Safety training operation control method based on behavior trace

Abstract

This invention discloses a safety training operation control method based on behavior tracing, belonging to the field of safety training operation control technology. The method includes the following steps: extracting behavior events and service response events corresponding to user operation behaviors, generating operation behavior identifiers and response behavior identifiers respectively, and constructing a behavior response difference mapping sequence containing an operation instruction index, a response time field, and a state offset label; calculating a delay drift factor based on the distribution characteristics of the state offset labels in the behavior response difference mapping sequence; and determining that an operation behavior response state is out of sync when the delay drift factor changes continuously or the response behavior identifier is missing. This invention solves the problem of misjudgment in safety training and operation control caused by the asynchrony between operation behavior and service response state during behavior tracing, and achieves accurate identification and reasonable classification of the actual processing results of operation behaviors, as well as dynamic and accurate control of the safety training process and operation control process.

Description

Technical Field

[0001] This invention relates to the field of safety training operation control technology, and specifically to a safety training operation control method based on behavior tracing. Background Technology

[0002] Behavior-based security training operation control is a security management method that integrates user behavior analysis and dynamic training scheduling. Its core idea is to dynamically identify potential risky behaviors or non-compliant operations by recording, analyzing, and tracing user actions within the business system in real time. This triggers targeted security training tasks and controls the entire process. In existing technologies, such methods typically rely on log management systems, behavior monitoring platforms, or endpoint security systems. First, they achieve full-process tracking and archiving of user actions, including login behavior, system access, data operations, and permission changes. Then, through a pre-set rule engine or a machine learning-based behavior recognition model, the system analyzes the user's behavioral data to identify risk levels or potential violations. Once a risky behavior is identified, the system automatically associates it with corresponding security training templates or content, triggering targeted training tasks and performing closed-loop tracking and control of the training reception status, completion status, and training effectiveness. In addition, the control method also includes training plan configuration, execution monitoring, result feedback, and accountability, ensuring that training not only covers the educational needs after the behavior occurs, but also dynamically improves the overall organizational safety and compliance level, realizing an intelligent linkage mechanism of behavior identification, response training, and closed-loop control.

[0003] The existing technology has the following shortcomings: In the process of implementing security training operation control based on behavior tracing, when a user performs an operation that relies on server-side processing results (such as submitting for review or saving configuration), the behavior tracing system typically records the behavior event immediately when the user clicks the operation command, serving as the basis for behavior evaluation. However, in actual system execution processes, such operations often involve server-side processing delays or failures, and the lack of synchronous binding between behavior tracing and service response mechanisms means that behavior records only reflect the initiation of the operation, failing to reflect whether the operation was effectively adopted by the system. Consequently, without obtaining the true response status, the system treats the behavior as completed and participates in subsequent security training evaluation and operation control judgments, leading to misjudgments. Existing behavior tracing-based security training operation control technology cannot accurately determine whether to trigger the security training process or execute corresponding operation control actions based on the behavior processing results when the operation behavior response status is not synchronized. This easily leads to incorrect responses to behaviors that have not yet taken effect or have been rejected by the system, resulting in incorrect generation of training tasks and unintended triggering of access control measures, affecting the accuracy of behavior recognition and the rationality of system operation control.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a safety training operation control method based on behavior tracing to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a safety training operation control method based on behavior traceability, specifically including the following steps: S1. Extract the behavior events and service response events corresponding to user operation behaviors, generate operation behavior identifiers and response behavior identifiers respectively, and construct a behavior response difference mapping sequence containing operation instruction index, response time field and status offset label; S2. Calculate the delay drift factor based on the distribution characteristics of the state offset labels in the behavior response difference mapping sequence. When the delay drift factor changes continuously or the response behavior identifier is missing, it is determined that the operation behavior response state is not synchronized, and the corresponding operation behavior identifier is marked as the offset state. S3. Perform trajectory deduction on the operation behavior identifier marked as offset state, construct a processing trajectory inference matrix including behavior chain sequence score, response jump node and state rollback value, and output behavior processing result type through matrix operation; S4. Combine the behavior processing result type with the behavior sensitivity level and permission level to form input, establish a response level assignment mapping diagram, classify the behavior risk group in the response level assignment mapping diagram, and determine whether to enter the security training process or the operation control process based on the classification result. S5. Adjust the boundary of the behavior risk group according to the behavior feedback flow formed by each response level assignment mapping, and update the behavior processing result judgment logic through error offset feedback to realize dynamic control of the safety training process and operation control process.

[0007] Preferably, S1 is as follows: Collect user-triggered operation behavior data, extract corresponding behavior events from the operation behavior data, and extract corresponding service response events from the service processing records associated with the operation behavior data. Establish a one-to-one correspondence between behavior events and service response events through operation request identifiers, and complete the extraction of behavior events and service response events corresponding to user operation behaviors. Based on the extracted behavior event, an operation behavior identifier is generated by selecting the operation trigger time, operation type information and operation path information. Based on the extracted service response event, a response behavior identifier is generated by selecting the response completion time, response status information and processing path information. Both the operation behavior identifier and the response behavior identifier are bound to the same operation request identifier to maintain data consistency. Using the operation request identifier in the operation behavior identifier as the operation instruction index, the response completion time in the response behavior identifier as the response time field, and generating a status offset label based on the deviation relationship between the operation trigger time and the response completion time and the response status information, a behavior response difference mapping sequence containing the operation instruction index, the response time field and the status offset label is constructed.

[0008] Preferably, S2 specifically includes the following steps: S201. Read the state offset labels in the behavior response difference mapping sequence, extract the label change trajectory according to the time sequence of the operation instruction index, count the number of times the label changes direction continuously and the time sequence difference between adjacent labels, and generate a distribution feature group to reflect the label distribution trend. S202. Based on the combination pattern of the label change direction in the distribution feature group and the interval change relationship of the response time field, calculate the delay drift factor. When the value of the delay drift factor shows alternating changes in direction in multiple consecutive operation instruction indices, or when no corresponding response behavior identifier can be found in the behavior response difference mapping sequence, it is determined that the operation behavior response status is not synchronized. S203. The operation behavior identifiers corresponding to the operation instruction indexes that are determined to have asynchronous operation behavior response states are uniformly included in the offset state identifier set. An offset state flag indicating that the response state is not synchronized is written into each operation behavior identifier, which serves as the input source for deducing the result type of subsequent behavior processing.

[0009] Preferably, S202 specifically refers to: Arrange the label change directions in the distribution feature group according to the operation instruction index order, extract any two consecutive label change directions to form a direction combination pair, and count the direction switching frequency between adjacent direction combination pairs to form a direction change frequency sequence. Extract the response time field corresponding to the direction combination pair, calculate the time interval difference between the response time fields of adjacent combination pairs, and construct a one-to-one correspondence between the difference sequence and the direction change frequency sequence to form a combination feature set containing time fluctuations and direction switching. In the combined feature set, the frequency value of the direction change is normalized and multiplied with the corresponding response time interval difference. The resulting product is used as the delay drift factor for each group of operation instruction indices. When the value of the delay drift factor shows alternating direction changes in multiple consecutive operation instruction indices, or when no corresponding response behavior identifier can be found in the behavior response difference mapping sequence, it is determined that the operation behavior response state is not synchronized.

[0010] Preferably, S3 specifically includes the following steps: S301. Process the trajectory of the operation behavior identifier marked as offset state according to the index order, extract the preceding and following behavior path nodes of each operation behavior identifier in the behavior response difference mapping sequence, sort the nodes in ascending order based on the operation trigger time, calculate the time interval weight, and generate a behavior chain order score by combining the dwell ratio of each operation behavior identifier in the continuous path. S302. Combine the behavior chain sequence score to identify the location where each operation behavior identifier changes path in the service response flow. By extracting the path start point and target triggered by a response jump in the service response flow, construct the response jump node and calculate the change range of its state identifier before and after as the state rollback value, which is used to measure the path degradation behavior in the response result. S303. Arrange the behavior chain sequence score, response jump node and state rollback value in the processing trajectory inference matrix according to the time order of the operation behavior identifier. Use the continuous offset magnitude of the matrix row vector and the change trend of the column vector for coupling analysis, and output the behavior processing result type corresponding to each operation behavior identifier to identify the actual response attribution under the offset state.

[0011] Preferably, S302 is as follows: Based on the service response flow corresponding to the operation behavior identifier, and combined with the numerical sorting result of the behavior chain sequence score, the operation behavior identifiers that are adjacent in the operation instruction index sequence and whose sequence scores have changed are selected. The corresponding response time field and processing path information are compared. By the change of the processing node identifier in the processing path information, the position where the path change occurs in the service response flow is identified. The processing node before the path change is recorded as the path start point, and the processing node after the path change is recorded as the path target. Using the path start point and path destination as boundaries, extract the response status information sequence of the two in the response behavior identifier. Based on the numerical range transformation of the operation result in the status information field, generate the before and after status identifiers, calculate the numerical change amplitude of the before and after status identifiers, and map the change amplitude to the status rollback value to represent the degree of response degradation generated by the operation behavior identifier in the response jump. The path start point and path target are used to form a response jump node, and the corresponding state rollback value is bound to the operation behavior identifier. This serves as a structural factor for measuring path degradation behavior in the derivation of behavior processing result type, and participates in the construction and calculation of the subsequent processing trajectory inference matrix.

[0012] Preferably, S4 is as follows: Each operation behavior identifier is assigned a behavior processing result type, behavior sensitivity level, and permission level in chronological order to form a joint input item. Three data fields are embedded in each joint input item and standardized encoding is performed to generate a multi-dimensional input index set, which serves as the input basis for constructing the response-level dispatch mapping graph. Based on the combination of fields such as behavior processing result type, behavior sensitivity level and permission level in the multidimensional input index set, the mapping dimensions are divided, multi-level mapping paths are established according to the field nesting order, a response-level dispatch mapping diagram is generated, and node identification rules are set in the response-level dispatch mapping diagram to cluster similar index sets and form candidate behavior risk groups. Based on the scope of the behavior processing result type corresponding to each behavior risk group within the permission level and the security threshold segment in its behavior sensitivity level, the matching node position in the response level assignment mapping diagram is retrieved. When the node is above the upper limit of the security threshold segment and the permission level is controlled, the security training process is initiated. When the node is below the lower limit of the security threshold segment and the permission level is restricted, the operation control process is initiated, and the determination flag is bound to the corresponding operation behavior identifier for subsequent execution logic judgment.

[0013] Preferably, S5 is as follows: After each response-level dispatch mapping map completes the classification of behavioral risk groups and executes the safety training process or operation control process, the behavioral feedback flow is obtained. The behavioral feedback flow contains the actual execution process type corresponding to the operation behavior identifier and the original classified behavioral risk group information, which is used to depict the correspondence between the mapping map judgment result and the actual execution result. Based on the behavior feedback flow, the actual execution process type of each operation behavior identifier is compared with its behavior risk group affiliation in the response level assignment map. The deviation relationship between the two at the risk boundary position is extracted, and error offset feedback is generated according to the deviation direction and deviation accumulation. The error offset feedback is applied to the boundary of the behavior risk group to adjust the boundary position of different behavior risk groups in the response level assignment map. Based on the adjusted boundary of the behavior risk group, the error offset feedback is introduced into the behavior processing result judgment logic to update the mapping relationship of behavior processing result types in the response level assignment mapping diagram. This enables subsequent operational behaviors to be classified according to the updated judgment logic when entering the response level assignment mapping diagram, thereby achieving continuous dynamic control of the safety training process and the operation control process.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: 1. This invention solves the misjudgment problem caused by the asynchrony between operational behavior and service response status by constructing a behavior response difference mapping sequence, introducing a delay drift factor, processing a trajectory inference matrix, and a response-level dispatch mapping diagram. Unlike existing technologies that rely solely on static behavior judgment based on user operation time points, this solution dynamically captures the response fluctuation characteristics of operational behavior during system execution, accurately identifies behaviors that are not adopted by the system or have abnormal response statuses, and supplements the missing information of the server-side processing status by reconstructing the trajectory and extrapolating the results of operations with offset states. This enables a deeper judgment of the actual effectiveness of operational behavior, improving the accuracy of behavior evaluation and the matching degree of response decisions.

[0015] 2. This invention constructs a response-level dispatch mapping map through a multi-dimensional input index and classifies and judges it based on the behavior processing result type, behavior sensitivity level, and permission level. This not only achieves fine-grained grouping of behavioral risks but also supports intelligent triggering of security training and operational control processes. Furthermore, by leveraging behavioral feedback streams and error offset feedback mechanisms, real-time updates to the mapping map boundaries and judgment logic are achieved, enabling the system to possess closed-loop capabilities for behavior perception, anomaly judgment, attribution analysis, and self-adjustment. Overall, this solution has the advantages of high recognition accuracy, strong response rationality, and adjustable control strategies, enhancing the dynamic adaptability and execution efficiency of behavior-based security training and access control systems. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a flowchart illustrating the safety training operation control method based on behavior tracing of the present invention. Detailed Implementation

[0018] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0019] This invention provides, for example Figure 1 The safety training operation control method based on behavior traceability shown includes the following steps: S1. Extract the behavior events and service response events corresponding to user operation behaviors, generate operation behavior identifiers and response behavior identifiers respectively, and construct a behavior response difference mapping sequence containing operation instruction index, response time field and status offset label; In this embodiment, S1 specifically refers to: Collect user-triggered operation behavior data, extract corresponding behavior events from the operation behavior data, and extract corresponding service response events from the service processing records associated with the operation behavior data. Establish a one-to-one correspondence between behavior events and service response events through operation request identifiers, and complete the extraction of behavior events and service response events corresponding to user operation behaviors. When a user performs actions such as "click to save" or "submit for review" on the interface, logs are generated at the front-end or application layer. These logs contain information such as operation time, operation type, target page, and parameter content. This data constitutes the user-triggered operation behavior data. By listening to the front-end event stream or collecting business operation logs, data blocks with complete behavior descriptions can be extracted to form an operation behavior data set. After the operation is completed, the server generates a corresponding processing record, which includes information such as the start time, end time, return result, and processing status code of the request. This is the service response event. To establish the association between behavior events and service response events, it is necessary to extract the unique identifier field carried in the operation request (such as request serial number, transaction ID, or interface call number). Using this as a bridge, a one-to-one correspondence is established in the data, thereby accurately matching each user action with its system processing result. The reason for this is that only by establishing a clear operation-response binding relationship can subsequent behavior tracing have practical significance, avoiding misjudgments caused by simply recording click actions without considering the actual backend processing status.

[0020] User-triggered action data refers to the front-end interaction records automatically generated after a user performs any business operation in the user interface. These records typically include the operation time, content, location, and parameters. Action events are core operational elements extracted from action data, focusing on the intent and timing of the action, and are used for subsequent identifier generation. Service response events are system response logs generated after the backend receives a request, reflecting the system's actual processing status for that operation. The action request identifier is a unique transaction identifier that runs through the front-end and back-end communication chain, such as a unified call ID or signature tag, used to bind action behaviors to response behaviors. A one-to-one correspondence means that each action behavior can only correspond to one response event. The key to establishing this mapping relationship lies in the stability and extractability of the action request identifier. Only through this precise binding can the logical consistency between action records and response results be ensured, laying an accurate data foundation for action judgment and subsequent training and control.

[0021] Based on the extracted behavior event, an operation behavior identifier is generated by selecting the operation trigger time, operation type information and operation path information. Based on the extracted service response event, a response behavior identifier is generated by selecting the response completion time, response status information and processing path information. Both the operation behavior identifier and the response behavior identifier are bound to the same operation request identifier to maintain data consistency. To construct behavior identifiers for behavior analysis and response determination, key feature information from user behavior events and service response events needs to be extracted and structured. For behavior events, the timestamp of the user behavior can be extracted as the operation trigger time by parsing front-end logs or operation audit records. Action types such as clicks, inputs, and switches can be extracted from the operation elements as operation type information. Operation path information can be extracted from the page path, module path, or interface path bound to the event. Combining these three types of information and standardizing the format, for example, generating a structured string in the order of timestamp_type_path, allows the construction of the operation behavior identifier. For service response events, the timestamp of response processing completion can be extracted as the response completion time by analyzing server-side logs or call records. Response status information can be obtained from status codes, return results, or exception identifiers. Processing path information can be extracted from the specific business logic path. Combining these three items in the same way generates the response behavior identifier. The operation behavior identifier and the response behavior identifier are bound together by a pre-extracted operation request identifier, ensuring a one-to-one logical relationship between the two throughout the data tracing process. This binding method supports accurate comparison of behavior state offsets and forms a sustainable data chain for tracing.

[0022] Operation trigger time refers to the moment when a user actually clicks, submits, or executes a certain action on the client or interface layer, usually recorded with a timestamp in milliseconds. Operation type information indicates the type of action within the system, such as "clicking the confirmation button," "filling out a form," or "switching function pages," reflecting the intent of the action. Operation path information is used to locate the location where the action occurred; it can be a page path or a function module path, used to track the action context. Operation behavior identifier is a unique identifier composed of the operation trigger time, operation type information, and operation path information, used to identify an independent operation. Response completion time is the end time of the backend's completion of the operation processing logic, reflecting the actual feedback point of the system to the operation. Response status information is the return status result after the system completes processing, such as whether it was successful, abnormal, or blocked, used to evaluate the consequences of the action. Processing path information refers to the business path or logical flow traversed during the service response process, such as the actual service interface called or the triggered business chain, used to assist in action determination. The response behavior identifier is an identifier composed of the response completion time, response status information, and processing path information, reflecting the server's processing conclusion on the frontend action. Binding the operation behavior identifier and the response behavior identifier to a unified operation request identifier is the foundation for building a complete closed loop of behavior-response, ensuring the temporal consistency and logical traceability of subsequent offset judgment and behavior evaluation.

[0023] Using the operation request identifier in the operation behavior identifier as the operation instruction index, the response completion time in the response behavior identifier as the response time field, and generating a status offset label based on the deviation relationship between the operation trigger time and the response completion time and the response status information, a behavior response difference mapping sequence containing the operation instruction index, the response time field and the status offset label is constructed.

[0024] To construct the behavior response difference mapping sequence, the operation request identifier is first extracted from the operation behavior identifier and used as the operation instruction index to identify a complete operation interaction unit. Then, the response completion time is extracted from the response behavior identifier and set as the response time field, describing the time taken by the system to process the operation. Next, by comparing the time interval between the operation trigger time and the response completion time, and combining response status information (such as success, failure, interruption, retry), deviation levels are classified. Deviation levels can be defined into several intervals based on time difference thresholds and status types, such as "normal response," "delayed response," and "no response," further subdivided by whether a failure status is returned. Based on this calculation process, a status offset label is generated for each pair of operation behavior identifiers and response behavior identifiers. The label can be represented using encoding, symbol bits, color coding, or phrase structures. Finally, using the operation instruction index as the primary key, the response time field and the status offset label are combined as supporting attributes to form a ternary structure, arranged chronologically to constitute the behavior response difference mapping sequence. This sequence will serve as the core data foundation for subsequent judgments of behavior synchronization status, determination of behavior validity, and triggering of training control processes.

[0025] The operation instruction index is an operation request identifier extracted from the operation behavior identifier, used to uniquely identify a specific action-response interaction. The response time field is the response completion time recorded in the response behavior identifier, representing the actual time the server completed processing, used in conjunction with the operation trigger time to calculate response latency. The deviation between the operation trigger time and the response completion time is a metric measuring the time difference between the user operation and the system response, reflecting whether the system responds in real-time or experiences delays. Response status information is the code or description of the system's feedback status, such as success, failure, or abort, used to assist in determining whether the action was adopted by the system. The status offset label is an identifier jointly generated based on the time deviation and status information, used to distinguish whether the operation behavior has experienced response anomalies or synchronization deviations. The behavior response difference mapping sequence is a structured sequence composed of the operation instruction index, response time field, and status offset label, which can be sorted by time order or priority order, serving as the data foundation for visual traceability of behavior, response quality assessment, and subsequent process control in global auditing and local judgment. The construction of this mapping sequence provides quantifiable, traceable, and distinguishable input support for solving the problem of asynchronous operation behavior response statuses.

[0026] S2. Calculate the delay drift factor based on the distribution characteristics of the state offset labels in the behavior response difference mapping sequence. When the delay drift factor changes continuously or the response behavior identifier is missing, it is determined that the operation behavior response state is not synchronized, and the corresponding operation behavior identifier is marked as the offset state. In this embodiment, S2 specifically includes the following steps: S201. Read the state offset labels in the behavior response difference mapping sequence, extract the label change trajectory according to the time sequence of the operation instruction index, count the number of times the label changes direction continuously and the time sequence difference between adjacent labels, and generate a distribution feature group to reflect the label distribution trend. Before identifying state synchronization anomalies, a distribution feature set reflecting the label distribution trend needs to be constructed. First, the state offset label corresponding to each record in the behavior response difference mapping sequence is read, and these labels are arranged sequentially based on the chronological order of the operation instruction index, forming a complete label change trajectory. Then, the values ​​of the labels in the sequence are compared sequentially to identify the direction of continuous change; for example, a label value changing from positive to negative or vice versa is recorded as a directional change, and the number of directions in this process is counted. Simultaneously, the response time field between each pair of adjacent labels is compared, and their temporal sequence difference, i.e., the fluctuation value of the response interval, is calculated. By combining the number of directions and the time difference, a distribution feature set is constructed to express the label change rate, periodicity, and jump amplitude. This process helps capture the dynamic characteristics of behavior response state changes, supporting the accuracy of subsequent anomaly fluctuation detection.

[0027] The temporal order of the operation instruction index refers to the index order sorted according to the time when the behavior record is generated or triggered, ensuring the causal continuity of the label comparison. The label change trajectory represents the continuous evolution of the state offset label value in the sequence, usually a sequence of symbols such as "+, +, -, -, +", used to reflect the trend of the behavior response. The number of times the label changes direction continuously refers to the number of times the label value changes directionally, such as a jump from positive to negative or from negative to positive. This frequency of change is highly correlated with the volatility of system state switching. The temporal order difference between adjacent labels refers to the difference in the response time field between two behavior records, the magnitude of which reflects the time delay fluctuation of the behavior response. The distribution feature group is a statistical set composed of these direction counts and time differences, used to describe the trend and rhythm of state offset changes throughout the entire behavior response process, playing a fundamental supporting role in behavior synchronization state recognition.

[0028] S202. Based on the combination pattern of the label change direction in the distribution feature group and the interval change relationship of the response time field, calculate the delay drift factor. When the value of the delay drift factor shows alternating changes in direction in multiple consecutive operation instruction indices, or when no corresponding response behavior identifier can be found in the behavior response difference mapping sequence, it is determined that the operation behavior response status is not synchronized. S203. The operation behavior identifiers corresponding to the operation instruction indexes that are determined to have asynchronous operation behavior response states are uniformly included in the offset state identifier set. An offset state flag indicating that the response state is not synchronized is written into each operation behavior identifier, which serves as the input source for deducing the result type of subsequent behavior processing.

[0029] To ensure accurate identification of which operational behaviors exhibit asynchronous response states during subsequent behavior processing result type derivation, the previously identified operation instruction indexes need centralized processing. First, each operation instruction index identified as having asynchronous response states is mapped to its corresponding operation behavior identifier, and these identifiers are collectively compiled into a separate dataset named the Offset Status Identifier Set. Then, a field indicating asynchronous response states—the Offset Status Flag—is added to each operation behavior identifier record in this set. This flag can be represented using a Boolean value or encoded bits, clearly indicating whether the behavior differs from the server's response state. The purpose of writing the Offset Status Flag is to provide a reliable basis for downstream data analysis and logical decision-making, enabling subsequent derivation of behavior processing result types to avoid misjudging these inconsistent behaviors and improving the accuracy of safety training operation control responses.

[0030] The offset status identifier set is a dedicated collection for centrally managing all operational behavior identifiers that are in a state of asynchronous response determination. It ensures that subsequent analysis only evaluates behaviors with consistent responses. Operational behavior identifiers are the core index units in the behavior tracing process, while offset status identifiers are logical judgment identifiers attached to this index, used to distinguish which behavior records have missing response information or drift anomalies. By deeply binding this type of status information with behavior identifiers, a structured input basis can be provided for subsequent operations such as behavior trajectory deduction, behavior risk assessment, and safety training process division, preventing control failures or accidental triggering of response processes due to status errors. This structured processing method enhances the classification capability of behavior tracing data and the accuracy of response control.

[0031] In this embodiment, S202 specifically refers to: Arrange the label change directions in the distribution feature group according to the operation instruction index order, extract any two consecutive label change directions to form a direction combination pair, and count the direction switching frequency between adjacent direction combination pairs to form a direction change frequency sequence. To further quantify the rhythmic characteristics of label change trends, a direction change frequency sequence needs to be constructed based on the label change direction. First, the label change directions in the distribution feature group are arranged sequentially according to the chronological order of the operation command index, for example, forming a change sequence of "positive, positive, negative, positive, negative". Then, any two consecutive label change directions are extracted to form a direction pair, such as "positive-positive", "positive-negative", "negative-positive", etc. The direction pair represents the directional relationship of label changes at two consecutive time points and is a local encoding of label jump behavior. Next, according to the sequence of pair combinations, adjacent direction pairs are compared sequentially, and it is counted whether a direction switch occurs between each pair, i.e., whether the direction pattern of the pair has changed. For example, a jump from "positive-positive" to "positive-negative" constitutes a direction switch. A sliding scan is performed on the entire combination sequence, accumulating the number of direction switches occurring in each jump segment, and the switching frequency under each operation command index is recorded as a frequency value. Finally, this set of frequency values ​​is arranged sequentially according to the index position of the operation command to construct a direction change frequency sequence, which is used to express the strength and rhythm density of the fluctuation of the label change direction throughout the entire behavioral response cycle. The direction combination pair is the basic unit for analyzing the continuity and switching of direction; the frequency of direction switching reveals the frequency of trend reversals during the behavioral response process; and the direction change frequency sequence is an important input indicator for assessing synchronization stability and drift risk. This analysis process provides a refined fluctuation basis for subsequent calculations of the delay drift factor, helping to more accurately determine whether there is a lack of synchronization in the operational behavioral response state.

[0032] Extract the response time field corresponding to the direction combination pair, calculate the time interval difference between the response time fields of adjacent combination pairs, and construct a one-to-one correspondence between the difference sequence and the direction change frequency sequence to form a combination feature set containing time fluctuations and direction switching. To integrate the rhythm of direction switching with the latency fluctuations of behavioral responses, the response time field corresponding to each direction pair needs to be extracted first. The response time field represents the timestamp of the actual completion of the corresponding response event after the operation is completed, reflecting the system's processing time in that response path. After constructing the direction pair sequence, the response time field recorded in the associated response behavior identifier is sequentially searched according to the index position of the operation instruction corresponding to the pair. Then, the difference between the response time fields of any two adjacent direction pairs is calculated, yielding the time interval difference, which represents the degree of temporal fluctuation between the two adjacent behavioral responses. The time interval differences between all pairs are arranged sequentially to generate a time fluctuation difference sequence. Simultaneously, maintaining the index order that corresponds one-to-one with the previously constructed direction change frequency sequence, the two sequences are combined to form a two-parameter feature set, constructing a combined feature set containing time fluctuation information and direction switching frequency. This set achieves a two-dimensional analysis of the stability and drift probability of the operational behavior response state by synchronously comparing the temporal instability of the behavioral response with the complexity of direction switching. The response time field provides the basis for time axis positioning, the time interval difference quantifies the response fluctuation amplitude, and the combined feature set provides a fusion input for the subsequent calculation of the delay drift factor, which can effectively enhance the ability to identify abnormalities in behavioral response consistency.

[0033] In the combined feature set, the frequency value of the direction change is normalized and multiplied with the corresponding response time interval difference. The resulting product is used as the delay drift factor for each group of operation instruction indices. When the value of the delay drift factor shows alternating direction changes in multiple consecutive operation instruction indices, or when no corresponding response behavior identifier can be found in the behavior response difference mapping sequence, it is determined that the operation behavior response state is not synchronized.

[0034] To achieve quantitative analysis of the stability of operational response states, in the constructed combined feature set, the frequency value of directional change and the corresponding response time interval difference in each feature group are first normalized. The normalization process uses min-max normalization to compress the values ​​of the two parameters to the same scale range, eliminating the weight imbalance caused by their different dimensions. Subsequently, the normalized frequency value of directional change and the normalized response time interval difference are multiplied term by term to obtain a product value that integrates the response fluctuation amplitude and the instability of the behavioral path. This product value serves as the delay drift factor corresponding to the current operational command index, reflecting the probability of a synchronous shift in the response chain for a certain operational behavior. This calculation process is applied to all operational command indices to form a delay drift factor sequence. Then, it is observed whether the direction of change of the delay drift factor of multiple consecutive operational command indices in this sequence alternates, i.e., from increasing to decreasing or from decreasing to increasing. If alternating patterns appear consecutively, or if response behavior identifiers cannot be matched at certain operation command index positions, it indicates that there is a missing or inconsistent response information between the behavior record and the service response, thus indicating an unsynchronized operation behavior response state. The latency drift factor is a key criterion; the frequency of direction changes reflects the complexity of the behavior chain, and the difference in response time intervals reveals processing latency fluctuations. The fusion of these two factors ensures that drift anomalies can be accurately identified. Alternating direction changes represent discontinuous switching of behavior states, which is an important characteristic for identifying unsynchronized response states.

[0035] S3. Perform trajectory deduction on the operation behavior identifier marked as offset state, construct a processing trajectory inference matrix including behavior chain sequence score, response jump node and state rollback value, and output behavior processing result type through matrix operation; In this embodiment, S3 specifically includes the following steps: S301. Process the trajectory of the operation behavior identifier marked as offset state according to the index order, extract the preceding and following behavior path nodes of each operation behavior identifier in the behavior response difference mapping sequence, calculate the time interval weight after arranging the nodes in ascending order based on the operation trigger time, and generate a behavior chain order score by combining the dwell ratio of each operation behavior identifier in the continuous path, which is used to represent its guidance strength and path coherence in the complete operation chain. In trajectory extrapolation, all operation behavior identifiers marked as offset are processed sequentially according to their order in the operation instruction index. The preceding and following path nodes of each operation behavior identifier in the behavior response difference mapping sequence are extracted, thus locating the context behavior position of the behavior in the operation chain. Using the operation trigger time as the sorting criterion, all nodes are sorted in ascending order. The time interval between any two consecutive nodes is calculated, and a corresponding time interval weight is generated based on the time interval length. Furthermore, for each operation behavior identifier, the proportion of its dwell time between adjacent path nodes is calculated, i.e., the ratio of the time period of the behavior in the operation chain to the total time of its path segment. Finally, this dwell proportion and the time interval weight are fused to construct a behavior chain sequence score, used to evaluate whether the behavior has stage guidance or path continuity in the entire operation process. For example, in a form configuration operation, if the user spends a long time between field editing and submission, the behavior position is early, and the path is clear and continuous, then the sequence score of this behavior identifier is high, indicating strong path coherence.

[0036] Trajectory deduction refers to reconstructing the dynamic evolution path of operational behavior identifiers within a behavioral chain based on temporal sequence and path connectivity information. Preceding and subsequent behavioral path nodes refer to two adjacent and path-related behavioral identifiers in the behavioral response difference mapping sequence that are temporally close to the current operational behavior identifier; they jointly define the forward relationship of the behavior within the path. The time interval weight is a value obtained by calculating the trigger time difference between two nodes and is used to measure the importance of path continuity. The dwell ratio of each operational behavior identifier in a continuous path refers to the proportion of time that behavior spends within its path node segment, reflecting whether it plays a role in pausing or transitioning within the operational chain. The behavioral chain sequence score comprehensively considers the time interval weight and dwell ratio to form a quantitative expression of the guiding role and sequential stability of the behavior within the chain, serving as a core input factor for subsequent response jump judgment and trajectory analysis. By constructing a behavioral chain sequence score, it is possible to effectively identify which behaviors have key control value, thus improving the accuracy of behavioral attribution judgment.

[0037] S302. Combine the behavior chain sequence score to identify the location where each operation behavior identifier changes path in the service response flow. By extracting the path start point and target triggered by a response jump in the service response flow, construct the response jump node and calculate the change range of its state identifier before and after as the state rollback value, which is used to measure the path degradation behavior in the response result. S303. Arrange the behavior chain sequence score, response jump node and state rollback value in the processing trajectory inference matrix according to the time order of the operation behavior identifier. Use the continuous offset magnitude of the matrix row vector and the change trend of the column vector for coupling analysis, and output the behavior processing result type corresponding to each operation behavior identifier to identify the actual response attribution under the offset state.

[0038] To extract the response attribution features of operational behaviors under offset states, three analysis factors can be used: behavior chain sequence score, response jump node, and state rollback value. These factors are arranged according to the chronological order of the operational behavior identifiers to construct a multi-dimensional processing trajectory inference matrix. Each row in the matrix represents an analysis record for one operational behavior identifier, and the columns correspond to the values ​​of guidance intensity, path change, and response degradation, respectively. By analyzing the continuous offset amplitude of the matrix row vectors, the coherence of changes in the same behavior across paths and states can be captured. Simultaneously, the evolutionary patterns of the behavioral response chain can be mined by combining the changing trends of the column vectors. Based on this, coupled analysis is performed to identify behavioral state evolution patterns in the data, thereby accurately deriving the behavioral processing result type corresponding to each offset state operational behavior identifier, such as whether the submission was successful, whether it was rolled back by the system, or whether it entered an abnormal waiting state.

[0039] The processing trajectory inference matrix is ​​a behavior response analysis container built on a time sequence. It arranges analysis units for each operational behavior identifier by row and fills in three categories of indicators by column: behavior chain sequence score, response jump node, and status rollback value. The continuous offset magnitude of the matrix row vectors characterizes the degree of coherent deviation of the behavior path over time, reflecting the response stability of the operational behavior. The changing trend of the column vectors reveals the distribution patterns of various structural indicators among similar behaviors, reflecting potential trajectories of path degradation or guidance failure. Coupling analysis cross-compares the dynamic features of these two dimensions to accurately identify typical attribution patterns in response behavior. The behavior processing result type is a classification identifier derived from the analysis of the matrix structure, used to describe the actual execution result of the operational behavior in the service response. It is a key data foundation for safety training and operational control process initiation judgment.

[0040] In this embodiment, S302 specifically refers to: Based on the service response flow corresponding to the operation behavior identifier, and combined with the numerical sorting result of the behavior chain sequence score, the operation behavior identifiers that are adjacent in the operation instruction index sequence and whose sequence scores have changed are selected. The corresponding response time field and processing path information are compared. By the change of the processing node identifier in the processing path information, the position where the path change occurs in the service response flow is identified. The processing node before the path change is recorded as the path start point, and the processing node after the path change is recorded as the path target. Based on the service response flow, by acquiring the service response data associated with each operation behavior identifier, the corresponding response time field and processing path information are extracted. Combined with the numerical sorting results of the behavior chain sequence score, two adjacent operation behavior identifiers are selected according to the operation instruction index order. When the numerical change in the sequence score of these two behavior identifiers is considered a trigger point for a possible path change, the processing path information corresponding to these two behavior identifiers is compared, and the sequence of processing node identifiers contained in the path is analyzed. When the processing node in the later path no longer contains the tail node of the previous path, or the path structure of both paths shifts, it indicates that the service response path has jumped. Further, the last processing node before the jump is extracted as the path start point, and the first processing node after the jump is extracted as the path target, and this jump point is recorded. For example, after a user performs an approval submission action, if the system response path changes from "pending approval node" to "error rollback node," a path shift can be identified, with the path start point being "pending approval node" and the path target being "error rollback node."

[0041] The response time field records the time when a service response is completed, typically used to measure response latency, processing rhythm, or time-series analysis. Processing path information refers to the set of processing nodes experienced by each action response during the service response process, including node identifiers, processing status, and their order. Changes in the processing node identifiers within the path information are key elements in determining whether the action processing flow has jumped. Interruptions in the continuity of the node identifier sequence or changes in node content can identify whether the path has deviated or rolled back. The path start point refers to the last processing node in the action response path before a structural change occurs, and the path target refers to the first new node after the change. Together, they constitute a path jump pair, which is crucial foundational data for subsequent analysis of response degradation or rollback behavior. By identifying the path jump location, further analysis of status changes can determine whether the behavior poses an operational risk or requires triggering a training process.

[0042] Using the path start point and path destination as boundaries, extract the response status information sequence of the two in the response behavior identifier. Based on the numerical range transformation of the operation result in the status information field, generate the before and after status identifiers, calculate the numerical change amplitude of the before and after status identifiers, and map the change amplitude to the status rollback value to represent the degree of response degradation generated by the operation behavior identifier in the response jump. After determining the path start point and path destination, it is necessary to characterize the actual processing of the operation behavior based on the corresponding response state changes. Specifically, firstly, a set of response state information is continuously extracted from the response behavior identifier corresponding to the path start point, and then subsequent response state information is extracted from the response behavior identifier corresponding to the path destination, thus forming a sequence of response state information with path changes as the boundary. Next, the state information fields in the response state information sequence are parsed, mapping the operation results to preset numerical ranges, such as mapping different results like success, processing, failure, and rollback to different range levels. By comparing the state ranges before and after the path change, before-and-after state identifiers can be generated to reflect the hierarchical change in state before and after the path jump. Then, the difference between the range values ​​corresponding to the before-and-after state identifiers is calculated to obtain the magnitude of the state change, and this magnitude is mapped to a state rollback value. For example, if an operation's response state is in the normal processing range at the path start point but transitions to a rollback or abnormal range at the path destination, there is a significant difference between the before-and-after state identifiers, and the corresponding state rollback value is large, indicating that the operation experienced significant response degradation during the path jump.

[0043] The response state information sequence refers to a continuous set of response state records extracted around the path start point and path target, used to describe the state evolution process of the operation behavior at different processing stages. The numerical range transition of the operation result in the state information field refers to the process of the operation result shifting from one preset range to another, used to quantify the direction and magnitude of the state level change. The preceding and following state identifiers represent the state range identifiers corresponding to the path start point stage and the path target stage, respectively, and are the basis for state comparison. The magnitude of the numerical change of the preceding and following state identifiers is used to characterize the degree of change from normal and stable to abnormal and regressive states. The state rollback value is a unified expression of this magnitude of change, used to describe the degree of processing degradation that occurs during the response transition, providing a key quantitative factor for the construction of the subsequent processing trajectory inference matrix.

[0044] The path start point and path target are used to form a response jump node, and the corresponding state rollback value is bound to the operation behavior identifier. This serves as a structural factor for measuring path degradation behavior in the derivation of behavior processing result type, and participates in the construction and calculation of the subsequent processing trajectory inference matrix.

[0045] To incorporate the impact of path transitions on operational response results into the derivation process of behavior processing result types, a structured binding relationship needs to be established between the boundary nodes of path changes and their corresponding state rollback states. Specifically, this can be achieved by encapsulating the path start and path target into a set of response transition nodes with unique indices. These nodes not only carry the start and target information of the path change but also possess a dynamic attribute: the state rollback value corresponding to that transition. Binding each response transition node to its associated operational behavior identifier clearly marks the response degradation impact caused by path mutations in the processing trajectory. For example, if an operational behavior transitions from an approval flow to a rollback flow in the service response flow, and its state rollback value is a significantly negative value, a corresponding response transition node can be constructed, and a structural label can be assigned to indicate the directionality and severity of the transition. This response transition node, combined with the state rollback value, becomes a structural factor used in the processing trajectory inference matrix to measure the degradation weight of different operational behaviors in the execution path, thereby influencing the final classification of the behavior processing result type. Response jump nodes are data pairs consisting of path change events triggered by operational actions. They include processing nodes before and after the path change, used to accurately locate structural jumps in the response flow. State rollback values ​​are numerical measures of the state difference before and after the jump, representing the degree of response degradation experienced by the operational action during the jump. Operational action identifiers are behavioral units that uniquely identify a specific operational event. Their binding with response jump nodes and state rollback values ​​constitutes a key unit in the processing trajectory inference matrix for evaluating the rationality of operational action processing. Applying this structural factor to the construction of the trajectory matrix allows for the extraction of essential features of behavioral responses across multi-dimensional path, state, and time relationships, providing data support for subsequent behavioral classification, response attribution, and control decisions.

[0046] S4. Combine the behavior processing result type with the behavior sensitivity level and permission level to form input, establish a response level assignment mapping diagram, classify the behavior risk group in the response level assignment mapping diagram, and determine whether to enter the security training process or the operation control process based on the classification result. In this embodiment, S4 specifically refers to: Each operation behavior identifier is assigned a behavior processing result type, behavior sensitivity level, and permission level in chronological order to form a joint input item. Three data fields are embedded in each joint input item and standardized encoding is performed to generate a multi-dimensional input index set, which serves as the input basis for constructing the response-level dispatch mapping graph. When constructing input data for determining the risk level of actions, three key pieces of information can be relied upon for each action identifier: the action processing result type, the action sensitivity level, and the permission level. These are then sorted according to the trigger time of the action identifier to ensure the semantic sequence of actions. For each sorted action record, the three information fields are combined into a joint input item and subjected to unified format conversion and encoding. For example, the action processing result type is discretized numerically encoded according to the response offset, the action sensitivity level is coded according to the data operation scope or system impact, and the permission level is coded according to the control level from open to restricted. After encoding, the three pieces of data are merged to generate a multi-dimensional input index set with a three-dimensional structure. This set serves as the basic input source for subsequently constructing the response level assignment mapping map. This is done to achieve measurable comparison of different action states in a structured data space, thereby supporting downstream risk classification and process scheduling calculations.

[0047] The behavior processing result type refers to the response attribution information, such as whether the operation was completed, failed, withdrawn, or abnormal, determined based on the analysis of the processing trajectory. The behavior sensitivity level describes the importance of the data or functions involved in the system, such as accessing user information, modifying key parameters, or configurations. The permission level reflects the scope of account or entity permissions upon which the behavior execution depends, typically categorized as basic, controlled, or restricted. The combined input item is a record combining the above three pieces of information; each record forms a multi-field entity used to express the risk dimension of the operation. The three data fields refer to the behavior processing result type field, the behavior sensitivity level field, and the permission level field, respectively. Standardized coding processing refers to converting the original category information into a discrete numerical code with a unified dimension for subsequent graphing and comparison. The multidimensional input index set is a dataset composed of multiple combined input items, where each dimension represents a coded field, forming a structured input matrix with data analysis and graph construction capabilities. This set provides a unified input interface for behavior risk classification and response path modeling.

[0048] Based on the combination of fields such as behavior processing result type, behavior sensitivity level and permission level in the multidimensional input index set, the mapping dimensions are divided, multi-level mapping paths are established according to the field nesting order, a response-level dispatch mapping diagram is generated, and node identification rules are set in the response-level dispatch mapping diagram to cluster similar index sets and form candidate behavior risk groups. To classify operational behaviors by risk, a combination mapping is needed based on three fields from a multidimensional input index set: behavior processing result type, behavior sensitivity level, and permission level. These three fields can be initially set as mapping dimensions, and the nesting order can be determined according to the priority of the fields' impact on behavior risk. For example, behavior processing result type could be the first-level dimension, followed by behavior sensitivity level and permission level. Under this structure, a hierarchical path mapping graph is constructed, where each path represents a specific combination pattern of behavioral characteristics. In the graph, node identification rules, such as field value equality and consistency of occurrence frequency within adjacent behavior time windows, are used to identify sets of nodes with similar characteristics. These nodes identified as having similar behavioral characteristics are merged into cluster units, ultimately forming candidate behavior risk groups for subsequent determination of whether to enter a specific control process. This approach effectively improves the accuracy of identifying behavioral differences and enhances the ability to classify abnormal behavior sets.

[0049] Mapping dimensions refer to the key field categories used when constructing the mapping graph, in this case, the three fields: behavior processing result type, behavior sensitivity level, and permission level. Field nesting order indicates the order in which multi-dimensional fields are nested in the graph, usually arranged from largest to smallest in terms of their influence in risk assessment. Multi-level mapping paths are paths formed by expanding sequentially along the nesting order from the root node of the mapping graph; each path represents a specific combination of behavioral risks. The response-level assignment mapping graph is a graphical structure composed of all multi-level paths, possessing hierarchical indexing and classification capabilities. Node identification rules are the criteria used in the graph to determine whether nodes belong to the same type of behavioral characteristics, and can be formulated based on logic such as field value matching, overlapping behavior types, and similar time distribution. Clustering similar index sets refers to grouping nodes with consistent or similar characteristics together, thereby reducing redundant judgments. Candidate behavioral risk groups are the collection of these clustering results, representing a set of pending behaviors that may trigger control or training processes, for use in subsequent risk assessment stages.

[0050] Based on the scope of the behavior processing result type corresponding to each behavior risk group within the permission level and the security threshold segment in its behavior sensitivity level, the matching node position in the response level assignment mapping diagram is retrieved. When the node is above the upper limit of the security threshold segment and the permission level is controlled, the security training process is initiated. When the node is below the lower limit of the security threshold segment and the permission level is restricted, the operation control process is initiated, and the determination flag is bound to the corresponding operation behavior identifier for subsequent execution logic judgment.

[0051] To implement appropriate interventions for different behavioral risk groups, it is necessary to jointly analyze the behavioral processing result type corresponding to each risk group with the permission level and behavioral sensitivity level. First, determine the scope of the behavioral processing result type within the permission level, such as its impact on configuration, data transmission, and account management, mapping it to the permission levels defined in the system. Then, locate the corresponding security threshold range for its behavioral sensitivity level under the current policy, identifying which boundary interval of the security zone the behavior falls within. Subsequently, retrieve the graph node positions matching the above conditions from the response level assignment mapping graph. If the behavioral sensitivity level corresponding to the node is above the upper limit of the threshold range, and the permission level belongs to the controlled level set by the system, it indicates that the operation behavior has high sensitivity in a high-privilege environment and should be determined to enter the security training process. Conversely, if the node is below the lower limit of the threshold and the permission level is restricted, it indicates that the behavioral risk has significantly exceeded the tolerance range and should be determined to enter the operation control process. After the determination is completed, the conclusion is written into the operation behavior identifier in the form of a tag for subsequent execution path judgment and security action scheduling.

[0052] The scope of a behavior processing result type within the permission hierarchy refers to the degree of impact a certain operation has on the functions within a specific system permission range, such as whether it triggers permission changes or data modifications. The security threshold segment within a behavior sensitivity level is a continuous interval that quantifies the risk level of a behavior according to the system security policy, typically divided into low-risk, medium-risk, and high-risk segments. The upper limit of the security threshold segment represents the position with the highest risk level within the current segment; if a behavior exceeds the upper limit, it indicates a deviation from the safe range. The controlled level refers to a permission area with a higher permission level and a greater impact on the system, usually requiring mandatory management and early warning measures. A security training process is a guidance mechanism that guides users to operate correctly through education, simulation, or interactive methods. The lower limit of the security threshold segment is the critical point with the lowest risk within the segment; behaviors below the lower limit indicate significant deviations. The restricted level refers to a permission area where the system has stricter permission control and does not allow operations to exceed the boundaries. The operational control process is an operational procedure for directly intervening in abnormal behavior using technical means, such as freezing accounts, suspending services, or resetting permissions, aiming to prevent the spread of risk.

[0053] S5. Adjust the boundary of the behavior risk group according to the behavior feedback flow formed by each response level assignment mapping, and update the behavior processing result judgment logic through error offset feedback to realize dynamic control of the safety training process and operation control process.

[0054] In this embodiment, S5 specifically refers to: After each response-level dispatch mapping map completes the classification of behavioral risk groups and executes the safety training process or operation control process, the behavioral feedback flow is obtained. The behavioral feedback flow contains the actual execution process type corresponding to the operation behavior identifier and the original classified behavioral risk group information, which is used to depict the correspondence between the mapping map judgment result and the actual execution result. Obtaining the behavioral feedback stream generated after each response-level dispatch mapping execution result can be achieved by recording the actual execution result type of the operation behavior identifier in the real system process and matching it with the behavioral risk group to which it belongs in the mapping diagram. Specifically, after the system completes a security training process or operational control process, the final processing path of all operation behavior identifiers in that execution is extracted, such as whether access control was actually entered or whether the training process was actually completed. This is combined with the behavior processing records to identify the process type. Then, it is compared with the original behavioral risk group record corresponding to that behavior identifier in the mapping diagram to form a one-to-one combination of judgment and execution results. Each combination is recorded as a feedback item, and all items are aggregated to form a complete behavioral feedback stream for analyzing classification accuracy. The significance of this is that it continuously collects the system's classification and execution deviations, providing a quantitative basis for adjusting subsequent judgment logic. For example, if the system judges that a certain behavioral risk group should enter the security training process, but the process is not actually triggered, it indicates that the current judgment logic has a deviation, and the risk boundary of the mapping diagram needs to be adjusted.

[0055] In the behavior feedback stream, the "Operation Behavior Identifier" uniquely identifies a specific user operation behavior. The "Actual Execution Process Type" refers to the response process to which the operation behavior is ultimately guided during real system execution, including safety training processes or operational control processes. The "Original Classified Behavior Risk Group Information" indicates the risk group identifier to which the operation behavior belongs during the response-level assignment mapping process. These three elements together constitute the basic fields of the feedback entry. The "Correspondence" between the judgment result and the actual result measures the degree of fit between the system prediction and the actual behavior, serving as an important benchmark for adjusting the mapping judgment boundary. For example, if multiple operation behavior identifiers consistently exhibit the same classification deviation trend across multiple execution cycles, it means that the "Risk Group Boundary" setting deviates from the actual risk-bearing distribution, and its boundary position should be optimized in the next cycle to improve the accuracy of behavior classification.

[0056] Based on the behavior feedback flow, the actual execution process type of each operation behavior identifier is compared with its behavior risk group affiliation in the response level assignment map. The deviation relationship between the two at the risk boundary position is extracted, and error offset feedback is generated according to the deviation direction and deviation accumulation. The error offset feedback is applied to the boundary of the behavior risk group to adjust the boundary position of different behavior risk groups in the response level assignment map. Adjusting the boundaries of behavioral risk groups based on behavioral feedback flows can be achieved by comparing the process type executed in the real system for each operational behavior identifier with the behavioral risk group it belongs to in the response-level assignment mapping. Specifically, for each feedback flow entry, its actual execution process type and the original mapping classification result are extracted and compared for consistency. If inconsistent, the deviation of their risk boundary positions in the mapping is calculated. For example, if an operational behavior is classified as medium-risk in the mapping but is actually guided to a high-risk operation control process by the system, its boundary classification in the diagram can be considered too low. The direction (upward or downward) of this type of deviation is accumulated with its frequency to form a deviation direction set. Based on this set, an error offset feedback model is constructed to locally shift or expand the corresponding risk group boundaries, thereby making the classification of similar behaviors in the future more realistic. This mechanism has adaptive adjustment capabilities, continuously optimizing classification boundaries and enhancing the system's adaptability to behavioral fluctuations.

[0057] The actual execution process type refers to the processing path that a certain operation enters in the final system response, such as a safety training process or an operation control process. Behavioral risk group classification refers to the risk group to which the behavior is categorized in the mapping rules, such as low, medium, and high risk zones. The deviation relationship at the risk boundary position is the relative offset of these two classifications in the graph structure, such as whether it crosses a set interval threshold line. The direction of the offset indicates whether the classification result is too high or too low, and the cumulative offset counts the frequency and consistency of this type of deviation in multiple classifications. Error offset feedback is a numerical correction mechanism used to map the classification deviation amount into a boundary adjustment factor. Through continuous calculation and application of error offset feedback, the boundary positions corresponding to different behavioral risk groups in the graph can be dynamically changed, thereby improving the classification accuracy and risk perception capability of the response-level assignment mapping graph in the actual environment.

[0058] Based on the adjusted boundary of the behavior risk group, the error offset feedback is introduced into the behavior processing result judgment logic to update the mapping relationship of behavior processing result types in the response level assignment mapping diagram. This enables subsequent operational behaviors to be classified according to the updated judgment logic when entering the response level assignment mapping diagram, thereby achieving continuous dynamic control of the safety training process and the operation control process.

[0059] Introducing error offset feedback into the behavior processing result determination logic can be achieved by updating the mapping relationship between behavior processing result types and behavior risk groups in the response-level assignment mapping graph. Specifically, after each error offset feedback and adjustment of the behavior risk group boundary, the classification position of each behavior processing result type under different permission levels and sensitivity levels is recalculated, and the adjusted boundary value is used as the new mapping determination basis. For example, if a certain processing result type was originally classified as low-risk in the medium sensitivity level and medium permission level, but multiple consecutive error offset feedbacks indicate that its actual execution flow is a run control flow, the system should adjust the determination boundary of this type to the high-risk area and update the mapping graph structure so that future behaviors under the same conditions directly enter the high-risk classification path. Through this mechanism, subsequent newly entered operations can be classified based on the latest determination logic, ensuring the timeliness and accuracy of system decisions and effectively preventing risky behaviors from being misjudged or missed due to outdated classification rules.

[0060] The adjusted behavioral risk group boundaries represent a redefinition of the risk level range corresponding to different behavioral processing result types in the response-level assignment mapping diagram. Error offset feedback is a set of dynamic correction parameters reflecting the deviation trend of the original judgment result, used to correct the critical threshold for behavioral classification in the judgment logic. Behavioral processing result judgment logic refers to the combination of conditions and mapping rules used when classifying operational behavior identifiers for risk, including the mapping relationship between fields such as sensitivity level, authority level, and processing result type. Mapping relationship update refers to adjusting the correspondence between the original judgment conditions and risk groups based on offset feedback. The response-level assignment mapping diagram is a graph structure model used to express different risk classification paths, achieving classification decisions through the mapping of nodes and paths in the graph. Dynamic control refers to the ability of this mapping relationship to be continuously adjusted based on data feedback, enabling the system to make timely classification judgments and correct process response strategies for different risk situations.

[0061] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0062] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0063] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0064] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0065] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0066] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0067] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A safety training operation control method based on behavior traceability, characterized in that, Specifically, the following steps are included: S1. Extract the behavior events and service response events corresponding to user operation behaviors, generate operation behavior identifiers and response behavior identifiers respectively, and construct a behavior response difference mapping sequence containing operation instruction index, response time field and status offset label; S2. Calculate the delay drift factor based on the distribution characteristics of the state offset labels in the behavior response difference mapping sequence. When the delay drift factor changes continuously or the response behavior identifier is missing, it is determined that the operation behavior response state is not synchronized, and the corresponding operation behavior identifier is marked as the offset state. S3. Perform trajectory deduction on the operation behavior identifier marked as offset state, construct a processing trajectory inference matrix including behavior chain sequence score, response jump node and state rollback value, and output behavior processing result type through matrix operation; S4. Combine the behavior processing result type with the behavior sensitivity level and permission level to form input, establish a response level assignment mapping diagram, classify the behavior risk group in the response level assignment mapping diagram, and determine whether to enter the security training process or the operation control process based on the classification result. S5. Adjust the boundary of the behavior risk group according to the behavior feedback flow formed by each response level assignment mapping, and update the behavior processing result judgment logic through error offset feedback to realize dynamic control of the safety training process and operation control process.

2. The safety training operation control method based on behavior tracing according to claim 1, characterized in that, S1 specifically refers to: Collect user-triggered operation behavior data, extract corresponding behavior events from the operation behavior data, and extract corresponding service response events from the service processing records associated with the operation behavior data. Establish a one-to-one correspondence between behavior events and service response events through operation request identifiers, and complete the extraction of behavior events and service response events corresponding to user operation behaviors. Based on the extracted behavior event, an operation behavior identifier is generated by selecting the operation trigger time, operation type information and operation path information. Based on the extracted service response event, a response behavior identifier is generated by selecting the response completion time, response status information and processing path information. Both the operation behavior identifier and the response behavior identifier are bound to the same operation request identifier to maintain data consistency. Using the operation request identifier in the operation behavior identifier as the operation instruction index, the response completion time in the response behavior identifier as the response time field, and generating a status offset label based on the deviation relationship between the operation trigger time and the response completion time and the response status information, a behavior response difference mapping sequence containing the operation instruction index, the response time field and the status offset label is constructed.

3. The safety training operation control method based on behavior tracing according to claim 1, characterized in that, S2 specifically includes the following steps: S201. Read the state offset labels in the behavior response difference mapping sequence, extract the label change trajectory according to the time sequence of the operation instruction index, count the number of times the label changes direction continuously and the time sequence difference between adjacent labels, and generate a distribution feature group to reflect the label distribution trend. S202. Based on the combination pattern of the label change direction in the distribution feature group and the interval change relationship of the response time field, calculate the delay drift factor. When the value of the delay drift factor shows alternating changes in direction in multiple consecutive operation instruction indices, or when no corresponding response behavior identifier can be found in the behavior response difference mapping sequence, it is determined that the operation behavior response status is not synchronized. S203. The operation behavior identifiers corresponding to the operation instruction indexes that are determined to have asynchronous operation behavior response states are uniformly included in the offset state identifier set. An offset state flag indicating that the response state is not synchronized is written into each operation behavior identifier, which serves as the input source for deducing the result type of subsequent behavior processing.

4. The safety training operation control method based on behavior traceability according to claim 3, characterized in that, S202 specifically refers to: Arrange the label change directions in the distribution feature group according to the operation instruction index order, extract any two consecutive label change directions to form a direction combination pair, and count the direction switching frequency between adjacent direction combination pairs to form a direction change frequency sequence. Extract the response time field corresponding to the direction combination pair, calculate the time interval difference between the response time fields of adjacent combination pairs, and construct a one-to-one correspondence between the difference sequence and the direction change frequency sequence to form a combination feature set containing time fluctuations and direction switching. In the combined feature set, the frequency value of the direction change is normalized and multiplied with the corresponding response time interval difference. The resulting product is used as the delay drift factor for each group of operation instruction indices. When the value of the delay drift factor shows alternating direction changes in multiple consecutive operation instruction indices, or when no corresponding response behavior identifier can be found in the behavior response difference mapping sequence, it is determined that the operation behavior response state is not synchronized.

5. The safety training operation control method based on behavior traceability according to claim 1, characterized in that, S3 specifically includes the following steps: S301. Process the trajectory of the operation behavior identifier marked as offset state according to the index order, extract the preceding and following behavior path nodes of each operation behavior identifier in the behavior response difference mapping sequence, sort the nodes in ascending order based on the operation trigger time, calculate the time interval weight, and generate a behavior chain order score by combining the dwell ratio of each operation behavior identifier in the continuous path. S302. Combine the behavior chain sequence score to identify the location where each operation behavior identifier changes path in the service response flow. By extracting the path start point and target triggered by a response jump in the service response flow, construct the response jump node and calculate the change range of its state identifier before and after as the state rollback value, which is used to measure the path degradation behavior in the response result. S303. Arrange the behavior chain sequence score, response jump node and state rollback value in the processing trajectory inference matrix according to the time order of the operation behavior identifier. Use the continuous offset magnitude of the matrix row vector and the change trend of the column vector for coupling analysis, and output the behavior processing result type corresponding to each operation behavior identifier to identify the actual response attribution under the offset state.

6. The safety training operation control method based on behavior tracing according to claim 5, characterized in that, S302 specifically refers to: Based on the service response flow corresponding to the operation behavior identifier, and combined with the numerical sorting result of the behavior chain sequence score, the operation behavior identifiers that are adjacent in the operation instruction index sequence and whose sequence scores have changed are selected. The corresponding response time field and processing path information are compared. By the change of the processing node identifier in the processing path information, the position where the path change occurs in the service response flow is identified. The processing node before the path change is recorded as the path start point, and the processing node after the path change is recorded as the path target. Using the path start point and path destination as boundaries, extract the response status information sequence of the two in the response behavior identifier. Based on the numerical range transformation of the operation result in the status information field, generate the before and after status identifiers, calculate the numerical change amplitude of the before and after status identifiers, and map the change amplitude to the status rollback value to represent the degree of response degradation generated by the operation behavior identifier in the response jump. The path start point and path target are used to form a response jump node, and the corresponding state rollback value is bound to the operation behavior identifier. This serves as a structural factor for measuring path degradation behavior in the derivation of behavior processing result type, and participates in the construction and calculation of the subsequent processing trajectory inference matrix.

7. The safety training operation control method based on behavior traceability according to claim 1, characterized in that, S4 specifically refers to: Each operation behavior identifier is assigned a behavior processing result type, behavior sensitivity level, and permission level in chronological order to form a joint input item. Three data fields are embedded in each joint input item and standardized encoding is performed to generate a multi-dimensional input index set, which serves as the input basis for constructing the response-level dispatch mapping graph. Based on the combination of fields such as behavior processing result type, behavior sensitivity level and permission level in the multidimensional input index set, the mapping dimensions are divided, multi-level mapping paths are established according to the field nesting order, a response-level dispatch mapping diagram is generated, and node identification rules are set in the response-level dispatch mapping diagram to cluster similar index sets and form candidate behavior risk groups. Based on the scope of the behavior processing result type corresponding to each behavior risk group in the permission level and the security threshold segment in the behavior sensitivity level, the matching node position in the response level assignment mapping is retrieved. When the node is above the upper limit of the security threshold segment and the permission level is controlled, it is determined to enter the security training process. When a node is below the lower limit of the security threshold segment and the permission level is restricted, it is determined to enter the operation control process, and the determination flag is bound to the corresponding operation behavior identifier for subsequent execution logic judgment.

8. The safety training operation control method based on behavior tracing according to claim 1, characterized in that, S5 specifically refers to: After each response-level dispatch mapping map completes the classification of behavioral risk groups and executes the safety training process or operation control process, the behavioral feedback flow is obtained. The behavioral feedback flow contains the actual execution process type corresponding to the operation behavior identifier and the original classified behavioral risk group information, which is used to depict the correspondence between the mapping map judgment result and the actual execution result. Based on the behavior feedback flow, the actual execution process type of each operation behavior identifier is compared with its behavior risk group affiliation in the response level assignment map. The deviation relationship between the two at the risk boundary position is extracted, and error offset feedback is generated according to the deviation direction and deviation accumulation. The error offset feedback is applied to the boundary of the behavior risk group to adjust the boundary position of different behavior risk groups in the response level assignment map. Based on the adjusted boundary of the behavior risk group, the error offset feedback is introduced into the behavior processing result judgment logic to update the mapping relationship of behavior processing result types in the response level assignment mapping diagram. This enables subsequent operational behaviors to be classified according to the updated judgment logic when entering the response level assignment mapping diagram, thereby achieving continuous dynamic control of the safety training process and the operation control process.

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