Data lifecycle sharing management method and system and medium

Abstract

The application discloses a data full-life-cycle sharing management and control method and system and a medium, relates to the technical field of data management, and comprises the following steps: acquiring a shared channel monitoring data set and a shared data real-time life-cycle characteristic, performing shared behavior compliance detection under full-life-cycle correlation, and acquiring a shared behavior compliance detection result; if the shared behavior compliance detection result is that the shared behavior is not compliant, performing hierarchical blocking management and control on the real-time data sharing channel; if the shared behavior compliance detection result is that the shared behavior is compliant, constructing a shared hidden danger evolution process model, performing dynamic reconstruction adjustment on the real-time data sharing channel, and establishing a shared channel reconstruction mechanism. The application solves the technical problem that, in the prior art, the data sharing process lacks effective management and control on the full life cycle of data, and thus the compliance of shared behavior is difficult to guarantee, and achieves the technical effects of realizing full-life-cycle compliance control of data sharing behavior and improving the safety and reliability of data sharing.

Description

Technical Field

[0001] This invention relates to the field of data management technology, specifically to methods, systems, and media for the sharing and management of data throughout its entire lifecycle. Background Technology

[0002] In the process of data sharing, data typically goes through multiple stages, including collection, storage, transmission, use, and destruction. The sharing entities, sharing methods, and risk characteristics differ at each stage. Existing data sharing management methods often focus on single-stage or post-event control, lacking continuous correlation and unified constraints on the entire data lifecycle's state and behavior. This makes it difficult to promptly detect changes in behavior and the evolution of risks during the sharing process, easily leading to problems such as sharing activities exceeding authorized scope and lagging control measures, making it difficult to effectively guarantee the compliance of data sharing activities. Summary of the Invention

[0003] This application provides a method, system, and medium for managing and controlling data sharing throughout its entire lifecycle, which addresses the technical problem in existing technologies where the lack of effective management and control over the entire lifecycle of data sharing makes it difficult to guarantee the compliance of sharing activities.

[0004] In view of the above problems, this application provides a method, system and medium for data sharing and management throughout the entire lifecycle.

[0005] The first aspect of this application provides a method for managing and controlling data sharing throughout its entire lifecycle, the method comprising:

[0006] The real-time data sharing channel of the data sharing platform is monitored in all dimensions to obtain the monitoring dataset and real-time lifecycle characteristics of the shared data. Based on the real-time lifecycle characteristics of the shared data, compliance checks on the sharing behavior are performed on the monitoring dataset under full lifecycle correlation to obtain compliance check results. If the compliance check result indicates non-compliance, a sharing channel blocking mechanism is triggered to implement tiered blocking control of the real-time data sharing channel. If the compliance check result indicates compliance, a sharing hazard evolution process model is constructed based on the monitoring dataset, and the real-time data sharing channel is dynamically reconstructed and adjusted based on the sharing hazard evolution process model, establishing a sharing channel reconstruction mechanism.

[0007] A second aspect of this application provides a data lifecycle sharing and management system, the system comprising:

[0008] The real-time monitoring module is used to perform full-dimensional real-time monitoring of the real-time data sharing channel of the data sharing platform, and to obtain the shared channel monitoring dataset and the real-time lifecycle characteristics of the shared data. The compliance detection module is used to perform full-lifecycle-related compliance detection of the shared behavior on the shared channel monitoring dataset based on the real-time lifecycle characteristics of the shared data, and to obtain the compliance detection results of the shared behavior. The hierarchical blocking and control module is used to trigger the shared channel blocking mechanism to perform hierarchical blocking and control of the real-time data sharing channel if the compliance detection result of the shared behavior is non-compliant. The dynamic reconstruction and adjustment module is used to construct a shared hidden danger evolution process model based on the shared channel monitoring dataset if the compliance detection result of the shared behavior is compliant, and to dynamically reconstruct and adjust the real-time data sharing channel based on the shared hidden danger evolution process model, thereby establishing a shared channel reconstruction mechanism.

[0009] A third aspect of the embodiments of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the data lifecycle sharing and management method provided in this application.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] This application performs full-dimensional real-time monitoring of the real-time data sharing channel of a data sharing platform, acquiring a shared channel monitoring dataset and real-time lifecycle characteristics of the shared data. Based on the real-time lifecycle characteristics of the shared data, it performs full-lifecycle compliance detection on the shared channel monitoring dataset, obtaining compliance detection results. If the compliance detection result indicates non-compliance, a shared channel blocking mechanism is triggered to implement tiered blocking control of the real-time data sharing channel. If the compliance detection result indicates compliance, a shared risk evolution process model is constructed based on the shared channel monitoring dataset, and the real-time data sharing channel is dynamically reconstructed and adjusted according to the shared risk evolution process model, establishing a shared channel reconstruction mechanism. This invention addresses the technical problem in existing technologies where the lack of effective control over the entire data lifecycle during data sharing leads to difficulties in ensuring compliance of shared behaviors. By performing compliance detection and control on the real-time data sharing channel based on the full lifecycle characteristics of the data, it achieves the technical effect of realizing full-lifecycle compliance control of data sharing behaviors and improving the security and reliability of data sharing. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the data lifecycle sharing and management method provided in the embodiments of this application;

[0014] Figure 2 This is a schematic diagram of the data lifecycle sharing and management system provided in the embodiments of this application.

[0015] Figure labeling: Real-time monitoring module 11, compliance detection module 12, graded blocking and control module 13, dynamic reconfiguration and adjustment module 14. Detailed Implementation

[0016] This application addresses the technical problem of insufficient effective control over the entire data lifecycle in existing technologies, which makes it difficult to guarantee the compliance of data sharing activities. By providing a data lifecycle sharing management method, system, and medium, this application achieves the technical effect of realizing full lifecycle compliance control of data sharing activities and improving the security and reliability of data sharing by conducting compliance detection and management of real-time data sharing channels based on the characteristics of the entire data lifecycle.

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0018] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0019] Example 1, as Figure 1 As shown, this application provides a data lifecycle sharing and management method, the method including:

[0020] Step S100: Perform full-dimensional real-time monitoring of the real-time data sharing channel of the data sharing platform to obtain the monitoring dataset of the sharing channel and the real-time lifecycle characteristics of the shared data.

[0021] In this embodiment, during the operation of the data sharing platform, the real-time data sharing channel is continuously monitored. Through the platform's existing interface gateway, log recording component, and link monitoring component, request information, response information, and channel operating status generated during the data sharing process are collected synchronously. The collected information includes the data request occurrence time, data provider and data user identifiers, interface identifier, data object identifier, sharing operation type, data transmission status, transmission time, and abnormal situations. Simultaneously, the throughput, number of concurrent connections, and resource usage of the data sharing channel are collected, thereby achieving real-time monitoring of the real-time data sharing channel in multiple aspects.

[0022] Next, the collected monitoring information will be organized according to the data sharing channel and time sequence. Request information will be matched with corresponding response information, and data with inconsistent formats or missing key information will be processed to form a shared channel monitoring dataset reflecting data sharing behavior and channel operation status. This shared channel monitoring dataset is used to describe the data interactions that occur during the operation of the real-time data sharing channel and its corresponding operational status.

[0023] Based on the shared channel monitoring dataset, and combined with the existing data catalog and data management information in the platform, the monitored data objects are identified, and the stage of the data in the sharing process is determined, including data generation, data transmission, data usage, data retention, and data deletion. According to the time of sharing, sharing method, and participating entities, the duration, stage changes, and flow path of the data in each stage are recorded, thereby extracting the real-time lifecycle characteristics of the shared data to reflect the stage and changes of the data in the sharing process.

[0024] Step S200: Perform a full lifecycle-based compliance detection of the shared channel monitoring dataset based on the real-time lifecycle characteristics of the shared data, and obtain the compliance detection results of the shared behavior.

[0025] In this embodiment, when performing full lifecycle-based compliance detection of shared behavior on the shared channel monitoring dataset based on the real-time lifecycle characteristics of shared data, the process first involves retrieving and organizing historical normal data sharing processes based on the data lifecycle chain, extracting shared behavior records that conform to established sharing rules and authorization constraints, and constructing a full lifecycle benchmark shared behavior space covering each stage of the data lifecycle. Then, the real-time lifecycle characteristics of shared data are used to perform stage correspondence and state mapping on the full lifecycle benchmark shared behavior space to obtain a current benchmark shared behavior space that matches the current data sharing state. Subsequently, shared behavior characteristics formed during the current data sharing process are identified from the shared channel monitoring dataset to form a current shared behavior feature set. The current shared behavior feature set is then compared and analyzed with the current benchmark shared behavior space to determine whether the shared behavior conforms to the full lifecycle sharing rules, thereby generating a shared behavior compliance detection result.

[0026] Furthermore, in the method provided in the application embodiments, the method further includes performing full lifecycle-related compliance detection of the shared channel monitoring dataset based on the real-time lifecycle characteristics of the shared data, and obtaining the compliance detection result of the shared behavior, as well as:

[0027] Retrieve normal sharing behavior records based on the data lifecycle chain to establish a full lifecycle benchmark sharing behavior space; map and match the full lifecycle benchmark sharing behavior space based on the real-time lifecycle characteristics of the shared data to obtain the current benchmark sharing behavior space; identify sharing behavior features based on the shared channel monitoring dataset to obtain the current sharing behavior feature set; perform compliance detection based on the current benchmark sharing behavior space and the current sharing behavior feature set to generate the sharing behavior compliance detection result.

[0028] In this embodiment, when conducting compliance testing of sharing behavior, the system first retrieves normal sharing behavior records based on the data lifecycle chain and establishes a baseline sharing behavior space for the entire lifecycle. Specifically, it reads the key fields of each sharing from the historical sharing records of the data sharing platform. The key fields include at least the data object identifier, caller identifier, callee identifier, interface identifier, sharing operation type, sharing purpose, authorized sharing scope, occurrence time, and response status. Then, it checks each historical sharing record against the sharing rules, authorization relationships, and access control information configured in the platform, retaining only records that meet the sharing rules and authorized sharing scope as normal sharing behavior records. Subsequently, it categorizes the normal sharing behavior records according to the data lifecycle chain, marking each record with its lifecycle stage. Within each lifecycle stage, it statistically analyzes the allowed caller set, callee set, interface set, sharing operation type set, and sharing purpose set, while recording the allowed value range and allowed combination relationships of each field. This forms the baseline sharing behavior space for the entire lifecycle, which is used to provide the scope of allowed data sharing behavior in each lifecycle stage.

[0029] After obtaining the full lifecycle baseline shared behavior space, it is mapped and matched according to the real-time lifecycle characteristics of the shared data. These real-time lifecycle characteristics include at least the data object identifier, current lifecycle stage, data type, authorized sharing scope, and sharing purpose. In this process, the current lifecycle stage from the real-time lifecycle characteristics is first read, and the baseline behavior scope for the corresponding stage is located in the full lifecycle baseline shared behavior space. Then, the data type, authorized sharing scope, and sharing purpose from the real-time lifecycle characteristics are read, and the above information is compared item by item with the allowed conditions in the located stage's baseline behavior scope to determine whether it belongs to the allowed set or falls within the allowed range, and whether the combination relationship between fields satisfies the allowed combination. When both field comparison and combination comparison are satisfied, the corresponding baseline behavior scope is retained; when there are unsatisfactory items, the corresponding baseline behavior scope is removed. After filtering, the current baseline shared behavior space applicable to the current data state is obtained. This current baseline shared behavior space is used to limit the allowed data sharing behaviors under the current lifecycle stage and current authorized conditions.

[0030] Next, based on the shared channel monitoring dataset, shared behavior characteristics are identified to obtain the current shared behavior feature set. Specifically, the currently occurring data sharing records are read from the shared channel monitoring dataset, and fields related to the shared behavior are extracted. These fields include at least the data object identifier, caller identifier, callee identifier, interface identifier, shared operation type, occurrence time, number of bytes transmitted, transmission time, and response status. Records with the same data object identifier within a continuous time window are summarized, and the frequency of shared occurrences, number of consecutive calls, number of failures, and number of exceptions are calculated. At the same time, the correspondence between the caller identifier and the callee identifier is preserved, thus forming the current shared behavior feature set, which is used to describe the actual occurrence of the current data sharing behavior.

[0031] Finally, compliance checks are performed on the current shared behavior feature set based on the current baseline shared behavior space, and a shared behavior compliance check result is generated. Specifically, each shared record in the current shared behavior feature set is checked one by one. First, the applicable scope of the current baseline shared behavior space is determined by the data object identifier. Then, it is checked in sequence whether the caller identifier is within the allowed caller set, whether the callee identifier is within the allowed callee set, whether the interface identifier is within the allowed interface set, whether the shared operation type is within the allowed shared operation type set, and whether the shared purpose meets the authorized sharing scope requirements. The combination relationship between the above fields is also checked to see if it belongs to the allowed combination. At the same time, it is checked whether the sharing frequency, the number of consecutive calls, the number of failures, and the number of exceptions fall within the range limited by the current baseline shared behavior space. When all the checks are satisfied, a shared behavior compliance check result is generated as compliant. When any check item is not satisfied, a shared behavior compliance check result is generated as non-compliant, thus completing the shared behavior compliance check based on the data lifecycle association.

[0032] Step S300: If the compliance detection result of the sharing behavior is that the sharing behavior is non-compliant, the sharing channel blocking mechanism is triggered to perform hierarchical blocking and control on the real-time data sharing channel.

[0033] In this embodiment, when the compliance detection result of the sharing behavior is non-compliant, the non-compliant sharing behavior is analyzed based on the compliance detection result to extract the core non-compliance information. A risk assessment is then performed on the non-compliant behavior based on this core non-compliance information to generate a corresponding non-compliance risk level. Subsequently, according to a pre-set sharing channel blocking mechanism, the non-compliance risk level is mapped to a blocking level. This blocking mechanism includes a level 1 blocking mechanism, a level 2 blocking mechanism, and a level 3 blocking mechanism. When the non-compliance risk level is mild non-compliance, the level 1 blocking mechanism is activated to block the real-time data sharing channel. When the non-compliance risk level is moderate non-compliance, the level 2 blocking mechanism is activated to block the real-time data sharing channel. When the non-compliance risk level is severe non-compliance, the level 3 blocking mechanism is activated to block the real-time data sharing channel. This achieves non-compliance risk level determination based on core non-compliance information and hierarchical blocking control of the real-time data sharing channel.

[0034] Furthermore, in the method provided in the application embodiments, if the compliance detection result of the sharing behavior is that the sharing behavior is non-compliant, triggering the sharing channel blocking mechanism to perform graded blocking control on the real-time data sharing channel, it further includes:

[0035] If the compliance detection result of the sharing behavior is non-compliant, the core non-compliance information is extracted based on the compliance detection result, and a non-compliance risk level is generated based on the core non-compliance information. The sharing channel blocking mechanism includes a level 1 blocking mechanism, a level 2 blocking mechanism, and a level 3 blocking mechanism. If the non-compliance risk level is mild non-compliance, the level 1 blocking mechanism is activated to block the real-time data sharing channel. If the non-compliance risk level is moderate non-compliance, the level 2 blocking mechanism is activated to block the real-time data sharing channel. If the non-compliance risk level is severe non-compliance, the level 3 blocking mechanism is activated to block the real-time data sharing channel.

[0036] In this embodiment, when the compliance detection result of the sharing behavior is non-compliant, the compliance detection result is first analyzed item by item. The shared records judged as non-compliant are read from the detection results, and non-compliance core information is extracted from the corresponding records. This non-compliance core information includes the identifier of the data object where the non-compliance occurred, the corresponding real-time data sharing channel identifier, the data lifecycle stage where the non-compliance occurred, the type of non-compliant sharing operation, the identifier of the calling entity, and the start and end times of the non-compliant behavior. For example, when a data object is called by an unauthorized entity through a sharing interface during its usage phase, the non-compliance core information clearly records the data object identifier, the real-time data sharing channel identifier where the interface is located, the usage stage, the actual type of calling operation, the identifier of the entity initiating the call, and the specific time the call occurred, thereby accurately locating the position and scope of the non-compliant behavior.

[0037] After extracting the core information on non-compliance, a non-compliance risk level is generated based on this information. Specifically, the level is determined according to the scope and degree of impact of the non-compliance on the data sharing process. When the core information indicates that the violation involves only a single data object and occurs only in one sharing process—for example, when a piece of data is abnormally accessed during transmission but not further propagated—a mild non-compliance risk level is generated. When the core information indicates that the violation continues to occur within the same data lifecycle stage—for example, when the same type of data is continuously provided to unauthorized entities during the usage stage—a moderate non-compliance risk level is generated. When the core information indicates that the violation has affected the overall operation of the real-time data sharing channel or caused abnormal data flow across multiple lifecycle stages, a severe non-compliance risk level is generated. This ensures that the non-compliance risk level reflects the severity of the non-compliance.

[0038] After generating a non-compliance risk level, a corresponding shared channel blocking mechanism is selected based on the non-compliance risk level. This mechanism includes Level 1, Level 2, and Level 3 blocking mechanisms. When the non-compliance risk level is minor, the Level 1 blocking mechanism is activated. By controlling the transmission rules in the real-time data sharing channel, only the non-compliant data identified in the core non-compliant information is blocked from transmission, while maintaining the normal flow of other data in the real-time data sharing channel. A rectification notice is sent to the data sharing platform administrator and the entity responsible for the non-compliant operation, clearly specifying the non-compliant data object, the lifecycle stage of the violation, and the type of non-compliant operation. The Level 1 blocking is lifted after rectification is completed and verified.

[0039] When the non-compliance risk level is moderate, the level 2 blocking mechanism is activated. By adjusting the access permissions of the real-time data sharing channel, the sharing capability of the data corresponding to the non-compliant core information at the data lifecycle stage is suspended, so that the data at that lifecycle stage cannot continue to flow through the channel. At the same time, the non-compliant data marked in the non-compliant core information is isolated and stored, and the sharing permissions of the non-compliant entity in the data sharing platform are frozen. During the blocking period, the cause of the non-compliant behavior is verified.

[0040] When the non-compliance risk level is severe, a three-level blocking mechanism is activated. By controlling the connection status of the real-time data sharing channel, all data transmission connections of the real-time data sharing channel are cut off, prohibiting any data from entering or leaving the channel. At the same time, the operation permissions of the non-compliant core information and its related data are frozen to prevent the non-compliant data from being tampered with, leaked or spread in the later stages of the data lifecycle. The operation log and data flow record of the real-time data sharing channel are saved simultaneously for subsequent non-compliance verification, accountability and rectification. This achieves hierarchical blocking control based on the non-compliant core information and the non-compliance risk level.

[0041] Step S400: If the shared behavior compliance detection result is that the shared behavior is compliant, construct a shared hidden danger evolution process model based on the shared channel monitoring dataset, and dynamically reconstruct and adjust the real-time data sharing channel according to the shared hidden danger evolution process model to establish a shared channel reconstruction mechanism.

[0042] In this embodiment, if the compliance detection result of the sharing behavior is compliant, a shared risk evolution process model is constructed based on the shared channel monitoring dataset. In this process, based on the shared channel monitoring dataset, a time-series analysis of shared security risks is first performed to extract the security risk process trajectory; then, a time-series analysis of shared privacy risks is performed based on the shared channel monitoring dataset to obtain the privacy risk process trajectory; subsequently, a time-series analysis of shared quality risks is performed to obtain the quality risk process trajectory, recording the quality problems that may arise during data sharing and their evolution. Finally, by aligning and fusing the security risk process trajectory, the privacy risk process trajectory, and the quality risk process trajectory in multiple dimensions, a shared risk evolution process model is generated.

[0043] Next, the real-time data sharing channel is dynamically reconstructed and adjusted based on the shared hazard evolution process model. In this process, the real-time data sharing channel is dynamically mapped and adjusted according to the shared hazard evolution process model to obtain the channel adjustment decision space. Subsequently, based on the shared channel risk factors, the channel adjustment decision space is optimized under risk constraints to select channel adjustment candidate spaces that meet risk control requirements. Then, weights are allocated according to the shared channel risk factors to establish a channel reliability calculation model. Finally, the channel reliability calculation model is used to iteratively optimize the channel reliability of the channel adjustment candidate space, thereby generating a shared channel reconstruction mechanism. This ensures that risks can be effectively controlled and the stability and reliability of the channel can be improved when dynamically adjusting the shared channel.

[0044] Furthermore, in the method provided in the application embodiments, constructing a shared hazard evolution process model based on the shared channel monitoring dataset further includes:

[0045] Based on the shared channel monitoring dataset, a time-series analysis of shared security risks is performed to obtain the process trajectory of security risks; a time-series analysis of shared privacy risks is performed based on the shared channel monitoring dataset to obtain the process trajectory of privacy risks; a time-series analysis of shared quality risks is performed based on the shared channel monitoring dataset to obtain the process trajectory of quality risks; and a multi-dimensional risk process alignment and fusion is performed based on the process trajectories of security risks, privacy risks, and quality risks to generate the shared risk evolution process model.

[0046] In this embodiment of the application, when performing time-series analysis of shared safety hazards based on the shared channel monitoring dataset, the process first involves a shared safety correlation evaluation based on the shared channel monitoring dataset to obtain a safety correlation network of monitoring parameters; then, by analyzing the safety correlation network of monitoring parameters, the process captures the safety anomaly features to form a safety anomaly feature network; finally, based on the safety anomaly feature network, the process fit of the hazard time-series trend is performed to generate the safety hazard process trajectory.

[0047] Next, a time-series analysis of privacy risks in shared data is conducted based on the shared channel monitoring dataset. This process begins by extracting privacy-related monitoring fields from the dataset and sorting the records by timestamp. Access to the same data object identifier across different caller identifiers is then organized chronologically. Subsequently, within fixed time intervals, indicators such as the number of entities accessing the data object identifier, cross-entity sharing frequency, access frequency, number of changes in access scope, and the proportion of abnormal accesses are statistically analyzed. The statistical results are then checked against the authorized sharing scope and purpose registered on the platform. If an accessing entity is outside the authorized sharing scope, the sharing purpose does not conform to the registered purpose, or the access scope exceeds the authorized scope, a privacy risk is marked as occurring in the corresponding time period. The start and end times of the privacy risk period, the data object identifiers involved, the caller identifier, the callee identifier, and the interface identifier are recorded. By connecting the marking results of each time period chronologically, a privacy risk process trajectory is formed. This trajectory represents the emergence, spread, and convergence of privacy risks over time.

[0048] Subsequently, when conducting time-series analysis of shared quality risks based on the shared channel monitoring dataset, quality-related monitoring fields are first extracted from the dataset, and the records are sorted by timestamp. Transmission results with the same interface identifier and the same data object identifier are then organized in chronological order. Next, within fixed time intervals, indicators such as transmission success rate, error return rate, number of data retransmissions, number of data missing times, number of field format validation failures, and transmission latency fluctuations are statistically analyzed. These indicators are then compared item by item with pre-set quality thresholds. If the transmission success rate is lower than the quality threshold, or the error return rate is higher than the quality threshold, or if there is an abnormal increase in the number of field format validation failures, a quality risk is marked as occurring in the corresponding time period. The start and end times of the time period in which the quality risk occurs, the interface identifiers involved, the data object identifiers, and the response status are recorded. By connecting the marking results of each time period in chronological order, a quality risk process trajectory is formed. This trajectory represents the generation and change of quality risks during the data sharing process.

[0049] Finally, after obtaining the process trajectories of security risks, privacy risks, and quality risks, multi-dimensional risk process alignment and fusion are performed. Specifically, a unified time axis and a unified time interval are first determined, and the process trajectories of security risks, privacy risks, and quality risks are mapped onto this unified time axis, so that the three trajectories have one-to-one corresponding risk status records within the same time period. For each time period, a joint risk status containing security risk markers, privacy risk markers, and quality risk markers is generated, and the joint risk status is associated with and saved with the corresponding real-time data sharing channel identifier, interface identifier, caller identifier, callee identifier, and data object identifier. Then, the joint risk statuses of consecutive time periods are connected in chronological order to form a shared risk evolution process model that can simultaneously reflect the coordinated changes of security risks, privacy risks, and quality risks on the same time axis. This shared risk evolution process model is used to describe the overall evolution law of multi-dimensional risks from emergence, superposition, to change during the data sharing process.

[0050] Furthermore, in the method provided in the application embodiments, the process of performing time-series analysis of shared security risks based on the shared channel monitoring dataset to obtain the trajectory of the security risk process also includes:

[0051] Based on the shared channel monitoring dataset, a shared safety correlation evaluation is performed to obtain a monitoring parameter safety correlation network; based on the monitoring parameter safety correlation network, safety anomaly features are captured to obtain a safety anomaly feature network; based on the safety anomaly feature network, a hazard time series trend is fitted to generate the safety hazard process trajectory.

[0052] In this embodiment, when performing a shared security correlation evaluation based on a shared channel monitoring dataset to obtain a security correlation network of monitoring parameters, the following steps are taken: First, monitoring parameters related to security status are extracted from the shared channel monitoring dataset. These monitoring parameters include the number of access failures, authentication failures, abnormal interface calls, connection interruptions, timeouts, and retries. Then, the monitoring parameters are sorted according to timestamps, and statistical analysis is performed on them at fixed time intervals, obtaining a sequence of statistical values ​​for each monitoring parameter within each time period. Next, correlation calculation is performed on any two types of monitoring parameters. This involves forming two equal-length sequences of statistical values ​​for the two types of monitoring parameters within the same time period, calculating their respective averages, and calculating the difference between the statistical value and the corresponding average for each time period. The differences between the two types of monitoring parameters within the same time period are multiplied and summed over all time periods to obtain a value reflecting the degree of common change in the two types of monitoring parameters. Simultaneously, the squares of the sums of the squares of the differences between the two types of monitoring parameters are taken, and the square root is taken to obtain the magnitude of change for each. Finally, the degree of common change is divided by the product of the magnitudes of change in the two types of monitoring parameters to obtain the correlation coefficient between them. Subsequently, the correlation coefficient is used to determine whether there is a synchronous change relationship between the two types of monitoring parameters. When the absolute value of the correlation coefficient is close to 1, it indicates that the two types of monitoring parameters show a clear synchronous change trend in the time dimension. When the correlation coefficient is close to 0, it indicates that there is no clear synchronous change relationship between the two types of monitoring parameters. Based on this, the correlation coefficient is compared with a pre-set judgment threshold. When the absolute value of the correlation coefficient is consistently higher than the judgment threshold and remains stable over multiple consecutive time periods, a safety correlation is identified between the two types of monitoring parameters, and this safety correlation is recorded. Finally, a monitoring parameter safety correlation network is constructed using monitoring parameters as nodes and safety correlations as connections. This monitoring parameter safety correlation network is used to describe the overall correlation structure between safety-related monitoring parameters.

[0053] After obtaining the security correlation network of monitoring parameters, security anomaly features are captured based on this network. Specifically, within the current time period, the statistical values ​​of each monitoring parameter are compared item by item with the baseline statistical range formed during historical normal operation. If the current statistical value exceeds the corresponding baseline range, the monitoring parameter is marked as an abnormal monitoring parameter. Simultaneously, the security correlation relationships in the monitoring parameter security correlation network are checked. The correlation coefficient recalculated for the current time period is compared with the baseline correlation coefficient range of the corresponding security correlation relationship during historical normal operation. If the current correlation coefficient deviates from the baseline range, the security correlation relationship is considered abnormal, and the corresponding connection relationship is marked as an abnormal correlation. Abnormal monitoring parameters and their abnormal correlation relationships are extracted from the monitoring parameter security correlation network to form a security anomaly feature network. This network is used to centrally reflect the abnormal states of security-related monitoring parameters and their correlation relationships during data sharing.

[0054] After obtaining the safety anomaly feature network, a time-series trend fitting of potential hazards is performed based on the network. Specifically, the safety anomaly feature networks formed over multiple consecutive time periods are arranged chronologically. Within each time period, the number of abnormal monitoring parameters, the number of abnormal correlations, and the duration of the abnormal state are statistically analyzed. These statistical results are then arranged chronologically to form a safety anomaly state sequence, and a trend fitting is performed on this sequence to obtain a continuous curve showing the change in safety anomaly intensity over time. The safety anomaly intensity is characterized by both the number of abnormal monitoring parameters and the number of abnormal correlations. Based on the trend curve, the process of safety anomalies from their appearance, enhancement, maintenance, weakening, and eventual disappearance is determined, and this process is mapped to the corresponding time periods to generate a safety hazard process trajectory.

[0055] Furthermore, in the method provided in the application embodiments, the real-time data sharing channel is dynamically reconstructed and adjusted according to the shared hidden danger evolution process model to establish a shared channel reconstruction mechanism, which further includes:

[0056] The real-time data sharing channel is dynamically mapped and adjusted according to the shared hidden danger evolution process model to obtain the channel adjustment decision space; the channel adjustment decision space is optimized by risk constraints according to the shared channel risk factors to obtain the channel adjustment candidate space; the channel reliability calculation model is obtained by weight allocation according to the shared channel risk factors; the channel reliability is iteratively optimized according to the channel reliability calculation model and the channel adjustment candidate space to generate the shared channel reconstruction mechanism.

[0057] In this embodiment, when dynamically mapping and adjusting the real-time data sharing channel according to the shared hazard evolution process model, the following steps are first taken: First, the safety hazard process trajectory, privacy hazard process trajectory, and quality hazard process trajectory corresponding to the current time point are read from the shared hazard evolution process model, and it is determined whether there are safety hazard markers, privacy hazard markers, or quality hazard markers within the current time period. Then, the adjustable operational content in the real-time data sharing channel is organized, including the start / stop status of the sharing channel, interface call permission configuration, allowed data sharing frequency per unit time, concurrent connection limit, and data transmission path settings between different systems. Next, based on the presence or absence of safety hazard markers, privacy hazard markers, and quality hazard markers... Quality hazard markers restrict the corresponding operational content. Specifically, when a security hazard marker is present, restrictions are placed on the shared channel's start / stop status and transmission path. When a privacy hazard marker is present, restrictions are placed on interface call permissions. When a quality hazard marker is present, restrictions are placed on data sharing frequency and the number of concurrent connections. Based on this, multiple optional configuration methods are set for each operational content, and these optional configuration methods are combined to form various channel adjustment schemes. Finally, each channel adjustment scheme is checked against system resource conditions and operational constraints, eliminating schemes that cannot be practically implemented, and retaining the set of schemes that can be implemented in the real-time data sharing channel, thus obtaining the channel adjustment decision space.

[0058] Next, when optimizing the channel adjustment decision space based on shared channel risk factors, we first learn from the shared channel risk factors to construct a shared channel risk prediction model. Then, we use the shared channel risk prediction model to perform multi-dimensional risk prediction on various channel adjustment schemes in the channel adjustment decision space to obtain the corresponding decision risk characteristics. After that, we obtain the shared risk constraints, which include shared security risk constraints, shared privacy risk constraints, and shared quality risk constraints. Finally, based on the decision risk characteristics and the shared risk constraints, we screen the adjustment schemes in the channel adjustment decision space, retain the adjustment schemes that meet the risk constraints, and generate the channel adjustment candidate space.

[0059] Subsequently, when allocating weights based on shared channel risk factors, the shared channel risk factors are first determined to include shared security risk, shared privacy risk, and shared quality risk. Within a preset time range, the number of abnormal records corresponding to each of the three types of shared channel risk factors is counted. Then, the number of abnormal records for the three types is added together to obtain the total number of abnormal records. The number of abnormal records corresponding to shared security risk, shared privacy risk, and shared quality risk is then divided by the total number of abnormal records to obtain the weights for shared security risk, shared privacy risk, and shared quality risk, respectively. This completes the weight allocation of the shared channel risk factors, ensuring that the sum of the three weights is 1. Based on this, for each channel adjustment scheme in the channel adjustment candidate space, the number of abnormal records corresponding to the shared security risk, shared privacy risk, and shared quality risk under the scheme is counted. The number of abnormal records is multiplied by the corresponding risk weight and summed to obtain the weighted total risk corresponding to the channel adjustment scheme. Then, the channel reliability is calculated by dividing the channel reliability by 1 and the reciprocal of the sum of the weighted total risk, so that the channel reliability is always positive and the smaller the weighted total risk, the greater the channel reliability, thereby constructing a channel reliability calculation model.

[0060] Finally, when iteratively optimizing the channel adjustment candidate space based on the channel reliability calculation model, the channel reliability is first calculated for each channel adjustment scheme in the candidate space using the channel reliability calculation model, and a one-to-one correspondence between the channel adjustment scheme and the channel reliability is established. Then, the channel adjustment schemes are sorted in descending order of channel reliability, and the channel adjustment scheme with the highest channel reliability is selected as the current preferred scheme. Next, using the current preferred scheme as a comparison benchmark, the remaining channel adjustment schemes in the channel adjustment candidate space are screened round by round, eliminating channel adjustment schemes with channel reliability lower than the current preferred scheme, and the channel reliability of the retained channel adjustment schemes is recalculated and sorted again. When the retained channel adjustment schemes no longer change after multiple rounds of screening, the channel reliability iterative optimization process ends, and the channel adjustment scheme with the highest final channel reliability is determined as the shared channel reconstruction mechanism.

[0061] Furthermore, in the method provided in the application embodiments, the process of optimizing the channel adjustment decision space based on shared channel risk factors to obtain a channel adjustment candidate space further includes:

[0062] Deep learning is performed on the shared channel risk factors to obtain a shared channel risk prediction model. These risk factors include shared security risks, shared privacy risks, and shared quality risks. Multidimensional risk prediction is then performed on the channel adjustment decision space based on this model to obtain multiple decision risk characteristics. Shared risk constraints are then obtained, including shared security risk constraints, shared privacy risk constraints, and shared quality risk constraints. Finally, the channel adjustment decision space is filtered based on these multiple decision risk characteristics and the shared quality risk constraints to generate a channel adjustment candidate space.

[0063] In this embodiment of the application, when performing deep learning based on the risk factors of the shared channel, training samples are first constructed from the shared channel monitoring dataset and historical channel operation records. The shared security risk, shared privacy risk, and shared quality risk within each time period are used as input features, and the corresponding risk results within the same time period are used as learning targets. Subsequently, the training samples are time-aligned and standardized so that the input features of different time periods have a unified dimension and value range, forming a feature sequence arranged in chronological order. Based on this, a network architecture for the shared channel risk prediction model is constructed. The network architecture consists of an input layer, at least two fully connected layers, and an output layer. The input layer contains input nodes corresponding to shared security risk, shared privacy risk, and shared quality risk, respectively. Each fully connected layer contains a weight parameter matrix and a bias parameter vector. The weight parameter matrix is ​​used to determine the influence strength of the output of the previous layer on each node of this layer, and the bias parameter vector is used to adjust the output of this layer. The output of the fully connected layer is obtained by weighted summation of the output of the previous layer and the weight parameter matrix, superimposed with the bias parameter vector, and then passed through an activation function, where the activation function is used to introduce nonlinear mapping capability. The output layer contains three output nodes, which are used to output the prediction results of shared security risk, shared privacy risk, and shared quality risk, respectively. During model training, in each training iteration, the training samples are first input into the shared channel risk prediction model to obtain the output layer results. Then, the output layer results are compared with the learning target to obtain the error value. Subsequently, based on the error value, the weight parameter matrix and bias parameter vector of each layer are updated by backpropagation to gradually reduce the error value until the preset termination condition is met, thereby completing the training of the shared channel risk prediction model and obtaining the shared channel risk prediction model.

[0064] Next, when performing multi-dimensional risk prediction on the channel adjustment decision space based on the shared channel risk prediction model, input fields representing the adjustment content are first extracted for each channel adjustment scheme in the channel adjustment decision space. The input fields include the shared channel start / stop status, interface call permission configuration, allowed data sharing frequency per unit time, concurrent connection limit, and data transmission path settings. After ensuring that the input fields are consistent with the input format of the shared channel risk factors, they are input into the shared channel risk prediction model. Subsequently, the output layer of the shared channel risk prediction model provides the prediction results of the shared security risk, shared privacy risk, and shared quality risk corresponding to the channel adjustment scheme. The three types of prediction results are then correlated with the channel adjustment scheme to form multiple decision risk characteristics. These decision risk characteristics are used to characterize the risk performance of the channel adjustment scheme under the three dimensions of shared security risk, shared privacy risk, and shared quality risk.

[0065] After obtaining multiple decision risk characteristics, shared risk constraints are obtained, including shared security risk constraints, shared privacy risk constraints, and shared quality risk constraints. Among them, the shared security risk constraints are used to limit the permissible range of shared security risk, the shared privacy risk constraints are used to limit the permissible range of shared privacy risk, and the shared quality risk constraints are used to limit the permissible range of shared quality risk. The shared risk constraints serve as the basis for judgment when screening channel adjustment schemes.

[0066] Finally, the channel adjustment decision space is screened based on multiple decision risk characteristics and shared risk constraints. Specifically, firstly, for each channel adjustment scheme in the channel adjustment decision space, its corresponding decision risk characteristics are read one by one; then, it is determined whether the prediction results of shared security risks, shared privacy risks, and shared quality risks satisfy the shared security risk constraints, respectively; when any risk dimension does not meet the corresponding shared risk constraints, the channel adjustment scheme is removed from the channel adjustment decision space; when all three risk dimensions meet the corresponding shared risk constraints, the channel adjustment scheme is retained; by completing the above screening process for all channel adjustment schemes in the channel adjustment decision space, a channel adjustment candidate space that meets the shared risk constraints is obtained.

[0067] Furthermore, in the method provided in the application embodiments, triggering the shared channel blocking mechanism to perform tiered blocking control of the real-time data sharing channel further includes:

[0068] Real-time acquisition of blocking monitoring data of the real-time data sharing channel; and closed-loop optimization of the blocking control mechanism of the sharing channel based on the blocking monitoring data.

[0069] In this embodiment, when acquiring real-time data sharing channel blocking monitoring data, after the sharing channel blocking mechanism takes effect on the real-time data sharing channel, the blocking-related information is synchronously collected by continuously reading the existing interface access logs, channel operation logs, and access control records in the data sharing platform. The collected information includes the blocking start time, the restricted data object identifier, the restricted interface identifier, the restricted sharing operation type, the blocking duration, and the number of sharing requests arriving at the real-time data sharing channel, the number of rejected requests, and the number of abnormal requests during the blocking period. Subsequently, the above information is organized according to the real-time data sharing channel identifier and time sequence, and new records are continuously added during the blocking period, thereby forming blocking monitoring data that can continuously reflect the blocking execution process and changes in channel operation.

[0070] Next, when optimizing the shared channel blocking mechanism through a closed-loop control based on blocking monitoring data, the blocking monitoring data is first compared and analyzed. Changes in the number of shared requests, rejected requests, and abnormal requests are statistically analyzed before, during, and after the blocking implementation. Then, the effectiveness of the current blocking method in suppressing non-compliant sharing behavior is assessed based on whether abnormal requests decrease during the blocking period and whether abnormal requests reappear after the blocking ends. When blocking monitoring data shows that abnormal requests continue to occur after the blocking implementation, the blocking trigger conditions are adjusted or the scope of restricted objects is expanded in the shared channel blocking mechanism. When blocking monitoring data shows that abnormal requests have been effectively controlled but the number of rejected requests remains high, the scope of restricted objects is adjusted or the blocking duration is shortened in the shared channel blocking mechanism. By updating the above adjustments to the shared channel blocking mechanism configuration, a closed-loop optimization process based on blocking monitoring data feedback is formed, allowing the shared channel blocking mechanism to be continuously corrected and improved through multiple executions.

[0071] In summary, the embodiments of this application have at least the following technical effects:

[0072] This application performs full-dimensional real-time monitoring of the real-time data sharing channel of a data sharing platform, acquiring a shared channel monitoring dataset and real-time lifecycle characteristics of the shared data. Based on the real-time lifecycle characteristics of the shared data, it performs full-lifecycle compliance detection on the shared channel monitoring dataset, obtaining compliance detection results. If the compliance detection result indicates non-compliance, a shared channel blocking mechanism is triggered to implement tiered blocking control of the real-time data sharing channel. If the compliance detection result indicates compliance, a shared risk evolution process model is constructed based on the shared channel monitoring dataset, and the real-time data sharing channel is dynamically reconstructed and adjusted according to the shared risk evolution process model, establishing a shared channel reconstruction mechanism. This invention addresses the technical problem in existing technologies where the lack of effective control over the entire data lifecycle during data sharing leads to difficulties in ensuring compliance of shared behaviors. By performing compliance detection and control on the real-time data sharing channel based on the full lifecycle characteristics of the data, it achieves the technical effect of realizing full-lifecycle compliance control of data sharing behaviors and improving the security and reliability of data sharing.

[0073] Example 2, based on the same inventive concept as the data lifecycle sharing and management method in the foregoing examples, such as... Figure 2 As shown, this application provides a data lifecycle sharing and management system. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0074] The real-time monitoring module 11 is used to perform full-dimensional real-time monitoring of the real-time data sharing channel of the data sharing platform, and to obtain the shared channel monitoring dataset and the real-time lifecycle characteristics of the shared data; the compliance detection module 12 is used to perform full-lifecycle related compliance detection of the shared behavior on the shared channel monitoring dataset based on the real-time lifecycle characteristics of the shared data, and to obtain the compliance detection results of the shared behavior; the hierarchical blocking control module 13 is used to trigger the shared channel blocking mechanism to perform hierarchical blocking control of the real-time data sharing channel if the compliance detection result of the shared behavior is non-compliant; the dynamic reconstruction and adjustment module 14 is used to construct a shared hidden danger evolution process model based on the shared channel monitoring dataset if the compliance detection result of the shared behavior is compliant, and to perform dynamic reconstruction and adjustment of the real-time data sharing channel based on the shared hidden danger evolution process model, and to establish a shared channel reconstruction mechanism.

[0075] Furthermore, the system is also used to implement the following functions:

[0076] Retrieve normal sharing behavior records based on the data lifecycle chain to establish a full lifecycle benchmark sharing behavior space; map and match the full lifecycle benchmark sharing behavior space based on the real-time lifecycle characteristics of the shared data to obtain the current benchmark sharing behavior space; identify sharing behavior features based on the shared channel monitoring dataset to obtain the current sharing behavior feature set; perform compliance detection based on the current benchmark sharing behavior space and the current sharing behavior feature set to generate the sharing behavior compliance detection result.

[0077] Furthermore, the system is also used to implement the following functions:

[0078] If the compliance detection result of the sharing behavior is non-compliant, the core non-compliance information is extracted based on the compliance detection result, and a non-compliance risk level is generated based on the core non-compliance information. The sharing channel blocking mechanism includes a level 1 blocking mechanism, a level 2 blocking mechanism, and a level 3 blocking mechanism. If the non-compliance risk level is mild non-compliance, the level 1 blocking mechanism is activated to block the real-time data sharing channel. If the non-compliance risk level is moderate non-compliance, the level 2 blocking mechanism is activated to block the real-time data sharing channel. If the non-compliance risk level is severe non-compliance, the level 3 blocking mechanism is activated to block the real-time data sharing channel.

[0079] Furthermore, the system is also used to implement the following functions:

[0080] Based on the shared channel monitoring dataset, a time-series analysis of shared security risks is performed to obtain the process trajectory of security risks; a time-series analysis of shared privacy risks is performed based on the shared channel monitoring dataset to obtain the process trajectory of privacy risks; a time-series analysis of shared quality risks is performed based on the shared channel monitoring dataset to obtain the process trajectory of quality risks; and a multi-dimensional risk process alignment and fusion is performed based on the process trajectories of security risks, privacy risks, and quality risks to generate the shared risk evolution process model.

[0081] Furthermore, the system is also used to implement the following functions:

[0082] Based on the shared channel monitoring dataset, a shared safety correlation evaluation is performed to obtain a monitoring parameter safety correlation network; based on the monitoring parameter safety correlation network, safety anomaly features are captured to obtain a safety anomaly feature network; based on the safety anomaly feature network, a hazard time series trend is fitted to generate the safety hazard process trajectory.

[0083] Furthermore, the system is also used to implement the following functions:

[0084] The real-time data sharing channel is dynamically mapped and adjusted according to the shared hidden danger evolution process model to obtain the channel adjustment decision space; the channel adjustment decision space is optimized by risk constraints according to the shared channel risk factors to obtain the channel adjustment candidate space; the channel reliability calculation model is obtained by weight allocation according to the shared channel risk factors; the channel reliability is iteratively optimized according to the channel reliability calculation model and the channel adjustment candidate space to generate the shared channel reconstruction mechanism.

[0085] Furthermore, the system is also used to implement the following functions:

[0086] Deep learning is performed on the shared channel risk factors to obtain a shared channel risk prediction model. These risk factors include shared security risks, shared privacy risks, and shared quality risks. Multidimensional risk prediction is then performed on the channel adjustment decision space based on this model to obtain multiple decision risk characteristics. Shared risk constraints are then obtained, including shared security risk constraints, shared privacy risk constraints, and shared quality risk constraints. Finally, the channel adjustment decision space is filtered based on these multiple decision risk characteristics and the shared quality risk constraints to generate a channel adjustment candidate space.

[0087] Furthermore, the system is also used to implement the following functions:

[0088] Real-time acquisition of blocking monitoring data of the real-time data sharing channel; and closed-loop optimization of the blocking control mechanism of the sharing channel based on the blocking monitoring data.

[0089] In Example 3, based on the same inventive concept as the data lifecycle sharing and management method in the foregoing embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed, implements the steps of any one of the methods in Example 1 above.

[0090] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0091] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A data lifecycle sharing and management method, characterized in that, The method includes: Perform full-dimensional real-time monitoring of the real-time data sharing channel of the data sharing platform to obtain the monitoring dataset of the sharing channel and the real-time lifecycle characteristics of the shared data; Based on the real-time lifecycle characteristics of the shared data, the shared channel monitoring dataset is subjected to full lifecycle-related compliance detection of shared behavior to obtain the compliance detection results of shared behavior. If the compliance detection result of the sharing behavior is that the sharing behavior is non-compliant, the sharing channel blocking mechanism is triggered to implement graded blocking control of the real-time data sharing channel; If the compliance detection result of the shared behavior is that the shared behavior is compliant, a shared hidden danger evolution process model is constructed based on the shared channel monitoring dataset, and the real-time data sharing channel is dynamically reconstructed and adjusted based on the shared hidden danger evolution process model to establish a shared channel reconstruction mechanism.

2. The data lifecycle sharing and management method as described in claim 1, characterized in that, Based on the real-time lifecycle characteristics of the shared data, the shared channel monitoring dataset is subjected to full lifecycle-based compliance detection of shared behavior to obtain the compliance detection results, including: Based on the data lifecycle chain, retrieve normal sharing behavior records and establish a lifecycle benchmark sharing behavior space. Based on the real-time lifecycle characteristics of the shared data, the full lifecycle benchmark shared behavior space is mapped and matched to obtain the current benchmark shared behavior space; Based on the shared channel monitoring dataset, the shared behavior features are identified to obtain the current shared behavior feature set; Based on the current benchmark shared behavior space, compliance detection is performed on the current shared behavior feature set to generate the shared behavior compliance detection result.

3. The data lifecycle sharing and management method as described in claim 1, characterized in that, If the compliance detection result of the sharing behavior is non-compliant, the sharing channel blocking mechanism is triggered to implement tiered blocking control of the real-time data sharing channel, including: If the compliance detection result of the shared behavior is that the shared behavior is non-compliant, the core information of non-compliance is extracted based on the compliance detection result of the shared behavior, and a non-compliance risk level is generated based on the core information of non-compliance; The shared channel blocking mechanism includes a first-level blocking mechanism, a second-level blocking mechanism, and a third-level blocking mechanism; If the non-compliance risk level is minor, the first-level blocking mechanism will be activated to block the real-time data sharing channel. If the non-compliance risk level is moderate non-compliance, the secondary blocking mechanism will be activated to block the real-time data sharing channel; If the non-compliance risk level is severe non-compliance, the three-level blocking mechanism will be activated to block the real-time data sharing channel.

4. The data lifecycle sharing and management method as described in claim 1, characterized in that, Based on the shared channel monitoring dataset, a shared hazard evolution process model is constructed, including: Based on the shared channel monitoring dataset, a time-series analysis of shared security risks is performed to obtain the process trajectory of security risks. Based on the shared channel monitoring dataset, a time-series analysis of shared privacy risks is performed to obtain the trajectory of the privacy risk process. Based on the shared channel monitoring dataset, a time-series analysis of shared quality hazards is performed to obtain the process trajectory of quality hazards. The shared hazard evolution process model is generated by aligning and fusing the process trajectories of the security hazard, privacy hazard, and quality hazard to achieve multidimensional hazard process alignment and fusion.

5. The data lifecycle sharing and management method as described in claim 4, characterized in that, Based on the shared channel monitoring dataset, a time-series analysis of shared security risks is performed to obtain the process trajectory of the security risks, including: Based on the shared channel monitoring dataset, a shared security correlation evaluation is performed to obtain the monitoring parameter security correlation network. Based on the monitoring parameters, a security anomaly feature network is obtained by capturing security anomaly features through a security association network. Based on the safety anomaly feature network, the time-series trend of the hidden danger is fitted to generate the process trajectory of the safety hazard.

6. The data lifecycle sharing and management method as described in claim 1, characterized in that, Based on the shared hidden danger evolution process model, the real-time data sharing channel is dynamically reconstructed and adjusted to establish a shared channel reconstruction mechanism, including: Based on the shared hidden danger evolution process model, the real-time data sharing channel is dynamically mapped and adjusted to obtain the channel adjustment decision space; Based on the shared channel risk factors, the channel adjustment decision space is optimized under risk constraints to obtain a channel adjustment candidate space. Based on the shared channel risk factors, weights are allocated to obtain the channel reliability calculation model; Based on the channel reliability calculation model, the channel adjustment candidate space is iteratively optimized to generate the shared channel reconstruction mechanism.

7. The data lifecycle sharing and management method as described in claim 6, characterized in that, Based on the shared channel risk factors, the channel adjustment decision space is optimized under risk constraints to obtain a channel adjustment candidate space, including: Deep learning is performed based on the shared channel risk factors to obtain a shared channel risk prediction model. The shared channel risk factors include shared security risks, shared privacy risks, and shared quality risks. Based on the shared channel risk prediction model, multidimensional risk prediction is performed on the channel adjustment decision space to obtain multiple decision risk characteristics; Obtain shared risk constraints, which include shared security risk constraints, shared privacy risk constraints, and shared quality risk constraints. Based on the multiple decision risk characteristics and the shared quality hazard risk constraints, the channel adjustment decision space is screened to generate the channel adjustment candidate space.

8. The data lifecycle sharing and management method as described in claim 1, characterized in that, Triggering the shared channel blocking mechanism to implement tiered blocking control of the real-time data sharing channel, including: Real-time acquisition of blocking monitoring data of the real-time data sharing channel; The shared channel blocking mechanism is optimized through a closed-loop blocking control system based on the blocking monitoring data.

9. A data lifecycle sharing and management system, characterized in that: The system is used to execute the data lifecycle sharing management and control method as described in any one of claims 1-8, and the system includes: The real-time monitoring module is used to perform full-dimensional real-time monitoring of the real-time data sharing channel of the data sharing platform, and to obtain the monitoring dataset of the sharing channel and the real-time lifecycle characteristics of the shared data. The compliance detection module is used to perform full lifecycle-related compliance detection of the shared channel monitoring dataset based on the real-time lifecycle characteristics of the shared data, and obtain the compliance detection results of the shared behavior. The graded blocking control module is used to trigger the sharing channel blocking mechanism to perform graded blocking control on the real-time data sharing channel if the compliance detection result of the sharing behavior is non-compliant. The dynamic reconstruction and adjustment module is used to construct a shared hidden danger evolution process model based on the shared channel monitoring dataset if the shared behavior compliance detection result is compliant, and to dynamically reconstruct and adjust the real-time data sharing channel based on the shared hidden danger evolution process model, thereby establishing a shared channel reconstruction mechanism.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the data lifecycle sharing management method as described in any one of claims 1-8.

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