Product shooting image data security management method and system

Abstract

The invention belongs to the field of information security, and discloses a product shooting image data security management method and system, and the method comprises the steps: 1, obtaining a purpose label based on borrowing application information and purpose description submitted by a user when borrowing equipment, and generating a structured data recording tuple based on the purpose label; step 2, obtaining a purpose legality judgment result of the image shot by the user based on the purpose label; 3, acquiring a spatial position of borrowed equipment during image shooting, and acquiring a comprehensive judgment result based on the spatial position; 4, acquiring an identity fingerprint of the image shot by the user, and recording an operation log about the image shot by the user based on the identity fingerprint; and 5, calculating a risk score based on the operation log, and executing a risk-driven cleaning strategy based on the risk score. According to the invention, the normal use convenience of the image equipment is ensured, and the compliance control and data security of the shooting behavior are obviously enhanced.

Description

Technical Field

[0001] The present invention relates to the field of information security, and in particular to a method and system for secure management of product image data. Background Art

[0002] In enterprises or scientific research institutions with increasingly stringent information security requirements, employees are generally prohibited from bringing personal devices with camera functions into office areas to prevent them from taking photos of confidential documents, screen contents, or the internal environment without authorization. However, there are still legitimate and necessary needs for taking photos at work, such as recording meeting whiteboards, collecting information when visiting, or providing on-site problem feedback. Therefore, some companies have deployed a "shared imaging equipment borrowing management system," whereby the unit provides camera equipment (such as dedicated mobile phones, portable cameras, etc.), and employees apply in advance when they need to use it, stating key information such as purpose, time period, and location, and can only borrow it after approval by the relevant person in charge. This system ostensibly achieves controllable image data and process review, but in actual implementation, there are still a large number of security loopholes and regulatory blind spots.

[0003] First, current systems often manage devices solely based on "process compliance." That is, as long as the requested purpose is filled out and approved, employees can use the device to record images. However, the system itself cannot determine whether the content captured by the user actually meets the requested purpose. For example, an employee may borrow a device under the pretext of "meeting minutes" but actually use it to record computer screens or sensitive documents. This "inconsistent purpose" violation is difficult to detect. Second, devices are shared by multiple people, and image data is not firmly tied to specific individuals. Even if the system records who borrowed the device, it is difficult to verify who captured which images after they are exported or leaked. Third, current devices are unable to detect the security level of the environment during use. That is, even if a user enters a "no-photography zone" such as a computer room or a confidential meeting room, no restrictions or alerts are triggered. Finally, image data management often relies on a "fixed-time automatic purge" policy, failing to integrate image usage frequency, access behavior, and data value into more intelligent lifecycle management. These issues collectively result in a situation where, while processes appear controllable, image data usage and storage remain out of control and difficult to track afterward, failing to meet the actual needs of high-security scenarios. Summary of the Invention

[0004] The purpose of the present invention is to disclose a method and system for securely managing product image data, and to solve the technical problems mentioned in the background art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for securely managing product image data, comprising:

[0007] Step 1: Obtain a usage tag based on the borrowing application information and usage description submitted by the user when borrowing the device, and generate a structured data record tuple based on the usage tag;

[0008] Step 2: Obtain the result of the legality judgment of the use of the image taken by the user based on the use label;

[0009] Step 3, obtaining the spatial position of the borrowed device when taking the image, and obtaining a comprehensive judgment result based on the spatial position;

[0010] Step 4: Obtain the identity fingerprint of the image taken by the user, and record the operation log of the image taken by the user based on the identity fingerprint;

[0011] Step 5: Calculate risk scores based on the operation logs and execute risk-driven cleanup strategies based on the risk scores;

[0012] The structured usage tags are obtained based on the borrowing application and usage description submitted by the user when borrowing the device, including:

[0013] Get the usage label corresponding to the usage description;

[0014] Based on the usage label and the borrowing application information, it is determined whether the user's borrowing application is approved. If so, a structured data record tuple is generated based on the usage label.

[0015] Furthermore, the borrowing application information includes user information u, device information d and borrowing time period rt r , denote the usage description as D.

[0016] Furthermore, the usage label corresponding to the usage description is obtained, including:

[0017] Calculate the similarity between D and each tag in the tag library respectively;

[0018] The tag with the highest similarity in the tag library is used as the usage tag T corresponding to D.

[0019] Furthermore, judging whether to approve the user's borrowing application based on the usage tag and the borrowing application information includes:

[0020] Obtain the job level λ(u) contained in the user information u and determine whether λ(u) meets the minimum job level requirement corresponding to the usage tag T. If so, approve the user's borrowing application; otherwise, reject the user's borrowing application.

[0021] Furthermore, a structured data record tuple is generated based on the usage tag, including:

[0022] R=(u,T,d,rt r )

[0023] R represents a structured data record tuple.

[0024] Furthermore, step 2 includes:

[0025] Get the feature vector v of the image I taken by the user I ;

[0026] Get the feature vector v corresponding to the usage label T T ;

[0027] Calculate v I and v T The similarity S between them;

[0028] Let y represent the result of the legality judgment of the use of the image taken by the user;

[0029]

[0030] δ represents the preset similarity threshold.

[0031] Furthermore, step 3 includes:

[0032] L represents the spatial position of the device used by the user when taking the image I; λ(L) represents the minimum level that can be used to take a picture at L; y final Indicates the comprehensive judgment result;

[0033]

[0034] allowed_areas indicates the areas where photography is allowed; restricted_areas indicates the areas where photography is not allowed; λ(u) ≥ λ(L) indicates that the user's rank is greater than or equal to the minimum rank that allows photography in L; λ(u) < λ(L) indicates that the user's rank is less than the minimum rank that allows photography in L;

[0035] If the comprehensive judgment result is legal, the image I is stored in the database; otherwise, an early warning is issued according to the preset warning mechanism.

[0036] Furthermore, obtaining the identity fingerprint of the image taken by the user includes:

[0037] Perform content hash calculation on image I to obtain the hash value H of the content of image I I ;

[0038] The shooting time t of image I r , the spatial location L of the device, the device information d and the user information u are concatenated into a string and hashed to generate a metadata hash value H M ;

[0039] For H I and H M Perform splicing to obtain the identity fingerprint F of image I;

[0040] The identity fingerprint F is embedded into the metadata of the image I.

[0041] Furthermore, the operation log includes the operation type when operating on the image I in the database, the identity fingerprint of the image I, the user who performed the operation, and the start time and end time of the operation.

[0042] In a second aspect, the present invention provides a product shooting image data security management system, comprising a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module and a risk control module;

[0043] The first acquisition module is used to acquire a usage tag based on the borrowing application information and usage description submitted by the user when borrowing the device, and generate a structured data record tuple based on the usage tag;

[0044] The second acquisition module is used to obtain the use legality judgment result of the image taken by the user based on the use label;

[0045] The third acquisition module is used to obtain the spatial position of the borrowed device when taking the image, and obtain a comprehensive judgment result based on the spatial position;

[0046] The fourth acquisition module is used to obtain the identity fingerprint of the image taken by the user, and record the operation log of the image taken by the user based on the identity fingerprint;

[0047] The risk control module is used to calculate risk scores based on operation logs and execute risk-driven cleanup strategies based on risk scores;

[0048] The structured usage tags are obtained based on the borrowing application and usage description submitted by the user when borrowing the device, including:

[0049] Get the usage label corresponding to the usage description;

[0050] Based on the usage label and the borrowing application information, it is determined whether the user's borrowing application is approved. If so, a structured data record tuple is generated based on the usage label.

[0051] Beneficial effects:

[0052] The core of the present invention is to build a multi-dimensional security control mechanism with compliance review of shooting behavior, traceability of shooting content, and perception and regulation of safe areas as the core. The innovation of the present invention is reflected in the following aspects: First, the system can judge in real time whether the image content is consistent with the application purpose when the user uses the device to shoot, thereby identifying potential abuse and solving the problem of "disconnection between application content and actual use" in the existing system; Second, the system automatically embeds the identification information of the responsible person when the shooting image is generated, realizing a strong binding between the image and the individual behavior. Even if the image is exported, renamed, or forwarded, it can be traced back to the source, overcoming the core defect of the current "unbinding of the image and the responsible person"; Third, the system has the ability to perceive the use environment, and can identify whether it is currently in a high-sensitivity area where shooting is prohibited based on information such as vision and sensor data, and dynamically adjust the device permissions accordingly, filling the loophole of the existing system's lack of spatial restrictions; Fourth, the present invention provides a data cleaning strategy that combines image usage behavior and access trajectory, which is different from the simple "periodic deletion" and realizes a dynamic assessment of the value of image data and a risk-driven cleaning mechanism. Through the above-mentioned multi-level and mutually coordinated security management technology, the present invention significantly enhances the compliance control and data security of shooting behavior while ensuring the normal use convenience of imaging equipment. It is suitable for all kinds of enterprises, scientific research and government scenarios that have strict requirements on information shooting security. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 Schematic diagram of the product shooting image data security management method of the present invention. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0056] like Figure 1In one embodiment shown, the present invention provides a method for securely managing product image data, comprising:

[0057] Step 1: Obtain a usage tag based on the borrowing application information and usage description submitted by the user when borrowing the device, and generate a structured data record tuple based on the usage tag;

[0058] Step 2: Obtain the result of the legality judgment of the use of the image taken by the user based on the use label;

[0059] Step 3, obtaining the spatial position of the borrowed device when taking the image, and obtaining a comprehensive judgment result based on the spatial position;

[0060] Step 4: Obtain the identity fingerprint of the image taken by the user, and record the operation log of the image taken by the user based on the identity fingerprint;

[0061] Step 5: Calculate risk scores based on the operation logs and execute risk-driven cleanup strategies based on the risk scores;

[0062] The structured usage tags are obtained based on the borrowing application and usage description submitted by the user when borrowing the device, including:

[0063] Get the usage label corresponding to the usage description;

[0064] Based on the usage label and the borrowing application information, it is determined whether the user's borrowing application is approved. If so, a structured data record tuple is generated based on the usage label.

[0065] The primary task of step 1 is to convert the user-submitted borrowing application and usage description into structured usage tags, providing standardized input data for subsequent steps such as image compliance determination, identity tracing, and spatial compliance detection. This process not only helps the present invention automatically identify the legitimacy of each borrowing application but also provides a foundation for comprehensive system auditing while ensuring data consistency.

[0066] Furthermore, the borrowing application information includes user information u, device information d and borrowing time period rt r , denote the usage description as D.

[0067] User information u includes employee ID, job level and other identity information;

[0068] Device information d includes device number, type, etc.;

[0069] Borrowed time period rt r Including the use period of the equipment applied for (including start and end time);

[0070] The usage description D includes the user's natural language description of the purpose of borrowing the device, such as "taking meeting minutes" or "taking product problem feedback."

[0071] Furthermore, the usage label corresponding to the usage description is obtained, including:

[0072] Calculate the similarity between D and each tag in the tag library respectively;

[0073] The tag with the highest similarity in the tag library is used as the usage tag T corresponding to D.

[0074] For example, if the user description is "I want to take photos of meeting minutes," the similarity between this description and each tag in the tag library is calculated to find a matching tag. Assuming there are multiple tags in the tag library, "meeting minutes" has the highest similarity with the user description. Therefore, the application is assigned the purpose tag T = meeting minutes.

[0075] Furthermore, the similarity between D and each tag in the tag library is calculated separately, including:

[0076] The usage description is converted into a high-dimensional semantic vector v through a semantic understanding model (such as MiniLM) D ;

[0077] Use v Ti Represents the i-th tag T in the tag library i The semantic vector of D and T i The calculation formula for the similarity between them is:

[0078] S i =cos(v D ,v Ti )

[0079] S i Indicates that the cosine similarity calculation algorithm is used to calculate v D and v Ti The similarity obtained by calculation.

[0080] Furthermore, judging whether to approve the user's borrowing application based on the usage tag and the borrowing application information includes:

[0081] Obtain the job level λ(u) contained in the user information u and determine whether λ(u) meets the minimum job level requirement corresponding to the usage tag T. If so, approve the user's borrowing application; otherwise, reject the user's borrowing application.

[0082] Once the usage tag T is determined, the user information u will be further verified to see if its rank meets the tag requirements. Each tag in the tag library has a corresponding minimum rank, ensuring that only qualified users can apply for equipment for a specific purpose.

[0083] For example, the "Photographing Confidential Documents" tag might require users with a manager rank or higher to apply for equipment. If the user's rank λ(u) is an employee, which is lower than the minimum rank required for the "Photographing Confidential Documents" tag, the user does not meet the minimum rank requirement, and the borrowing application will be rejected.

[0084] Furthermore, a structured data record tuple is generated based on the usage tag, including:

[0085] R=(u,T,d,rt r )

[0086] R represents a structured data record tuple.

[0087] Once a user's borrowing application passes semantic matching and rank verification, a structured data record tuple R is generated. This tuple contains user information, usage tags, device information, and the borrowing period. R serves as input for subsequent steps (such as image compliance assessment and fingerprint embedding). This information ensures that all subsequent image reviews, behavioral audits, and data cleansing are based on accurate, structured data.

[0088] In step 1, the present invention has generated a usage tag T, which defines the expected usage scenario when the user applies for the device (such as "meeting minutes" or "customer feedback"). The task of step 2 is to confirm whether the captured image meets the usage tag T through image analysis.

[0089] Furthermore, step 2 includes:

[0090] Get the feature vector v of the image I taken by the user I ;

[0091] The image I captured by the user's borrowed device is input into an image recognition model (such as ResNet or Inception). The image recognition model has been trained on large-scale datasets and can extract semantic features of the image. These semantic features can reflect the objects, scenes, and other high-level information in the image.

[0092] Through this process, the image I is transformed into a high-dimensional feature vector v I , which contains the visual feature information of image I, such as the text, objects, environment and their relative positions in image I.

[0093] For example, if the image contains a meeting whiteboard and several attendees, the model will extract features related to "meeting minutes" and generate a feature vector v I .

[0094] Extracting complex features from images through deep learning can more comprehensively understand the high-level semantics of images and significantly improve the accuracy of judgments compared to traditional image processing methods based on low-level features such as color and texture.

[0095] Get the feature vector v corresponding to the usage label T T ;

[0096] In step 1, the usage description provided by the user when borrowing the device is converted into a structured usage label T. Each label is pre-defined with a corresponding feature vector in the label library, which represents the typical features of the image in that category.

[0097] In this step, the feature vector v of image I is I The feature vector v corresponding to the usage label T in the label library T Compare and evaluate whether the image I meets the requirements of the application. By calculating the image feature vector v I The feature vector v corresponding to the usage label T T The present invention can judge the degree of fit between the image content and the usage label based on the similarity between the two.

[0098] Calculate v I and v T The similarity S between them;

[0099]

[0100] cos means calculating cosine similarity;

[0101] Let y represent the result of the legality judgment of the use of the image taken by the user;

[0102]

[0103] δ represents the preset similarity threshold.

[0104] By calculating v I and v T The present invention determines whether the image content of image I complies with the user's declared purpose based on a preset similarity threshold δ. If S ≥ δ, the image content is consistent with the user's declared purpose and is judged as "legitimate." If S < δ, the image content does not comply with the user's declared purpose and is judged as "suspected abuse."

[0105] For example, if the user applies to borrow the device for the purpose of "taking meeting minutes", the feature vector v of image I is extracted through the image recognition model I and compare it with the feature vector v corresponding to the usage label T TFor comparison, suppose the calculated cosine similarity S = 0.92 and the threshold δ = 0.85. Since S is greater than δ, the image is considered "legal".

[0106] If the similarity is low, for example, S=0.72, the image will be marked as "suspected abuse" and may trigger subsequent manual review or other monitoring measures.

[0107] Step 2 is the core component of the behavior review process within the entire system, ensuring that non-compliant photography behavior does not occur during device borrowing. Through semantic matching and legality determination, the present invention automatically identifies and marks images that do not meet usage requirements, thereby preventing unauthorized use of image data. In this way, Step 2 provides an image behavior compliance review mechanism for the entire system, a critical component of image data security management.

[0108] The core task of step 3 is to ensure the legality of the image capture environment through spatial identification and shooting permission determination. Specifically, this step requires confirming that the capture occurred within a legal area and that the user has permission to capture within that area. Step 3 relies on the output y from step 2 to further determine the legality of the space and ultimately determine whether the image capture was legal.

[0109] Furthermore, step 3 includes:

[0110] Let L represent the spatial position of the device borrowed by the user when taking image I;

[0111] While the device is capturing images, it collects its spatial location data through various sensors, including GPS, barometers, and Bluetooth, to generate a spatial location, L. For example, the longitude and latitude coordinates returned by GPS, combined with the triangulation results of multiple Bluetooth sensors installed in a company, can determine whether the device is in a defined area, such as a "conference room," "confidential area," or "rest area."

[0112] The system maintains a database of spatial zones, assigning each zone a legal shooting location and job level requirement. This information will be used for subsequent permission determination.

[0113] Let λ(L) represent the minimum rank that can be used for filming at L;

[0114] Use y final It represents the comprehensive judgment result, combining the dual factors of content and space;

[0115]

[0116] allowed_areas indicates the areas where photography is allowed; restricted_areas indicates the areas where photography is not allowed; λ(u) ≥ λ(L) indicates that the user's rank is greater than or equal to the minimum rank that allows photography in L; λ(u) < λ(L) indicates that the user's rank is less than the minimum rank that allows photography in L;

[0117] If the comprehensive judgment result is legal, the image I is stored in the database; otherwise, an early warning is issued according to the preset warning mechanism.

[0118] For example, when y final If it is illegal, the information of the user who took the image I is sent to the company's information security manager.

[0119] Furthermore, considering that some areas are only allowed to shoot during specific time periods (such as shooting is prohibited during late night hours), a regularization term R that links time and space conditions is introduced. adj (t, L), the comprehensive judgment result can be fine-tuned according to the current time t and the regional time strategy.

[0120] The formula is as follows:

[0121] yb final =R(y final )+α·R adj (t,L)

[0122] If y final is "illegal", then R(y final )=1; if y final is "legal", then R(y final )=0.

[0123] R adj (t, L) is a regularization term that combines time t and location L to judge special scenes (such as nighttime or temporarily closed areas); yb final This is the result obtained after fine-tuning;

[0124] If yb final If it is greater than the set comparison threshold (for example) 0.5, it means the result is illegal, otherwise the result is legal.

[0125] Specific calculation of the regularization term:

[0126] If the shooting time is during a period when shooting is prohibited (for example, from 10 PM the previous day to 9 AM the next day), then the behavior is considered to be very high-risk and a high value, such as 1, is given.

[0127] If the shooting time is within the period when shooting is allowed, such as 9:00 to 21:00, the risk is lower and a medium value is given, such as 0.5.

[0128] α is the regularization term adjustment strength coefficient, and its value is configured by the system strategy, for example, it can be 0.6, which controls its effect on y final impact.

[0129] For example:

[0130] The user applies to take a photo of a "meeting record," and the image content is determined to be "legal" in step 2;

[0131] Spatial location L is in "Conference Room A", where filming is allowed and the filming time is 10:30, which complies with the regional time policy;

[0132] L requires the job level to be “ordinary employee”, while the job level in u is “supervisor”, that is, λ(u) ≥ λ(L);

[0133] Final judgment yb final =Legal.

[0134] This step introduces spatial recognition, job level comparison, and spatiotemporal linkage rule judgment, forming the "environmental compliance modeling layer" in the system to ensure that the equipment is not abused for shooting in illegal areas.

[0135] The core task of step 4 is to generate a unique fingerprint for each image and embed it into the image's metadata. This, combined with comprehensive logging of the image's usage, ensures traceability throughout the image's lifecycle. Through image fingerprints and detailed logging, the system can trace each image's generation, processing, transmission, and deletion, ensuring the integrity, security, and legality of the image data.

[0136] Identity fingerprint definition: An image identity fingerprint is a unique identifier that combines a hash value of the image content and image metadata (such as capture time, location, and user information). This fingerprint ensures the authenticity of the image content and provides traceability throughout the image's lifecycle.

[0137] Furthermore, obtaining the identity fingerprint of the image taken by the user includes:

[0138] Perform content hash calculation on image I to obtain the hash value H of the content of image I I ;

[0139] Content hash calculation of image I can be achieved through algorithms such as MD5 and SHA256;

[0140] The shooting time t of image I r , the spatial location L of the device, the device information d and the user information u are concatenated into a string and hashed to generate a metadata hash value H M ;

[0141] H M =hash(t r ||L||u||d)

[0142] For H I and H M Perform splicing to obtain the identity fingerprint F of image I;

[0143] F=hash(H I ||H M )

[0144] The identity fingerprint F is embedded into the metadata of the image I.

[0145] Images and their fingerprints are stored in an encrypted format to prevent data leakage or tampering. Every image upload, download, access, and deletion operation is logged to ensure that image usage and management always meet regulatory compliance requirements.

[0146] This method combines the image's content hash with the metadata from the time it was captured to generate a comprehensive identity fingerprint. This approach prevents traditional hash values from being forged or tampered with, ensuring the image's immutability.

[0147] Furthermore, the operation log includes the operation type when operating on the image I in the database, the identity fingerprint of the image I, the user who performed the operation, and the start time and end time of the operation.

[0148] Operation types include transfer, storage, access, and deletion.

[0149] The user performing the operation includes the user's rank, ID, etc.

[0150] Every time an image passes the system's legality check, the system will generate a new record of the storage operation in the log database.

[0151] The operation logs are classified according to the operation type and correspond one-to-one with the identity fingerprint F of the image.

[0152] For example, if a user accesses an image or performs a download operation, the system will record information such as the access time, operation type, and the user who performed the operation, ensuring that every image operation is recorded in detail.

[0153] Operation logs are stored in a dedicated log management system to ensure efficient query and secure storage. In the event of any illegal activity in the system, administrators can accurately trace all image operation history based on the log information.

[0154] Furthermore, the operation log can be used for regular audits. Using the image's fingerprint and operation log, the system can generate a detailed audit report that records all operations on each image.

[0155] The introduction of image fingerprints and logs enables comprehensive management and tracking of each image from creation, transmission, to deletion. Any illegal operation on the image will be recorded and quickly reported to the police, ensuring the security of the image.

[0156] The above solution comprehensively designs image identity fingerprint generation, operation logs, and image lifecycle management, ensuring the complete traceability of images throughout the system and enhancing data security and compliance.

[0157] The core task of step 5 is to build a risk model based on user access behavior and image usage history to predict potential risks and abuse, and to develop corresponding policies for image data cleanup, storage duration, and access rights management. By modeling and analyzing user access behavior, the system can trigger automatic cleanup policies or permission changes based on risk assessment throughout the image data storage lifecycle, thereby reducing the risks of data misuse, leakage, or long-term storage.

[0158] Furthermore, risk scores are calculated based on operation logs, including:

[0159] The access frequency, access period, access duration and job level of the accessing user of the image within a preset period (eg, one week) are counted based on the operation log.

[0160] Through these factors, the system will calculate a risk score R u ,is used to measure whether each user’s access to the image poses a potential risk.,Different from traditional data cleaning strategies, this step introduces a risk prediction model based on,access behavior.,This model not only relies on the user’s direct behavior data, but also,considers multiple factors such as time and frequency, enabling a more,accurate prediction of potential risks.

[0161] In order to more accurately assess the risk of an image, the present invention weights multiple factors of the user's access behavior to generate a comprehensive risk score R u . Assume that each factor f i (such as access frequency, access duration, etc.) have corresponding weights w i , the present invention can use the following formula to calculate the risk score:

[0162] R u =w1·f1+w2·f2+w3·f3

[0163] f1 represents the score of access frequency. For example, the access frequency can be divided into 10 intervals, and the total score is 100 points. The higher the access frequency, the higher the score. For example, when the access frequency is 95%, it belongs to the interval (90, 100), and the score is 100 points; for example, when the access frequency is 5%, it belongs to the interval [0, 10], and the score is 10 points.

[0164] f2 represents the score of the access period, which is scored based on whether the access period falls within the working hours. For example, if the working hours are from 9:00 to 18:00 and the access period is from 10:00 to 11:00, the access period falls within the working hours and is scored 50 points; if the access period is from 19:00 to 21:00, the access period falls within the working hours and is scored 100 points;

[0165] f3 represents the score of the access time. The access time and the score are in direct proportion. For example, if the full score is 100, when the access time is 12 hours, the score is

[0166] w1, w2, and w3 are the weights of each score, which can be adjusted based on historical data of risk analysis;

[0167] R u Higher values indicate greater risk.

[0168] According to the risk score R u Ability to classify users into different risk levels, such as low-risk users, medium-risk users and high-risk users, and design corresponding risk management strategies for each risk level.

[0169] For example, when the sum of the three weights is 1 and the maximum values of f1, f2, and f3 are all 100, then when the risk score belongs to [0,30), the user is a low-risk user; when the risk score belongs to [30,60), the user is a medium-risk user; when the risk score belongs to (60,100), the user is a high-risk user.

[0170] Furthermore, risk-driven cleanup strategies are implemented based on risk scores, including:

[0171] Using R threshold Represents the threshold of risk score, if R u More than R threshold , then execute the risk-driven cleanup strategy, which includes:

[0172] High-risk users: Their related images will be encrypted, removed, or marked as pending images;

[0173] Medium-risk users: restrict their access rights or require additional authentication;

[0174] Low-risk users: Continue to maintain normal access permissions.

[0175] This cleanup strategy is not only based on image storage time and usage frequency, but also incorporates user behavior analysis. For example, if a user frequently accesses high-privilege images at night and accesses them for a long time, the system will identify this behavior as potential abuse and trigger cleanup measures.

[0176] Based on the risk score, the system can dynamically adjust the user's access rights to images. For example, if the system determines that a user's access behavior poses a high risk, the system can adjust the permissions in the following ways:

[0177] Demoting a user's rank (e.g., from "Manager" to "Staff");

[0178] restricting access to certain high-risk images;

[0179] Force users to perform two-factor authentication to access images.

[0180] Step 5 provides a dynamic and intelligent management framework for the entire image management system through user access behavior modeling and risk-driven cleanup strategies. It can assess potential risks based on users' actual behavior and respond in a timely manner.

[0181] The core of the present invention is to build a multi-dimensional security control mechanism with compliance review of shooting behavior, traceability of shooting content, and perception and regulation of safe areas as the core. The innovation of the present invention is reflected in the following aspects: First, the system can judge in real time whether the image content is consistent with the application purpose when the user uses the device to shoot, thereby identifying potential abuse and solving the problem of "disconnection between application content and actual use" in the existing system; Second, the system automatically embeds the identification information of the responsible person when the shooting image is generated, realizing a strong binding between the image and the individual behavior. Even if the image is exported, renamed, or forwarded, it can be traced back to the source, overcoming the core defect of the current "unbinding of the image and the responsible person"; Third, the system has the ability to perceive the use environment, and can identify whether it is currently in a high-sensitivity area where shooting is prohibited based on information such as vision and sensor data, and dynamically adjust the device permissions accordingly, filling the loophole of the existing system's lack of spatial restrictions; Fourth, the present invention provides a data cleaning strategy that combines image usage behavior and access trajectory, which is different from the simple "periodic deletion" and realizes a dynamic assessment of the value of image data and a risk-driven cleaning mechanism. Through the above-mentioned multi-level and mutually coordinated security management technology, the present invention significantly enhances the compliance control and data security of shooting behavior while ensuring the normal use convenience of imaging equipment. It is suitable for all kinds of enterprises, scientific research and government scenarios that have strict requirements on information shooting security.

[0182] The present invention also provides a product shooting image data security management system, comprising a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module and a risk control module;

[0183] The first acquisition module is used to acquire a usage tag based on the borrowing application information and usage description submitted by the user when borrowing the device, and generate a structured data record tuple based on the usage tag;

[0184] The second acquisition module is used to obtain the use legality judgment result of the image taken by the user based on the use label;

[0185] The third acquisition module is used to obtain the spatial position of the borrowed device when taking the image, and obtain a comprehensive judgment result based on the spatial position;

[0186] The fourth acquisition module is used to obtain the identity fingerprint of the image taken by the user, and record the operation log of the image taken by the user based on the identity fingerprint;

[0187] The risk control module is used to calculate risk scores based on operation logs and execute risk-driven cleanup strategies based on risk scores;

[0188] The structured usage tags are obtained based on the borrowing application and usage description submitted by the user when borrowing the device, including:

[0189] Get the usage label corresponding to the usage description;

[0190] Based on the usage label and the borrowing application information, it is determined whether the user's borrowing application is approved. If so, a structured data record tuple is generated based on the usage label.

[0191] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for securely managing product image data, characterized in that: include: Step 1: Obtain a usage tag based on the borrowing application information and usage description submitted by the user when borrowing the device, and generate a structured data record tuple based on the usage tag; Step 2: Obtain the result of the legality judgment of the use of the image taken by the user based on the use label; Step 3, obtaining the spatial position of the borrowed device when taking the image, and obtaining a comprehensive judgment result based on the spatial position; Step 4: Obtain the identity fingerprint of the image taken by the user, and record the operation log of the image taken by the user based on the identity fingerprint; Step 5: Calculate risk scores based on the operation logs and execute risk-driven cleanup strategies based on the risk scores; The structured usage tags are obtained based on the borrowing application and usage description submitted by the user when borrowing the device, including: Get the usage label corresponding to the usage description; Based on the usage label and the borrowing application information, it is determined whether the user's borrowing application is approved. If so, a structured data record tuple is generated based on the usage label.

2. The method for securely managing product image data according to claim 1, wherein: Borrowing application information includes user information u, device information d and borrowing time period rt r , denote the usage description as D.

3. The method for securely managing product image data according to claim 2, wherein: Get the usage label corresponding to the usage description, including: Calculate the similarity between D and each tag in the tag library respectively; The tag with the highest similarity in the tag library is used as the usage tag T corresponding to D.

4. The method for securely managing product image data according to claim 3, wherein: Determine whether to approve the user's borrowing application based on the usage tag and borrowing application information, including: Obtain the job level λ(u) contained in the user information u and determine whether λ(u) meets the minimum job level requirement corresponding to the usage tag T. If so, approve the user's borrowing application; otherwise, reject the user's borrowing application.

5. The method for securely managing product image data according to claim 3, wherein: Generates structured data record tuples based on usage tags, including: R=(u,T,d,rt r ) R represents a structured data record tuple.

6. The method for securely managing product image data according to claim 5, wherein: Step 2 includes: Get the feature vector v of the image I taken by the user I ; Get the feature vector v corresponding to the usage label T T ; Calculate v I and v T The similarity S between them; Let y represent the result of the legality judgment of the use of the image taken by the user; δ represents the preset similarity threshold.

7. The method for securely managing product image data according to claim 6, wherein: Step 3 includes: Let L represent the spatial position of the device used by the user when taking the image I; let λ(L) represent the minimum level that can be used to take a picture at L; let y final Indicates the comprehensive judgment result; allowed_areas indicates the areas where photography is allowed; restricted_areas indicates the areas where photography is not allowed; λ(u) ≥ λ(L) indicates that the user's rank is greater than or equal to the minimum rank that allows photography in L; λ(u) < λ(L) indicates that the user's rank is less than the minimum rank that allows photography in L; If the comprehensive judgment result is legal, the image I is stored in the database; otherwise, an early warning is issued according to the preset warning mechanism.

8. The method for securely managing product image data according to claim 7, wherein: Obtain the identity fingerprint of the image taken by the user, including: Perform content hash calculation on image I to obtain the hash value H of the content of image I I ; The shooting time t of image I r , the spatial location L of the device, the device information d and the user information u are concatenated into a string and hashed to generate a metadata hash value H M ; For H I and H M Perform splicing to obtain the identity fingerprint F of image I; The identity fingerprint F is embedded into the metadata of the image I.

9. The method for securely managing product image data according to claim 8, wherein: The operation log includes the operation type when operating on the image I in the database, the identity fingerprint of the image I, the user who performed the operation, and the start time and end time of the operation.

10. Product shooting image data security management system, characterized by: It includes a first acquisition module, a second acquisition module, a third acquisition module, a fourth acquisition module and a risk control module; The first acquisition module is used to acquire a usage tag based on the borrowing application information and usage description submitted by the user when borrowing the device, and generate a structured data record tuple based on the usage tag; The second acquisition module is used to obtain the use legality judgment result of the image taken by the user based on the use label; The third acquisition module is used to obtain the spatial position of the borrowed device when taking the image, and obtain a comprehensive judgment result based on the spatial position; The fourth acquisition module is used to obtain the identity fingerprint of the image taken by the user, and record the operation log of the image taken by the user based on the identity fingerprint; The risk control module is used to calculate risk scores based on operation logs and execute risk-driven cleanup strategies based on risk scores; The structured usage tags are obtained based on the borrowing application and usage description submitted by the user when borrowing the device, including: Get the usage label corresponding to the usage description; Based on the usage label and the borrowing application information, it is determined whether the user's borrowing application is approved. If so, a structured data record tuple is generated based on the usage label.

Images