Multi-mode illiterate form safety monitoring method oriented to one-stop student community

Abstract

The invention discloses a'one-stop 'student community-oriented multi-modal illiterate form safety monitoring method, which comprises the following steps of: adaptively triggering an acquisition task by dynamically monitoring a sudden change rate of a data source; generating a modal label based on the feature vector, constructing a dynamic routing graph, and outputting unified structure data after asynchronous decoding and anomaly correction; organizing the time sequence content into a three-dimensional semantic tensor, clustering topics by using a potential energy field and detecting risk units; constructing a three-dimensional tension space to quantitatively evaluate a risk level and a dominant factor; planning a minimum intervention path based on the propagation impedance diagram, and evaluating the priority to form a scheduling strategy; a meta-learning mechanism is introduced through a game network, strategy closed-loop adaptive optimization is realized, and full-link intelligent management and control from monitoring to intervention are completed. According to the method, a series of core technology bottlenecks of a traditional multi-modal data processing method in real-time performance, unified decoding paths, weak semantic extraction, fuzzy risk modeling, intervention path rigidity and the like are broken through.

Description

Technical Field

[0001] The present invention relates to the field of monitoring technologies, and particularly to a multi-modal ideological security monitoring method for a "one-stop" student community. Background Art

[0002] In the current highly information-based campus environment, student communities have gradually become the forefront positions for ideological exchanges, public opinion generation, and value collisions. With the deep penetration of various media means such as mobile Internet, social platforms, and graphic, audio, and video, the information exchange among students shows an evolutionary trend of "high frequency, fast flow, and multi-modal". Although this highly active communication characteristic has greatly enriched the campus public opinion ecosystem, it has also provided a breeding ground for the rapid spread and mutation of ideological risks. Especially after a specific sensitive period or the occurrence of an event that is prone to trigger public opinion, some potentially confrontational, inflammatory, or misleading content often spreads rapidly through multiple modalities such as text, voice, images, and videos, forming a strong public opinion inertia and cluster effect, which will then have a subtle impact on the cognitive structure and ideological security of the student group.

[0003] Under this background, there is an urgent need for a technical path in the ideological management of colleges and universities that can not only support the collaborative processing of multi-modal data streams but also achieve the rapid identification and precise response to sudden public opinion events. However, the current mainstream multi-modal data analysis methods generally rely on large-scale offline modeling and multi-stage pipeline processing mechanisms, which not only have problems such as long response delays and weak decoupling of upstream and downstream systems, but also often lack a sensitive "trigger-focus-intervene" closed-loop ability when facing information mutations, and it is difficult to meet the high requirements for real-time performance, accuracy, and intervenability in the "one-stop" student community management system.

[0004] Therefore, how to construct a new ideological security monitoring method for the "one-stop" student community scenario, which can achieve real-time collection, dynamic decoding, semantic clustering, risk identification, and intervention decision-making closed-loop linkage of high-frequency multi-modal data, has become the core technical problem that needs to be solved urgently at present. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a multi-modal ideological security monitoring method for a "one-stop" student community.

[0006] To achieve the above object, the technical solutions adopted by the present invention are as follows:

[0007] The present invention discloses a multi-modal ideological security monitoring method for a "one-stop" student community, including the following steps:

[0008] Step 1: Adaptive trigger the acquisition task by dynamically monitoring the mutation rate of the data source;

[0009] Step 2: Generate modal labels based on feature vectors and construct a dynamic routing graph. After asynchronous decoding and anomaly correction, output unified structured data;

[0010] Step 3: Organize the temporal content into a three-dimensional semantic tensor, use the potential field to cluster topics and detect risk units;

[0011] Step 4: Construct a three-dimensional tension space to quantitatively evaluate the risk level and dominant factors;

[0012] Step 5: Plan the minimum intervention path based on the propagation impedance map, evaluate the priority to form a scheduling strategy;

[0013] Step 6: Introduce a meta-learning mechanism through a game network to achieve closed-loop adaptive optimization of the strategy, and complete the full-link intelligent control from monitoring to intervention.

[0014] Furthermore: Step 1 includes: Initialize through modal registration and modal listeners, monitor multi-modal data sources, calculate the mutation rate of modal content, and dynamically trigger the data collection tasks of corresponding modalities according to the adaptive acquisition trigger factor to achieve resource data collection;

[0015] Step 2 includes: Based on the multi-modal content segments and their feature vectors collected in Step 1, dynamically generate modal label vectors, construct a reconfigurable modal routing graph, allocate data to different decoding paths according to the modal decision routing function, load the processing modules of the corresponding paths for asynchronous concurrent decoding, and ensure the decoding accuracy through the anomaly routing fast correction mechanism. Finally, format the decoding results into a unified structure and send them to the next step of processing;

[0016] Step 3 includes: Organize the decoded multi-modal content units into a three-dimensional semantic time tensor in chronological order, define a time-topic semantic potential function, construct a total potential field and reverse generate topic centers, perform semantic adsorption clustering based on the potential gradient field to form cross-modal topic clusters, and at the same time detect abnormal potential clusters and mark them as high-risk ideological topic units;

[0017] Step 4 includes: Construct an ideological tension three-dimensional vector space with three orthogonal factor dimensions of semantic confrontation tension, emotional intensification tension, and structural propagation tension, quantitatively evaluate the risk tension of each topic cluster obtained in Step 3, assign risk level labels according to the tension intensity value and the relative density of spatial distribution, and output the dominant risk factors;

[0018] Step 5 includes: constructing an ideological impedance map based on the dissemination structure diagram, determining the set of risk node driving source points in the high-tension cluster, inversely planning the minimum impedance intervention path, injecting corresponding intervention strategy labels into the end nodes of the path, evaluating and sorting the intervention priorities through the comprehensive intervention cost function, forming an intervention task scheduling diagram, and performing multi-threaded intervention scheduling;

[0019] Step 6 includes: constructing an ideological reflux game network, introducing an adaptive update mechanism for the policy structure based on meta-learning and evolutionary regression optimization of the intervention policy mapping function, automatically recompiling the weight structure, and forming a closed-loop optimization flow from event triggering, intervention path, community response to policy evolution.

[0020] Furthermore: Step 1 includes:

[0021] Performing modal registration and initializing the modal listener, clarifying the types, addresses, and embedding functions of each modal data source, and initializing a listener for each modality;

[0022] Mapping the monitored modal data to a feature vector through the modal content embedding function; then calculating the mutation rate of the modal content to measure the change amplitude of the semantic content;

[0023] Calculating the adaptive acquisition trigger factor according to the historical steady-state mutation threshold and the dynamically adjusted trigger coefficient, and comparing the real-time mutation rate with the trigger factor. Once the condition is met, the data acquisition of the corresponding modality is triggered;

[0024] Entering the modal acquisition scheduling stage, dynamically allocating acquisition resources according to factors such as the burst intensity, resource priority, and acquisition window size to ensure that burst modalities are preferentially responded to.

[0025] Furthermore: Step 2 includes:

[0026] Dynamically generating a modal label vector for each modal data unit entering the processing link, including information such as modal type, mutation rate, scheduling priority, and feature embedding entropy value;

[0027] Constructing a reconfigurable modal routing graph, which consists of multiple routing nodes and processing modules, supports dynamic path reconstruction and shared paths between modalities, and has the ability of hot update;

[0028] According to the modal label vector, judging the decoding path into which the data should flow through the dynamic routing decision function to ensure that high-mutation and high-complexity modalities preferentially flow into high-performance routing paths;

[0029] Loading all the processing modules required for the corresponding path and performing asynchronous concurrent decoding, and uniformly mapping the outputs of all decoding modules to the shared semantic space;

[0030] When the average information density entropy or processing delay of a certain path is abnormal, automatically reroute the modal data to the alternate path and feedback to the modal label generation module to adjust the subsequent label calculation strategy;

[0031] Repackage all decoded modal content into a unified structure and send it to the next step of processing.

[0032] Further: The said step 3 includes:

[0033] Organize data units of different modalities into a unified three-dimensional semantic time tensor in chronological order as the input for semantic potential field construction;

[0034] Define a time-topic semantic potential energy function to simulate the aggregation trend of different modal contents towards a certain topic, and couple semantic proximity and temporal proximity into a unified physical quantity;

[0035] Calculate the total potential energy through full tensor traversal and use a high-dimensional potential energy optimizer to inversely generate the topic center;

[0036] Perform semantic adsorption clustering based on the potential energy gradient field to form cross-modal topic clusters. At the same time, evaluate the abnormality degree of each clustering result in terms of semantic distribution and temporal continuity. If the anomaly score exceeds the threshold, mark it as a high-risk ideological topic unit;

[0037] Format and output the clustering results to provide a semantic basis for subsequent ideological risk assessment.

[0038] Further: The said step 4 includes:

[0039] Construct a three-dimensional tension vector space, including three orthogonal factor dimensions: semantic adversarial tension, emotional intensification tension, and structural propagation tension;

[0040] Calculate each factor respectively. The semantic adversarial tension measures the degree of semantic opposition through the average cosine dissimilarity of the semantic vector tensor, the emotional intensification tension evaluates the degree of emotional polarization using the sum of squared emotional deviation, and the structural propagation tension measures the content diffusion potential based on the structural entropy of the propagation path;

[0041] Map the tension vectors of each cluster to the tension space to form a tension cloud map, and assign high, medium, and low risk level labels to each cluster according to the tension intensity value and spatial distribution density;

[0042] Output the risk label and the dominant risk factor of each cluster to provide a decision-making basis for subsequent intervention strategies.

[0043] Further: The said step 5 includes:

[0044] An ideological impedance graph is constructed based on the communication structure graph. The impedance value of each edge is calculated by weighting semantic consistency, node influence, and trust gap.

[0045] Determine the nodes with high centrality and high excitation tension contribution in the propagation path of the high tension cluster as the set of intervention source points;

[0046] An improved multi-source and multi-target impedance minimum path algorithm is used to calculate the minimum impedance path to all potential public opinion receiving nodes starting from the source point set, and dynamically adjust the impedance value to optimize the path selection;

[0047] Inject corresponding intervention strategy labels according to the semantic stance, emotional intensity and structural position of the end node of the path;

[0048] Define a comprehensive intervention cost function, sort all paths in ascending order of the cost function, and decide the order of intervention execution;

[0049] An intervention task scheduling diagram is formed to schedule asynchronous intervention execution threads to carry out strategy delivery, public opinion intervention or administrative guidance, so as to achieve low-cost and high-efficiency public opinion guidance and topic intervention.

[0050] Further: Step 6 comprises:

[0051] The key behavior outputs of the first five steps are structured and archived to build a feedback data structure, recording the acquisition event number, modality decoding path and processing delay, clustering cluster semantic evolution trajectory, tension factor tensor history sequence, intervention path execution log and response status, user feedback response and topic status change;

[0052] Introducing game theory, we construct an ideological reflux game network, taking the confrontation between intervention strategies and the real reactions of the community as the basic unit, forming a dynamically evolving game network, with each edge endowed with a response offset vector;

[0053] Design a meta-learning-based adaptive update mechanism for strategy structure, extract the weight parameters most sensitive to strategy updates from historical game trajectories, and quickly update them in small sample intervention windows;

[0054] Introducing the evolutionary regression mechanism of the strategy influence function, through genetic algorithms or Bayesian optimization, continuously updating the strategy set, and screening out a stable, moderate and efficient strategy subset;

[0055] Under the joint action of the feedback network and the meta-learner, the weight structure is automatically recompiled to form a closed-loop optimization flow from event triggering, intervention path, community response to strategy evolution.

[0056] Compared with the prior art, the present invention has the following technical advances:

[0057] The present invention realizes highly sensitive detection of data mutation through the modal change rate difference function, breaking the lag mode of traditional timing acquisition and passive monitoring, and fundamentally solving the problem that it is impossible to timely perceive and schedule acquisition at the initial stage of sudden ideological events, winning a crucial time window for subsequent processing.

[0058] By constructing a second-step algorithm of a dynamic modal parallel routing network, rapid identification, label guidance, and multi-channel processing routing of different modal data are realized. Compared with the traditional unified processing path method, the present invention allows each modality to be processed independently in parallel, avoiding problems such as information loss, modal conflict, and waste of processing resources, and fundamentally improving the parsing efficiency and system reconfigurability.

[0059] In the third step, the idea of "potential energy field" is introduced to construct a cross-modal semantic energy field model to achieve semantic fusion under the dual constraints of time and topic. Compared with traditional clustering models such as K-means or LDA, this method is more adaptable to the sudden semantic aggregation law in unstructured and multi-modal information fields, significantly enhancing the semantic extraction ability of potential ideological clusters. In the fourth step, three factors, namely emotional tension, semantic confrontation tension, and structural diffusion tension, are introduced to form an "awareness tension field" to realize the visual and quantifiable expression of ideological risks, overcoming the problem that traditional methods cannot accurately numerically model awareness risks, and making risk assessment have stronger interpretability and decision-making support value.

[0060] In the fifth step, inspired by the principle of minimum impedance of circuit networks, a multi-dimensional network impedance tensor is constructed by comprehensively considering factors such as student relationship structure, historical sensitivity, and event propagation path, so as to deduce a path that takes into account both the minimum propagation rebound risk and the maximum intervention effectiveness. Compared with the extensive control strategy, this method has more "gentle intervention" characteristics and reduces group resistance.

[0061] Finally, through the self-evolution strategy refluxer in the sixth step, a reflux mechanism integrating game network and meta-learning is constructed, which can cyclically self-optimize and re-train parameters for acquisition frequency, decoding structure, risk model, and intervention strategy based on the real response of the community, forming an evolutionary intelligent agent system with long-term adaptability and the ability to resist the evolution of ideological information.

[0062] In summary, the present invention breaks through a series of core technical bottlenecks in traditional multi-modal data processing methods, such as real-time performance, unified decoding path, weak semantic extraction, fuzzy risk modeling, and rigid intervention path, and establishes a full-process intelligent mechanism from modal mutation trigger to feedback closed-loop evolution, having highly practical and forward-looking technological leading advantages in complex public opinion scenarios facing "one-stop" student communities. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings are used to provide a further understanding of the present invention and form a part of the specification. They are used in conjunction with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention.

[0064] In the accompanying drawings:

[0065] Figure 1 is a flowchart of the present invention. Detailed implementation manners

[0066] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0067] As Figure 1 shown, the present invention discloses a multi-modal ideological security monitoring method for an "one-stop" student community, including:

[0068] Step 1: Adaptive trigger the acquisition task by dynamically monitoring the mutation rate of the data source;

[0069] Step 2: Generate modal labels based on the feature vectors and construct a dynamic routing graph. After asynchronous decoding and anomaly correction, output unified structured data;

[0070] Step 3: Organize the time-series content into a three-dimensional semantic tensor, use the potential field to cluster topics and detect risk units;

[0071] Step 4: Construct a three-dimensional tension space to quantitatively evaluate the risk level and dominant factors;

[0072] Step 5: Plan the minimum intervention path based on the propagation impedance graph, evaluate the priority to form a scheduling strategy;

[0073] Step 6: Introduce a meta-learning mechanism through a game network to achieve closed-loop adaptive optimization of the strategy, and complete the full-link intelligent control from monitoring to intervention.

[0074] Step 1 includes: Initialize by modal registration and modal listener, monitor the multi-modal data source, calculate the mutation rate of the modal content, and dynamically trigger the data acquisition task of the corresponding modality according to the adaptive acquisition trigger factor to achieve resource data acquisition;

[0075] Step 2 includes: Based on the multi-modal content segments and their feature vectors collected in Step 1, dynamically generate modal label vectors, construct a reconfigurable modal routing graph, allocate data to different decoding paths according to the modal decision routing function, load the processing modules of the corresponding paths for asynchronous concurrent decoding, and ensure the decoding accuracy through the anomaly routing fast correction mechanism. Finally, format the decoding result into a unified structure and send it to the next step for processing;

[0076] Step 3 includes: organizing the decoded multi-modal content units into a three-dimensional semantic temporal tensor in chronological order, defining a time-topic semantic potential energy function, constructing a total potential energy field and inversely generating a topic center, performing semantic adsorption clustering based on the potential energy gradient field to form cross-modal topic clusters, and simultaneously detecting abnormal potential clusters and labeling them as high-risk ideological topic units;

[0077] Step 4 includes: constructing an ideological tension three-dimensional vector space with three orthogonal factor dimensions of semantic adversarial tension, emotional intensification tension, and structural propagation tension, quantitatively evaluating the risk tension of each topic cluster obtained in Step 3, assigning risk level labels according to the tension intensity value and the relative density of spatial distribution, and outputting the dominant risk factor;

[0078] Step 5 includes: constructing an ideological impedance map based on the communication structure diagram, determining the set of risk node driving source points in the high-tension clusters, inversely planning the minimum impedance intervention path, injecting corresponding intervention strategy labels into the end nodes of the path, evaluating and sorting the intervention priorities through a comprehensive intervention cost function, forming an intervention task scheduling diagram, and performing multi-threaded intervention scheduling;

[0079] Step 6 includes: constructing an ideological reflux game network, introducing an adaptive update mechanism for the policy structure based on meta-learning and an evolutionary regression optimization of the intervention strategy mapping function, automatically recompiling the weight structure, and forming a closed-loop optimization flow from event triggering, intervention path, community response to policy evolution.

[0080] Specifically, the implementation process of Step 1 includes:

[0081] The core objective of this step is to implement a mechanism that can quickly identify content variation trends in multi-modal data sources (such as text, images, audio, video, etc.) and dynamically trigger corresponding modal data collection tasks accordingly, so as to solve the response delay and resource waste problems brought by the traditional timing collection method in a high-frequency dynamic environment such as a student community.

[0082] Sub-step 1.1: Modality registration and initialization of modality listeners

[0083] In the system initialization stage, all modality data sources (Modality Sources) that need to be connected to the monitoring system need to complete the modality registration process (Modality Registration Protocol), including but not limited to:

[0084] Modality types (text, images, audio, video, etc.);

[0085] Data source addresses (API endpoints, database nodes, listening paths, etc.);

[0086] Modality content embedding function Fm (x), which is used to map the original modal data to a unified semantic space.

[0087] Subsequently, a modality listener is initialized for each modality, whose task is to capture modality segments and perform preprocessing within a set unit time interval.

[0088] Sub-step 1.2: Modality content embedding and feature vector generation

[0089] For each segment of modality data obtained by the listener (where m represents the modality and t represents the time point), the system uses the modality content embedding function F set in the registration phase m to map it to a feature representation in a high-dimensional vector space:

[0090]

[0091] For the text modality, a graph-semantic embedding structure can be used;

[0092] For the image modality, visual attention embedding can be used;

[0093] For the audio modality, spectro conv embedding can be used;

[0094] All modality embedding results are uniformly normalized and mapped to the modality feature space

[0095] Sub-step 1.3: Modality content mutation rate modeling

[0096] For each modality m, the system calculates the content mutation rate (CMR) of the modality content within two consecutive time slices t and t - Δt:

[0097]

[0098] This value represents the change amplitude of the semantic content of the modality per unit time and is the core variable of the subsequent acquisition trigger mechanism.

[0099] Sub-step 1.4: Adaptive acquisition trigger factor calculation

[0100] The system maintains the historical steady-state mutation threshold θ of each modality during operation m , and introduces a dynamically adjusted trigger coefficient τ m (t), which is used to enhance the sensitivity of the trigger mechanism:

[0101]

[0102] γ: The basic acquisition sensitivity parameter;

[0103] σ: The standard deviation adjustment weight;

[0104] std: The standard deviation of CMR in the past K time windows.

[0105] Subsequently, compare the real-time mutation rate with the acquisition trigger factor:

[0106]

[0107] Once the output of a certain modality trigger Immediately start the data acquisition program of this modality, and mark the current mutation point as the starting point of the "modality burst interval".

[0108] Sub-step 1.5: Modality acquisition scheduling and dynamic resource allocation

[0109] When any modality is triggered for acquisition, the system enters the modality acquisition scheduling stage (Modality Sampling Scheduler). In this stage, the system dynamically schedules the acquisition resources of the system according to factors such as modality burst intensity, resource priority, and acquisition window size.

[0110] The scheduling priority is determined by the following function:

[0111]

[0112] The community usage frequency of this modality in the current time period;

[0113] The rarity of this modality being triggered in the recent period;

[0114] α, β, ζ: Scheduling weighting parameters.

[0115] Finally, the collector sorts all modalities according to this priority value and performs queued concurrent acquisition to ensure that burst modalities are preferentially responded to.

[0116] To sum up, the asynchronous mutation-aware acquisition trigger (asynchronous mutation-aware acquisition trigger) algorithm constructs a set of real-time response, resource-friendly, sensitive and efficient multi-modal acquisition mechanisms for the student community through five closely linked technical steps: modality registration, content embedding, mutation rate modeling, dynamic trigger judgment, and scheduling acquisition resources.

[0117] This step serves as the first technical entry point for the entire ideological security monitoring method, providing high-quality, time-sensitive multimodal input data for subsequent processing processes such as decoding, fusion, judgment and intervention.

[0118] Based on the high-frequency, bursty modal feature data stream obtained in step 1, step 2 will serve as the key scheduling component for the data to enter the perception-understanding stage, realizing decoupled diversion and structural decoding between modalities, so as to maximize the parallelism and modality-specific accuracy of subsequent processing.

[0119] Specifically, the implementation process of step 2 includes:

[0120] The core task of step 2 is to build a routing mechanism that is guided by modal labels, has a reconfigurable path structure, and supports modal heterogeneity processing based on the triggered multimodal content fragments and their corresponding feature vectors (provided by step 1), so as to avoid the error accumulation problem of "unified model hard decoding".

[0121] This step includes the following six sub-steps, forming a complete modal routing and decoding scheduling chain.

[0122] Sub-step 2.1: Modal tag generation and binding

[0123] The system will generate a modal data unit for each modal data unit that enters the processing link through step 1 A modality tag vector (MTV) is dynamically generated, which constitutes the starting point for modality decoding and routing path planning.

[0124] The modal label vector is in the form:

[0125]

[0126] Type m : Modal type identifier (such as Text, Image, Audio, Video);

[0127] Mutation rate (calculated in step 1);

[0128] Scheduling priority (obtained from the scheduling phase in step 1);

[0129] The feature embedding entropy value is used to represent the internal complexity of the modal content and is defined as:

[0130]

[0131] This tag will be bound to the corresponding modality data as metadata throughout the entire diversion-decoding process.

[0132] Sub-step 2.2: Reconfigurable Modality Routing Graph Construction

[0133] In the initial stage of system operation, a Modality Routing Graph (MRG) is constructed. This graph consists of multiple routing nodes and processing units, and supports dynamic path reconstruction and shared paths between modalities.

[0134] Each node R i is defined with a receiving rule in the form of:

[0135]

[0136] This graph adopts a directed acyclic graph (DAG) structure to ensure that there is no loop interference after information is split;

[0137] Each processing unit can be a dedicated modality processor (such as a semantic parser, a visual decoder) or a cross-modal sharer (such as a general semantic mapper);

[0138] The routing graph has the ability of hot update, and can automatically optimize the node structure (refinement, high-speed channelling, module replacement, etc.) according to the processing efficiency in the past 24 hours.

[0139] Sub-step 2.3: Modality Decision Routing Function Design

[0140] The system, according to the modality label vector through a dynamic routing decision function judges the decoding path into which the data should flow:

[0141]

[0142] Where:

[0143] ω1, ω2, ω3 are weights dynamically adjusted according to the task scenario;

[0144] is the reciprocal of the current average processing efficiency of the node (to avoid overload);

[0145] This function ensures that modalities with high mutation and high complexity flow into high-performance routing paths preferentially.

[0146] Sub-step 2.4: Modality-Specific Decoding Path Loading and Activation

[0147] Once a data segment is assigned to a certain path (such as the image path G-3), the system will load all the processing modules required for the corresponding path and perform asynchronous concurrent decoding:

[0148] Image modality loading module: Multi-scale Semantic Vision Parser;

[0149] Text modality loading module: Nested Graph Decoder;

[0150] Audio modality loading module: Spectro Texture Parser;

[0151] The outputs of all decoding modules are uniformly mapped to the shared semantic space for subsequent processing.

[0152] Sub-step 2.5: Abnormal Routing Fast Correction Mechanism

[0153] To avoid data routing errors caused by abnormal modal labels or mutation noise, a real-time feedback reflection mechanism called "Path Corrector" is designed in this step.

[0154] Whenever the average information density entropy or processing delay of a certain path is significantly higher than the historical mean (the deviation exceeds 2 standard deviations), the correction module is triggered;

[0155] The modal data will be automatically re-routed to the alternate path, and the original path will initiate automatic parameter re-estimation to avoid system resource congestion or semantic distortion;

[0156] The correction mechanism will also feedback to the modal label generation module to adjust the subsequent label calculation strategy.

[0157] Sub-step 2.6: Shunt Decoded Result Formatting and Semantic Unification

[0158] All decoded modal contents will be re-encapsulated into a unified structure:

[0159]

[0160] where represents the high-dimensional vector representation of the modality in the unified semantic space. All will be fed into the cross-modal semantic fusion clustering in Step 3.

[0161] Based on the mutation-driven modal acquisition structure obtained in Step 1, this step establishes a highly adaptive, strongly structurally elastic, and path-optimizable decoding and routing system. Through the combined design of the modal label-driven DAG-style path planning mechanism, modal differentiation processing module, and real-time correction channel, the processing efficiency, decoding accuracy, and autonomy of modal information are greatly improved, which is the key relay link for multi-modal ideological risk analysis.

[0162] After completing the modal mutation perception trigger acquisition in Step 1 and realizing the modal shunt decoding and routing in Step 2, the system has obtained a set of multi-modal content units with a unified structure and a shared semantic space representation. Although these modal units have completed the preliminary semantic alignment, they have not yet formed an overall understanding and clustering judgment of the topics related to ideological risks.

[0163] Therefore, the core task of cross-modal semantic fusion clustering in Step 3 is to construct a temporal topic clustering mechanism similar to a physical potential energy field on the basis of maintaining the original modal time continuity and semantic space distribution, so as to achieve two-way collaborative perception between semantic continuity and time evolution trajectory.

[0164] Specifically, the implementation process of Step 3 includes:

[0165] In this step, by introducing the "Temporal-Topical Joint Potential Field (TT-JPF)" modeling paradigm, the parsed modal units are mapped to a physical-like multi-dimensional potential field space, and "topic center adsorption type" clustering is completed in this space, greatly enhancing the dynamic capture ability of semantic evolution paths and mutation nodes.

[0166] This step is divided into the following six key sub-steps:

[0167] Sub-step 3.1: Construct a cross-modal semantic time tensor

[0168] First, all data units from different modalities are organized into a unified three-dimensional tensor structure in chronological order:

[0169]

[0170] Among them:

[0171] T represents the time window length;

[0172] M represents the number of modalities;

[0173] z represents the dimension of the unified semantic space;

[0174] The (t, m) element is the semantic representation vector of a certain modality at a certain moment

[0175] This tensor will be used as the original input for constructing the semantic potential field, ensuring the linkage of the time-modal-semantic three domains.

[0176] Sub-step 3.2: Define the time-topic semantic potential energy function

[0177] Taking the "topic center" as the potential clustering target, the system introduces the Joint Potential Function to simulate the aggregation trend of different modal contents towards a certain topic. Each topic center is regarded as a gravitational source point in the semantic space. Then, for a certain semantic unit the potential energy under this center is:

[0178]

[0179] Where:

[0180] represents the semantic distance;

[0181] τ k : the time center point corresponding to the topic center;

[0182] σ: the semantic diffusion scale;

[0183] α: the time decay factor, which controls the time perception sensitivity.

[0184] This potential energy function couples "semantic proximity" and "temporal proximity" into a unified physical quantity, enabling cross-modal data to naturally aggregate in the "topic - time" dual field.

[0185] Sub-step 3.3: Construct the total potential energy field and reverse generate the topic center

[0186] Through full tensor traversal, calculate the total potential energy of all modal units under all potential topic centers:

[0187]

[0188] The generation of the topic center is derived reversely through the maximum potential energy response point, that is:

[0189]

[0190] The system uses a high-dimensional potential energy optimizer (HP-Field Finder) for iterative sampling to dynamically find the local extreme points as candidate topic kernels.

[0191] Sub-step 3.4: Perform semantic adsorption clustering based on the potential energy gradient field

[0192] Once multiple topic centers {μ k} are determined, the system generates a semantic potential energy gradient field on the original tensor:

[0193]

[0194] The system adopts the gradient descent method + time constraint factor to simulate the contraction process in which modal content naturally tends to the topic center, and completes the potential energy adsorption clustering of semantic units.

[0195] The final clustering results form multiple cross-modality topic blobs, and each blob represents a latent ideological event with continuous time and unified semantics.

[0196] Sub-step 3.5: Abnormal potential energy cluster detection

[0197] For each clustering result, the system will automatically evaluate its abnormality degree in semantic distribution and time continuity:

[0198]

[0199] where:

[0200] Internal distribution entropy of semantic vectors;

[0201] Mutation density within the time window;

[0202] λ1 and λ2 are adjustable weights.

[0203] When the abnormal score of a potential energy cluster exceeds the threshold, the system marks it as a high-risk ideological topic unit and sends it to the next step for in-depth intervention.

[0204] Sub-step 3.6: Formatting of clustering results and output caching

[0205] All clustering results are output in a unified structure as:

[0206]

[0207] where is the index set of modal content, and is finally passed to step 4, the ideological risk decision interpreter.

[0208] In this step, the potential energy physical field model is integrated into the semantic clustering link in ideological security analysis, and a cross-modal clustering paradigm of time-topic coupling is constructed. By introducing semantic-time two-dimensional attenuation, reverse derivation of potential centers, and gradient adsorption clustering paths, this algorithm greatly enhances the system's recognition accuracy for latent, evolving, and mutating ideological topics, providing a high-quality semantic basis for subsequent decision interpretation and intervention strategies.

[0209] After completing the entire process from mutation perception acquisition to modal shunt routing and then to time-topic joint potential clustering in steps 1 to 3, the system has successfully identified multiple clusters of ideological potential topic units with temporal continuity and semantic aggregation characteristics. However, the existence of these topic clusters does not necessarily mean they pose ideological security risks. The core objectives to be precisely answered by the ideological risk assessment in step 4 are whether they constitute real risks, how to classify the risk levels, and how to rank the intervention priorities.

[0210] Specifically, the implementation process of step 4 includes:

[0211] In this step, by designing an innovative algorithm with a three-dimensional tension vector modeling structure - the multi-factor awareness tension mapper, an ideological security field is established in the high-dimensional latent space, and a quantitative risk tension assessment and risk level mapping are carried out for each clustered potential topic cluster within this field.

[0212] Sub-step 4.1: Construction of the three-factor tension field

[0213] This algorithm constructs an ideological tension three-dimensional vector space with three mutually orthogonal key factor dimensions, namely:

[0214] 1. Semantic Dissonance Tension;

[0215] 2. Emotional Polarization Tension;

[0216] 3. Propagation Topology Tension.

[0217] The ideological tension vector T k of each block C k is defined as:

[0218]

[0219] where each tension factor is defined as follows:

[0220] Semantic Dissonance Tension;

[0221] Emotional Polarization Tension;

[0222] Propagation Topology Tension.

[0223] 1. Semantic Dissonance Tension

[0224] Measures the blob C k The degree of opposition between internal semantic expressions can be defined by the average cosine dissimilarity of the adversarial semantic vector tensor as:

[0225]

[0226] Where:

[0227] v i , v j Are the semantic vectors of the i-th and j-th texts in the blob (which can be encoded by the semantic embedding model)

[0228] cos(·,·) represents the cosine similarity between vectors

[0229] The larger the value, the more conflicting the semantics inside, the stronger the adversarial nature, and the greater the tension.

[0230] 2. Emotional intensification tension

[0231] Measures the polarization degree of the emotional distribution of the texts within the blob C k It is modeled by the sum of squares of emotional deviation (EmotionalDeviation Index):

[0232]

[0233] Where:

[0234] e i ∈[-1, +1]: The emotional polarity score of the i-th text (given by the emotion recognition model)

[0235] The average emotional polarity of the whole blob

[0236] This indicator reflects whether there is a phenomenon of polarization (such as the coexistence of extreme positive and extreme negative emotions) inside the blob, and is an important indicator of intensification potential.

[0237] 3. Structural propagation tension

[0238] Measures the structural diffusion force of the blob C k In the propagation network, it is defined based on the structural entropy (information complexity) of the propagation path as follows:

[0239]

[0240] Where:

[0241] The propagation degree distribution of the propagation node i (which can be based on the reconstructed forwarding network or comment relationship network)

[0242] n k : The number of propagation nodes involved in the blob

[0243] d i : The degree value (number of connections) of the i-th node

[0244] The more complex and dispersed the propagation structure is, the higher the entropy value, indicating that the content has stronger potential diffusibility and is more difficult to control.

[0245] Overall three-factor tension field model (ideological tension vector)

[0246] Finally, the ideological risk tension vector of each multimodal semantic blob C k is defined as:

[0247]

[0248] Sub-step 4.2: Semantic adversarial tension calculation

[0249] This factor measures the degree of tension between different semantic positions in the topic cluster, specifically by constructing a semantic position embedding vector field and calculating the internal adversarial degree index of the tensor.

[0250] Let be the semantic vectors in the blob. First, calculate the tensor covariance matrix of the embedding field:

[0251]

[0252] Subsequently, use the maximum eigenvalue λ max as the tension index:

[0253]

[0254] The larger the tension value, the more intense the internal semantic differentiation and the stronger the potential antagonism.

[0255] Sub-step 4.3: Emotional intensification tension calculation

[0256] The emotional intensification tension measures the degree of polarization in the emotional expression of multimodal content. Assume that the output of the emotion classification model is the emotion distribution probability vector set ε k ={p1, p2,... p n}. Define the intensification degree as the emotion entropy difference - extreme polarization index (EPI):

[0257]

[0258] Here c is the number of emotion categories, u is the uniform emotion distribution, measures the degree of deviation from the center, and the larger the value, the more polarized the emotion.

[0259] Sub-step 4.4: Calculation of Structural Propagation Tension

[0260] The propagated structural tension focuses on whether the diffusion mode of the topic in the social network structure shows an explosive, fragmented, or extremely centralized tendency. Let the propagation graph be G k =(V, E), and the propagation tension index is defined as:

[0261]

[0262] DegreeVar measures the variance of the node degrees (centralization degree);

[0263] Assortativity measures the tendency of similar nodes to connect;

[0264] CascadeDepth measures the propagation depth.

[0265] The parameters β1, β2, β3 are used as adjustable hyperparameters.

[0266] Sub-step 4.5: Mapping of the Ideological Security Tension Field

[0267] Map the tension vectors of all topic clusters to a three-dimensional tension space to form a tension cloud map. The position of each point is determined by:

[0268]

[0269] Define the overall ideological tension intensity as:

[0270]

[0271] According to the tension intensity value and the relative density of the spatial distribution, the system assigns a risk level label to each cluster:

[0272] High risk (red area): ||T k || > θ H

[0273] Medium risk (orange area): θ M < ||T k || ≤ θ H

[0274] Low risk (green area): ||T k || ≤ θ M

[0275] Sub-step 4.6: Output of Risk Label and Factor Contribution Explanation

[0276] Finally, each cluster outputs the following structure:

[0277] RiskEval(Ck ) = {T k , ||T k ||, RiskLevel, TopContributor}

[0278] Among them, TopContributor represents the dominant risk factor, which is used to support the decision-making of subsequent intervention strategies.

[0279] By constructing a high-dimensional tension space based on three factors: semantic confrontation tension, emotional intensification tension, and communication structure tension, the multi-factor awareness tension mapper can not only accurately depict the potential risk level of ideological events, but also has strong factor interpretation ability and intervention priority guidance, laying a solid foundation for subsequent personalized response and strategy orientation.

[0280] In the first four steps, this embodiment has successively completed mutation-driven acquisition trigger, modal shunt routing decoding, cross-modal semantic potential clustering, and multi-factor awareness tension mapping. At this time, the system has successfully identified and evaluated multiple topic clusters in the student community that may trigger ideological security risks, and has assigned them accurate risk levels and tension factor weights. However, identifying risks is only a prerequisite, and the real governance core lies in how to select the most effective, least frictional, and most accessible intervention path.

[0281] Specifically, the implementation process of step 5 includes:

[0282] In this step, by designing an innovative path modeling algorithm inspired by the minimum resistance path of the circuit network - the minimum impedance intervention path planner, an ideological impedance map is constructed on the real social topology, and the minimum impedance intervention path inversion and multi-point intervention scheduling planning are carried out based on the risk node set to achieve low-cost, high-efficiency, and personalized public opinion guidance and topic intervention.

[0283] Sub-step 5.1: Ideological impedance network construction

[0284] Based on the communication structure graph G k = (V k , E k ) obtained in the previous stage, impedance modeling is introduced, and the ideological communication impedance value R ij of each edge e k ∈ E ij is defined, and a weighted directed graph The weight of each edge is defined as:

[0285]

[0286] Where:

[0287] IdeoAffinity ij: It represents the semantic consistency degree of nodes i and j in ideological positions;

[0288] InfluenceScore j : The influence of node j (such as the number of fans, the number of reposts, the repost centrality);

[0289] TrustGap ij : It represents the trust gap of node i with respect to node j in historical interactions (which can be statistically modeled by historical comment tendencies, like / dislike behaviors, etc.);

[0290] α1, α2, α3: They are adjustable hyperparameter weights, reflecting the impedance contribution degrees of each factor.

[0291] Finally, It constitutes the "ideological impedance graph".

[0292] Sub-step 5.2: Determination of the risk node-driven source point set

[0293] Using the high-tension cluster set output by the fourth step Define the nodes with high centrality and high contribution degree of intensifying tension in the propagation path among them as the intervention source point set:

[0294] S = {s1, s2,..., s l}, where s i ∈V k

[0295] These nodes are the public opinion detonation points that the system should focus on seeking intervention and control.

[0296] Sub-step 5.3: Inversion planning of the minimum impedance path

[0297] In this step, an improved multi-source multi-target minimum impedance path algorithm (MS-MinRes-Dijkstra) is adopted. For each risk propagation path, starting from the source point set S, calculate the minimum impedance path to all potential public opinion receiving nodes Its total cost is:

[0298]

[0299] The algorithm improvement points include:

[0300] Introduce a dynamic impedance adjustment mechanism to assign higher edge impedance (self-attenuation) to the already controlled nodes;

[0301] Introduce a propagation potential prediction function to avoid selecting intervention paths with high costs but low marginal benefits of propagation;

[0302] Support multi-point concurrent path deduction to improve the scheduling efficiency.

[0303] Sub-step 5.4: Intervention Strategy Type Mapping and Path Label Injection

[0304] After obtaining the minimum impedance path, inject strategy labels into the end node t of each path:

[0305] If the awareness stance of t is moderate and easy to guide → inject positive guiding corpus

[0306] If t has intense emotions and a high structural centrality → inject "turning semantic breakpoint content"

[0307] If t is located at the structural edge → inject adaptive ending encapsulation content

[0308] The injection strategy is jointly determined and generated by the previous semantic labels, emotion distribution, and propagation depth.

[0309] Sub-step 5.5: Intervention Cost Function Evaluation and Intervention Priority Ranking

[0310] To guide the intervention scheduling, define a comprehensive intervention cost function:

[0311]

[0312] Where:

[0313] represents the mutation gradient of semantic stance;

[0314] represents the mutation gradient of emotional fluctuation;

[0315] The system arranges all paths in ascending order of the cost function and determines the intervention execution order accordingly.

[0316] Sub-step 5.6: Forming an Intervention Task Graph and Multithreaded Scheduling Execution

[0317] Finally, the system outputs an intervention task scheduling graph, whose nodes are the social nodes that need to execute interventions, and the edges represent the dependency order (such as guiding the core nodes first and then infiltrating the edge users), and schedules asynchronous intervention execution threads for strategy delivery, public opinion intervention, or administrative guidance.

[0318] Inspired by the minimum resistance path of the circuit system, the minimum impedance intervention path planner first introduces "impedance modeling" into the ideological risk governance path design. Through the fine quantization modeling and intelligent path inversion of the impedance of the multi-factor propagation network, it constructs an accurate, efficient, and intervention psychology law-compliant public opinion response framework, providing a technical means with intelligence, flexibility, and cost control ability for ideological risk management.

[0319] After completing the first five steps with a logical closed-loop, that is, from the mutation perception of sudden information to the path intervention deduction, the system has basically acquired the ideological security management ability from perception to response. However, in a continuously evolving and rapidly iterating information environment, static strategies alone are not sufficient to adapt to the variability and complexity of sudden ideological events in the long term. Therefore, the core goal of the sixth step is to introduce a systematic feedback regulation mechanism on the entire system basis to achieve the automatic evolution and strategy update closed-loop of algorithm parameters, model structures, and intervention strategies.

[0320] Specifically, the implementation process of step 6 includes:

[0321] In this embodiment, an innovative algorithm based on game theory-driven reflux optimization mechanism and meta-learning adaptive structure reconstruction - self-evolving strategy refluxer is introduced here. By designing an ideological monitoring reflux game network (IdeoMetaGameNet), each processing module of the system not only has the "historical experience memory ability" but also can continuously self-correct in dynamic confrontation, and finally realizes the closed-loop evolutionary optimization of the whole process.

[0322] Sub-step 6.1: Construction of multi-stage policy execution feedback log archiving module

[0323] First of all, the system conducts unified structured archiving on the key behavior outputs generated by the first five steps to construct a feedback data structure Each feedback record contains:

[0324] Collected event number;

[0325] Modal decoding path and processing delay;

[0326] Clustering cluster semantic evolution trajectory;

[0327] Tension factor tensor historical sequence;

[0328] Intervention path execution log and response situation;

[0329] User feedback response and topic status change.

[0330] This structure serves as the support data set for meta-learning input and is used for subsequent policy evolution.

[0331] Sub-step 6.2: Ideological reflux game network modeling

[0332] In this sub-step, the idea of game network is introduced. Taking the confrontation relationship between each intervention strategy (such as information guidance, blockade reminder, structure penetration, etc.) and the real reaction of the community as the basic unit, a dynamically evolving "consciousness game network" is constructed, where:

[0333] Each node represents a specific intervention strategy πi ;

[0334] Each edge (π i , π j ) represents the evolutionary relationship formed after the game between two strategies in a continuous time window;

[0335] Each edge is assigned a response offset vector:

[0336]

[0337] This game network is continuously updated to form a time-weighted graph reflecting the evolutionary trajectory of the advantages and disadvantages among strategies.

[0338] Sub-step 6.3: Adaptive update of the policy structure based on meta-learning

[0339] This embodiment designs a special meta-learner structure: Conscious Tension Response - Weight Reconstruction Network, whose core is to extract the weight parameters most sensitive to policy updates from historical game trajectories and perform rapid updates in a small-sample intervention window.

[0340] Assume that the current policy network weight is θ, and it is updated through the game feedback gradient as:

[0341]

[0342] where:

[0343] represents the meta-loss function composed of the conscious tension trend;

[0344] β: is the meta-update rate;

[0345] represents the subset of feedback logs corresponding to a certain type of conscious risk intervention task.

[0346] Through this structure, the system can quickly adjust its intervention parameters or semantic decoding model structure using a small number of samples when facing the same type of events in the future, so as to achieve high adaptability.

[0347] Sub-step 6.4: Evolutionary regression optimization of the intervention strategy mapping function

[0348] To further improve the policy stability of the system, this embodiment introduces an evolutionary regression mechanism of the policy influence function and constructs a policy influence function:

[0349] Φ(π i ) = γ1·RiskDrop(π i ) + γ2·TensionDecay(π i ) + γ3·UserRepelScore(πi )

[0350] Through genetic algorithms or Bayesian optimization, continuously update the policy set and screen out a stable, mild, and efficient policy subset for subsequent tasks.

[0351] Sub-step 6.5: Reallocation of system-wide weights and automatic recompilation scheduling

[0352] Finally, under the combined action of the feedback network and the meta-learner, the system automatically recompiles the weight structures of the following modules:

[0353] The mutation threshold scheduling function of the asynchronous mutation-aware acquisition trigger;

[0354] The modal label accuracy improvement model of the dynamic modal parallel routing network;

[0355] The potential aggregation function structure of the time-topic joint potential clustering device;

[0356] The tension weight coefficient of the multi-factor awareness tension mapper;

[0357] The path cost parameter and node scheduling strategy of the minimum impedance intervention path planner;

[0358] Enable the system to form a closed-loop optimization flow from event triggering, intervention path, community response, to policy evolution.

[0359] The biggest highlight of the self-evolving policy refluxer lies in its comprehensive modeling ability of multi-source feedback, multi-policy confrontation, and interpretable evolution linkage, enabling the entire "one-stop ideological security monitoring system for student communities" to possess human-like policy correction, self-regulation, and experience migration capabilities. With this structure, the system no longer relies on static models to operate but has the ability to quickly adapt to sudden ideological events and form counterfactual improvements.

[0360] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A multi-modal ideological security monitoring method for the "one-stop" student community, characterized in that, It includes the following steps: Step 1: Dynamically monitor the mutation rate of the data source and adaptively trigger the acquisition task; Step 2: Generate modal labels based on feature vectors and construct a dynamic routing graph, and output unified structured data after asynchronous decoding and anomaly correction; Step 3: Organize the time-series content into a three-dimensional semantic tensor, use the potential field to cluster topics and detect risk units; Step 4: Construct a three-dimensional tension space to quantitatively evaluate the risk level and dominant factors; Step 5: Plan the minimum intervention path based on the propagation impedance map, evaluate the priority to form a scheduling strategy; Step 6: Introduce a meta-learning mechanism through the game network to achieve closed-loop adaptive optimization of the strategy, and complete the full-link intelligent control from monitoring to intervention.

2. A multi-modal ideological security monitoring method for a "one-stop" student community according to claim 1, characterized in that: The said Step 1 includes: Through modal registration and modal listener initialization, monitor the multi-modal data source, calculate the mutation rate of the modal content, and dynamically trigger the data acquisition task of the corresponding modality according to the adaptive acquisition trigger factor to realize resource data acquisition; The said Step 2 includes: Based on the multi-modal content segments and their feature vectors collected in Step 1, dynamically generate modal label vectors, construct a reconfigurable modal routing graph, allocate data to different decoding paths according to the modal decision routing function, load the processing modules of the corresponding paths for asynchronous concurrent decoding, and ensure the decoding accuracy through the anomaly routing fast correction mechanism. Finally, format the decoding result into a unified structure and send it to the next step for processing; The said Step 3 includes: Organize the decoded multi-modal content units into a three-dimensional semantic time tensor in chronological order, define the time-topic semantic potential function, construct the total potential field and inversely generate the topic center, perform semantic adsorption clustering based on the potential gradient field to form cross-modal topic clusters, and at the same time detect abnormal potential clusters and mark them as high-risk ideological topic units; The said Step 4 includes: Construct a three-dimensional vector space of ideological tension with three orthogonal factor dimensions of semantic confrontation tension, emotional intensification tension, and structural propagation tension, quantitatively evaluate the risk tension of each topic cluster obtained in Step 3, assign risk level labels according to the tension intensity value and the relative density of the spatial distribution, and output the dominant risk factor; The said Step 5 includes: Construct an ideological impedance map based on the propagation structure diagram, determine the set of risk node driving source points in the high-tension cluster, inversely plan the minimum impedance intervention path, inject the corresponding intervention strategy label into the end node of the path, evaluate and sort the intervention priority through the comprehensive intervention cost function, form an intervention task scheduling diagram and execute multi-threaded intervention scheduling; The said Step 6 includes: Construct an ideological reflux game network, introduce an adaptive update mechanism of the strategy structure based on meta-learning and the evolutionary regression optimization of the intervention strategy mapping function, automatically recompile the weight structure, and form a closed-loop optimization flow from event trigger, intervention path, community response to strategy evolution.

3. A multimodal ideological security monitoring method for a "one-stop" student community according to claim 2, characterized in that The said Step 1 includes: Perform modal registration and modal listener initialization, clarify the types, addresses, and embedded functions of each modal data source, and initialize listeners for each modality; Map the monitored modal data to feature vectors through the modal content embedding function; then calculate the mutation rate of the modal content to measure the variation range of the semantic content; Calculate the adaptive acquisition trigger factor based on the historical steady-state mutation threshold and the dynamically adjusted trigger coefficient, and compare the real-time mutation rate with the trigger factor. Once the condition is met, trigger the data acquisition of the corresponding modality; Enter the modal acquisition scheduling stage, dynamically allocate acquisition resources according to factors such as burst intensity, resource priority, and acquisition window size to ensure that burst modalities are preferentially responded to.

4. A multimodal ideological security monitoring method for a "one-stop" student community according to claim 3, characterized in that The step 2 includes: Dynamically generate a modal label vector for each modal data unit entering the processing link, including modal type, mutation rate, scheduling priority, and feature embedding entropy value information; Construct a reconfigurable modal routing graph, which consists of multiple routing nodes and processing modules, supports dynamic path reconstruction and shared paths between modalities, and has the ability of hot update; According to the modal label vector, judge the decoding path that the data should flow into through the dynamic routing decision function to ensure that modalities with high mutation and high complexity flow into high-performance routing paths preferentially; Load all the required processing modules for the corresponding path and perform asynchronous concurrent decoding. The outputs of all decoding modules are uniformly mapped to the shared semantic space; When the average information density entropy or processing delay of a certain path is abnormal, automatically reroute the modal data to the backup path and feedback to the modal label generation module to adjust the subsequent label calculation strategy; Repackage all the decoded modal contents into a unified structure and send them to the next step of processing.

5. A multimodal ideological security monitoring method for a "one-stop" student community according to claim 4, characterized in that The step 3 includes: Organize the data units of different modalities into a unified three-dimensional semantic time tensor in chronological order as the input for semantic potential field construction; Define the time-topic semantic potential energy function to simulate the aggregation trend of different modal contents towards a certain topic, and couple semantic proximity and temporal proximity into a unified physical quantity; Calculate the total potential energy through full tensor traversal and use a high-dimensional potential energy optimizer to generate the topic center in the reverse direction; Perform semantic adsorption clustering based on the potential energy gradient field to form cross-modal topic clusters. At the same time, evaluate the abnormality degree of each clustering result in terms of semantic distribution and temporal continuity. If the anomaly score exceeds the threshold, mark it as a high-risk ideological topic unit; Format and output the clustering results to provide a semantic basis for subsequent ideological risk assessment.

6. A multimodal ideological security monitoring method for a "one-stop" student community according to claim 5, characterized in that The step 4 includes: Construct a three-dimensional tension vector space, including three orthogonal factor dimensions: semantic adversarial tension, emotional intensification tension, and structural propagation tension; Calculate each factor respectively. The semantic adversarial tension measures the degree of semantic opposition through the average cosine dissimilarity of the semantic vector tensor, the emotional intensification tension evaluates the degree of emotional polarization using the sum of squared emotional deviation, and the structural propagation tension measures the content diffusion potential based on the structural entropy of the propagation path; Map the tension vectors of each cluster to the tension space to form a tension cloud map, and assign high, medium, and low risk level labels to each cluster according to the tension intensity value and spatial distribution density; Output the risk labels and dominant risk factors of each cluster, providing a decision-making basis for subsequent intervention strategies.

7. A multimodal ideological security monitoring method for a "one-stop" student community according to claim 6, characterized in that Step 5 includes: Construct an ideological impedance graph based on the propagation structure graph, and the impedance value of each edge is calculated by weighting semantic consistency, node influence, and trust gap; Determine the nodes with high centrality and high intensification tension contribution in the propagation paths of high-tension clusters as the set of intervention source points; Adopt an improved multi-source multi-target minimum impedance path algorithm, starting from the source point set, calculate the minimum impedance paths to all potential public opinion receiving nodes, and dynamically adjust the impedance values to optimize path selection; According to the semantic stance, emotional intensity, and structural position of the end nodes of the paths, inject corresponding intervention strategy labels; Define a comprehensive intervention cost function, sort all paths in ascending order of the cost function, and determine the intervention execution order; Form an intervention task scheduling graph, schedule asynchronous intervention execution threads for strategy delivery, public opinion intervention, or administrative guidance, and achieve low-cost and high-efficiency public opinion guidance and topic intervention.

8. A multimodal ideological security monitoring method for a "one-stop" student community according to claim 7, characterized in that Step 6 includes: Structurally archive the key behavior outputs of the first five steps, construct a feedback data structure, and record the acquisition event number, modal decoding path and processing delay, semantic evolution trajectory of the clustering cluster, historical sequence of tension factor tensors, intervention path execution log and response situation, user feedback response, and topic status change; Introduce the idea of game theory, construct an ideological reflux game network, use the confrontation relationship between intervention strategies and the real reactions of the community as the basic unit to form a dynamically evolving game network, and assign a response offset vector to each edge; Design a policy structure adaptive update mechanism based on meta-learning, extract the weight parameters most sensitive to policy update from historical game trajectories, and perform rapid updates in a small-sample intervention window; Introduce a policy influence function evolutionary regression mechanism, continuously update the policy set through genetic algorithms or Bayesian optimization, and screen out a stable, mild, and efficient policy subset; Under the joint action of the feedback network and the meta-learner, automatically recompile the weight structure to form a closed-loop optimization flow from event trigger, intervention path, community response to policy evolution.

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