A risk identification system for small and micro power utilization printing security

Abstract

The application discloses a kind of small and micro power utilization printing security risk identification systems, the system includes: multi-modal data acquisition and fusion module, responsible for collecting multidimensional data from each relevant system, and the multidimensional data collected are fused, form unified printing behavior portrait;Lightweight dynamic risk identification module, its architecture design adopts double-tower structure, including feature encoding tower and risk prediction tower;Risk quantification and early warning module, improved fuzzy comprehensive evaluation method is used to quantify risk, membership function is constructed using the correlation degree output by lightweight dynamic risk identification module, determine the membership degree of each index to different risk levels, finally get the comprehensive risk assessment result and risk score by fuzzy synthesis operation.The application constructs a complete, efficient and particularly suitable for small and micro power utilization printing security risk identification solution in complex scenarios, effectively overcomes many defects of prior art.

Description

Technical Field

[0001] This invention relates to the field of digital governance and information security technology, specifically to a risk identification system for the security of seal usage by small and micro-sized individuals at the grassroots level. Background Technology

[0002] The use of seals in the exercise of power at the grassroots level is a crucial link in the standardized operation of grassroots governance. With the popularization of digital governance methods, the traditional physical seal management method is gradually shifting to a technology-enabled model. However, the characteristics of grassroots seal usage scenarios, such as diverse matters, complex personnel, and open environments, make it difficult to effectively address actual risks by simply applying conventional corporate seal management models or general security identification technologies. Therefore, existing technical solutions still have significant shortcomings in terms of the real-time nature, accuracy, and adaptability of risk identification.

[0003] Currently, there is an approval control system based on a rule engine. This system equates the identification of security risks associated with using seals with the verification of process compliance. It typically pre-sets a series of rules and judges risks by comparing the degree of conformity between actual seal usage and the rule base. This type of solution performs well in structured scenarios and can effectively intercept obvious violations. However, the rigidity of the rule engine makes it difficult to cope with the complexity of seal usage at the grassroots level. For example, the system cannot effectively identify behaviors that use legal forms to cover up violations, such as situations where the content of the sealed document is maliciously altered but the approval process is complete. At the same time, the rule base requires manual maintenance and is updated late, making it unable to adapt to new risk patterns.

[0004] Meanwhile, there exists a seal comparison system based on traditional image processing. This system focuses on identifying the authenticity of the seal impression by comparing the image of the seal impression after it has been used with a preset standard seal impression to determine whether they match. Early systems used simple pixel comparison or contour matching algorithms, while more recent systems have introduced more complex feature extraction methods. However, traditional image processing methods face serious challenges in grassroots scenarios: on the one hand, the grassroots seal-using environment is uncontrollable, which can easily lead to seal impression deformation and blurring, significantly reducing the accuracy of recognition; on the other hand, the system only focuses on the seal impression itself, while neglecting the comprehensive judgment of key risk dimensions such as the legality of the content of the sealed document and the compliance of the user's behavior. Summary of the Invention

[0005] To address the aforementioned issues, the purpose of this invention is to propose a risk identification system for the security of small-scale official seal usage at the grassroots level. This system constructs a complete, efficient, and highly adaptable solution for identifying security risks related to the use of official seals in complex grassroots scenarios, effectively overcoming many shortcomings of existing technologies.

[0006] This was achieved through the following technical solutions: A risk identification system for the security of seal usage by grassroots micro-enterprises is proposed. This system includes: a multimodal data acquisition and fusion module, responsible for collecting multi-dimensional data on seal usage events to be identified from various relevant systems, and fusing the collected multi-dimensional data to form a unified seal usage behavior profile. The multi-dimensional data includes: approval process data, document content data, real-time behavior data, environmental context data, historical data, and business system data, and is divided into three categories: text data, numerical sequences, and image features; a lightweight dynamic risk identification module, whose architecture adopts a dual-tower structure, including a feature encoding tower and a risk prediction tower. The feature encoding tower uses a lightweight neural network to extract features from the three types of multi-dimensional data collected. The system uses semantic encoding to generate corresponding high-dimensional feature vectors that characterize the risks associated with seal usage. A risk prediction tower receives these fused high-dimensional feature vectors and employs a meta-learning mechanism to maintain a security support set and multiple risk domain support sets internally. It comprehensively assesses risk by calculating the correlation between the high-dimensional feature vector of the seal usage event to be identified and all support sets. The risk quantification and early warning module uses an improved fuzzy comprehensive evaluation method to quantify risk. It first selects evaluation indicators from multiple dimensions to determine the set of evaluation factors, then uses the correlation output from the lightweight dynamic risk identification module to construct a membership function, determining the degree of membership of each indicator to different risk levels. Finally, it obtains the comprehensive risk assessment result and risk score through fuzzy synthesis operations. This invention, through an innovative multimodal data fusion method, a lightweight dynamic risk identification model with meta-learning capabilities, and an intelligent early warning mechanism based on fuzzy comprehensive evaluation, constructs a complete, efficient, and particularly adaptable solution for identifying security risks associated with small-scale seal usage in complex grassroots scenarios, effectively overcoming many shortcomings of existing technologies.

[0007] Preferably, the approval process data includes at least the applicant for the seal, the approval chain, and the approval opinion; the document content data includes the text content and key fields of the document to be sealed; the real-time behavior data includes the force, angle, and duration of the seal-applying action, as well as a video stream of the sealing environment; the environmental context data includes the seal-applying time, geographical location, and device status; the historical record data includes at least the user's historical seal-applying records, violation records, and seal usage frequency; and the business system data includes business background information related to the seal-applying matter. By collecting multi-source heterogeneous data and constructing a multi-modal data fusion mechanism, risks can be perceived from multiple dimensions, eliminating monitoring blind spots.

[0008] Preferably, the document content data is extracted from the document to be stamped using OCR technology. Obtaining the document content data using OCR technology enables the identification of the core data content within the document to be stamped.

[0009] Preferably, real-time behavioral data is collected through sensors built into the smart seal device and cameras installed at the seal-using site. By collecting real-time behavioral data, it is possible to analyze whether the user is engaging in behaviors such as obscuring or switching the seal.

[0010] Preferably, the multi-dimensional data fusion method based on knowledge graphs forms a unified profile of seal-using behavior from the collected multi-dimensional data. This unified profile uses seal-using events as the core hub, linking six types of entity nodes: seal-using personnel, seal-using institutions, seals, documents to be sealed, seal-using devices, and business scenarios. It also designs dedicated relationship edges across the entire chain to construct the network relationship of the entire seal-using process. By using the knowledge graph-based multi-modal data association method, the information silos problem of traditional multi-source data collection is broken down, achieving a leap from physical collection to semantic fusion of multi-modal data.

[0011] Preferably, the feature encoding tower also performs triple customization and optimization for the scene characteristics of multi-dimensional data, including: first, for text data, a pruning Transformer module is used to efficiently encode key text information related to the seal application; second, for numerical sequences, a lightweight fully connected subnetwork is designed to dynamically adjust the number of neurons to adapt to the dimensional features of the seal application values; and third, for image features, a depthwise separable convolutional subnetwork is used to extract image features related to the authenticity of the seal application. By performing triple customization and optimization for the scene characteristics of multi-dimensional data, the reliability of the evaluation results can be improved.

[0012] Preferably, when performing feature extraction and semantic encoding on multi-dimensional data, a feature fusion mechanism for the stamping scenario is designed to collaboratively integrate the high-dimensional feature vectors of the three types of data through a scenario weight matrix. This feature fusion mechanism ensures that the final output feature vector not only contains the feature information of the data itself but also accurately maps the risk-related features in the stamping process.

[0013] Preferably, the lightweight dynamic risk identification module has a dynamic weight adjustment mechanism, where the weight of each risk assessment indicator is variable. By adjusting the weights of the corresponding indicators through the dynamic weight adjustment mechanism, the true risk level under different scenarios can be reflected more accurately.

[0014] Preferably, a mapping rule is established between the comprehensive risk assessment results and the risk level of seal usage, and based on the final risk score, the system activates a three-level early warning mechanism of red, yellow, and blue. By establishing a mapping rule, the ambiguity and uncertainty in risk judgment can be better addressed.

[0015] Preferably, the system's comprehensive risk assessment results communicate directly with business processes and physical devices via a standardized API interface. When a high risk is identified, the system will send a locking command to the smart seal device in real time, or require additional authentication to be added to the approval process. By communicating directly with business processes and physical devices through a standardized API interface, a closed-loop management system from risk perception to real-time intervention is achieved, eliminating potential security risks at their inception.

[0016] The beneficial effects of this invention compared to the prior art are: The technical solution of this invention constructs a technical framework integrating a data acquisition layer, a feature engineering layer, a core model layer, and an application service layer through an innovative multimodal data fusion method, a lightweight dynamic risk identification model with meta-learning capabilities, and an intelligent early warning mechanism based on fuzzy comprehensive evaluation. It is a solution for identifying security risks of small-scale rights and seals in complex grassroots scenarios, effectively overcoming many shortcomings of existing technologies. At the same time, this framework accesses multi-source heterogeneous data, utilizes innovative feature extraction and fusion methods, inputs the data into the core risk identification model of this invention for calculation, and finally outputs dynamic risk assessment results to provide service support for different application scenarios. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a risk identification system for the security of seal usage by small and micro-sized enterprises at the grassroots level. Detailed Implementation

[0018] The following will refer to the appendices in the embodiments of the present invention. Figure 1 The technical solutions in the embodiments of the present invention will be described in detail below.

[0019] like Figure 1 The diagram illustrates the principle of a risk identification system for the security of seal usage by small-scale grassroots entities. It includes: a multimodal data acquisition and fusion module, responsible for collecting multi-dimensional data from various related systems and fusing the collected data to form a unified profile of seal usage behavior; a lightweight dynamic risk identification module, whose architecture adopts a dual-tower structure, including a feature encoding tower and a risk prediction tower; and a risk quantification and early warning module, which uses an improved fuzzy comprehensive evaluation method to quantify risks, constructs a membership function using the correlation output of the lightweight dynamic risk identification module, determines the membership degree of each indicator to different risk levels, and finally obtains a comprehensive risk assessment result and risk score through fuzzy synthesis calculation. This invention constructs a complete, efficient, and particularly adaptable risk identification solution for the security of seal usage by small-scale grassroots entities, effectively overcoming many shortcomings of existing technologies.

[0020] The system specifically includes the following: The multimodal data acquisition and fusion module is responsible for collecting multi-dimensional data of the seal-using events to be identified from various related systems, and fusion of the collected multi-dimensional data to form a unified seal-using behavior profile, namely the seal-using knowledge graph. The multi-dimensional data includes: approval process data, document content data, real-time behavior data, environmental context data, historical record data, and business system data, and is divided into three categories: text data, numerical sequences, and image features.

[0021] In this embodiment, the approval process data comes from the OA system, which includes at least the applicant for the seal, the approval chain, the approval opinion, and the reason for the seal. The OA system stands for Office Automation System. The document content data is extracted from the document to be sealed using OCR technology, including the text content and key fields in the document. The key fields include information such as amount, date, and signatory, in order to identify the core data content in the document to be sealed. Real-time behavior data is collected by the sensors built into the smart seal device and the camera installed at the sealing site, including the force, angle, and duration of the sealing action and the video stream of the sealing environment, to analyze whether the seal user has engaged in behaviors such as obstruction or substitution. Environmental context data includes the sealing time, the geographical location of the sealing, and the device status, in order to clarify whether the sealing time is during working hours, whether the geographical location of the sealing is in a designated area, and the security of the network and the device fingerprint. Historical data includes at least the user's historical sealing records, violation records, and the frequency of seal use. Business system data comes from the three-capital supervision platform and the financial system, including business background information related to the sealing matter. Here, OCR stands for Optical CharacterRecognition is a technology that can recognize and convert text images in media such as pictures, photos and scanned documents into editable and searchable text data. This invention can perceive risks from multiple dimensions and eliminate monitoring blind spots by collecting multi-source heterogeneous data and building a multimodal data fusion mechanism.

[0022] In this embodiment, the multi-dimensional data fusion method based on knowledge graph multimodal data association forms a unified seal-using behavior profile from the collected multi-dimensional data. The unified seal-using behavior profile takes the seal-using event as the core hub and links six types of entity nodes: seal-using personnel, seal-using institutions, seals, documents to be sealed, seal-using equipment, and business scenarios. It also designs a full-link exclusive relationship edge from initiating a seal-using application to the approval and review of the seal-using application, to the execution of the sealing action, and finally the traceability of the seal-using record. This constructs a network relationship for the entire seal-using process, thereby breaking the information silo problem of traditional multi-source data collection and realizing the leap from physical collection to semantic fusion of multimodal data.

[0023] The lightweight dynamic risk identification module adopts a dual-tower architecture, including a feature encoding tower and a risk prediction tower. The feature encoding tower uses a lightweight neural network to extract features and semantically encode the collected three types of multi-dimensional data, generating corresponding high-dimensional feature vectors with the ability to represent the risk of stamp use. The risk prediction tower receives the fused high-dimensional feature vectors and uses a meta-learning mechanism to maintain a security support set and multiple risk domain support sets internally. It comprehensively judges the risk by calculating the correlation between the high-dimensional feature vector of the stamp use event to be identified and all support sets.

[0024] In this embodiment, the feature coding tower also performs triple customization and optimization for the scene characteristics of multi-dimensional data. Specifically, it includes: First, for text data, such as document clauses and approval notes, a pruning Transformer module is used to efficiently encode key text information related to the use of seals; second, for numerical sequences, such as the frequency of use of seals, permission level, and document number, a lightweight fully connected subnetwork is designed to adapt to the dimensional features of the seal numerical values ​​by dynamically adjusting the number of neurons, and an abnormal value detection layer is embedded to identify seal applications with frequencies far exceeding the norm; third, for image features, such as seal impressions and signature positions, a depthwise separable convolutional subnetwork is used to reduce computational load while extracting image features related to the authenticity of the use of seals through customized seal contour detection convolutional kernels and signature position deviation calculation layers. Thus, by performing triple customization and optimization for the scene characteristics of multi-dimensional data, the reliability of the evaluation results can be improved.

[0025] The pruning Transformer module retains the semantic association capture capabilities of the traditional Transformer while achieving lightweight and efficient encoding by focusing on risk information and pruning redundant computations. Its core structure adds three core units to the traditional Transformer Encoder layer: key information localization and pruning, lightweight attention, and feature compression. The overall process proceeds from text preprocessing to key information localization and pruning, then to lightweight attention computation, and finally feature compression output. This structured optimization achieves a balance between efficiency and performance.

[0026] In this embodiment, when performing feature extraction and semantic encoding on multi-dimensional data, a feature fusion mechanism for the seal application scenario is designed. The high-dimensional feature vectors of the three types of data are collaboratively integrated through the scenario weight matrix. The scenario weight matrix plays a role in increasing the weight ratio of seal image features and sensitive text features, which can ensure that the final output feature vector not only contains the feature information of the data itself, but also accurately maps the risk-related features in the seal application process.

[0027] In this embodiment, the security support set is a standard feature library for secure seal usage under the meta-learning mechanism. It contains a massive number of verified, risk-free seal usage events with high-dimensional feature vectors. Each vector is associated with a security label, comprehensively covering the legitimate features of the three core dimensions of the seal user, the seal usage behavior, and the seal usage content. Furthermore, a dynamic update mechanism that regularly incorporates new compliant samples and removes outdated standards ensures the timeliness of the security benchmark. Its mechanism is based on security relevance. After the feature vector of the seal usage event to be identified is input, it first calculates the similarity with all features in the support set to obtain a security relevance score of 0-100. Then, the risk level of the seal usage event to be identified is determined based on the security relevance score, eliminating the need for subsequent complex matching.

[0028] The multiple risk domain support sets are categorized feature libraries split by risk type, consisting of several sub-support sets such as illegal entity, process violation, content risk, operational anomaly, and document tampering. Each sub-support set corresponds to a specific risk type and stores typical feature vectors for that type of risk. Its dynamic update mechanism supports adding new risk domains and optimizing existing ones. It can adapt to new risks with only a small number of samples, enhancing the model's generalization ability. The working mechanism of the multiple risk domain support sets is based on risk correlation. It calculates the risk correlation between the feature vector of the seal-using event to be identified and the risk of each risk domain support set, and then determines the risk level of the corresponding risk type of the seal-using event based on the risk correlation. This allows for precise location of any type of risk and the superposition of levels, while also supporting the determination of composite risks by associating multiple risk domains.

[0029] In this embodiment, the lightweight dynamic risk identification module features a dynamic weight adjustment mechanism, where the weight of each risk assessment indicator is variable, achieving deep collaboration between the spatiotemporal and business dimensions. In the time dimension, while adjusting the basic weight based on working or non-working time periods, it can also dynamically optimize the weight adjustment threshold by considering the periodic characteristics of seal usage. In the spatial and business dimensions, for scenarios with frequent seal applications, it does not rely solely on the historical approval rate as a single parameter, but integrates multiple dimensions such as the applicant's permission level, the sensitivity of associated documents, and the seal usage frequency threshold to establish a weighted calculation model, achieving refined and multi-factor driven adjustment of frequency indicator weights. Simultaneously, this mechanism also has a risk prediction and feedback function. When weight adjustment triggers a preset risk threshold, it automatically associates historical risk cases in the seal usage knowledge graph, reversely optimizing the weight calculation factors, thus forming a closed loop from adjustment to prediction to optimization, which can more accurately reflect the true risk level under different seal usage scenarios.

[0030] The risk quantification and early warning module uses an improved fuzzy comprehensive evaluation method to quantify risks. First, it selects evaluation indicators from multiple dimensions to determine the set of evaluation factors. Then, it uses the correlation output of the lightweight dynamic risk identification module to construct a membership function to determine the degree of membership of each indicator to different risk levels. Finally, it obtains the comprehensive risk assessment results and risk scores through fuzzy synthesis operations.

[0031] The risk quantification and early warning module also features a dynamic iteration mechanism for weight calculation. Through the risk case feedback iteration submodule, the feature data of historical risk events in the seal knowledge graph are transmitted to the weight calculation model in real time. The weight calculation model then recalculates the weights of the corresponding evaluation indicators. For example, when the incidence of any type of file tampering risk events increases, the weight of the corresponding file anti-counterfeiting feature matching degree indicator will automatically increase, realizing the upgrade of weights from static preset to dynamic adaptation.

[0032] In this embodiment, the final dynamic weight calculation method for the i-th risk assessment indicator at time t is as follows: the product of the basic weight, normalization coefficient, scenario matching coefficient, and real-time feedback coefficient of the current assessment indicator. The basic weight is initialized based on business experience and historical data. The normalization coefficient is used to ensure that the sum of the weights of all indicators is 1. The scenario matching coefficient is used to quantify the differences in the importance of each assessment indicator under different seal usage scenarios. The real-time feedback coefficient is obtained by multiplying the risk correlation factor and the identification accuracy factor. The risk correlation factor is calculated based on the correspondence between the assessment indicator anomalies and the occurrence of risks in multiple seal usage events that occur in a fixed period. The identification accuracy factor is adjusted according to the system's identification accuracy of assessment indicator anomalies.

[0033] The dynamic weight update adopts a dual mechanism of scheduled updates and event-triggered updates. By default, it updates the data based on the previous day's data at midnight every day. When three consecutive stamping events fail to identify the same abnormal indicator, or when new risk types are added or the frequency of scene switching increases, an update will be triggered immediately to ensure the speed of risk response. At the same time, it adapts to the low computing power requirements of small and micro scenarios through triple lightweight optimization. All calculations use linear formulas and rule judgments, and set upper and lower limits of 0.05-0.3 for the weight of individual indicators to prevent excessive weight skew.

[0034] In this embodiment, a mapping rule is established between the comprehensive risk assessment results and the seal usage risk level. Based on the final risk score, the system activates a three-level warning mechanism: red, yellow, and blue. Red indicates a high-risk seal usage scenario, yellow indicates a medium-risk scenario, and blue indicates a low-risk scenario. Specifically, when the risk score is ≥0.8, the current seal usage scenario is considered high-risk, triggering a dual-blocking mechanism: the comprehensive risk assessment results communicate directly with the business process and physical devices via a standardized API interface. When a high risk is identified, the system automatically freezes the seal application process and requires additional authentication in the approval process. Simultaneously, the system sends physical locking commands to the smart seal device in real time, such as cutting off the seal motor power and locking the seal module, and pushes warning information to the highest administrator in real time. The warning information includes risk details. The response latency of the dual-blocking mechanism is ≤100ms. The API stands for Application Programming Interface. Interface is an application programming interface. When the risk score is between 0.5 and 0.8, the current seal-using scenario is considered medium-risk. In this case, the conventional single-method approach of secondary authentication will be broken through, and a dynamic matching solution will be used based on the risk type. If the risk stems from doubts about the identity of the person using the seal, the person using the seal will be required to undergo dual authentication through facial recognition and employee badge scanning. If the risk stems from incomplete document information, a supplementary material list will be automatically pushed, such as requiring the upload of legal review opinions. When the risk score is less than 0.5, the current seal-using scenario is considered low-risk. In this case, on the basis of completing the seal-using operation normally, a risk traceability module will be added to automatically link the seal-using process data, such as the applicant's operation log, document scans, and seal usage trajectory, to the seal-using knowledge graph, forming a full lifecycle archive from seal-using to recording to traceability, thereby better handling the ambiguity and uncertainty in risk assessment.

[0035] In this embodiment, the risk score for any seal usage scenario is calculated as follows: Where w(i) is the weight value of any evaluation index, R 异常程度量化值 The deviation between the actual data of the current evaluation indicator and the set threshold is given by r, which is the correction coefficient for the current seal usage scenario, i is any evaluation indicator, and n is the total number of evaluation indicators. This enables accurate scoring, and the calculation logic is simple and can objectively reflect the true risk level of the seal usage event.

[0036] In summary, this invention constructs a technical framework integrating a data acquisition layer, a feature engineering layer, a core model layer, and an application service layer through an innovative multimodal data fusion method, a lightweight dynamic risk identification model with meta-learning capabilities, and an intelligent early warning mechanism based on fuzzy comprehensive evaluation. This framework is a solution for identifying security risks associated with the use of official seals in complex grassroots scenarios, effectively overcoming many shortcomings of existing technologies. Furthermore, by accessing multi-source heterogeneous data and utilizing innovative feature extraction and fusion methods, the framework inputs these data into the core risk identification model for calculation, ultimately outputting dynamic risk assessment results to provide service support for different application scenarios, demonstrating significant advancements.

[0037] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A risk identification system for the security of official seal usage by small and micro-sized individuals at the grassroots level, characterized in that, include: The multimodal data acquisition and fusion module is responsible for collecting multi-dimensional data of the seal-using events to be identified from various related systems, and fusing the collected multi-dimensional data to form a unified seal-using behavior profile. The multi-dimensional data includes: approval process data, document content data, real-time behavior data, environmental context data, historical data, and business system data, and is divided into three categories: text data, numerical sequences, and image features. The lightweight dynamic risk identification module adopts a dual-tower architecture, including a feature encoding tower and a risk prediction tower. The feature encoding tower uses a lightweight neural network to extract features and semantically encode the collected three types of multi-dimensional data, generating corresponding high-dimensional feature vectors with the ability to represent the risk of stamp use. The risk prediction tower receives the fused high-dimensional feature vectors and uses a meta-learning mechanism to maintain a security support set and multiple risk domain support sets internally. It comprehensively judges the risk by calculating the correlation between the high-dimensional feature vector of the stamp use event to be identified and all support sets. The risk quantification and early warning module uses an improved fuzzy comprehensive evaluation method to quantify risks. First, it selects evaluation indicators from multiple dimensions to determine the set of evaluation factors. Then, it uses the correlation output of the lightweight dynamic risk identification module to construct a membership function to determine the degree of membership of each indicator to different risk levels. Finally, it obtains the comprehensive risk assessment results and risk scores through fuzzy synthesis operations.

2. The risk identification system for the security of seal usage by small and micro-sized enterprises at the grassroots level, as described in claim 1, is characterized in that... The approval process data includes at least the applicant for the seal, the approval chain, and the approval opinion; the document content data includes the text content and key fields of the document to be sealed; the real-time behavior data includes the force, angle, and duration of the sealing action, as well as the video stream of the sealing site environment; the environmental context data includes the sealing time, the sealing location, and the device status; the historical record data includes at least the user's historical sealing records, violation records, and the frequency of seal use; and the business system data includes business background information related to the sealing matter.

3. A risk identification system for the security of official seal usage by small and micro-sized enterprises at the grassroots level, as described in claim 2, is characterized in that... The document content data is extracted from the document to be printed using OCR technology.

4. A risk identification system for the security of official seal usage by small and micro-sized enterprises at the grassroots level, as described in claim 2, is characterized in that... Real-time behavioral data is collected through sensors built into the smart stamp device and cameras installed at the stamping site.

5. A risk identification system for the security of official seal usage by small and micro-sized enterprises at the grassroots level, as described in claim 1, is characterized in that... Multi-dimensional data fusion, based on the knowledge graph-based multimodal data association method, forms a unified profile of seal usage behavior from the collected multi-dimensional data. The unified profile of seal usage behavior takes the seal usage event as the core hub and links six types of entity nodes: seal user, seal-using organization, seal, document to be sealed, seal-using equipment, and business scenario. It also designs exclusive relationship edges for the entire chain to build the network relationship of the entire seal usage process.

6. A risk identification system for the security of official seal usage by small and micro-sized enterprises at the grassroots level, as described in claim 1, is characterized in that... The feature coding tower also performs triple customization and optimization for the scene characteristics of multi-dimensional data, including: First, for text data, a cropped Transformer module is used to efficiently encode the key text information of the seal; second, for numerical sequences, a lightweight fully connected sub-network is designed to adapt to the dimensional features of the seal values ​​by dynamically adjusting the number of neurons; and third, for image features, a deep separable convolutional sub-network is used to extract image features related to the authenticity of the seal.

7. A risk identification system for the security of official seal usage by small and micro-sized enterprises at the grassroots level, as described in claim 1, is characterized in that... When performing feature extraction and semantic encoding on multi-dimensional data, a feature fusion mechanism for the seal application scenario is designed to collaboratively integrate the high-dimensional feature vectors of the three types of data through the scenario weight matrix.

8. A risk identification system for the security of official seal usage by small and micro-sized enterprises at the grassroots level, as described in claim 1, is characterized in that... The lightweight dynamic risk identification module has a dynamic weight adjustment mechanism, and the weight of each risk assessment indicator is variable.

9. A risk identification system for the security of official seal usage by small and micro-sized enterprises at the grassroots level, as described in claim 1, is characterized in that... A mapping rule is established between the comprehensive risk assessment results and the risk level of seal usage, and the system activates a three-level early warning mechanism of red, yellow, and blue based on the final risk score.

10. A risk identification system for the security of official seal usage by small and micro-sized enterprises at the grassroots level, as described in claim 1, is characterized in that... The system's comprehensive risk assessment results communicate directly with business processes and physical devices through standardized API interfaces. When a high risk is identified, the system will send a locking command to the smart seal device in real time or require additional authentication to be added to the approval process.

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