Intelligent early warning and processing method and system for information content release

Abstract

The application relates to the technical field of data processing, and discloses an intelligent early warning and processing method and system for information content publishing, which comprises the following steps: firstly, quantifying information content into an initial risk vector and constructing a risk potential field; secondly, predicting risk evolution by using a dynamic model, identifying risk points and clusters; thirdly, combining the ability of auditors, and generating action instructions through an optimization scheduling algorithm; and finally, updating the model and auditor data according to the instruction execution result feedback, forming a closed-loop learning system; the system comprises an information content quantization module, a risk potential field construction module, a risk evolution prediction module, an action instruction generation module and a feedback updating module. The application constructs a risk potential field and a dynamic model, realizes prospective early warning, accurately allocates audit resources through an optimization scheduling model, and utilizes a closed-loop feedback mechanism, so that the system can self-adaptively evolve and the information content risk control level is comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an intelligent early warning and processing method and system for information content publishing. Background Technology

[0002] With the rapid development of internet technology, various online platforms centered on user-generated content (UGC) are flourishing. While the rapid dissemination of massive amounts of information enriches social and cultural life, it also poses a severe challenge to the security governance of information content on these platforms. How to effectively identify and handle potentially risky information content to maintain a healthy and orderly online ecosystem has become a critical issue that all platform operators must address.

[0003] Currently, the industry's commonly used technical solutions mainly rely on artificial intelligence models for initial screening, followed by in-depth processing through human review. However, such solutions still have significant shortcomings in practical applications. Their risk identification process is often isolated and lagging, typically performing static analysis only on individual pieces of information. It is difficult to discern the interconnected risks arising from semantic or thematic relationships between pieces of information, and it also lacks forward-looking predictions of future risk evolution trends.

[0004] Furthermore, in the subsequent manual review stage, the task allocation mechanism is rather crude, and there is a general lack of accurate matching between the reviewers' professional capabilities, real-time work status and the types of risks to be handled. This not only affects the processing efficiency, but also makes it difficult to guarantee the review quality in complex risk scenarios, making the entire information content security system seem powerless when dealing with large-scale and highly dynamic risk impacts.

[0005] Therefore, this invention proposes an intelligent early warning and processing method and system for information content publishing to address the shortcomings of existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent early warning and processing method and system for information content publishing, which solves the problems of isolated information content risk identification, passive handling, and low resource scheduling efficiency.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent early warning and processing method for information content publishing, the method comprising the following steps:

[0008] S1. Obtain the information content to be processed and quantify the information content to be processed into an initial risk vector;

[0009] S2. Construct a risk potential energy field based on the preset generalized distance between the initial risk vector and the information content;

[0010] S3. Using the real-time interactive data of the information content, the risk potential field is predicted through a dynamic evolution prediction model to identify risk points and risk clusters.

[0011] S4. Construct an audit resource capability matrix, match the identified risk points and risk clusters with the audit resource capability matrix, and generate action instructions by optimizing the scheduling objective function;

[0012] S5. Execute and update the dynamic evolution prediction model and the audit resource capability matrix based on the execution result of the action instructions.

[0013] Preferably, in step S1, the steps of obtaining the information content to be processed and quantifying the information content to be processed into an initial risk vector include:

[0014] The acquired information content is analyzed by calling an artificial intelligence model, the risk attributes of the information content on a preset risk dimension are identified, and a corresponding risk quantification value is generated for the risk dimension corresponding to the identified risk attribute based on the identified risk attribute.

[0015] Based on the risk quantification values, an initial risk vector is constructed. This initial risk vector is a vector composed of the risk quantification values ​​and is used to characterize the initial multidimensional risk state of the information content. The initial risk vector is...

[0016] In the formula, Let q be the initial risk vector for information content j, n be the total number of preset risk dimensions, and q be the initial risk vector for information content j. j,n Let be the risk quantification value of information content j in the nth risk dimension, where n is a positive integer greater than 1.

[0017] Preferably, in step S2, the step of constructing a risk potential field based on the preset generalized distance between the initial risk vector and the information content includes:

[0018] The risk source is determined based on the initial risk vector of the information content to be processed, and the risk potential field is constructed based on the impact of the risk source on other information content in the platform and the preset generalized distance between the information content. The intensity of the impact of the risk source on other information content in the platform is proportional to the risk quantification value of the risk source itself, and decreases as the preset generalized distance between the information content increases.

[0019] Based on the aforementioned risk potential energy field and risk potential energy calculation formula, the local risk potential energy of the information content to be processed is calculated by summing the impact of the risk source on all other information content. The risk potential energy calculation formula is as follows:

[0020]

[0021] In the formula, Φ j Let be the local risk potential energy of the information content j to be processed; i is the unique identifier of other information content; j is the unique identifier of the information content to be processed; κ is the basic potential energy scaling constant. Let i be the initial risk vector for other information content i; D is a scenario weight vector used to adjust the importance of different risk dimensions; ij The preset generalized distance between other information content i and the information content j to be processed.

[0022] Preferably, in step S3, the step of using real-time interactive data of the information content to predict the evolution of the risk potential field through a dynamic evolution prediction model, and identifying risk points and risk clusters, includes:

[0023] The risk potential energy of the risk potential energy field is a dynamic quantity that changes over time. The rate of change of the risk potential energy is determined by both the risk growth effect and the natural decay effect. The risk growth effect is driven by the real-time interactive data of the information content, and the natural decay effect causes the risk potential energy to decrease over time.

[0024] The dynamic evolution prediction model is constructed based on the risk growth effect and the natural decay effect, wherein the dynamic evolution prediction model is a differential equation characterizing the change of the risk potential energy over time, and the dynamic evolution prediction model is as follows:

[0025]

[0026] In the formula, Let g be the rate of change of the risk potential energy of information content j over time t; g(·) is a nonlinear function used to aggregate multiple risk growth factors. Let j be the real-time interactive vector of information content j; The sentiment gradient of the comment section for information content j; V d,j (t) represents the propagation speed of information content j; λ is the attenuation coefficient characterizing the natural attenuation effect; Φ j (t) represents the risk potential energy of information content j at time t.

[0027] Preferably, in step S3, the step of using real-time interactive data of the information content to predict the evolution of the risk potential field through a dynamic evolution prediction model, and identifying risk points and risk clusters, further includes:

[0028] Information content whose risk potential exceeds a preset risk threshold is identified as the risk point;

[0029] An information content association graph is constructed based on a preset generalized distance between the information content, wherein the nodes of the information content association graph are the information content, and the edges of the information content association graph represent the association strength between the information content.

[0030] A community detection algorithm is applied to the information content association graph, and the set of closely connected nodes identified by the community detection algorithm is taken as the risk cluster.

[0031] Preferably, in step S4, the steps of constructing an audit resource capability matrix, matching the identified risk points and risk clusters with the audit resource capability matrix, and generating action instructions by optimizing the scheduling objective function include:

[0032] The audit resource capability matrix is ​​composed of auditor capability vectors, which are used to characterize the auditor's capabilities and status information. The auditor capability vectors include: scoring vectors, work efficiency values, real-time fatigue indexes, and current workload. The scoring vectors are composed of the auditor's professional capability scores for preset risk dimensions.

[0033] The work efficiency value is used to characterize the efficiency of the auditor in handling tasks;

[0034] The real-time fatigue index is used to characterize the auditor's current fatigue state;

[0035] The current workload is used to characterize the amount of tasks assigned to the auditor.

[0036] Preferably, in step S4, the step of constructing an audit resource capability matrix, matching the identified risk points and risk clusters with the audit resource capability matrix, and generating action instructions by optimizing the scheduling objective function further includes:

[0037] For each action instruction to be evaluated, determine the total predicted future platform risk arising from executing the action instruction, as well as the total cost required to execute the action instruction.

[0038] Based on the predicted total risk and total cost of the future platform, an optimized scheduling objective function for evaluating the comprehensive benefits of the action instructions is constructed by weighted summation, and the optimal action instructions are found by solving the optimized scheduling objective function.

[0039] The objective function for the optimized scheduling is:

[0040] In the formula, The target value is a comprehensive one. Action instructions that need optimization; In order to execute action orders Then, the predicted total risk of the future platform; To execute the action order The total cost required; α and β are weighting coefficients used to balance the total risk and total cost of the future platform.

[0041] Preferably, the optimal action instruction also satisfies both professional competence constraints and workload constraints. The professional competence constraint is as follows: when the action instruction is a manual review task, the task is assigned to the reviewer of the manual review task based on the professional competence score of the score vector in the reviewer's competence vector on the risk dimension corresponding to the task. The professional competence score of the score vector in the reviewer's competence vector corresponding to the reviewer on the risk dimension corresponding to the task is greater than or equal to the preset competence requirement threshold of the manual review task.

[0042] The workload constraint is as follows: when the action instruction is to assign a new audit task to the auditor, the new audit task is assigned to the auditor based on the weighted sum of the auditor's current workload and the real-time fatigue index. If the weighted sum of the auditor's current workload and the real-time fatigue index is less than or equal to the upper limit of workload preset by the platform to ensure audit quality, then a new audit task is assigned to the auditor.

[0043] Preferably, in step S5, the step of executing and updating the dynamic evolution prediction model and the audit resource capability matrix based on the execution result of the action instruction includes:

[0044] Obtain the actual execution result of the action instruction, compare the actual execution result with the predicted execution result before the action instruction was executed, and calculate the feedback error;

[0045] The internal parameters of the dynamic evolution prediction model are adjusted using the feedback error, and the score vector, work efficiency value, real-time fatigue index, and current workload of the auditors executing the action instructions in the audit resource capability matrix are updated based on the auditors' work performance reflected in the actual execution results.

[0046] This invention also provides an intelligent early warning and processing system for information content publishing, the system comprising:

[0047] The information content quantification module is used to call an artificial intelligence model to analyze the acquired information content on preset risk dimensions in order to construct an initial risk vector that represents the multidimensional risk status of the information content.

[0048] The risk potential field construction module is used to take the initial risk vector as a risk source and calculate the local risk potential of each information content by accumulating the influence generated by all risk sources and decaying with a preset generalized distance, so as to construct a risk potential field.

[0049] The risk evolution prediction module is used to predict the evolution of the risk potential field based on a dynamic evolution prediction model driven by real-time interactive data of information content, which is jointly determined by the risk growth effect and the natural decay effect. The risk evolution prediction module identifies risk points by using risk thresholds and identifies risk clusters by applying a community discovery algorithm on the information content association graph.

[0050] The action instruction generation module is used to generate optimal action instructions based on the audit resource capability matrix, which includes the auditor scoring vector, work efficiency value, real-time fatigue index and current workload, by solving an optimization scheduling objective function that aims to minimize the weighted sum of predicted future platform total risk and total cost, and under the conditions of satisfying professional capability constraints and workload constraints.

[0051] The feedback update module is used to calculate the feedback error based on the actual execution results of the action instructions, so as to adjust the internal parameters of the dynamic evolution prediction model; and the feedback update module updates the scoring vector, work efficiency value, real-time fatigue index and current workload in the audit resource capability matrix according to the auditer's work performance.

[0052] This invention provides an intelligent early warning and processing method and system for information content publishing. It has the following beneficial effects:

[0053] 1. This invention introduces the concept of a risk potential energy field and combines it with a dynamic evolution prediction model to comprehensively extrapolate risks across the entire platform, achieving a global and forward-looking early warning of information content risks. Compared to existing technologies that rely heavily on static keyword matching or post-event popularity analysis of isolated information content, this invention addresses the shortcomings of these technologies in effectively identifying the risk linkage effects between information content and lacking the ability to predict potential risks in advance, thereby improving the initiative and foresight in risk discovery.

[0054] 2. This invention constructs an optimized scheduling model that incorporates the total future platform risk and execution cost into the objective function, and comprehensively considers the professional capabilities, workload, and real-time status information of auditors, achieving precise and efficient allocation of audit resources. This differs from existing technologies that rely on simple queues or coarse rules for task assignment, solving the problems of blind resource allocation, mismatch between audit manpower and risky tasks, and difficulty in balancing processing costs and risk control in existing technologies.

[0055] 3. This invention also designs a closed-loop feedback mechanism from execution results to model parameters, using the handling effect to continuously calibrate the risk evolution model, and using the auditor's actual work performance to update their competency profile. This achieves the technical effect of continuous adaptive optimization. In contrast, existing early warning models and scheduling rules are usually static. The design of this invention solves the shortcomings of their system performance becoming rigid over time and unable to automatically adapt to new risk patterns and changes in personnel status, enabling the entire system to have the ability to self-evolve. Attached Figure Description

[0056] Figure 1 This is a flowchart of the method of the present invention;

[0057] Figure 2 This is a flowchart illustrating the risk identification and prediction process of the present invention.

[0058] Figure 3 This is a flowchart illustrating the risk response and update process of the present invention.

[0059] Figure 4 This is a system architecture diagram of the present invention. Detailed Implementation

[0060] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Please see Figures 1-3 This invention provides an intelligent early warning and processing method for information content publishing, the method comprising the following steps:

[0062] S1. Obtain the information content to be processed and quantify the information content to be processed into an initial risk vector;

[0063] In this embodiment, to conduct an initial assessment of the risks associated with information content publication, the system acquires the content to be processed. The information content can be in the form of text, images, audio, video, or any combination thereof, to adapt to diverse platform publishing scenarios.

[0064] Specifically, to transform unstructured content information into structured data suitable for analysis, the system invokes a pre-trained artificial intelligence model to perform deep analysis of the acquired information. In one possible implementation, the artificial intelligence model is a deep learning model. For example, for text-based content, a natural language processing model based on a transformer architecture can be used; for image or video content, a model based on a convolutional neural network (CNN) can be used. These models, trained on massive amounts of labeled data, acquire the ability to identify complex risk patterns.

[0065] One of the core tasks of artificial intelligence models is to identify the risk attributes of information content across multiple preset risk dimensions. These preset risk dimensions are a predefined classification system based on the platform's information content security policy, used to achieve fine-grained risk management. As an option, preset risk dimensions may include politically sensitive dimensions, pornography dimensions, violent and terrorist dimensions, advertising and marketing dimensions, vulgar information content dimensions, and controversial topic dimensions.

[0066] After identifying risk attributes, the AI ​​model generates a corresponding risk quantification value for each risk dimension. The risk quantification value is a numerical value used to precisely measure the degree of risk or confidence score of information content within that risk dimension. This quantification process transforms the abstract concept of risk into a concrete, calculable numerical value.

[0067] Based on the risk quantification values ​​generated across various dimensions, the system constructs an initial risk vector to provide a unified mathematical representation of the initial multidimensional risk state of the information content. The construction process of the initial risk vector can be expressed by the following formula:

[0068]

[0069] In the formula, Let q be the initial risk vector for information content j; n is the total number of preset risk dimensions; j,k Let be the risk quantification value of information content j in the nth risk dimension, where n is a positive integer greater than 1.

[0070] This initial risk vector, as a structured mathematical object containing rich risk information, provides a fundamental and quantifiable input for constructing the risk potential field in subsequent steps.

[0071] S2. Construct a risk potential field based on the preset generalized distance between the initial risk vector and the information content;

[0072] In this embodiment, after obtaining the initial risk vectors of each piece of information, the system constructs a global risk potential field based on the preset generalized distance between these vectors and the information content. This aims to transform the risk assessment of information content from an isolated, static perspective to an interconnected, global one.

[0073] Specifically, the system treats the initial risk vector of each piece of information as a risk source. A risk source exerts an influence within the platform space, similar to a field in physics. The strength of this influence, i.e., the risk potential energy exerted by a risk source on other information content within the platform, follows two fundamental principles:

[0074] First, the intensity of the impact is directly proportional to the risk quantification value of the risk source itself; that is, the higher the risk of a piece of information itself, the greater its contribution to the risk of the surrounding environment.

[0075] Secondly, the intensity of the influence decreases as the pre-defined generalized distance between the information contents increases; that is, the farther apart two pieces of information are, the weaker their mutual influence becomes.

[0076] Other information content within the platform refers to all other information content entities within the system's analysis scope relative to the target information content when calculating risk potential. Alternatively, the analysis scope can cover all currently active information content on the platform, or be limited to a set of information content within a specific time window and under a specific theme, to ensure the comprehensiveness and relevance of the potential calculation.

[0077] The predefined generalized distance between information content is not a simple physical or network topological distance, but a comprehensive metric used to quantify the degree of correlation between information content. In one possible implementation, the predefined generalized distance D... ij This can be calculated by fusing various heterogeneous information sources. For example, it can include semantic similarity based on the embedding vectors of the information content's text or images, similarity based on the user profiles or historical behavioral patterns of the information content's publishers, and relevance based on the topic tags or community affiliation of the information content. This multi-dimensional distance definition method allows the transmission of risk potential energy to simulate the complex paths of information dissemination in the real world.

[0078] To calculate the local risk potential energy of a specific target information content j, the system needs to accumulate the impact of all other risk source information content i on information content j. This accumulation process is accomplished using the risk potential energy calculation formula, which is:

[0079]

[0080] In the formula, Φ jLet be the local risk potential energy of the target information content j, representing the initial risk pressure borne by information content j due to the presence of other surrounding information content; i and j are unique identifiers for information content, used to distinguish different information content entities in the calculation; the summation symbol Σ. i≠j This indicates that all other information content i in the system, except for information content j itself, are traversed and accumulated; κ is the basic potential energy scaling constant, used to adjust the calculated total potential energy value to a standardized numerical range with business interpretation significance; The initial risk vector is the risk source information content i, which is calculated in step S1; This is a scenario weight vector, with the same dimensions as the initial risk vector. It is used to dynamically adjust the importance of different risk dimensions based on different scenarios or operational strategies. For example, during a specific event, the weight of the advertising and marketing dimension can be increased. This is achieved by calculating the dot product. A weighted total risk value can be obtained, reflecting the overall risk intensity of information content i in the current scenario; D ij Let be the preset generalized distance between information content i and information content j.

[0081] By performing the above calculations on every piece of information within the platform, the system can construct an initial risk potential field covering the entire platform. This potential field presents the global risk distribution at the initial stage of information content release in a quantitative manner.

[0082] S3. Using real-time interactive data of information content, a dynamic evolution prediction model is used to predict the evolution of the risk potential field and identify risk points and risk clusters.

[0083] In this embodiment, in order to achieve dynamic and forward-looking management of platform risks, after constructing an initial risk potential field, the system further utilizes real-time interactive data of information content to predict the evolution of the risk potential field through a dynamic evolution prediction model.

[0084] Specifically, the system will determine the local risk potential Φ of each piece of information. j It is considered as a dynamic quantity that changes with time t, denoted as Φ. j (t). The rate of change of risk potential energy is determined by two antagonistic effects.

[0085] One is the risk growth effect, which is driven by real-time user interaction data. Users' attention, discussion, and dissemination behaviors amplify the risk potential of information content.

[0086] The second is the natural decay effect, which means that in the absence of new external interactive stimuli, the risk intensity of information content will naturally decrease over time.

[0087] To accurately model this complex dynamic process mathematically, a differential equation characterizing the change of risk potential energy over time is established as a dynamic evolution prediction model. In one possible implementation, the model can be defined by a risk potential energy evolution equation, which is:

[0088]

[0089] In the formula, Risk potential energy Φ for information content j j (t) is the rate of change at time t; g(·) is a nonlinear function used to aggregate multiple risk growth factors, and its nonlinear characteristics can simulate complex phenomena such as risk amplification and saturation during the propagation process. Let j be the real-time interaction vector of information content j at time t. This vector can be composed of multiple interaction indicators such as the number of likes, comments, and shares per unit time. V represents the sentiment gradient of information content j in the comment section at time t. This value is calculated through real-time sentiment analysis of the comment text and is used to characterize the polarization trend and speed of change in public opinion. d,j (t) represents the propagation speed of information content j at time t, which can be quantified as the growth rate of the number of views or shares of information content per unit time; λ is the decay coefficient characterizing the natural decay effect, which is a positive constant, and its value determines the rate at which the risk potential energy dissipates without external stimulus; Φ j (t) represents the risk potential value of information content j at time t.

[0090] By solving this differential equation, the system can predict the distribution of the overall risk potential field of the platform over a future period.

[0091] After obtaining the evolved risk potential field, the system further identifies high-risk objects within it, a process that includes the identification of risk points and risk clusters.

[0092] For risk identification, the system identifies information content with a predicted risk potential value exceeding a preset risk threshold as risk points. Specifically, the preset risk threshold is not a fixed, single value. Alternatively, the threshold can be dynamic; for example, it can be determined based on the real-time statistical distribution of risk potential values ​​across all information content on the platform, identifying the top 1% of information content with the highest risk potential as risk points. Another possible implementation is to set multiple tiered risk thresholds, each corresponding to a different warning level, such as "Attention," "Warning," and "High Risk," to facilitate differentiated handling.

[0093] The purpose of identifying risk clusters is to discover sets of information content with interconnected risks. The system first uses a preset generalized distance D between information content, defined in step S2. ij Let's construct an information content association graph. In this graph, each piece of information is treated as a node, and the edges between nodes represent the association strength between the information contents. The weight of the edge can be set as the reciprocal of the generalized distance, that is, the association strength is inversely proportional to the generalized distance.

[0094] The system applies community detection algorithms to the constructed information content association graph. Alternatively, the Louvain-method or the Girvan-Newman-algorithm can be used. These algorithms effectively identify sets of tightly connected nodes in the graph, where internal associations are much stronger than external associations. These tightly connected sets of nodes identified by the algorithm are defined as risk clusters. A risk cluster represents a group of information content that is highly related in terms of topic, semantics, or propagation path, and may collectively ferment to form a wider-ranging public opinion event.

[0095] S4. Construct an audit resource capability matrix, match the identified risk points and risk clusters with the audit resource capability matrix, and generate action instructions by optimizing the scheduling objective function;

[0096] In this embodiment, after identifying risk points and risk clusters, the system enters the action instruction generation stage. The core purpose of this step is to generate an optimal and executable intervention and disposal plan, i.e., action instructions, based on the identified risks and the platform's currently available review resources, through optimized scheduling algorithms.

[0097] To achieve refined management and scheduling of audit resources, the system first constructs and maintains an audit resource capability matrix. The audit resource capability matrix is ​​a structured dataset composed of multiple auditor capability vectors, each vector comprehensively representing an auditor's capabilities and real-time status. In one possible implementation, the auditor capability vector specifically includes:

[0098] The scoring vector is composed of the auditor's professional competence scores for each preset risk dimension. The scores can be derived by comprehensively evaluating the auditor's accuracy, recall rate, and training and assessment results in specific areas of their past audit tasks, in order to achieve a precise match between professional competence and task risk type.

[0099] Work efficiency score: This value characterizes the average rate at which auditors process audit tasks, such as the number of tasks completed per unit of time. Real-time fatigue index: This is a dynamically updated value calculated based on factors such as the auditor's continuous working hours and recent trends in task processing speed. It is used to assess their current work status and prevent a decline in audit quality due to fatigue.

[0100] Current workload, this value is used to characterize the amount of tasks assigned to the auditor but not yet completed, and is a direct indicator of their task saturation.

[0101] After quantifying the review resources, the system constructs the process of generating action instructions as a mathematical optimization problem. The goal of this optimization problem is to find a set of action instructions that minimizes a comprehensive objective value among all possible combinations of action instructions. The comprehensive objective value is obtained by weighted summing of the predicted total future platform risk and the total cost required to execute the instruction.

[0102] To achieve this goal, the system determines the final action instruction by solving an optimal scheduling objective function. The optimal scheduling objective function is:

[0103]

[0104] In the formula, The comprehensive objective value to be minimized; This is a set of action instructions to be optimized, serving as variables in the optimization problem. Action instructions may include various operations such as assigning a risk point or risk cluster to a specific reviewer for manual review, limiting or removing information content, and issuing warnings or banning users. In order to execute the set of action instructions Next, the total future platform risk is deduced using the dynamic evolution prediction model in step S3. This calculation reflects the intervention effect of the action directive on the risk evolution trend; To execute the set of action instructions The total cost required. Alternatively, this cost may include the human and time costs of reviewers, potential user experience losses due to information content processing, etc.; α and β are preset weighting coefficients, both normal values, used to balance the two optimization objectives of risk control and cost control. The platform can dynamically adjust the values ​​of these two coefficients according to the operational strategies at different times to reflect changes in risk tolerance and cost sensitivity.

[0105] When solving the aforementioned objective function, the output action instructions of the optimization scheduling algorithm must be considered a feasible solution to the optimization problem. A feasible solution must simultaneously satisfy multiple pre-defined constraints to ensure that the generated action instructions are reasonable and executable in reality. Specifically, these constraints include:

[0106] The professional competence constraint stipulates that when an action instruction involves a manual review task, the assigned reviewer's professional competence score in the risk dimension corresponding to the task must not be lower than the preset competence requirement threshold for that task. This constraint ensures that complex and difficult risk information can be handled by reviewers with the corresponding professional competence.

[0107] The workload constraint limits the weighted sum of an auditor's current workload and their real-time fatigue index after a new review task is assigned, ensuring that it does not exceed the workload limit preset by the platform to guarantee review quality. This constraint aims to prevent a decline in review quality or a reduction in efficiency due to auditor overload.

[0108] By running optimization algorithms (such as genetic algorithms, simulated annealing, or integer linear programming solvers), the optimal scheduling objective function can be found while satisfying all constraints. Set of action instructions to obtain the minimum value This will be the final output of this step and will be sent to the execution system.

[0109] S5. Execute and update the dynamic evolution prediction model and the audit resource capability matrix based on the execution results of the action instructions;

[0110] In this embodiment, to ensure the continuous optimization and adaptability of the entire early warning and handling method, the system enters a feedback update phase after the action command is executed. This step establishes a closed-loop learning mechanism, using real execution results to iteratively optimize key models and data within the system.

[0111] Specifically, the system first obtains the actual execution results of the action instructions. These results are multi-dimensional. As an option, when the action instructions involve the processing of information content, the actual execution results include the actual interactive data stream of that information content after processing, such as changes in page views, comments, and shares over a subsequent period. When the action instructions involve manual review tasks, the actual execution results include the final review conclusion given by the reviewer, the time taken to complete the task, and the accuracy of the review verified through subsequent quality checks or user feedback.

[0112] After obtaining the actual execution result, the system will quantitatively compare it with the predicted execution result before the action command was executed to calculate the feedback error. For example, for a dynamic evolution prediction model, the feedback error can be defined as the difference between the actual risk potential of the information content and the risk potential predicted by the model at a certain observation time point.

[0113] Using the calculated feedback error, the system adjusts the internal parameters of the dynamic evolution prediction model in step S3. In one possible implementation, this adjustment process can be viewed as a parameter optimization process. Its goal is to minimize the prediction error, thereby improving the model's prediction accuracy. For example, if the model's parameter set is defined as θ, including the decay coefficient λ in the risk potential evolution equation and the internal weights of the risk growth function g(·), then the parameter update can follow a model parameter update formula:

[0114]

[0115] In the formula, θ new The updated model parameters; θ old The model parameters before the update are given; η is a preset learning rate used to control the step size of parameter updates; L(E) model ) is a feedback error E model The constructed loss function could be, for example, the square of the prediction error; This is the gradient of the loss function with respect to the model parameters θ. This gradient indicates the direction in which the parameters should be adjusted to reduce the value of the loss function. In this way, the model can learn from each deviation between prediction and reality, gradually approximating the true evolution of risk.

[0116] Simultaneously, the system updates the audit resource capability matrix in step S4 based on the auditor's work performance as reflected in the actual execution results. This update is targeted, adjusting only the capability and status information related to the auditors who executed the action instructions.

[0117] In one possible implementation, the update process is specifically manifested as follows:

[0118] The system adjusts the scoring vector within an auditor's competency vector based on the accuracy of their task handling. For example, if an auditor accurately handles a highly complex piece of politically sensitive information, their professional competency score in the political dimension will be increased. Conversely, if a misjudgment or omission occurs, their score will be decreased accordingly.

[0119] The auditor's work efficiency value is updated based on the actual time spent completing the task. For example, the efficiency of completing the task can be included in the average calculation within a sliding time window to dynamically reflect changes in efficiency.

[0120] Based on the auditor's continuous work records, their fatigue index and current workload are updated in real time. Completing a task will reduce their current workload, while continuously completing multiple tasks will increase their fatigue index accordingly.

[0121] Through this continuous feedback and updates, the system can not only make the risk prediction model more and more accurate, but also make the auditor's competency profile clearer, thereby achieving better risk identification and more intelligent resource allocation in subsequent cycles.

[0122] Please see Figure 4 An intelligent early warning and processing system for information content publishing, the system comprising:

[0123] The information content quantification module acquires the information content to be published and quantifies it into an initial risk vector.

[0124] The information content quantification module is responsible for receiving information content in the form of text, images, or videos to be processed within the platform. This module integrates an artificial intelligence model that can perform multi-dimensional risk analysis on the information content and generate a quantitative score for each preset risk dimension. Finally, these scores are combined into a structured initial risk vector, which serves as the basis for subsequent processing.

[0125] The risk potential energy field construction module constructs a risk potential energy field based on the preset generalized distance between the initial risk vector and the information content.

[0126] The risk potential field construction module receives the initial risk vector output by the information content quantification module. This module treats each piece of information content as a risk source that will affect the rest of the information content, and calculates the local risk potential of each piece of information content based on the risk intensity of the information content itself and the generalized distance between information content at the semantic, thematic or user level, thereby constructing a risk potential field that can depict the global initial risk distribution of the platform.

[0127] The risk evolution prediction module uses real-time interactive data of information content to predict the evolution of the risk potential field through a dynamic evolution prediction model, and identifies risk points and risk clusters.

[0128] The risk evolution prediction module is responsible for dynamically extrapolating the static risk potential field. This module continuously acquires real-time interactive data of information content and uses a dynamic evolution prediction model that integrates risk growth and natural decay effects to predict changes in risk potential over time. After prediction, this module is responsible for identifying independent risk points whose risk potential values ​​exceed a preset threshold, and using a community detection algorithm to identify high-risk information content sets, i.e., risk clusters, in the information content association graph.

[0129] The action instruction generation module generates action instructions based on risk points and risk clusters, combined with a preset audit resource capability matrix, through an optimized scheduling algorithm.

[0130] The action instruction generation module formulates handling strategies based on the risk points and risk clusters identified by the risk evolution prediction module. This module matches a preset audit resource capability matrix that includes the auditor's professional competence, work efficiency, and real-time status, and uses an optimization scheduling algorithm to solve for the optimal action instruction under constraints such as professional competence matching and workload balancing, so as to achieve a comprehensive balance between platform risk and handling costs.

[0131] The feedback update module updates the dynamic evolution prediction model and the audit resource capability matrix based on the execution results of the action instructions.

[0132] The feedback update module is responsible for forming the system's closed-loop learning capability. This module collects the actual execution results of action instructions, compares them with the model's prediction results, and calculates the feedback error. Then, it uses the feedback error to adjust the internal parameters of the dynamic evolution prediction model to improve prediction accuracy, and updates the capability and status information in the audit resource capability matrix based on the actual work performance of the auditors, thereby enabling the entire system to continuously self-optimize and iterate.

[0133] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent early warning and processing of information content release, characterized in that, The method comprises the following steps: S1, obtaining information content to be processed, and quantizing the information content to be processed into an initial risk vector; S2. Construct a risk potential energy field based on the initial risk vector and the preset generalized distance between information content; wherein, a risk source is determined according to the initial risk vector of the information content to be processed, and the risk potential energy field is constructed based on the impact of the risk source on other information content within the platform and the preset generalized distance between information content, wherein the intensity of the impact of the risk source on other information content within the platform is proportional to the risk quantification value of the risk source itself, and decreases as the preset generalized distance between information content increases; based on the risk potential energy field and the risk potential energy calculation formula, the local risk potential energy of the information content to be processed is calculated by accumulating the impact of the risk source on all other information content, and the risk potential energy calculation formula is: In the formula, Information content to be processed The local risk potential energy; As a unique identifier for other information content, A unique identifier for the information content to be processed; The scaling constant for the fundamental potential energy; For other information content The initial risk vector; This is a scenario weight vector used to adjust the importance of different risk dimensions; For other information content Information content to be processed The presupposed generalized distance between them; S3, using the real-time interaction data of the information content to perform evolution prediction on the risk potential field through a dynamic evolution prediction model, to identify risk points and risk clusters; wherein, the risk potential of the risk potential field is a dynamic quantity that changes over time, the change rate of the risk potential is jointly determined by a risk growth effect and a natural decay effect, the risk growth effect is driven by the real-time interaction data of the information content, and the natural decay effect causes the risk potential to decrease over time; the dynamic evolution prediction model is constructed based on the risk growth effect and the natural decay effect, wherein, the dynamic evolution prediction model is a differential equation representing the change of the risk potential over time, and the dynamic evolution prediction model is: ; wherein, is the information content to be processed is the change rate of the local risk potential over time ; is a nonlinear function used to aggregate multiple risk growth factors; is the real-time interaction vector of the information content to be processed ; is the emotional gradient of the comment area of the information content to be processed ; is the propagation speed of the information content to be processed ; is a decay coefficient representing the natural decay effect; is the local risk potential of the information content to be processed at time ; S4, constructing an audit resource capability matrix, matching the identified risk points and risk clusters with the audit resource capability matrix, and generating action instructions through optimization of a scheduling objective function; S5, executing and updating the dynamic evolution prediction model and the audit resource capability matrix according to the execution result of the action instructions.

2. The intelligent pre-warning and processing method for information content publishing according to claim 1, characterized in that, In step S1, the step of obtaining information content to be processed and quantizing the information content to be processed into an initial risk vector comprises: calling an artificial intelligence model to analyze the obtained information content, identifying the risk attributes of the information content in the preset risk dimensions, and generating corresponding risk quantization values for the risk dimensions corresponding to the identified risk attributes; based on the risk quantification values, constructing an initial risk vector, the initial risk vector being a vector composed of the risk quantification values, for characterizing an initial multi-dimensional risk state of the information content, wherein the initial risk vector is ; wherein is the initial risk vector of the information content to be processed, is the initial risk vector of the information content to be processed, is the total number of preset risk dimensions, is the initial risk vector of the information content to be processed, is the risk quantization value on the nth risk dimension, n is a positive integer greater than 1.

3. The intelligent pre-warning and processing method for information content publishing according to claim 1, characterized in that, In step S3, the step of identifying risk points and risk clusters by using real-time interaction data of the information content through a dynamic evolution prediction model to evolve and predict the risk potential field comprises: identifying the information content whose risk potential exceeds a preset risk threshold as the risk point; constructing an information content correlation graph based on a preset generalized distance between the information content, wherein the nodes of the information content correlation graph are the information content, and the edges of the information content correlation graph represent the correlation strength between the information content; applying a community discovery algorithm on the information content correlation graph, and taking the node set connected closely identified by the community discovery algorithm as the risk cluster.

4. The intelligent pre-warning and processing method for information content publishing according to claim 1, characterized in that, In step S4, the step of constructing an audit resource capability matrix, matching the identified risk points and risk clusters with the audit resource capability matrix, and generating action instructions through optimization of a scheduling objective function comprises: The audit resource capability matrix is composed of an auditor capability vector, which is used to represent the capability and state information of the auditor, and the auditor capability vector includes a scoring vector, a work efficiency value, a real-time fatigue index and a current work load, wherein the scoring vector is composed of professional ability scores of the auditor for the preset risk dimensions; The work efficiency value is used to represent the efficiency of the auditor in processing tasks; The real-time fatigue index is used to represent the current fatigue state of the auditor; The current work load is used to represent the amount of tasks allocated to the auditor.

5. The intelligent pre-warning and processing method for information content publishing according to claim 4, characterized in that, In step S4, the step of constructing an audit resource capability matrix, matching the identified risk points and risk clusters with the audit resource capability matrix, and generating action instructions through optimization of a scheduling objective function further comprises: For the action instruction to be evaluated, the predicted future platform total risk generated by executing the action instruction and the total cost required to execute the action instruction are determined respectively; Based on the predicted future platform total risk and the total cost, an optimization scheduling objective function for evaluating the comprehensive benefit of the action instruction is constructed by weighted summation, and the optimal action instruction is found by solving the optimization scheduling objective function; The optimization scheduling objective function is: ； wherein, is a comprehensive target value; is an action instruction to be optimized; is a predicted future platform total risk after executing the action instruction ; is a total cost required for executing the action instruction ; and is a weight coefficient for balancing the future platform total risk and the total cost.

6. The intelligent early warning and processing method for information content release according to claim 5, characterized in that, The optimal action instruction also satisfies the professional ability constraint and the work load constraint, wherein, The professional ability constraint is that when the action instruction is a manual review task, the task is assigned to a reviewer of the manual review task according to a professional ability score of a score vector in a reviewer ability vector of the reviewer on a risk dimension corresponding to the task, wherein the professional ability score of the score vector in the reviewer ability vector corresponding to the reviewer on the risk dimension corresponding to the task is greater than or equal to a preset ability requirement threshold of the manual review task; The work load constraint is that when the action instruction is to assign a new review task to a reviewer, the new review task is assigned to the reviewer according to a weighted sum of a current work load and a real-time fatigue index of the reviewer, wherein if the weighted sum of the current work load and the real-time fatigue index of the reviewer is less than or equal to an upper limit value of the work load preset by the platform to guarantee review quality, the new review task is assigned to the reviewer.

7. The intelligent pre-warning and processing method for information content publishing according to claim 1, characterized in that, In step S5, the step of feeding back and updating the dynamic evolution prediction model and the review resource ability matrix according to the execution result of the action instruction includes: obtaining an actual execution result of the action instruction, and comparing the actual execution result with a predicted execution result before execution of the action instruction to calculate a feedback error; The feedback error is used to adjust internal parameters of the dynamic evolution prediction model, and the score vector, the work efficiency value, the real-time fatigue index and the current work load of the reviewer in the review resource ability matrix are updated according to the work performance of the reviewer embodied by the actual execution result.

8. An intelligent early warning and processing system for information content release, applied to the method of any one of claims 1-7, characterized in that, The system comprises: An information content quantification module configured to call an artificial intelligence model, analyze the obtained information content on a preset risk dimension, and construct an initial risk vector representing a multi-dimensional risk state of the information content; A risk potential field construction module configured to take the initial risk vector as a risk source, calculate a local risk potential of each information content by accumulating influences generated by all risk sources and decaying with a preset generalized distance, and construct a risk potential field; A risk evolution prediction module configured to perform evolution prediction on the risk potential field based on a dynamic evolution prediction model determined by a risk growth effect and a natural decay effect driven by real-time interaction data of the information content, identify a risk point by a risk threshold, and identify a risk cluster by applying a community discovery algorithm to an information content correlation graph; An action instruction generation module configured to generate an optimal action instruction by solving an optimization scheduling objective function aiming to minimize a weighted sum of a predicted future platform total risk and total cost, and under the condition of meeting a professional ability constraint and a work load constraint, based on a review resource ability matrix comprising a reviewer score vector, a work efficiency value, a real-time fatigue index and a current work load; A feedback updating module configured to calculate a feedback error according to an actual execution result of the action instruction to adjust internal parameters of the dynamic evolution prediction model, and update the score vector, the work efficiency value, the real-time fatigue index and the current work load in the review resource ability matrix according to the work performance of the reviewer.

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