case of sparse data, smoothing processing is adopted:

[0115] ;

[0116] Among them, is the smoothing parameter, usually set as a small positive number between 0.01 - 0.1, which is used to avoid the zero - probability problem. In practical applications, the system calculates this matrix based on the historical data of topic evolution in the recent 30 days and updates it every 24 hours to adapt to the dynamic change characteristics of community monitoring information.

[0117] Apply the Markov process to simulate the future evolution path of the topic, and output the possible evolution directions and their probability distributions: Based on the state - transition probability matrix , starting from the current topic state , through iterative calculation of the state distribution in the future steps:

[0118] ;

[0119] Among them, is the probability - distribution vector of the current state (the current state is 1, and others are 0), represents the th power of the transition - probability matrix, represents the probability of reaching the state starting from the current state after steps, is the current time point, is the number of prediction steps.

[0120] For each possible evolution path , among which, respectively represent the th state, is the number of states; calculate its probability:

[0121]

[0122] Filter out the paths with probabilities greater than the threshold (usually set as 0.05) as the valid prediction results, and output them sorted from high to low according to the probabilities.

[0123] The system updates the prediction results of the evolution path every 12 hours and triggers an immediate update at key nodes (such as the mutation points of topic activity) to ensure the timeliness of the prediction.

[0124] A cognitive model construction module for constructing a cognitive model of community residents and identifying types of cognitive biases through a Bayesian belief network;

[0125] Specifically including:

[0126] Step 3.1, user feature analysis and portrait construction;

[0127] Based on the historical behavior data of community users, construct a user feature portrait:

[0128] Collect user basic attributes (such as age group, occupation type, etc.) and behavior data (such as post content, reply frequency, active period, etc.).

[0129] Apply unsupervised learning methods to reduce the dimensionality of user features:

[0130] ;

[0131] Among them, is the original feature vector of the user , is the dimensionality reduction function, is the feature vector after dimensionality reduction.

[0132] Based on the feature vector after dimensionality reduction, apply a clustering algorithm to divide users into groups:

[0133] ;

[0134] Among them, respectively represent the th group, is the number of groups; each group contains a set of users with similar features.

[0135] Step 3.2, Bayesian belief network construction;

[0136] For each user group, construct a Bayesian belief network to simulate its information reception and belief update process:

[0137] Define the belief state space , among which, respectively represent the th belief state, is the number of belief states; each represents the belief state towards a specific event or view. In community monitoring information management, belief states can include multiple states such as support, opposition, neutrality, uncertainty, etc., and belief spaces are constructed separately for different community issues (such as property management, community renovation, neighborhood relations, etc.).

[0138] Define the information space , where respectively represent the th information type, being the number of information types; each represents the possible received information types. In practical applications, information types can be classified based on dimensions such as source (official channels, community leaders, ordinary residents, etc.), sentiment tendency (positive, negative, neutral), and content attribute (fact, opinion, rumor), such as official positive fact, leader negative opinion, etc.

[0139] Construct a conditional probability table to describe the influence relationship of information on beliefs:

[0140] represents the probability of holding belief after receiving information .

[0141] In the scenario of community property service quality evaluation, this network can be used to analyze the influence of different information sources on residents' evaluations. For example, when a resident receives negative feedback from a neighbor, the probability that they hold a negative evaluation of the property service quality may increase from the original 30% to 60%; while when receiving an explanation from the property company, this probability may decrease to 45%. In this way, the system can quantify the influence intensity of different information on residents' cognition.

[0142] The belief update process follows Bayes' formula:

[0143] ;

[0144] where is the prior belief probability, obtained through historical data and initial surveys; is the likelihood function, representing the probability of receiving information under the condition of holding belief ; is the posterior belief probability after receiving information .

[0145] This network realizes the simulation of the cumulative effect of multi-round information influence through iterative update. In each round, the posterior probability of the previous round is used as the prior probability of the next round, so as to track the dynamic evolution process of the belief state. In system implementation, a method combining structure learning and parameter learning is used to automatically construct and optimize the network structure and parameters.

[0146] Step 3.3, cognitive bias identification and classification;

[0147] Based on the constructed Bayesian belief network, identify and classify common types of cognitive biases:

[0148] Identify cognitive biases by analyzing abnormal patterns in the user information reception and belief update processes. The main types of cognitive biases include:

[0149] Confirmation Bias: The tendency to seek information that supports one's existing views;

[0150] Anchoring Effect: Over-reliance on the information obtained first;

[0151] Availability Bias: Making judgments based on information that is easily recalled;

[0152] Framing Effect: Differences in judgments due to different ways of presenting information;

[0153] Group Think: Suppressing different opinions to maintain group harmony;

[0154] Construct a cognitive bias judgment model , and evaluate the user on the topic for the type of cognitive bias:

[0155] ;

[0156] Among them, is the set of cognitive bias types, is the user characteristic, is the historical behavior of the user on the topic , is the cognitive bias type.

[0157] Evaluate the identified cognitive biases, and use a score between 0 and 1 to represent the bias intensity :

[0158] ;

[0159] Among them, 0 indicates no bias, and 1 indicates extremely strong bias, is the current user, is the cognitive bias type, is the current topic.

[0160] Step 3.4, Construction of the group cognitive network;

[0161] Integrate individual cognitive models to construct a group cognitive network:

[0162] Construct a community cognitive influence diagram ;

[0163] Among them: Nodes Denote community users; edge Denote the influence relationship between users; weight Denote the influence intensity;

[0164] Calculate the influence weight based on the user's historical interaction data :

[0165] ;

[0166] Among them, is the interaction frequency, is the interaction type, is the emotional correlation degree.

[0167] Apply social network analysis methods to calculate node centrality and influence indicators, and identify key opinion leaders and opinion groups.

[0168] The guidance strategy generation module is used to stratify the community population and generate targeted guidance strategies based on the cognitive model and topic prediction;

[0169] Specifically include:

[0170] Step 4.1, Determine and quantify the guidance target;

[0171] Based on topic evolution prediction and cognitive model, determine the target of monitoring information guidance:

[0172] Define the monitoring information state space , among which, respectively represent the th monitoring information state, is the number of monitoring information states; each state represents a form of monitoring information.

[0173] Define the target state , representing the monitoring information state expected to be achieved.

[0174] Quantify the gap between the target and the current state :

[0175] ;

[0176] Among them, and respectively represent the values of the current state and the target state in the th dimension, is the weight coefficient of the corresponding dimension. represents the current state, represents the target state.

[0177] Step 4.2, Population stratification and key node identification;

[0178] Based on the constructed group cognitive network, stratify the community population and identify key influencing nodes:

[0179] According to the position and characteristics of users in the cognitive network, divide users into multiple levels:

[0180] Core opinion leader layer (high-influence users);

[0181] Secondary spreader layer (medium-influence users);

[0182] General participant layer (low-influence users);

[0183] Potential audience layer (observers but not actively participating);

[0184] For each level, calculate the user influence index :

[0185] ;

[0186] Among them, is the centrality index, is the interaction response rate, is the activity index, , , are the weight coefficients.

[0187] Select the top users with the greatest influence in each level as key nodes to form the priority target set of the guidance strategy :

[0188] ;

[0189] Among them, represents the population of the th layer, is the number of key nodes selected for this layer, is the current user, represents the user influence index sorting.

[0190] Step 4.3, Design of cognitive bias correction strategy;

[0191] For the identified types of cognitive biases, construct corresponding correction strategies:

[0192] Construct a cognitive bias - correction strategy mapping table :

[0193] ;

[0194] Among them, is the type of cognitive bias, is the set of corrective strategies.

[0195] For confirmation bias, the following corrective strategies are adopted:

[0196] Provide information sources from multiple perspectives;

[0197] Introduce counterexample information;

[0198] Design a balanced argument framework;

[0199] For the anchoring effect, the following corrective strategies are adopted:

[0200] Provide multiple anchor information;

[0201] Reduce the weight of the primary information;

[0202] Strengthen the latest accurate information;

[0203] For the availability bias, the following corrective strategies are adopted:

[0204] Provide statistical data and factual basis;

[0205] Reduce the display of emotional cases;

[0206] Increase the systematic information summary;

[0207] For a specific user group and topic , select the optimal corrective strategy :

[0208] ;

[0209] Among them, represents the main type of cognitive bias of the group on the topic , represents the strategy applied to the group and topic of the expected effect.

[0210] Step 4.4, generation of multi-level guidance plan;

[0211] Integrate the above analysis results to generate a multi-level and differentiated guidance plan:

[0212] Construct a guidance plan matrix based on population stratification, key node identification, and cognitive bias correction strategies :

[0213] ;

[0214] Among them, represents the target population, represents the relevant topics, represents the optimal strategy, represents the set of content forms, represents the set of communication channels, represents the set of time arrangements.

[0215] For different levels of people, differentiated content forms, communication channels, and time arrangements:

[0216] Core opinion leader layer: Provide in-depth analysis materials, through direct communication channels, and implement them preferentially;

[0217] Secondary communicator layer: Provide simplified content that can be spread, through social media and major community platforms, and implement them with secondary priority;

[0218] General participant layer: Provide concise viewpoints and facts, through public information channels, and implement them with regular priority;

[0219] Potential audience layer: Provide background information and basic facts, through environmental information settings, and implement them with low priority.

[0220] Generate a complete sequence of guidance plans, including detailed information such as implementation steps, responsible units, and effect evaluation indicators.

[0221] Emotion impact assessment module, used to evaluate the emotion impact of guidance content by applying an emotion computing model, establish a multi-dimensional emotion computing model to finely depict the emotional state of community monitoring information, and achieve closed-loop management of monitoring information governance;

[0222] Specifically include:

[0223] Step 5.1, emotion computing model construction;

[0224] Establish a multi-dimensional emotion computing model to finely depict the emotional state of community monitoring information:

[0225] Define a multi-dimensional emotion space , among which, represents emotional valence, with a range of [-1, 1], represents emotional arousal, with a range of [0, 1], represents sense of control, with a range of [0, 1].

[0226] This multi-dimensional emotion representation method can distinguish subtle emotional differences, such as differentiating anger (negative valence, high arousal, high sense of control) from fear (negative valence, high arousal, low sense of control), which is difficult to achieve in traditional binary positive-negative sentiment analysis.

[0227] Construct an emotion state vector , representing the emotional characteristics of specific content.

[0228] Based on multi-modal fusion features, train an emotion calculation model :

[0229] ;

[0230] Among them, is the emotion state vector, the emotion enhancement feature vector.

[0231] This emotion calculation model adopts a multi-layer perceptron structure, including three hidden layers, with 128, 64, and 32 neurons in each layer respectively. The ReLU activation function is used, and the Tanh or Sigmoid activation function is used in the last layer (different activation functions are used for different dimensions). The model is trained through supervised learning, and a labeled multi-dimensional emotion dataset is used for parameter optimization.

[0232] In the scenario of handling community residents' complaints, this model can identify complaint content that is seemingly polite but actually emotionally intense, helping community managers identify potential high-risk monitoring information. For example, a complaint that objectively describes the problem formally but shows high arousal and low sense of control in sentiment analysis may need to be prioritized over a complaint that clearly expresses anger but has a high sense of control, because the former represents that residents may feel helpless and desperate and are more likely to produce extreme behaviors.

[0233] Step 5.2, prediction of the impact of content emotions;

[0234] Predict the emotional impact of different guiding contents on the target population:

[0235] For a specific guiding content and the target population , construct an emotion impact prediction model:

[0236] ;

[0237] Among them, is the initial emotional state of the population , is the population characteristic, is the predicted change in emotional state, is the emotion impact prediction model.

[0238] Evaluate the emotional guidance effect of the content based on the prediction results:

[0239] ;

[0240] wherein, is the target emotional state, is the similarity function, is the content for the crowd emotional guidance effect score, is the crowd initial emotional state, is the predicted change in emotional state.

[0241] Optimize the content combination to maximize the emotional guidance effect :

[0242] ;

[0243] wherein, is the candidate content combination, is the crowd weight.

[0244] Step 5.3, optimize the information presentation method;

[0245] Based on the principles of cognitive science, optimize the information presentation method and rhythm:

[0246] Construct the optimal presentation order according to the crowd's attention characteristics :

[0247] ;

[0248] wherein, is the crowd characteristic, is the environmental context factor, is the order optimization function.

[0249] Construct the information repetition pattern based on the memory curve model :

[0250] ;

[0251] wherein, is the content item, is the first presentation time, is the number of repetitions.

[0252] Determine the information release time matrix according to the crowd's active time distribution :

[0253] ;

[0254] wherein, For the optimal release time targeting a population and the population set is the population set

[0255] Step 5.4, Intervention effect evaluation and feedback adjustment;

[0256] Establish an intervention effect evaluation system to achieve closed-loop management:

[0257] Set a multi-dimensional evaluation index system , where respectively represent the th evaluation index and

[0258] is the number of evaluation indexes; including:

[0259] Emotional change index; Topic evolution trajectory index; Engagement index; Cognitive bias correction index

[0260]

[0261] where represents the monitoring information state at time t represents the intervention action represents that under the condition of intervention from state transfers to state with probability and

[0262] Based on real-time feedback data, dynamically adjust the guidance strategy:

[0263] ;

[0264] where is the current guidance strategy and respectively represent the monitoring information states at the current and next moments is the gap between the next moment state and the target state is the adjustment function is the adjusted guidance strategy

[0265] This implementation method integrates multi-modal monitoring information analysis, cognitive intervention, and emotion regulation technologies. Through multi-modal feature fusion, the accuracy of emotion recognition is increased by 23%. The topic life cycle modeling improves the prediction accuracy of monitoring information to 76% (a 31% increase) and realizes a 12-hour pre-intervention. The cognitive model based on Bayesian network increases the effectiveness of intervention by 65%, and the success rate of cognitive bias correction increases from 37% to 68%, achieving targeted and precise governance. This solution promotes the governance of monitoring information to shift from the closed-loop mode of discovery - analysis - response to prediction - guidance - shaping, significantly improving the accuracy and initiative of governance. While enhancing residents' participation and satisfaction, it promotes the harmonious and stable development of the community.

[0266] In an embodiment of the present invention, an example of the aforementioned management system for the comprehensive governance of smart communities is provided:

[0267] Application scenario description:

[0268] A large community (about 5000 households) plans to implement a property service fee adjustment plan, increasing it from the original 2.5 yuan per square meter per month to 3.2 yuan per square meter per month, and at the same time optimizing some service contents. The property company and the community management committee are worried that this adjustment may cause strong opposition from residents and hope to guide the formation of a rational and harmonious discussion atmosphere and reach the greatest consensus through scientific monitoring information management methods.

[0269] The main challenges faced by the management department include:

[0270] There are large differences in the age structure, professional background, and economic status of residents in the community, and their acceptance of the property service fee adjustment varies;

[0271] Some residents are dissatisfied with the existing property services and may take this opportunity to express their dissatisfaction intensively;

[0272] In past similar events, there have been phenomena of rumor spreading and emotional polarization, resulting in a tense community atmosphere;

[0273] It is necessary to reach a basic consensus before the formal implementation of the plan to avoid the intensification of subsequent contradictions.

[0274] The application of this method aims to achieve scientific and pre-emptive management of community monitoring information through accurate monitoring information analysis, topic evolution prediction, and hierarchical guidance strategies.

[0275] Multi-modal monitoring information data collection and feature extraction: First, starting 2 weeks before the announcement of the property service fee adjustment plan, the system collects data related to monitoring information from multiple channels within the community:

[0276] The data sources include: community forum text posts, WeChat group chat records, community official account messages, voice / video feedback submitted by residents, records of offline symposiums, etc.;

[0277] Data preprocessing: Standardize various types of data, including Chinese word segmentation, stop word removal, image size standardization, etc.;

[0278] Feature extraction: Apply a multi-modal feature extraction network to generate a fused representation vector.

[0279] The collection situation of different modal data is shown in Table 1:

[0280] Table 1: Data collection statistics of community property fee adjustment monitoring information:

[0281]

[0282] After fusing the features of each modality through a cross-modal attention algorithm, the system identifies the main emotional features related to property fee adjustment, mainly concentrated in worry (negative valence, medium arousal, low sense of control) and doubt (negative valence, medium arousal, medium sense of control).

[0283] Topic evolution trend analysis and prediction: Apply a temporal topic model to the collected multi-modal data, and the system identifies the following key sub-topics, as shown in Table 2:

[0284] Table 2: Sub-topics related to property fee adjustment and initial emotional distribution:

[0285]

[0286] The system predicts the activity trend of each sub-topic within the next two weeks through dynamic modeling of the topic life cycle:

[0287] It is predicted that the topic of the predicted fee increase rate will reach its peak 2 - 3 days after the plan is announced. Without intervention, the proportion of negative emotions may increase to more than 85%;

[0288] The topic of the service quality improvement plan has the potential to guide the monitoring information towards a positive direction and is expected to be used as a key guiding direction;

[0289] The emotional tone of the assistance policy topic is relatively positive and can be used as a means to buffer negative emotions;

[0290] There is a possibility that the topic of fee transparency and supervision mechanism will evolve into a secondary monitoring information hotspot and needs to be intervened in advance.

[0291] Resident group cognitive model and stratification strategy: Based on historical behavior data and the reaction of initial monitoring information, the system divides community residents into the following groups, as shown in Table 3:

[0292] Table 3: Division of community resident cognitive groups:

[0293]

[0294] The system simulates the belief update process of different groups after receiving different information based on the Bayesian belief network, and predicts that without guidance, the proportion of residents supporting the property fee adjustment will drop from the initial 23% to 15%, while the proportion of those strongly opposing will rise from 18% to 42%.

[0295] Based on this, the system generates a hierarchical and precise guidance strategy:

[0296] For opinion leaders: Provide complete and detailed basis for price adjustment and service improvement plan, arrange special communication meetings, and invite them to participate in the design of the supervision mechanism;

[0297] For active spreaders: Design graphic and text content that is easy to spread, highlight specific scenarios and benefits of service improvement, and release authoritative rumor refutation information in a timely manner;

[0298] For followers: Provide a concise breakdown of how the fees are used and a commitment to service improvement, emphasizing the consensus among the majority of residents;

[0299] For the indifferent group: Through environmental information settings (such as elevator advertisements, bulletin boards, etc.), convey positive information and reduce the probability of exposure to negative information.

[0300] Implementation effects and adjustment and optimization: One week after the implementation of the guidance strategy, the system continuously monitors and analyzes changes in monitoring information and finds the following effects:

[0301] The proportion of residents supporting the property fee adjustment rises to 42%, and the proportion of those strongly opposing drops to 19%;

[0302] The overall sentiment polarity of related topics turns positive, especially the positive sentiment proportion of the service quality improvement plan topic rises to 68%;

[0303] The speed of rumor spread decreases significantly, from an average of 500 people spreading in 3 hours to an average of 300 people spreading in 8 hours.

[0304] Based on real-time feedback data, the system dynamically adjusts the guidance strategy:

[0305] For residents who still hold opposing opinions, increase the publicity of the assistance policy;

[0306] For the topic of fee transparency and supervision mechanism, add specific plans for residents' representatives to participate in supervision;

[0307] Optimize the guidance content for the active spreader group, and more strongly emphasize the short-term visible service improvement points.

[0308] Verification of technical effects: To verify the actual effects of this method, the project team conducted a systematic evaluation of the entire process of monitoring information management for property fee adjustment in this community, and compared it with three similar-scale communities in the same region that did not adopt this method. The results proved the significant advantages of this method in two key indicators: the prediction accuracy of monitoring information and the effectiveness of intervention.

[0309] Verification of the prediction accuracy of monitoring information: The system predicted the development trend of monitoring information related to property fee adjustment through dynamic modeling of the topic life cycle, and compared it with the actual evolution. The results are shown in Table 4:

[0310] Table 4: Verification results of the prediction accuracy of monitoring information:

[0311]

[0312] As can be seen from Table 4, the accuracy rate of this method in predicting the trend of monitoring information reaches 91.9%, far higher than 57.1% of the traditional method, which early warns of potential risk points of monitoring information and provides strong support for precise intervention. Especially in the prediction of the change of emotional polarity, the accuracy rate is as high as 98.1%, reflecting the effectiveness of the multi-modal emotion calculation model.

[0313] Verification of the effectiveness of intervention: This method actively intervened in the community monitoring information through a hierarchical precise guidance strategy. The differences before and after the intervention and with the control group are shown in Table 5:

[0314] Table 5: Verification results of the effectiveness of monitoring information intervention:

[0315]

[0316] Table 5 shows that through the application of this method, the community has achieved many significant improvements during the process of property fee adjustment:

[0317] The support rate has increased by 82.6%, while that of the control group has only increased by 10.3%;

[0318] The proportion of strong opposition has only increased by 5.6%, while that of the control group has increased by 133.7%;

[0319] The proportion of residents actively participating in discussions has increased by 153.3%, indicating that the guidance strategy has stimulated the enthusiasm of residents to actively participate in community affairs;

[0320] Most notably, the polarization index of monitoring information has decreased by 50.0%, while that of the control group has increased by 15.7% instead, fully proving the unique advantage of this method in alleviating community emotional polarization.

[0321] Through the comparison of the above quantitative indicators, it can be clearly verified that this method has significant monitoring information prediction accuracy and intervention effectiveness, and has successfully achieved the paradigm shift of community monitoring information governance from discovery-analysis-response to prediction-guidance-shaping.

[0322] The successful application of this implementation method in the management of community property fee adjustment monitoring information not only effectively promotes the smooth implementation of the property fee adjustment plan, but also provides a replicable and scalable technical path for the comprehensive management of smart communities. The value of this application is mainly reflected in:

[0323] It provides a complete set of community monitoring information prediction, analysis and guidance methods. Compared with the traditional passive response mode, it can implement monitoring information intervention 12 hours in advance.

[0324] Through a layered and precise guidance strategy, community residents' participation and sense of identity in public affairs have been significantly improved, and the polarization index of monitored information has been reduced by an average of 50%;

[0325] A closed-loop feedback mechanism for community monitoring information governance has been established, enabling continuous optimization of intervention strategies and increasing the effectiveness of intervention plans by 65%;

[0326] It provides a scientific decision-making basis based on data and models for community management, reducing the uncertainty of human experience judgment;

[0327] To sum up, this implementation method has important practical value and promotion significance in the field of smart community monitoring information governance, and can effectively promote the harmonious and stable development of the community.

[0328] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make more forms of equivalent embodiments based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A management system for the comprehensive governance of smart communities, characterized in that, Including: A multi-modal data feature acquisition and fusion module, which is used to process community monitoring information data by using a multi-modal feature extraction network, and generate a representation vector by fusing multi-modal information through a cross-modal attention algorithm; A topic evolution analysis module, which is used to construct a topic life cycle evolution model, calculate topic activity indicators and predict their future change trends, including: The calculation formula for topic activity calculation and prediction is: ； Among them, is the current time point, is the time window, is the time decay weight, is the comprehensive function, is the number of messages, is the participating user feature, is the sentiment polarity; is the topic activity index; The time decay weight has the following calculation formula: ； wherein, is the attenuation coefficient, is the message release time; A cognitive model construction module, which is used to construct a community resident cognitive model and identify cognitive bias types through a Bayesian belief network; A guidance strategy generation module, which is used to stratify the community population and generate targeted guidance strategies based on the cognitive model and topic prediction. The multi-dimensional emotion calculation model is a three-dimensional emotion space based on valence, arousal, and control, and its calculation formula is: ； Among them, represents the content 's emotional state, represents the valence dimension, represents the arousal dimension, represents the control dimension; An emotion influence prediction model, and its calculation formula is: ； Among them, represents the content on the user emotional impact, is the user feature weight matrix, for the user feature vector; An emotion influence evaluation module, which is used to evaluate the emotion influence of guidance content by applying an emotion calculation model, establish a multi-dimensional emotion calculation model to finely depict the emotional state of community monitoring information, and realize the closed-loop management of monitoring information governance.

2. The management system for comprehensive governance of smart communities according to claim 1, characterized in that, In the multi-modal data feature acquisition and fusion module, the calculation formula for the attention weight of cross-modal attention fusion is: ； Among them, respectively represent the modality characteristics, is the cosine similarity function between modalities, represents the modality to the modality attention weight; represents the exponential function; ； Among them, is the global weight parameter for each modality, which is dynamically adjusted according to different community monitoring information scenarios, is the final fused feature vector.

3. The management system for comprehensive governance of smart communities according to claim 1, characterized in that, In the multi-modal data feature acquisition and fusion module, an improved dynamic topic model is applied to process time series data. By introducing the time dimension, the topic distribution parameters change smoothly over time, and its calculation formula is: ； Among them, , represent the topic distribution parameters at a certain moment, is the noise term, following a normal distribution with a mean of 0 and a variance of , and the parameter is dynamically adjusted according to the topic change speed, represents the identity matrix.

4. The management system for comprehensive governance of smart communities according to claim 1, characterized in that, In the cognitive model construction module, the belief update calculation formula of the Bayesian belief network is: ； Among them, represents the belief state, represents the received information, represents the belief probability after receiving the information ; represents the conditional probability of receiving the information under the belief state ; represents the prior belief probability, represents the marginal probability of the information .

5. The management system for comprehensive governance of smart communities according to claim 1, characterized in that, In the cognitive model construction module, a correction strategy mapping matrix is established for four main types of cognitive biases, namely confirmation bias, framing effect, anchoring effect, and availability bias. : ； Among them, is the cognitive bias feature vector, is the generated corrective strategy vector.

6. The management system for comprehensive governance of smart communities according to claim 1, characterized in that, It also includes intervention effect evaluation and feedback adjustment, and evaluates the guidance effect by calculating the change in the monitoring information state before and after the intervention: ； Among them, represents the intervention effect evaluation value, and respectively represent the status of the th indicator before and after the intervention, represents the th indicator weight, represents the number of evaluation indicators.

7. The management system for comprehensive governance of smart communities according to claim 1, characterized in that, In the emotion influence evaluation module, the emotion enhancement adopts the following formula: ； Among them, is the emotional feature extraction function, is the emotional enhancement coefficient, is the final feature vector after emotional enhancement, is the fused feature vector.

8. A storage medium stores non-transitory computer-readable instructions, characterized in that, When the non-transitory computer-readable instructions are executed by a computer, they can execute the modules in the management system for comprehensive governance of smart communities described in any one of claims 1-7.

Images