Cross-system identity permission adaptive reconciliation method and device for database management platform, computer device and storage medium

Abstract

The application discloses a cross-system identity permission adaptive reconciliation method and device of a database management platform, computer equipment and a storage medium, and the method comprises the following steps: constructing a cross-system identity permission reconciliation model; performing conflict detection on the cross-system operation chain perception layer according to a conflict event, and extracting subject features of the cross-system operation chain; performing reverse causal tracing based on the subject features by the causal attribution layer, and outputting causal correlation strength; performing consistency propagation and elimination based on the entity constraint relationship provided by the business entity topology graph and the causal correlation strength provided by the causal attribution layer by the state decision layer, obtaining a global optimal state belief, making a decision and performing by the adaptive reconciliation decision maker based on the global optimal state belief, and producing a composite repair instruction; and adjusting the cross-system identity permission according to the composite repair instruction, and eliminating the conflict event.

Description

Technical Field

[0001] This application belongs to the field of database management platform operation and maintenance technology, specifically referring to a cross-system identity and permission adaptive reconciliation method, device, computer equipment and storage medium for a database management platform. Background Technology

[0002] In the operation and maintenance of database management platforms, and specifically in identity synchronization management systems, the fundamental challenges faced by traditional identity synchronization systems are incomplete information and rigid rules. When conflicts occur, the system often only sees "what" (data inconsistency), but struggles to understand "why" (the root cause of the conflict) and "what will happen" (the scope of the conflict's impact). Existing technical solutions rely on manually predefined static rule matrices, which cannot adapt to dynamically changing business contexts and complex implicit dependencies. Summary of the Invention

[0003] To overcome the shortcomings of the prior art, this application provides a method, apparatus, computer device and storage medium for adaptive reconciliation of cross-system identity and permissions in a database management platform, which can dynamically adjust cross-system identity and permissions.

[0004] This invention provides a cross-system identity and permission adaptive reconciliation method for a database management platform, the method comprising:

[0005] A cross-system identity and permission reconciliation model is constructed, including a cross-system operation chain perception layer, a causal attribution layer, and a state decision layer. The cross-system operation chain perception layer includes a raw operation event stream generation layer, a cross-system operation chain splicer, and a cross-system operation chain topic model. The causal attribution layer includes an event tracing causal graph builder and a causal influence network. The state decision layer includes a business entity topology graph, a permission dependency consistency propagation algorithm, and an adaptive reconciliation decision-maker. Based on the conflict event, the cross-system operation chain perception layer performs conflict detection and extracts the topic features of the cross-system operation chain, which include intent topic distribution and anomaly score; The causal attribution layer performs reverse causal tracing based on the topic features and outputs the causal correlation strength; The state decision layer performs consistency propagation and resolution based on the entity constraint relationship provided by the business entity topology graph and the causal correlation strength provided by the causal attribution layer to obtain the globally optimal state belief. The adaptive harmonizing decision-maker makes decisions and executes based on the globally optimal state belief to produce composite repair instructions. Adjust cross-system identity permissions based on composite repair instructions to eliminate conflict events.

[0006] Furthermore, according to the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this application, the original operation event stream generation layer produces the cross-system operation chain, which is an ordered set of a series of atomic operation events that are related in business and span multiple systems in the database management platform; The cross-system operation chain splicer uses the business transaction ID, global GUID, time window, and causal relationship inference from the database management platform to splice isolated events scattered in various logs into a complete cross-system operation chain.

[0007] Furthermore, according to the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this application, the cross-system operation chain topic model includes: Construct documents collected from multiple subordinate systems related to the entities involved in the conflict event. set ; wherein, the document Represented as a cross-system operation chain of all operation events for a certain entity; For each document Produce one Dimensional Intent Topic Distribution ; in, The preset number of "potential operational intent topics" Assign functions to implicit Dirichlet functions. For standard Dirichlet prior parameters, For document Handcrafted feature vectors, The feature weight vector; Based on historical data, parameters are trained using an implicit Dirichlet assignment function to obtain a trained cross-system operation chain topic model.

[0008] Furthermore, based on the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this application, and according to the cross-system operation chain topic model, the cross-system operation chain of the new conflict event is input. The output is the distribution of intent topics for the new conflict event. and abnormal scores; among which, The probability that a new conflict event is driven by a potential intent of type i is represented; the anomaly score identifies the cross-system operation chain of the new conflict event. The degree of deviation from the cross-system operation chain described in the known normal mode.

[0009] Furthermore, according to the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this application, the event tracing cause-effect graph builder includes constructing a tracing cause-effect graph. ;in, A node representing an operation event is a specific operation. A state assertion node represents the state of a certain attribute of a certain business entity at a certain point in time; Cause and effect diagram Edge types include: Generate edges: This indicates that an operation has generated a new state or a state change. Influence edge: This indicates that the existence of one state is a prerequisite for triggering another operation, and there is a causal dependency between the two. Synchronous edges: This indicates that the states in two different systems should be kept synchronized through the identity command protocol; The event causal graph builder builds based on the causal graph. Update the causal influence network; New conflicting events are input into the causal attribution layer. Yes, through the causal influence network and the source causality graph. Perform reverse causal tracing to obtain and output the causal relationship strength of which "root cause operation" led to the current "state inconsistency" leaf node through which path.

[0010] Furthermore, based on the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this application, the business entity topology diagram is constructed. Include: Define topology graph nodes The nodes of the business entity topology graph It is a business entity, which includes at least users, user groups, devices, and service subscriptions; Define the edges of a topological graph The edges of the business entity topology graph It is a relationship with clear business semantics; The topology graph node and the edges of the topology graph The data model and configuration of the product originate from the database management platform.

[0011] Furthermore, according to the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this application, the permission-dependent consistency propagation algorithm includes: Initialization, each variable node Send its initial beliefs, derived from observational evidence or prior experience, to neighboring factor nodes; Factor edge To variable node Message passing, each factor edge Based on the messages of all the variable nodes it connects to and the edges The representative constraint rule is used to calculate and send a new "recommendation" message to each neighbor variable node; Variable Node To factor edge Message passing, each variable node Summarize edges from all adjacent factor edges The message updates its own belief distribution regarding various possible states; Iteration, filling the factor edges To variable node Message passing and the variable node To factor edge The message passing continues until the belief distributions of all nodes converge; the convergence condition is that the change is less than a threshold. ; Output decision, for each variable node The state with the highest belief level is selected as the "true state" inferred by the system.

[0012] This application also provides a cross-system identity and permission adaptive reconciliation device for a database management platform, the device comprising: The model generation module is used to construct a cross-system identity and permission reconciliation model, including constructing a cross-system operation chain perception layer, a causal attribution layer, and a state decision layer. The cross-system operation chain perception layer includes a raw operation event flow generation layer, a cross-system operation chain splicer, and a cross-system operation chain topic model; the causal attribution layer includes an event tracing causal graph builder and a causal influence network; the state decision layer includes a business entity topology graph, a permission dependency consistency propagation algorithm, and an adaptive reconciliation decision-maker. The cross-system operation chain topic feature extraction module is used to perform conflict detection of the cross-system operation chain perception layer based on conflict events, and extract the topic features of the cross-system operation chain, wherein the topic features include intent topic distribution and anomaly score; The reverse causal tracing module is used to perform reverse causal tracing based on the topic features according to the causal attribution layer, and output the causal association strength; The decision production and execution module is used to perform consistency propagation and resolution based on the entity constraint relationship provided by the state decision layer based on the business entity topology graph and the causal correlation strength provided by the causal attribution layer to obtain the global optimal state belief. The adaptive harmonizing decision-maker makes decisions and executes based on the global optimal state belief to produce composite repair instructions. The reconciliation and repair execution module is used to adjust cross-system identity permissions based on composite repair instructions to eliminate conflict events.

[0013] This application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this application.

[0014] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this application.

[0015] The beneficial effects of the present invention are as follows: The cross-system identity and permission adaptive reconciliation method, apparatus, computer equipment and storage medium provided by the present invention for database management platform, the method constructs a cross-system identity and permission reconciliation model, perceives the subject characteristics of the cross-system operation chain through the cross-system operation chain perception layer, performs reverse causal tracing through the subject characteristics of the causal attribution layer, attributes conflict events, and finally performs consistency propagation and resolution through the state decision layer based on the entity constraint relationship provided by the business entity topology diagram and the causal association strength provided by the causal attribution layer, obtains the globally optimal state belief, and makes decisions and executes to eliminate conflict events. Attached Figure Description

[0016] The technical solution and other beneficial effects of this application will become apparent from the following detailed description of specific embodiments in conjunction with the accompanying drawings.

[0017] Figure 1 This is a schematic diagram of the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this embodiment.

[0018] Figure 2 This is a schematic diagram of the architecture of the cross-system identity and permission reconciliation model provided in this embodiment. Detailed Implementation

[0019] 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.

[0020] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] The following disclosure provides many different embodiments or examples for implementing different structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. In addition, various specific examples of processes and materials are provided in this application, but those skilled in the art will recognize the application of other processes and / or the use of other materials.

[0022] The embodiments of this application will now be further described in conjunction with the accompanying drawings and specific implementation details.

[0023] Figure 1 This is a schematic diagram of the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this embodiment.

[0024] Figure 2 This is a schematic diagram of the architecture of the cross-system identity and permission reconciliation model provided in this embodiment.

[0025] like Figure 1 and Figure 2 As shown, the cross-system identity and permission adaptive reconciliation method of the database management platform includes: A cross-system identity and permission reconciliation model is constructed, including a cross-system operation chain perception layer, a causal attribution layer, and a state decision layer. The cross-system operation chain perception layer includes a raw operation event stream generation layer, a cross-system operation chain splicer, and a cross-system operation chain topic model. The causal attribution layer includes an event tracing causal graph builder and a causal influence network. The state decision layer includes a business entity topology graph, a permission dependency consistency propagation algorithm, and an adaptive reconciliation decision-maker. Based on the conflict event, the cross-system operation chain perception layer performs conflict detection and extracts the topic features of the cross-system operation chain, which include intent topic distribution and anomaly score; The causal attribution layer performs reverse causal tracing based on the topic features and outputs the causal correlation strength; The state decision layer performs consistency propagation and resolution based on the entity constraint relationship provided by the business entity topology graph and the causal correlation strength provided by the causal attribution layer to obtain the globally optimal state belief. The adaptive harmonizing decision-maker makes decisions and executes based on the globally optimal state belief to produce composite repair instructions. Adjust cross-system identity permissions based on composite repair instructions to eliminate conflict events.

[0026] Specifically, in this embodiment, the input in the cross-system identity and permission reconciliation model is a "cross-system operation chain." It is defined as follows: A cross-system operation chain is an ordered set of atomic operation events that are business-related and span multiple systems within the database management platform. For example, a user places an order for PCE (where PCE is an abbreviation for Cloud PBX) on PP (denoted as event e1) → creates a Reseller and assigns permissions in YCM (denoted as event e2) → the Reseller logs into PCE to activate the service (denoted as event e3). Events e1, e2, and e3 constitute a cross-system operation chain.

[0027] The cross-system operation chain splicer uses the business transaction ID, global GUID, time window, and causal relationship inference from the database management platform to splice isolated events scattered in various logs into a complete cross-system operation chain.

[0028] The cross-system operation chain topic model includes: Construct documents collected from multiple subordinate systems related to the entities involved in the conflict event. set ; wherein, the document Represented as a cross-system operation chain of all operation events for a certain entity; For each document Produce one Dimensional Intent Topic Distribution ; in, The preset number of "potential operational intent topics" Assign functions to implicit Dirichlet functions. For standard Dirichlet prior parameters, For document Handcrafted feature vectors, The feature weight vector; Based on historical data, parameters are trained using an implicit Dirichlet assignment function to obtain a trained cross-system operation chain topic model.

[0029] Based on the cross-system operation chain topic model, the cross-system operation chain that inputs new conflict events. The output is the distribution of intent topics for the new conflict event. and abnormal scores; among which, The probability that a new conflict event is driven by a potential intent of type i is represented; the anomaly score identifies the cross-system operation chain of the new conflict event. The degree of deviation from the cross-system operation chain described in the known normal mode.

[0030] In this embodiment, the anomaly score is an operation chain consisting of m events. The anomaly score (Anomaly(C)) is defined as the average minimum distance between the operation chain and all known normal operation chain patterns. The formula for calculating the anomaly score (Anomaly(C)) is: ; in, It is an operation chain The probability of belonging to the k-th topic (output by the cross-system operation chain topic model in this embodiment); It is the cluster center of topic k (composed of the mean of the event sequences of all training samples belonging to this topic). It is the distance function between sequences.

[0031] Specifically, in this embodiment, the topic of the cross-system operation chain topic model is an "intent-influence pattern" rather than a regular topic. A topic is defined as follows: each topic is no longer a meaningless distribution of words, but represents a cross-system collaborative business pattern. For example, topic A (set as purchase allocation): [PP:PURCHASE_PCE] Purchase PCE service → [YCM:CREATE_RESELLER] Create Reseller → [YCM:ASSIGN_PCE] Assign PCE to Reseller with a probability of 0.85.

[0032] Topic B (set to abnormal permission propagation): [YCM:CLONE_RESELLER] Copy clone Reseller user → [YCM:BULK_ASSIGN] Batch assign PCE → [PCE :UNUSUAL_LOGIN] Unusual login probability = 0.9.

[0033] According to the cross-system operation chain topic model, for a new conflict event, the cross-system operation chain... The cross-system operation chain topic model not only outputs the intention topic distribution of new conflict events, but also... Furthermore, it outputs an anomaly score. This anomaly score identifies the cross-system operation chain of the new conflict event. The degree of deviation from the cross-system operation chain described in the known normal mode.

[0034] like Figure 2 As shown, the event causality graph builder includes the function of constructing an causality graph. ; Cause-and-effect diagram The goal is to formally characterize the causal path of "how an operation, through a series of intermediate state changes, ultimately leads to inconsistencies in the permission states of the remote system".

[0035] Among them, the causal graph The structure is defined as follows: Cause and effect diagram The node types include: An O-Node represents an operation event; for example, Change your email address.

[0036] A State Assertion Node (S-Node) represents the state of a specific attribute of a business entity (such as User, PCE) at a specific point in time; for example, .

[0037] Cause and effect diagram Edge types include: Edges are generated: This indicates that an operation has generated a new state or a state change. Influences: This indicates that the existence of one state is a prerequisite for triggering another operation, and there is a causal dependency between the two. Synchronization edges (Syncs): This indicates that the states in two different systems should be kept synchronized through the identity command protocol; The event causal graph builder builds based on the causal graph. Update the causal influence network; New conflicting events are input into the causal attribution layer. Yes, through the causal influence network and the source causality graph. Perform reverse causal tracing to obtain and output the causal relationship strength of which "root cause operation" led to the current "state inconsistency" leaf node through which path.

[0038] That is, the database management platform continuously extracts data from operation logs and status snapshots. Yes, this cause-and-effect graph is dynamically constructed and updated using time sequence, business rules, and statistical correlation. When a conflict occurs, the system can perform reverse causal tracing on this graph to quickly locate which "root cause operation" led to the current "inconsistent state" leaf node through which path.

[0039] like Figure 2 As shown, in this embodiment, the business entity topology graph is constructed. Include: Define topology graph nodes The nodes of the business entity topology graph It is a business entity, which includes at least users, user groups, devices, and service subscriptions; Define the edges of a topological graph The edges of the business entity topology graph It is a relationship with clear business semantics; for example: some business semantics: Owner (owner), Belongs_to (who it belongs to).

[0040] The topology graph node and the edges of the topology graph This originates from the product's data model and configuration within the database management platform. Examples include the hierarchical relationship between Hosting User and PCE (PCE.owner = Hosting User), and between Hosting User and Reseller in YCM (Reseller Belongs_to Hosting User).

[0041] like Figure 2 As shown, in this embodiment, the permission-dependent consistency propagation algorithm includes: Initialization, each variable node Send its initial beliefs, derived from observational evidence or prior experience, to neighboring factor nodes; Factor edge To variable node Message passing, each factor edge Based on the messages of all the variable nodes it connects to and the edges Based on the constraint rules, F1 calculates and sends a new "suggestion" message to each neighbor variable node; for example, if F1 receives a message from V2 that "owner is GUID_A", but V1 observes that "GUID_A is disabled", F1 will strongly suggest that V2's state may be incorrect based on the rules.

[0042] Variable Node To factor edge Message passing, each variable node Summarize edges from all adjacent factor edges The message updates its own belief distribution regarding various possible states; Iteration, filling the factor edges To variable node Message passing and the variable node To factor edge The message is passed on until the belief distribution of all nodes converges; the convergence condition is that the change is less than the threshold ε. Output decision, for each variable node The state with the highest belief level is selected as the "true state" inferred by the system.

[0043] Its key parameters and formulas include: Message: , indicating factor edge Consider variable nodes Should be in state The confidence level.

[0044] Belief: , represents a variable node Ultimately in state The overall probability.

[0045] Factor Potential: , representing the coding business rules. For example, for the "consistency factor", if all variable states satisfy the rule, the potential function value is 1, otherwise it is 0 (or a minimum value ε).

[0046] Specifically, in this embodiment, the potential function design no longer uses a general consistency function, but instead defines a potential function that reflects the strength of business rules.

[0047] For example, a potential function for a "HA primary / standby shared service" rule:

[0048] Wherein, if “host_HA_status=enabled”; otherwise = 1.0 (if HA is not enabled, there is no strong constraint).

[0049] This is the rule strength coefficient, which can be dynamically adjusted according to the SLA level. SLA's... The larger the value, the stronger the constraint.

[0050] The permission dependency consistency propagation algorithm is a propagation algorithm that integrates multi-source evidence. The permission dependency consistency propagation algorithm receives three types of inputs as evidence, including: state observations from each system; causal correlation strength from the event causal graph (as prior weights on the edges); and anomaly scores from the operation chain topic model (as noise level parameters of the corresponding operation node observations).

[0051] The belief propagation process integrates these heterogeneous pieces of evidence to calculate a globally consistent state that best fits both the business topology constraints and the causal inference.

[0052] Example 2: In this embodiment, an example of diagnosing and fixing a "ghost permission" problem is used to illustrate the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this embodiment.

[0053] The "ghost permissions" issue manifests as follows: a YCM Reseller R1 that has been removed from Hosting User H1 on the database management platform can still access a Cloud PBX under its name.

[0054] The operation flow of the cross-system identity and permission adaptive reconciliation method of the database management platform provided in this embodiment is as follows: First, conflict detection is performed. The cross-system identity and permission reconciliation model finds that user R1's organizational relationship in PP and YCM is inconsistent, and that conflict still exists in YCM. Access history.

[0055] Then perform cross-system operation chain topic analysis to retrieve user R1 and Recent operational chain. A topic was discovered... Remove The operation chain that accessed PCE has an extremely high anomalous score because "access persists after removal" does not belong to any normal topic.

[0056] Perform causal graph tracing; in the event source causal graph, with Belonging to H1 and Starting from PCE_X, we traced backwards and found a path: operation... Will Allocated from the resource pool The administrator will Assigned to R1 state R1 has Permissions state R1 can access .

[0057] Key findings: When Will from Remove After it occurred, it did not trigger a proper event. Cleaning Inheritance rights Operation. Cause-effect graph display. and The "influence edge" between them is missing or invalid.

[0058] Then, consistency propagation is performed to resolve the issue. In the permission topology graph, with... Nodes are defined as such. Evidence of injection: PP observed... , Observed From the cause-and-effect diagram, we can see that... This state is caused by a flawed permission inheritance mechanism, and its causal strength is high. After propagation, the algorithm determines the outcome with high confidence. This is the true state that aligns with business logic (users should not access resources that have left the organization).

[0059] Then, the adaptive decision-maker performs a comprehensive analysis before making and executing the decision. A high anomaly score in the operation chain (indicating an anomalous event). The root cause located by the cause-effect graph (permission inheritance cleanup defect). High-confidence results from consistent propagation.

[0060] Finally, generate a composite repair command: Immediately: Force the removal of R1 in YCM Access permissions.

[0061] Generate a fix ticket: Submit a bug report to the YCM product team, along with a detailed cause-and-effect path, pointing out the vulnerability in the permission inheritance logic when a user is removed.

[0062] Prevention strategy: Update and reconcile strategies for any future events. After the operation, an audit and cleanup sub-process for all inherited permissions of the user is automatically triggered.

[0063] In summary, the cross-system identity and permission adaptive reconciliation method for the database management platform provided in this embodiment creates a unique data structure and analysis model for the domain of "cross-system identity and permission management": The "cross-system operation chain" serves as the analysis unit, and an "operation chain theme model" is designed for it; an "event causal graph" depicting the causal relationship between operations and states is constructed; and a "permission dependency topology graph" based on business entity relationships is defined. These models are closely aligned with the domain problem and are not simply applications of general algorithms.

[0064] Simultaneously, a three-layered intelligent architecture of "perception-attribution-decision" is constructed: The method provided in this embodiment is not a single algorithm, but an organically coordinated system. The operation chain topic model realizes anomaly perception, the event tracing cause-effect graph realizes root cause attribution, and the permission dependency consistency propagation realizes state decision-making. The three-layer model exchanges domain information (such as anomaly scores, causal strength, and rule coefficients) through specific interfaces to jointly complete complex diagnostic tasks.

[0065] Example 3: This embodiment also provides a cross-system identity and permission adaptive reconciliation device for a database management platform, the device comprising: The model generation module is used to construct a cross-system identity and permission reconciliation model, including constructing a cross-system operation chain perception layer, a causal attribution layer, and a state decision layer. The cross-system operation chain perception layer includes a raw operation event flow generation layer, a cross-system operation chain splicer, and a cross-system operation chain topic model; the causal attribution layer includes an event tracing causal graph builder and a causal influence network; the state decision layer includes a business entity topology graph, a permission dependency consistency propagation algorithm, and an adaptive reconciliation decision-maker. The cross-system operation chain topic feature extraction module is used to perform conflict detection of the cross-system operation chain perception layer based on conflict events, and extract the topic features of the cross-system operation chain, wherein the topic features include intent topic distribution and anomaly score; The reverse causal tracing module is used to perform reverse causal tracing based on the topic features according to the causal attribution layer, and output the causal association strength; The decision production and execution module is used to perform consistency propagation and resolution based on the entity constraint relationship provided by the state decision layer based on the business entity topology graph and the causal correlation strength provided by the causal attribution layer to obtain the global optimal state belief. The adaptive harmonizing decision-maker makes decisions and executes based on the global optimal state belief to produce composite repair instructions. The reconciliation and repair execution module is used to adjust cross-system identity permissions based on composite repair instructions to eliminate conflict events.

[0066] Example 4: This embodiment also provides a computer terminal device for a cross-system identity and permission adaptive reconciliation method for a database management platform. The terminal device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the embodiments of the method described in Embodiment 1 of the present invention.

[0067] Furthermore, as an executable solution, the computer terminal device for the cross-system identity and permission adaptive reconciliation method of the database management platform can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer terminal device for the cross-system identity and permission adaptive reconciliation method of the database management platform may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above-described composition of the computer terminal device for the cross-system identity and permission adaptive reconciliation method of the database management platform is merely an example and does not constitute a limitation on the computer terminal device for the cross-system identity and permission adaptive reconciliation method of the database management platform. It may include more or fewer components than described above, or combine certain components, or different components. For example, the computer terminal device for the cross-system identity and permission adaptive reconciliation method of the database management platform may also include input / output devices, network access devices, buses, etc., which are not limited in this embodiment of the invention.

[0068] Furthermore, as an executable solution, the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the computer terminal device for the cross-system identity and permission adaptive reconciliation method of the database management platform, connecting various parts of the computer terminal device for the cross-system identity and permission adaptive reconciliation method of the entire database management platform via various interfaces and lines.

[0069] The memory can be used to store the computer programs and / or modules. The processor, by running or executing the computer programs and / or modules stored in the memory and calling data stored in the memory, realizes various functions of the computer terminal device of the cross-system identity and permission adaptive reconciliation method of the database management platform. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0070] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the embodiments of the present invention.

[0071] If the modules / units integrated into the computer terminal device of the cross-system identity and permission adaptive reconciliation method of the database management platform are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc.

[0072] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the present invention. Finally, it should be noted that in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0073] The foregoing has provided a detailed description of a cross-system identity and permission adaptive reconciliation method, apparatus, computer device, and storage medium for a database management platform provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the technical solutions and core ideas of this application. Those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A cross-system identity and permission adaptive reconciliation method for a database management platform, characterized in that, The method includes: A cross-system identity and permission reconciliation model is constructed, including a cross-system operation chain perception layer, a causal attribution layer, and a state decision layer. The cross-system operation chain perception layer includes a raw operation event stream generation layer, a cross-system operation chain splicer, and a cross-system operation chain topic model. The causal attribution layer includes an event tracing causal graph builder and a causal influence network. The state decision layer includes a business entity topology graph, a permission dependency consistency propagation algorithm, and an adaptive reconciliation decision-maker. Based on the conflict event, the cross-system operation chain perception layer performs conflict detection and extracts the topic features of the cross-system operation chain, which include intent topic distribution and anomaly score; The causal attribution layer performs reverse causal tracing based on the topic features and outputs the causal correlation strength; The state decision layer performs consistency propagation and resolution based on the entity constraint relationship provided by the business entity topology graph and the causal correlation strength provided by the causal attribution layer to obtain the globally optimal state belief. The adaptive harmonizing decision-maker makes decisions and executes based on the globally optimal state belief to produce composite repair instructions. Adjust cross-system identity permissions based on composite repair instructions to eliminate conflict events.

2. The cross-system identity and permission adaptive reconciliation method for a database management platform according to claim 1, characterized in that, The original operation event stream generation layer produces the cross-system operation chain, which is an ordered set of a series of atomic operation events that are related in business and span multiple systems in the database management platform; The cross-system operation chain splicer uses the business transaction ID, global GUID, time window, and causal relationship inference from the database management platform to splice isolated events scattered in various logs into a complete cross-system operation chain.

3. The cross-system identity and permission adaptive reconciliation method for a database management platform according to claim 1 or 2, characterized in that, The cross-system operation chain topic model includes: Construct documents collected from multiple subordinate systems related to the entities involved in the conflict event. set ; wherein, the document Represented as a cross-system operation chain of all operation events for a certain entity; For each document Produce one Dimensional Intent Topic Distribution ; in, The preset number of "potential operational intent topics" Assign functions to implicit Dirichlet functions. For standard Dirichlet prior parameters, For document Handcrafted feature vectors, The feature weight vector; Based on historical data, parameters are trained using an implicit Dirichlet assignment function to obtain a trained cross-system operation chain topic model.

4. The cross-system identity and permission adaptive reconciliation method for a database management platform according to claim 3, characterized in that, Based on the cross-system operation chain topic model, the cross-system operation chain that inputs new conflict events. The output is the distribution of intent topics for the new conflict event. and abnormal scores; among which, The probability that a new conflict event is driven by a potential intent of type i is represented; the anomaly score identifies the cross-system operation chain of the new conflict event. The degree of deviation from the cross-system operation chain described in the known normal mode.

5. The cross-system identity and permission adaptive reconciliation method for a database management platform according to claim 4, characterized in that, The event causality graph builder includes the function of building a causality graph. ;in, A node representing an operation event is a specific operation. A state assertion node represents the state of a certain attribute of a certain business entity at a certain point in time; Cause and effect diagram Edge types include: Generate edges: This indicates that an operation has generated a new state or a state change. Influence edge: This indicates that the existence of one state is a prerequisite for triggering another operation, and there is a causal dependency between the two. Synchronous edges: This indicates that the states in two different systems should be kept synchronized through the identity command protocol; The event causal graph builder builds based on the causal graph. Update the causal influence network; New conflicting events are input into the causal attribution layer. Yes, through the causal influence network and the source causality graph. Perform reverse causal tracing to obtain and output the causal relationship strength of which "root cause operation" led to the current "state inconsistency" leaf node through which path.

6. The cross-system identity and permission adaptive reconciliation method for a database management platform according to claim 5, characterized in that, Construct the business entity topology graph Include: Define topology graph nodes The nodes of the business entity topology graph It is a business entity, which includes at least users, user groups, devices, and service subscriptions; Define the edges of a topological graph The edges of the business entity topology graph It is a relationship with clear business semantics; The topology graph node and the edges of the topology graph The data model and configuration of the product originate from the database management platform.

7. The cross-system identity and permission adaptive reconciliation method for a database management platform according to claim 6, characterized in that, The permission dependency consistency propagation algorithm includes: Initialization, each variable node Send its initial beliefs, derived from observational evidence or prior experience, to neighboring factor nodes; Factor edge To variable node Message passing, each factor edge Based on the messages of all the variable nodes it connects to and the edges The representative constraint rule is used to calculate and send a new "recommendation" message to each neighbor variable node; Variable Node To factor edge Message passing, each variable node Summarize edges from all adjacent factor edges The message updates its own belief distribution regarding various possible states; Iteration, filling the factor edges To variable node Message passing and the variable node To factor edge The message passing continues until the belief distributions of all nodes converge; the convergence condition is that the change is less than a threshold. ; Output decision, for each variable node The state with the highest belief level is selected as the "true state" inferred by the system.

8. A cross-system identity and permission adaptive reconciliation device for a database management platform, characterized in that, The device includes: The model generation module is used to construct a cross-system identity and permission reconciliation model, including constructing a cross-system operation chain perception layer, a causal attribution layer, and a state decision layer. The cross-system operation chain perception layer includes a raw operation event flow generation layer, a cross-system operation chain splicer, and a cross-system operation chain topic model; the causal attribution layer includes an event tracing causal graph builder and a causal influence network; the state decision layer includes a business entity topology graph, a permission dependency consistency propagation algorithm, and an adaptive reconciliation decision-maker. The cross-system operation chain topic feature extraction module is used to perform conflict detection of the cross-system operation chain perception layer based on conflict events, and extract the topic features of the cross-system operation chain, wherein the topic features include intent topic distribution and anomaly score; The reverse causal tracing module is used to perform reverse causal tracing based on the topic features according to the causal attribution layer, and output the causal association strength; The decision production and execution module is used to perform consistency propagation and resolution based on the entity constraint relationship provided by the state decision layer based on the business entity topology graph and the causal correlation strength provided by the causal attribution layer to obtain the global optimal state belief. The adaptive harmonizing decision-maker makes decisions and executes based on the global optimal state belief to produce composite repair instructions. The reconciliation and repair execution module is used to adjust cross-system identity permissions based on composite repair instructions to eliminate conflict events.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the cross-system identity and permission adaptive reconciliation method of the database management platform as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the cross-system identity and permission adaptive reconciliation method of the database management platform as described in any one of claims 1 to 7.

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