Intelligent control and order review method and system of versioning engine

Abstract

The application provides an order auditing method and system of an intelligent control and version engine, and relates to the technical field of intelligent control. The method comprises the following steps: obtaining to-be-audited order data and extracting a historical auditing record sequence to identify a version switching event and scene characteristics, to construct a causal relationship chain, to determine a target auditing rule version, to perform semantic matching to generate a preliminary auditing result, to set a dynamic auditing threshold based on resource occupancy and concurrent quantity changes to adjust verification, to calculate a version adaptation degree and to obtain a previous version switching from the causal relationship chain when the version adaptation degree is lower than a threshold, to determine whether to immediately audit or delay execution according to a delayed auditing strategy, to query a user library and order labels to determine whether to transfer to manual auditing, and to determine an auditing opportunity for a pre-sale order according to a paid deposit state and a warehouse condition. The method further comprises the following steps: performing an order auditing operation and appending a causal node, so that the accuracy and adaptability of order auditing are improved, dynamic version switching and resource optimization are realized, and multi-scene auditing strategy flexible configuration is supported.

Description

Technical Field

[0001] This invention relates to the field of intelligent control and versioning engine technology, and in particular to an order review method and system based on intelligent control and versioning engine. Background Technology

[0002] Existing order review methods typically pre-define a set of review rules, which are deployed in the review system in fixed versions. In practice, rule version switching mainly relies on periodic manual updates or triggers based on preset time cycles, lacking a dynamic correlation mechanism between changes in business scenario characteristics and review rule versions. When processing orders awaiting review, the review system generally matches order attribute information with statically configured rules to generate a preliminary review result, followed by secondary verification using fixed resource thresholds (such as CPU utilization and concurrent request count). Throughout this process, the iteration history of review rule versions is independent of the evolution of the business scenario, and review thresholds are often set based on empirical values ​​rather than adjusted in real time.

[0003] However, the above approach has significant drawbacks. The logic for switching review rule versions is disconnected from the causal evolution of business scenarios. When business scenarios undergo sudden changes (such as promotional activities, system upgrades, or shifts in user behavior patterns), the rule version cannot automatically trace the causal chain between previous scenarios and the current changes. This causes review standards to lag behind actual business needs, easily leading to mis-reviews or omissions. Furthermore, the setting of review thresholds lacks dynamic adaptability. Fixed thresholds cannot respond to real-time fluctuations in resource utilization and business request concurrency. For example, in sudden high-concurrency scenarios, fixed thresholds may overload the review system or leave resources idle, affecting order processing efficiency and system stability. These limitations make existing methods ill-suited for the complex and ever-changing enterprise-level order review environment. Summary of the Invention

[0004] This invention provides an order review method and system with intelligent control and versioning engine, which can solve the problems in the prior art.

[0005] A first aspect of the present invention provides an order review method using an intelligent control and versioning engine, comprising:

[0006] Obtain data on orders awaiting review, including order identifiers and order attribute information;

[0007] Extract the historical review record sequence corresponding to the order identifier, identify the review rule version switching event and business scenario characteristics, calculate the gradient of scenario feature changes before and after the switching event to construct causal relationship nodes, connect them in time sequence to form a version switching causal relationship chain, and determine the target review rule version through multi-hop path reasoning.

[0008] Semantic matching of order attribute information with the target review rule version generates preliminary review results;

[0009] Adjust and verify the preliminary review conclusions based on the dynamic review thresholds, and generate the verified review results.

[0010] Extract the decision feature vector of the audit result after verification and the rule feature vector of the target audit rule version, project them into the multi-dimensional semantic space to calculate the spatial overlap metric as the version adaptability. When the version adaptability is lower than the preset adaptability threshold, obtain the previous version from the version switching causal relationship chain and perform the version switching to generate the final audit result.

[0011] The order review operation is performed based on the final review result, and the final review result is added as a new causal link node to the version switch causal relationship chain.

[0012] In one optional embodiment, the historical review record sequence corresponding to the order identifier is extracted, the review rule version switching event and business scenario characteristics are identified, the gradient of scenario feature changes before and after the switching event is calculated to construct causal association nodes, and these nodes are connected in chronological order to form a version switching causal relationship chain. The target review rule version is determined through multi-hop path reasoning, including:

[0013] Extract the audit rule version switching event from the historical audit record sequence, obtain the order volume fluctuation, audit pass rate change and resource load intensity corresponding to the switching time as business scenario features, and calculate the difference vector norm of the business scenario features before and after the audit rule version switching event as the gradient of scenario feature change.

[0014] The version identifier, switching time, business scenario characteristics, and the gradient of changes in scenario characteristics are encapsulated as causal relationship nodes, and connected end to end in the order of switching time to form a causal relationship chain for version switching.

[0015] The current business scenario features are obtained by acquiring the order volume fluctuation range, the change in the approval rate, and the resource load intensity corresponding to the current order. Starting from the first node of the version switching causal relationship chain, the cosine similarity between the current business scenario features and the business scenario features stored in the causal relationship node is calculated at each causal relationship node.

[0016] Based on cosine similarity, a multi-path voting mechanism is used to determine the version of the target review rule.

[0017] In one optional embodiment, determining the target review rule version based on cosine similarity through a multi-path voting mechanism includes:

[0018] When the cosine similarity is greater than the preset matching threshold, the causal association node is marked as the scene matching node, the path hop count from the first node to the scene matching node is recorded, and the scene matching nodes with the path hop count greater than the preset hop count threshold are selected to form a candidate node set.

[0019] Extract the version identifiers stored in the matching nodes of each scenario in the candidate node set, count the frequency of each version identifier in the candidate node set as the version vote count, and select the version identifier with the largest version vote count as the target review rule version.

[0020] In one optional embodiment, the preliminary review conclusion is adjusted and verified based on a dynamic review threshold, and the verified review result is generated, including:

[0021] Extract order risk scores from the preliminary review results and construct a constrained dataset including order identifiers, risk scores, and preliminary review conclusions;

[0022] The dynamic review thresholds are divided into rejection threshold ranges, suspected threshold ranges, and approval threshold ranges to determine the threshold range type to which the order risk score belongs;

[0023] When an order's risk score falls within the rejection threshold range and the initial review conclusion is "pass," the review conclusion for that order is adjusted to "reject" and the reason for adjustment is marked as "threshold exceeding the limit." When an order's risk score falls within the pass threshold range and the initial review conclusion is "reject," the review conclusion for that order is adjusted to "pass" and the reason for adjustment is marked as "threshold regression," generating the review result after the conclusion adjustment.

[0024] From the revised review results, select orders whose review conclusions have been adjusted, and extract the transaction amount, user credit rating, and historical number of defaults of the adjusted orders to form a verification feature group;

[0025] The verification feature group is matched with the preset abnormal feature template in multiple dimensions, and the matching score is calculated.

[0026] When the matching score is lower than the preset security verification threshold, it is determined that the adjustment decision has the risk of misjudgment. The review conclusion of the order is restored to the preliminary review conclusion and a manual review mark is added. When the matching score is not lower than the security verification threshold, the adjusted review conclusion is maintained and a post-verification review result is generated.

[0027] In one optional embodiment, the decision feature vector of the verified review result and the rule feature vector of the target review rule version are extracted, projected onto a multi-dimensional semantic space to calculate a spatial overlap metric as the version fit. When the version fit is lower than a preset fit threshold, the preceding version is obtained from the version switching causal relationship chain to perform a version switch, generating the final review result, including:

[0028] Extract audit decision information from the verified audit results and form a decision feature vector;

[0029] Extract rule information from the target review rule version and form a rule feature vector;

[0030] Project the decision feature vector and the rule feature vector onto the multidimensional semantic space respectively, calculate the intersection volume and the union volume in the multidimensional semantic space, and use the ratio of the intersection volume to the union volume as the spatial overlap metric.

[0031] The spatial overlap metric is compared with the preset adaptation threshold as the version adaptation degree.

[0032] When the version compatibility is lower than the preset compatibility threshold, the preceding version of the target review rule version is obtained from the version switching causal relationship chain. The spatial overlap measure between the decision feature vector and the rule feature vector of the preceding version in the multidimensional semantic space is calculated as the preceding version compatibility. When the preceding version compatibility is higher than the version compatibility, the target review rule version is switched to the preceding version. The switched preceding version is used to perform rule matching on the verified review results.

[0033] Generate the final review results.

[0034] In one optional embodiment, obtaining the preceding version of the target audit rule version from the version switching causal chain includes:

[0035] Initialize the version compatibility record table and store the target review rule version and version compatibility in the version compatibility record table;

[0036] Obtain the preceding version of the target review rule version from the causal relationship chain of version switching, extract the rule information of the preceding version and form the rule feature vector of the preceding version;

[0037] Project the feature vector of the previous version rule onto the multidimensional semantic space, calculate the intersection volume and union volume of the decision feature vector and the feature vector of the previous version rule, and use the ratio of the intersection volume to the union volume as the fit degree of the previous version.

[0038] Store the compatibility between the previous version and the previous version in the version compatibility record table;

[0039] Determine whether the previous version has a previous version in the version switching causal relationship chain. If a previous version exists, use the previous version as the current previous version and repeat the rule information extraction and adaptation calculation. Otherwise, terminate the backtracking.

[0040] Select the version with the highest compatibility score from the version compatibility record table as the optimal version, and switch the target review rule version to the optimal version.

[0041] In one optional embodiment, performing an order review operation based on the final review result includes:

[0042] Obtain the textual description of the requirement, extract keywords of business intent, constraints and operation goals to construct semantic triples, encode the semantic triples with word vectors, input the encoding results into a multi-layer attention network to extract semantic features, and identify the requirement type and map it to the operation type through context association calculation;

[0043] Execution parameters are extracted based on operation type, combined to generate operation instruction sequence, the strength of dependency relationship between each operation instruction is calculated, and the execution queue is formed by sorting according to the strength of dependency relationship.

[0044] Extract operation type and execution parameters from operation instruction sequence, convert constraint conditions into trigger condition expressions, encapsulate operation type and execution parameters into execution actions, and combine trigger condition expressions and execution actions to form rule configuration scheme;

[0045] Extract the order attribute information from the final review result and calculate the matching degree with the trigger condition expression in the rule configuration scheme. When the matching degree exceeds the preset matching threshold, push it to the reviewer for confirmation.

[0046] After receiving the confirmation instruction, the rule configuration scheme is converted into an executable audit rule and stored in the target audit rule version. The operation instructions are executed sequentially according to the execution queue to complete the order audit operation.

[0047] The completed operation instructions, operation time, and operation results are encapsulated into operation log nodes and appended to the version switching causal relationship chain.

[0048] A second aspect of this invention provides an order approval system with intelligent control and versioning engine, comprising:

[0049] The data acquisition unit is used to acquire data on orders to be reviewed, including order identifiers and order attribute information;

[0050] The version inference unit is used to extract the historical review record sequence corresponding to the order identifier, identify the review rule version switching event and business scenario characteristics, calculate the gradient of scenario feature changes before and after the switching event to construct causal relationship nodes, connect them in time sequence to form a version switching causal relationship chain, and determine the target review rule version through multi-hop path inference.

[0051] The semantic matching unit is used to semantically match order attribute information with the target review rule version to generate preliminary review results;

[0052] The threshold adjustment unit is used to adjust and verify the preliminary review conclusion based on the dynamic review threshold, and generate the verified review result.

[0053] The adaptation and switching unit is used to extract the decision feature vector of the audit result after verification and the rule feature vector of the target audit rule version, project them onto the multi-dimensional semantic space to calculate the spatial overlap metric as the version adaptation degree. When the version adaptation degree is lower than the preset adaptation threshold, the previous version is obtained from the version switching causal relationship chain to perform the version switching and generate the final audit result.

[0054] The audit execution unit is used to perform order audit operations based on the final audit results, and to add the final audit results as new causal nodes to the version switch causal relationship chain.

[0055] A third aspect of the present invention provides an electronic device, comprising:

[0056] processor;

[0057] Memory used to store processor-executable instructions;

[0058] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0059] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0060] In this embodiment of the invention, by constructing a causal relationship chain for version switching and combining it with the gradient of changes in scenario features, the correlation between the changes in review rules and business scenarios can be accurately traced back. Multi-hop path reasoning is used to automatically lock the most matching target review rule version, significantly reducing mis-reviews and missed reviews caused by rule version mismatches. This effectively improves resource utilization efficiency and resistance to concurrent impacts. Real-time monitoring of resource occupancy rate changes and the frequency of sudden changes in business request concurrency allows for dynamic setting of threshold adjustment coefficients, enabling the review threshold to elastically change with system load. During peak periods, the threshold is automatically relaxed to avoid blocking, while during off-peak periods, the threshold is tightened to increase strictness. This ensures real-time response to critical business processing while avoiding resource idleness or overload, achieving a balanced optimization of review throughput and accuracy. Furthermore, it strengthens the version control of review results. This adaptability and self-correction capability map the verified review results to the target rule version in a multi-dimensional semantic space. It accurately determines the adaptability through spatial overlap measurement. When the adaptability is lower than a preset threshold, it automatically extracts the preceding version from the version switching causal relationship chain and switches to it, effectively avoiding review failures caused by scenario drift or rule outdatedness. It promotes the continuous evolution and self-learning of the review system. Each final review result is added to the version switching causal relationship chain as a new causal node, continuously enriching the historical trajectory data. As nodes accumulate, the causal relationship graph between the gradient of scenario feature changes and version switching events becomes more refined. The reasoning path for the target version in subsequent reviews is shorter and more accurate, forming a positive cycle of becoming smarter with use and maintaining a high degree of alignment with business dynamics in the long term. Attached Figure Description

[0061] Figure 1 A flowchart illustrating the order approval method using an intelligent control and versioning engine; Figure 2 Flowchart for adapting and switching audit rules. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0064] Figure 1 This is a flowchart illustrating the order review method of the intelligent control and versioning engine according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes:

[0065] Obtain data on orders awaiting review, including order identifiers and order attribute information;

[0066] Extract the historical review record sequence corresponding to the order identifier, identify the review rule version switching event and business scenario characteristics, calculate the gradient of scenario feature changes before and after the switching event to construct causal relationship nodes, connect them in time sequence to form a version switching causal relationship chain, and determine the target review rule version through multi-hop path reasoning.

[0067] Semantic matching of order attribute information with the target review rule version generates preliminary review results;

[0068] Adjust and verify the preliminary review conclusions based on the dynamic review thresholds, and generate the verified review results.

[0069] Extract the decision feature vector of the audit result after verification and the rule feature vector of the target audit rule version, project them into the multi-dimensional semantic space to calculate the spatial overlap metric as the version adaptability. When the version adaptability is lower than the preset adaptability threshold, obtain the previous version from the version switching causal relationship chain and perform the version switching to generate the final audit result.

[0070] In one optional embodiment, the historical review record sequence corresponding to the order identifier is extracted, the review rule version switching event and business scenario characteristics are identified, the gradient of scenario feature changes before and after the switching event is calculated to construct causal association nodes, and these nodes are connected in chronological order to form a version switching causal relationship chain. The target review rule version is determined through multi-hop path reasoning, including:

[0071] Extract the audit rule version switching event from the historical audit record sequence, obtain the order volume fluctuation, audit pass rate change and resource load intensity corresponding to the switching time as business scenario features, and calculate the difference vector norm of the business scenario features before and after the audit rule version switching event as the gradient of scenario feature change.

[0072] The version identifier, switching time, business scenario characteristics, and the gradient of changes in scenario characteristics are encapsulated as causal relationship nodes, and connected end to end in the order of switching time to form a causal relationship chain for version switching.

[0073] The current business scenario features are obtained by acquiring the order volume fluctuation range, the change in the approval rate, and the resource load intensity corresponding to the current order. Starting from the first node of the version switching causal relationship chain, the cosine similarity between the current business scenario features and the business scenario features stored in the causal relationship node is calculated at each causal relationship node.

[0074] Based on cosine similarity, a multi-path voting mechanism is used to determine the version of the target review rule.

[0075] In one specific implementation, when extracting review rule version switching events from the historical review record sequence, it is necessary to traverse all historical review records corresponding to the order identifier and identify the time node where the review rule version change occurred. Each version switching event corresponds to a specific switching moment, with an old version rule and a new version rule existing before and after that moment, respectively. For each switching moment, the order volume fluctuation, review approval rate change, and resource load intensity within the time window before the switching event are extracted from the historical review records, as well as the corresponding three indicators within the time window after the switching event. These three indicators are combined into a business scenario feature vector. The order volume fluctuation reflects the rise and fall of the number of order submissions per unit time near the switching moment; the review approval rate change reflects the change in the ratio of review conclusions changing from approval to rejection or from rejection to approval; and the resource load intensity reflects the overall utilization level of server-side computing and storage resources at that time.

[0076] After obtaining the business scenario feature vectors before and after the switching event, the difference vector between the two vectors is calculated, and the norm of this difference vector is taken as the gradient of the scenario feature change. Let the business scenario feature vector before the switching event be... The feature vector of the business scenario after the switching event is Then the gradient of scene feature changes Defined as ,in This represents the Euclidean norm of the vector. The larger the gradient value, the more significant the change in the business scenario before and after the version switch, and the stronger the causal relationship between the version switch and the version switch. The smaller the gradient value, the more the version switch is driven by rule iteration rather than by the business scenario.

[0077] The version identifier, switchover time, business scenario feature vector, and gradient of scenario feature changes are collectively encapsulated into a causal relationship node. Each causal relationship node carries complete contextual information, including the business environment state and magnitude of change during the version switch. All causal relationship nodes are linked end-to-end according to the chronological order of the switchover time, forming a causal chain for version switchover. There are clear temporal dependencies between adjacent nodes in the chain; that is, the version switch of a later node occurs based on the version corresponding to the previous node. This chain structure can completely preserve the historical path of version evolution, providing a structured basis for subsequent multi-hop path reasoning.

[0078] When determining the target review rule version, it is necessary to first obtain the real-time business scenario characteristics corresponding to the current orders pending review. Specifically, this involves collecting the order volume fluctuation range, the change in the review pass rate, and the resource load intensity at the current moment to construct the current business scenario feature vector. Starting from the first node of the causal chain of version switching, traverse each causal link node in turn, calculating at each node... The business scenario feature vector stored on this node The cosine similarity between them. The formula for calculating cosine similarity is: ,in This represents the dot product of two vectors. and Let represent the Euclidean norms of the two vectors respectively. The range of values ​​for cosine similarity is... The closer the value is This indicates that the current business scenario is more similar to the historical business scenarios recorded at this node.

[0079] Based on the cosine similarity calculated at each causal link node, a multi-path voting mechanism is used to determine the target review rule version. The specific implementation of the multi-path voting mechanism is as follows: all causal links in the version switching causal relationship chain whose cosine similarity exceeds a preset similarity threshold are selected as a candidate node set. The review rule versions corresponding to each node in the candidate node set are statistically analyzed, and the frequency of each version appearing in the candidate node set is used as the voting weight. Simultaneously, the gradient of scene feature changes is introduced. As a correction factor, the version corresponding to the node with the larger gradient value receives a higher voting weighting score, because a larger gradient means that the version switch occurred under a more significant change in the business scenario, and its reference value is higher. After combining frequency and gradient weighting, the review rule version with the highest number of votes is determined as the target review rule version. If the candidate node set is empty, it degenerates into selecting the review rule version corresponding to the single node with the highest cosine similarity to the current business scenario features as the target version, ensuring that a reasonable version selection result can still be given even when historical data is sparse.

[0080] In addition, after determining the target review rule version, the order attribute information is semantically matched with the target review rule version to generate preliminary review results. Order attribute information covers dimensions such as order amount, order type, counterparty information, payment method, and risk tags, while the target review rule version includes review conditions and threshold configurations for each of these dimensions. The semantic matching process involves mapping each field in the order attribute information to a rule entry in the target review rule version to determine whether the order attributes meet the constraints of the rule entry. For structured rule entries, numerical comparison or category matching is performed directly; for semantically described rule entries, the semantic similarity between the order attribute description and the rule description is calculated using vectorization. A successful match is considered achieved when the similarity exceeds a preset semantic matching threshold. Based on the matching results of all rule entries, a preliminary review conclusion is generated according to the predefined logical combination methods in the target review rule version (such as satisfying all, satisfying most, or satisfying key entries). This conclusion serves as the input for subsequent dynamic threshold verification.

[0081] This target version determination method, based on the causal relationship chain of historical version switching and the multi-path voting mechanism, makes full use of the version evolution patterns implicit in historical review data, avoids version selection bias caused by relying solely on a single similarity indicator, and at the same time, by introducing the gradient of scenario feature changes, it can prioritize referencing version switching experience with similar historical change patterns when business scenarios undergo drastic changes, thereby improving the accuracy and robustness of target review rule version selection.

[0082] In one alternative implementation, when an order enters the review process, the corresponding review rule version is located in the version switching causal chain based on the order creation time. The version switching causal chain records the time point and reason for each rule change, and by matching the time, the rule version that took effect at the time the order was created can be accurately found.

[0083] After obtaining the target rule version, the various attribute information of the order is extracted and matched item by item with the review conditions defined in the rule. Order attributes include multiple dimensions such as product price, shipping address, user history behavior, and payment method. Each review condition corresponds to a threshold, and the condition is approved by judging whether the order attribute value meets the threshold requirement. After all conditions are judged, a preliminary review conclusion is generated.

[0084] After the preliminary review results are generated, the thresholds are adjusted based on the current server operating status. Three resource metrics—CPU utilization, memory usage, and disk load—are collected in real time and weighted according to their respective impact on review performance to obtain the overall resource utilization rate. Simultaneously, the number of order review requests received within a unit time window is counted as a business concurrency metric.

[0085] The rate of change of resource utilization relative to the previous moment is calculated to determine the trend of resource pressure. A sliding time window is used to analyze the concurrency sequence, counting the number of times the difference between adjacent moments exceeds a fluctuation threshold, thus obtaining the frequency of concurrency spikes. A higher spike frequency indicates a more unstable business request arrival pattern.

[0086] When both the resource change rate and mutation frequency exceed the upper limit, it indicates that the server is under high load and experiencing significant business fluctuations. In this case, a higher adjustment coefficient should be set, proportionally relaxing the thresholds in the initial review conclusion to improve order approval rates and avoid a large backlog of orders. When both the resource change rate and mutation frequency are below the lower limit, it indicates that the server is under low load and the business is stable. In this case, a lower adjustment coefficient should be set, tightening the review thresholds and strengthening risk control. Otherwise, the standard adjustment coefficient should be maintained.

[0087] The order attribute information is re-verified against the adjusted threshold to generate the final audit result.

[0088] After adjusting the threshold, verify the suitability of the selected rule version for the current order. Extract the actually triggered rule entries, successfully matched attribute fields, and corresponding confidence scores from the review results to form a decision feature vector. Extract all rule entries, applicable order types, and historical review success rates from the target rule version to form a rule feature vector.

[0089] Two feature vectors are projected onto a pre-trained multi-dimensional semantic space. This semantic space is constructed using a large amount of historical review records and rule configuration data, enabling the mapping of different types of features to a unified representation space. The spatial overlap between the two projected vectors is calculated using the Gaussian radial basis function kernel similarity method, yielding a version fit score. A closer fit score indicates higher semantic consistency between the current review decision and the rule version.

[0090] If the fit is below the preset threshold, it indicates that the target rule version does not adequately cover the current order. The process involves tracing back through the version switching causal chain to find the predecessor version of the target version and re-executing the review process using that predecessor version. If the fit of the predecessor version still does not meet the requirements, the process continues tracing back to even earlier versions until a suitable version is found or the process returns to the beginning of the chain. If no suitable version is found even after tracing back to the beginning of the chain, a long-established, validated baseline rule version is used as a fallback.

[0091] Before executing the above review process, it is necessary to determine whether the order falls under the delayed review policy. This is done by matching the order type with the order attributes; if a match is found, the appropriate action is taken based on the policy type.

[0092] The relative time-based strategy calculates the expected review time based on the order's payment time or the final payment time for pre-sale orders, plus a configured delay duration. If the expected review time has not yet been reached, the order is placed in a delay queue and temporarily not reviewed. This approach is suitable for scenarios that require waiting for external data synchronization or for users to provide supplementary information.

[0093] The absolute time type strategy directly reads the configured absolute time point as the expected review time and uses the same judgment logic to determine whether to delay. This approach is suitable for scenarios that require processing batch orders uniformly during promotional activities.

[0094] The "no review" policy marks orders as blocked, directly initiating a manual review process without any automated review. This policy is suitable for high-risk or disputed order types.

[0095] The targeted review strategy reads multiple preset time points from the configuration and finds the nearest time point on the same day that is later than the order creation time as the target review time. If all time points on the same day are earlier than the creation time, the target time is set to the first time point of the next day. This approach concentrates order reviews within a specific time period, balances the workload, aligns with warehouse operation times, and improves supply chain collaboration efficiency.

[0096] Before reviewing an order, it's necessary to check if the user corresponding to the order is in the blacklist or special needs user database. The blacklist records users with a history of fraudulent behavior or violations, while the special needs user database records users who require customized services or are subject to special policies.

[0097] If a user is on the blacklist and the corresponding switch is off, the order will directly enter the manual review queue for detailed verification by risk control personnel. If the switch is on, the order will still be automatically reviewed according to the normal process, but a blacklist mark will be added to the log and stricter thresholds will be set or additional risk checks will be triggered.

[0098] Orders from users with special needs follow the same processing logic. If the switch is off, the order will be manually reviewed and processed individually by customer service or operations personnel. If the switch is on, the order will be automatically reviewed, but the rules will be adjusted based on the user's special tags, such as relaxing restrictions for VIP users or skipping some standard checks for customized orders.

[0099] Whether to perform automatic approval depends on the custom tags included with the order. These custom tags are added by business personnel or upstream suppliers when the order is created and are used to identify special attributes of the order, such as test orders, abnormal addresses, high-value goods, etc.

[0100] When an order contains one of the specified tags and is configured not to be reviewed, the system checks if the order carries any of the tags in the configuration list. If it does, the automatic review is skipped, and the order proceeds to manual processing. This logic is suitable for excluding test orders or scenarios where manual verification of address validity is required.

[0101] In the configuration where orders without any specified tags are not approved, the system checks if an order carries at least one tag from the configuration list. If no specified tag is included, automatic approval is not performed. This logic is suitable for scenarios where orders must have the necessary tags for automatic approval, such as only allowing orders with complete logistics information tags to be sent to the warehouse.

[0102] After the administrator deletes a tag from the configuration, orders that previously carried that tag will no longer be restricted during the next scheduling and can proceed with automatic review normally. Real-time reading of the latest configuration ensures that rule changes take effect immediately.

[0103] Pre-sale orders are divided into two stages: deposit payment and final payment. When the automatic review switch for deposit-paid orders is enabled, automatic review of deposit-paid orders is allowed. If the switch is disabled, the order status will change to final payment after the user pays the final payment before automatic review can begin.

[0104] With the automatic order review switch enabled for pre-sale orders with paid deposits, the system further supports configuring conditions to exclude pre-sale orders with paid deposits from automatic review. This can be done through two configuration options: warehouse type and specified warehouse. For example, configuring overseas warehouses to exclude automatic review means all pre-sale orders with paid deposits destined for overseas warehouses will not undergo automatic review, as overseas warehouse inventory transfer cycles are long and subject to other controls, requiring manual verification of inventory and customs clearance qualifications. If a specific warehouse is configured to exclude automatic review, only orders from that warehouse will not be automatically reviewed.

[0105] The final review result includes the review conclusion, the rule version identifier used, the list of triggered rule entries, the version compatibility value, and the review timestamp. The review conclusion is divided into three statuses: approved, rejected, or pending manual review.

[0106] The review results are written to the order review record table, which stores the complete review history of each order, including the rule version used each time, the review time, the review conclusion and the operator, supporting full-process traceability and problem investigation.

[0107] Simultaneously, the review results are asynchronously pushed to a message queue for downstream subscription. Upon receiving an approved message, the warehouse management system automatically generates a picking task and arranges shipment. Upon receiving a rejected message, the finance department triggers a refund process. The customer service department, after receiving a message awaiting manual review, pushes the order to the human review console. Through asynchronous decoupling via message queues, loosely coupled integration is achieved between the various systems.

[0108] Orders that pass the review process automatically trigger the subsequent shipping process without manual intervention. Rejected orders are automatically categorized based on the reason for rejection, such as abnormal amount, incomplete address, or insufficient inventory, and a notification is sent to operations staff who can contact the user to provide supplementary information or coordinate resources for resubmission. Orders awaiting manual review display details, review criteria, and any points of contention on the review interface to assist in quick manual decision-making.

[0109] In one optional embodiment, determining the target review rule version based on cosine similarity through a multi-path voting mechanism includes:

[0110] When the cosine similarity is greater than the preset matching threshold, the causal association node is marked as the scene matching node, the path hop count from the first node to the scene matching node is recorded, and the scene matching nodes with the path hop count greater than the preset hop count threshold are selected to form a candidate node set.

[0111] Extract the version identifiers stored in the matching nodes of each scenario in the candidate node set, count the frequency of each version identifier in the candidate node set as the version vote count, and select the version identifier with the largest version vote count as the target review rule version.

[0112] In one specific implementation, after completing the construction of the causal relationship chain for version switching and calculating the cosine similarity between the real-time business scenario feature vector corresponding to the current order to be reviewed and the business scenario feature vector stored in each causal relationship node, it is necessary to further determine the final target review rule version from the candidate nodes through a multi-path voting mechanism to ensure the robustness and noise resistance of version selection.

[0113] The cosine similarity calculation reflects the semantic similarity between the current business scenario and the scenarios recorded in historical nodes. The cosine similarity... Matching threshold When comparing, When this occurs, the causal relationship node is marked as a scene matching node. Preset matching threshold. The value is typically determined offline based on the distribution characteristics of historical review data, generally set between 0.75 and 0.90. The specific value can be adjusted according to the complexity of the business scenario and the dimension of the feature vector. The marking process traverses all nodes in the version switching causal chain, performing similarity calculations and threshold comparisons on each node sequentially, ultimately obtaining a batch of scenario-matching nodes that meet the conditions.

[0114] For each causal node marked as a scenario matching node, the path hop count from the head node of the causal relationship chain to that scenario matching node needs to be further recorded. The path hop count reflects the depth of the node in the time-series chain; a larger hop count indicates that the node is farther from the beginning of the chain, meaning the corresponding historical review record is newer and has undergone more complete version evolution. The core motivation for introducing the path hop count filtering mechanism is that nodes located in the shallow layers of the chain are often in the initial stage of version switching, and the review rule versions they record may not have been fully verified, resulting in lower stability; while nodes located in the deeper layers of the chain have undergone multiple business scenario tests, and their version information is more valuable. Let the preset hop count threshold be... Filter path hop count satisfy The scene matching nodes are aggregated to form a candidate node set. . The specific value can be dynamically set according to the average length of the causal relationship chain when switching versions. For example, the jump position corresponding to 30% of the total chain length can be used as a reference benchmark.

[0115] Candidate Node Set After construction, the version identifier stored in each scenario matching node in the set is extracted. The version identifier is a unique code for the version of the review rule, containing metadata information such as version number, release timestamp, and applicable business scenario type. All version identifiers appearing in the candidate node set are statistically analyzed, and the relationship between each version identifier and the relevant data is calculated. The number of times a node appears in a given context is defined as the version vote count for that version. Let the candidate node set be... CCP involved A different version identifier, the first The number of votes for each version identifier is denoted as . ,in Version vote count The calculation method is to count the number of candidate nodes in the set whose version identifier is the first. The total number of nodes in each version, i.e. ,in Represents a node The stored version identifier number.

[0116] After obtaining the number of votes for each version, the version with the highest number of votes is selected as the target review rule version. Specifically, the version identifier subscript corresponding to the target version is... satisfy When multiple version identifiers have the same number of votes, version timeliness is introduced as the deciding factor. The version identifier with the latest release timestamp is selected as the target review rule version. This ensures that in the event of a tie, the final selected version has the latest rule definition and can better adapt to the current business environment.

[0117] Multi-path voting mechanisms offer significant robustness advantages over single nearest neighbor matching methods. In real-world business scenarios, historical audit record sequences may introduce noisy nodes due to data collection anomalies, manual intervention, or unexpected business events. The scenario feature vectors of these noisy nodes may have accidental high similarity to the current scenario. If a single-point nearest neighbor strategy is directly adopted, it is highly likely that the audit rule version corresponding to the noisy node will be misjudged as the target version. By introducing path hop count filtering and multi-node voting mechanisms, the interference of noisy nodes can be effectively smoothed out, making the version selection result more stable and reliable. Path hop count filtering ensures that the nodes participating in the voting all come from fully verified historical segments in the chain, while the vote count further eliminates the impact of individual abnormal nodes on version selection through the majority voting principle.

[0118] At the engineering implementation level, to improve the execution efficiency of the multi-path voting mechanism, a directed acyclic graph structure can be used to store the causal relationship chain of version switching. Each node maintains a pointer to the next node and a cached field for the path hop count of the current node. When traversing nodes, the cached path hop count is used to directly complete the filtering and judgment, avoiding repeated calculation of the hop count for each query, thus keeping the overall time complexity within linear order of magnitude. The version vote count can be counted using a hash mapping structure, with the version identifier as the key and the vote count as the value, and is accumulated in real time. After traversing the candidate node set, the version identifier with the largest number of votes is directly output, and the target version can be determined without additional sorting operations.

[0119] For boundary cases where the candidate node set is empty, i.e., when there are no matching nodes in the version switching causal relationship chain that meet the filtering criteria in terms of path hop count, a preset hop count threshold can be used. The threshold is adaptively lowered to 0, which relaxes the path hop limit and includes all scenario matching nodes in the candidate pool for a re-execution of voting statistics. If the set of scenario matching nodes is still empty at this point, the default review rule version is reverted to ensure the continuity of the review process is not interrupted.

[0120] In one optional embodiment, the preliminary review conclusion is adjusted and verified based on a dynamic review threshold, and the verified review result is generated, including:

[0121] Extract order risk scores from the preliminary review results and construct a constrained dataset including order identifiers, risk scores, and preliminary review conclusions;

[0122] The dynamic review thresholds are divided into rejection threshold ranges, suspected threshold ranges, and approval threshold ranges to determine the threshold range type to which the order risk score belongs;

[0123] When an order's risk score falls within the rejection threshold range and the initial review conclusion is "passed," the review conclusion for that order will be adjusted to "rejected," and the reason for the adjustment will be marked as "threshold exceeded."

[0124] When an order's risk score falls within the threshold range and the initial review conclusion is rejection, the review conclusion for that order is adjusted to pass, and the reason for the adjustment is marked as threshold regression, generating the review result after the conclusion adjustment;

[0125] From the revised review results, select orders whose review conclusions have been adjusted, and extract the transaction amount, user credit rating, and historical number of defaults of the adjusted orders to form a verification feature group;

[0126] The verification feature group is matched with the preset abnormal feature template in multiple dimensions, and the matching score is calculated.

[0127] When the matching score is lower than the preset security verification threshold, it is determined that the adjustment decision has the risk of misjudgment. The review conclusion of the order is restored to the preliminary review conclusion and a manual review mark is added. When the matching score is not lower than the security verification threshold, the adjusted review conclusion is maintained and a post-verification review result is generated.

[0128] In one specific implementation, after obtaining the preliminary review results, the preliminary review conclusions need to be constrained and validated using dynamic review thresholds to eliminate the risk of misjudgment that may be introduced during the semantic matching stage. A risk score corresponding to each order is extracted from the preliminary review results. This risk score, output by the evaluation model in the semantic matching stage, reflects the overall risk level of the order under the current rule version, and its value range is a continuous real number interval. The order identifier, risk score, and preliminary review conclusion are jointly encapsulated to construct a constrained dataset, which serves as the unified input carrier for subsequent threshold interval judgments and conclusion adjustments. Each record in the constrained dataset corresponds to one order to be reviewed, ensuring the uniqueness of the order identifier and avoiding incorrect conclusion coverage due to identifier conflicts between multiple records.

[0129] Dynamic review threshold It is determined by the product of the basic audit threshold and the threshold adjustment factor, and its value changes dynamically with the resource load status. Before constraining the preliminary audit conclusions, it is necessary to... The threshold is divided into three continuous and non-overlapping intervals: the rejection threshold interval, the suspected threshold interval, and the pass threshold interval. Specifically, the upper bound of the rejection threshold interval is set as follows: The lower bound of the threshold interval is If the rejection threshold range corresponds to a risk score higher than [the threshold value], then [the rejection threshold range is not specified]. The range, corresponding to a risk score below the threshold interval. The range, the suspected threshold interval corresponds to a risk score between and The range between, where and All by The threshold is derived from a preset proportional coefficient. The three-segment threshold division method can distinguish between high-risk, ambiguous-risk, and low-risk orders, avoiding the boundary ambiguity problem caused by a single threshold.

[0130] For each record in the dataset to be constrained, determine the threshold range type to which its risk score belongs and cross-compare it with the preliminary review conclusion. If the order's risk score falls within the rejection threshold range and the preliminary review conclusion is "pass," it indicates a missed judgment in the semantic matching stage. The review conclusion for this order is forcibly adjusted to "reject," and the reason for adjustment is marked as "threshold out of bounds" in the record field, indicating that the adjustment was triggered by the risk score exceeding the rejection boundary. If the order's risk score falls within the pass threshold range and the preliminary review conclusion is "reject," it indicates an over-rejection in the semantic matching stage. The review conclusion for this order is adjusted to "pass," and the reason for adjustment is marked as "threshold regression," indicating that the adjustment was triggered by the risk score falling back to the safe range. For orders with risk scores falling within a suspected threshold range, regardless of the preliminary review conclusion, no conclusion adjustment is performed; the original preliminary review conclusion is retained for further processing in the subsequent verification stage. After the above conclusion adjustment logic processing, the adjusted review result is output, including an adjustment marker field for subsequent filtering and identification.

[0131] From the revised review results, orders whose review conclusions have been adjusted are selected, i.e., the set of records where the adjustment marker field is not empty. These are the processing objects in the verification phase. For each adjusted order, three dimensions of verification features are extracted: transaction amount, user credit rating, and historical default count. The transaction amount reflects the financial scale of the order, the user credit rating reflects the user's credit performance in historical transactions, and the historical default count reflects the frequency of the user's default behavior. These three features together constitute the verification feature group for this order, represented in vector form as follows: ,in The normalized transaction amount Numerical coding for credit rating. This is a normalized value for the number of historical defaults. The purpose of constructing the verification feature group is to verify the rationality of threshold adjustment decisions from an independent business perspective, preventing erroneous adjustments caused by dynamic changes in the threshold from propagating to the final audit results.

[0132] The verification feature set is matched against a preset anomaly feature template in multiple dimensions. The anomaly feature template is obtained statistically from the feature distribution of historically known misjudged samples, and includes multiple typical combinations of anomaly feature intervals, such as a combination of extremely high transaction amount, extremely low credit rating, and a historical default count exceeding a preset limit. The multi-dimensional matching process applies the verification feature set... Calculate the weighted similarity with each set of abnormal feature templates, and take the maximum value of all template matching scores as the final matching score. The weighted similarity is calculated by summing the differences of each feature dimension. The weights are determined by the contribution rate of each dimension to the discrimination of historical misjudged samples. Dimensions with higher contribution rates are given greater weights, making the matching score more sensitive to key abnormal features.

[0133] When the matching score Below the preset security verification threshold If the current threshold adjustment decision is deemed to have a risk of misjudgment, meaning the adjustment conclusion for this order does not match the known abnormal feature template sufficiently, it indicates that the threshold adjustment may incorrectly reject normal orders or incorrectly release high-risk orders. In this case, the order's review conclusion should be restored to the preliminary review conclusion, the threshold adjustment operation should be revoked, and a manual review mark should be added to the order record to prompt subsequent business personnel to conduct manual review of the order, thus compensating for the limitations of automated verification. When the matching score... Not less than If the threshold adjustment decision closely matches the known anomaly feature template, and the adjustment conclusion has sufficient feature support, the adjusted review conclusion remains unchanged, and no manual review mark is added. After the above verification logic processing, the review conclusions of all orders are summarized to generate the verified review result, which serves as the input for the subsequent version compatibility evaluation stage.

[0134] Each record in the post-verification audit results includes the final effective audit conclusion, adjustment marker, manual review marker, and matching score, fully preserving the complete decision-making chain from the initial audit conclusion to the verification conclusion. This facilitates the addition of nodes to the causal relationship chain and source tracing analysis during subsequent version switching. For orders with an added manual review marker, the manual review process is triggered simultaneously when performing the order audit operation, ensuring that high-risk boundary orders do not result in business losses due to automated misjudgments.

[0135] like Figure 2 The diagram shown illustrates the logic flowchart for adapting and switching review rules.

[0136] In one optional embodiment, the decision feature vector of the verified review result and the rule feature vector of the target review rule version are extracted, projected onto a multi-dimensional semantic space to calculate a spatial overlap metric as the version fit. When the version fit is lower than a preset fit threshold, the preceding version is obtained from the version switching causal relationship chain to perform a version switch, generating the final review result, including:

[0137] Extract audit decision information from the verified audit results and form a decision feature vector;

[0138] Extract rule information from the target review rule version and form a rule feature vector;

[0139] Project the decision feature vector and the rule feature vector onto the multidimensional semantic space respectively, calculate the intersection volume and the union volume in the multidimensional semantic space, and use the ratio of the intersection volume to the union volume as the spatial overlap metric.

[0140] The spatial overlap metric is compared with the preset adaptation threshold as the version adaptation degree.

[0141] When the version compatibility is lower than the preset compatibility threshold, the preceding version of the target review rule version is obtained from the version switching causal relationship chain. The spatial overlap measure between the decision feature vector and the rule feature vector of the preceding version in the multidimensional semantic space is calculated as the preceding version compatibility. When the preceding version compatibility is higher than the version compatibility, the target review rule version is switched to the preceding version. The switched preceding version is used to perform rule matching on the verified review results.

[0142] Generate the final review results.

[0143] In one specific implementation, the process of extracting audit decision information from the verified audit results to form a decision feature vector requires structured encoding of the multi-dimensional attributes of the audit conclusion. Specifically, the audit decision information includes fields such as audit pass probability score, rejection reason category encoding, risk level label, and rule hit path information. These fields are concatenated according to a predefined feature dimension order, and after normalization processing, the decision feature vector is obtained. Normalization ensures that the numerical ranges of each dimension component are consistent, avoiding deviations in semantic space projection calculations caused by differences in units. Decision feature vector It fully depicts the decision-making status of the current order during this review process and is a core input for evaluating the compatibility of subsequent versions.

[0144] Extract rule information from the target audit rule version and form a rule feature vector. At this time, semantic parsing of the rule set for this version is required. Rule information includes structured attributes such as the business scenario type encoding covered by the rule, the quantified value of the strictness of the rule constraints, the distribution of order types to which the rule applies, and the historical hit rate statistics of the rule. These attributes are mapped and encoded according to the same dimensional system as the decision feature vector, and then normalized to obtain the rule feature vector. Regular feature vectors With decision feature vector Maintaining alignment along the feature dimension is a prerequisite for semantic space projection and overlap calculation.

[0145] decision feature vector With regular feature vectors The process of projecting each feature vector into the multidimensional semantic space employs a hyperellipsoid approximation method to geometrically model the semantic region covered by the vector. Each feature vector is expanded in the multidimensional semantic space into a hyperellipsoid centered at the vector and with the confidence intervals of each dimension's features as its semi-axis lengths. Let the hyperellipsoid corresponding to the decision feature vector be denoted as... The hyperellipsoid corresponding to the regular feature vector is Intersection volume The volume of the union of two hyperellipsoids overlapping in a multidimensional semantic space. This represents the total geometric volume covered by the merged hyperellipsoids. Spatial overlap metric. Defined as the ratio of the intersection volume to the union volume, i.e. .when When the value approaches 1, it indicates that the decision feature vector and the rule feature vector highly overlap in the semantic space, suggesting good semantic consistency between the current review rule version and the review decision; when When the value approaches 0, it indicates that the semantic regions of the two are almost non-overlapping, suggesting a significant semantic discrepancy between the current rule version and the review decision.

[0146] Spatial overlap metric As version compatibility and preset compatibility threshold Comparison. Preset adaptation threshold. The configuration is set offline by the business configuration layer based on historical version adaptation statistics, reflecting the minimum acceptable level of version semantic consistency under normal business scenarios. If If the semantic matching between the target review rule version and the current review decision is deemed sufficient, no version switching is required, and the final review result is generated directly based on the current verified review result. If this happens, the version rollback mechanism will be triggered, and the previous version's compatibility evaluation process will begin.

[0147] When the version compatibility is lower than the preset compatibility threshold When the version switch occurs, the preceding version of the target review rule version is obtained from the version switch causal relationship chain. The nodes in the version switch causal relationship chain are connected sequentially, and each node records the version identifier and its causal context at the time the version switch event occurs. Therefore, it is possible to trace back along the chain to locate the direct preceding version of the target review rule version. After obtaining the preceding version, rule information is extracted from it, and a preceding version rule feature vector is formed according to the same feature dimension system. . Decision feature vector Compared with the previous version's rule feature vector Projected onto a multidimensional semantic space, the intersection volume of the preceding versions is calculated using the same hyperellipsoid approximation method. Union volume Get the compatibility score of the previous version .

[0148] Compatibility with previous versions Version compatibility with the target review rules Compare. If If the previous version is determined to have a higher degree of overlap with the current review decision in the semantic space, a version switching operation is performed, replacing the target review rule version with the previous version. The previous version is then used to perform rule matching on the verified review result, and a new review conclusion is generated. The version switching operation only affects the execution version in the rule matching stage of this review process and does not modify the historical node structure of the version switching causal chain, ensuring the integrity and traceability of the causal chain. If the compatibility of the previous version fails to surpass that of the current target version, no version switch will be performed. The review conclusion of the current target review rule version will be maintained, and the process will directly proceed to the final review result generation stage.

[0149] After completing the version compatibility comparison and version switch determination, the final review result is generated. The final review result is stored in the form of a structured record, including the order identifier, review decision type, effective review rule version identifier, and version compatibility value. This includes fields such as a flag indicating whether a version switch has been triggered. Based on the final review results, order review operations are performed, including triggering subsequent business processing flows for approved orders, sending rejection notifications for rejected orders, and recording the reasons for rejection.

[0150] In addition, after the order review process is completed, the final review result will be added as a new causal node to the version switch causal chain. The new node will include the decision feature vector of the final review result. The actual effective rule version identifier and version compatibility. The system adds version switch markers, and nodes are connected to the end of the link in chronological order. This addition allows the version switch causal chain to continuously accumulate the latest review decision context, providing richer historical data for multi-hop path reasoning and version compatibility assessment in subsequent order reviews, thus achieving a dynamic self-optimizing closed loop for review rule version management. As the number of link nodes continues to accumulate, the number of historical samples that can be referenced when identifying version switch patterns in multi-hop path reasoning increases, the accuracy of version compatibility assessment gradually improves, and the overall review quality is continuously improved.

[0151] In one optional embodiment, obtaining the preceding version of the target audit rule version from the version switching causal chain includes:

[0152] Initialize the version compatibility record table and store the target review rule version and version compatibility in the version compatibility record table;

[0153] Obtain the preceding version of the target review rule version from the causal relationship chain of version switching, extract the rule information of the preceding version and form the rule feature vector of the preceding version;

[0154] Project the feature vector of the previous version rule onto the multidimensional semantic space, calculate the intersection volume and union volume of the decision feature vector and the feature vector of the previous version rule, and use the ratio of the intersection volume to the union volume as the fit degree of the previous version.

[0155] Store the compatibility between the previous version and the previous version in the version compatibility record table;

[0156] Determine whether the previous version has a previous version in the version switching causal relationship chain. If a previous version exists, use the previous version as the current previous version and repeat the rule information extraction and adaptation calculation. Otherwise, terminate the backtracking.

[0157] Select the version with the highest compatibility score from the version compatibility record table as the optimal version, and switch the target review rule version to the optimal version.

[0158] In one specific implementation, when the version compatibility is lower than a preset compatibility threshold... In such cases, it is necessary to backtrack from the version switching causal chain to obtain the preceding version with better compatibility. To systematically manage the compatibility calculation results of each version during the backtracking process, a version compatibility record table is first initialized. The version identifier of the current target review rule version is matched with its corresponding spatial overlap metric. Write as the first record This record table uses the version identifier as the primary key and the fit value as the value range, supporting incremental appending and final comparison operations in subsequent multiple backtracking processes.

[0159] Tracing back along the causal chain of version switching, the direct predecessor version node of the target review rule version is retrieved. This node stores rule snapshot information at the time the version switching event occurred, including structured rule information such as the review rule's conditional logic, threshold configuration, field mapping relationships, and version metadata. After extracting the above rule information, the predecessor version's rule information is converted into a predecessor version rule feature vector using the same feature encoding method as the target review rule version. During the encoding process, the rule condition logic is processed using a structured semantic embedding method, the numerical threshold configuration is normalized and mapped, and the field mapping relationship is represented using sparse vectors. Finally, these are concatenated to form a... Consistent Dimensional Regular Feature Vectors This ensures that it can be projected into the same multidimensional semantic space for geometric measurement operations.

[0160] Will After projecting onto the multidimensional semantic space, a corresponding hyperellipsoid is constructed with it as the central axis. The radii of each axis of the hyperellipsoid are determined by The semantic confidence intervals for each dimension are determined. Simultaneously, the decision feature vector of the verified audit results is determined. Corresponding hyperellipsoid Remain unchanged. Calculate. and Intersection volume in multidimensional semantic space Union volume The intersection volume is estimated by numerical integration over the overlapping region of the two hyperellipsoids, and the union volume is obtained by... Calculation, where for volume, for The volume. and The ratio is used as the adaptation degree of the previous version. ,Right now After the calculation is complete, the version identifier of the current predecessor version is compared with... As a new record, it is added to the version compatibility record table. .

[0161] After appending a record, it is determined whether a previous version exists in the version switching causal chain. This is done by searching the parent node pointer of the current previous version node in the version switching causal chain. If the parent node pointer is not null and points to a valid historical version node, then a previous version exists. This previous version is then used as the new current previous version, and all operations, including rule information extraction, feature vector encoding, hyperellipsoid construction, intersection and union volume calculation, and fitness degree writing, are repeated until the parent node pointer of the current previous version node is null or points to the first sentinel node in a certain iteration. At this point, the backtracking process terminates. The entire backtracking process constitutes a recursive flow that moves forward hop by hop along the version switching causal chain. Each hop corresponds to a complete fitness degree calculation and record writing. The backtracking depth is determined by the actual length of the chain and is not intentionally truncated.

[0162] Version compatibility record table after rollback terminates It already contains all historical versions reachable along the chain from the target review rule version and their corresponding compatibility values. Traverse... Compare the compatibility scores of all versions in the database, and select the version with the highest compatibility score as the optimal version. If multiple versions have the same and highest compatibility score, the version that is most recent in the version switching causal chain is selected first, i.e., the version with the fewest hops from the target review rule version in the chain, to ensure the timeliness of the review rules and business continuity. Determine the optimal version. Then, switch the current target review rule version to The semantic matching, threshold calculation, and suitability assessment in the subsequent review process are all based on The rule feature vector is used as the basis for execution.

[0163] After the version switch is completed, the final review result will be based on A corresponding rule system is generated, and the final review result is added as a new causal link to the version switch causal chain. This records the business scenario characteristics and reasons for this version switch event, providing new historical evidence for multi-hop path reasoning in subsequent order reviews. Version Adaptability Record Table After the review process is completed, the data can be selectively persisted to support version adaptation trend analysis and rule version quality assessment across orders, and to assist in subsequent version management decisions.

[0164] Through the aforementioned backtracking mechanism, when there is a semantic discrepancy between the target review rule version and the current business decision, the system can automatically locate the semantically most matching rule version from the historical version sequence without relying on external manual intervention, ensuring the consistency of the review results and the accuracy of the business logic. The step-by-step construction of the version adaptation record table avoids the resource overhead of loading all historical versions at once. The backtracking process proceeds hop-by-hop as needed, maintaining a low peak computational load even when the chain is long.

[0165] In one optional embodiment, performing an order review operation based on the final review result includes:

[0166] Obtain the textual description of the requirement, extract business intent keywords, constraints and operation goals to construct semantic triples, encode the semantic triples with word vectors, input the encoding results into a multi-layer attention network to extract semantic features, and identify the requirement type and map it to the operation type through context association calculation;

[0167] Execution parameters are extracted based on operation type, combined to generate operation instruction sequence, the strength of dependency relationship between each operation instruction is calculated, and the execution queue is formed by sorting according to the strength of dependency relationship.

[0168] Extract operation type and execution parameters from operation instruction sequence, convert constraint conditions into trigger condition expressions, encapsulate operation type and execution parameters into execution actions, and combine trigger condition expressions and execution actions to form rule configuration scheme;

[0169] Extract the order attribute information from the final review result and calculate the matching degree with the trigger condition expression in the rule configuration scheme. When the matching degree exceeds the preset matching threshold, push it to the reviewer for confirmation.

[0170] After receiving the confirmation instruction, the rule configuration scheme is converted into an executable audit rule and stored in the target audit rule version. The operation instructions are executed sequentially according to the execution queue to complete the order audit operation.

[0171] The completed operation instructions, operation time, and operation results are encapsulated into operation log nodes and appended to the version switching causal relationship chain.

[0172] In one specific implementation, when performing order review operations, the requirement description in natural language form is transformed into an executable sequence of operation instructions. Business intent keywords, constraints, and operation goals are extracted using word segmentation and semantic parsing techniques, and these three are combined to construct semantic triples. The structured expression of a semantic triple is (intent subject, constraint, operation goal). For example, in the order review scenario, "orders exceeding 100,000 yuan require secondary review" can be parsed as (secondary review, amount exceeding 100,000 yuan, target order). Word vector encoding is applied to each element in the semantic triple, mapping discrete text symbols to continuous high-dimensional vector representations. This makes semantically similar words closer together in the vector space, thereby preserving semantic relevance information.

[0173] The word vector encoding results are input into a multi-layer attention network, which captures the contextual dependencies between elements of the triples through a self-attention mechanism, extracting semantic features layer by layer. Each layer of the attention network performs weighted aggregation of the input vectors, with weights determined by the relevance scores between positions, allowing the network to dynamically focus on the most critical parts for understanding the current semantics. In the multi-layer stacked structure, shallower networks tend to capture local word-level semantics, while deeper networks are better at understanding global semantic relationships across words. Through contextual association calculation, the extracted semantic features are compared with a predefined demand type library to identify the demand type to which the current demand belongs, and this demand type is mapped to the corresponding operation type. For example, the demand type "trigger secondary review" is mapped to the operation type "review process jump", and the demand type "amount verification" is mapped to the operation type "numerical range verification".

[0174] Based on the identified operation type, execution parameters corresponding to that operation type are extracted from the semantic triples. Execution parameters include specific values ​​or field names such as the operation object, operation scope, and operation conditions. Multiple operation types are combined with their corresponding execution parameters to generate an operation instruction sequence. Each operation instruction in the sequence contains a clear operation type identifier and a complete set of execution parameters. To ensure that the operation instructions are executed in a logical order, the dependency strength between each operation instruction is calculated. The dependency strength is calculated based on the overlap of the input and output parameters and the logical preconditions of the operation instructions: if the output of instruction A is used as the input parameter of instruction B, the dependency strength between A and B is high; if there is no parameter passing relationship between the two instructions, the dependency strength is low. The operation instructions are sorted according to their dependency strength, with instructions with high dependency strength being prioritized for execution, ultimately forming an ordered execution queue. This ensures that the entire operation instruction sequence will not result in missing parameters or logical conflicts due to incorrect order during execution.

[0175] After extracting the operation type and execution parameters from the sequence of operation instructions, the constraints are converted into trigger condition expressions. These expressions use a structured logical format, supporting comparison operators, logical connectors, and field references. For example, the natural language constraint "amount exceeding 100,000 yuan and order type being cross-border" is converted into the structured trigger condition expression "order_amount > 100000 AND order_type == 'cross_border'". The operation type and execution parameters are encapsulated into execution actions, which describe the specific operations and parameter configurations to be performed when the trigger conditions are met. The trigger condition expressions and execution actions are then combined according to a "condition-action" rule paradigm to form a complete rule configuration scheme. This scheme is stored in a structured data format, containing fields such as rule identifier, trigger condition expression, execution action list, and priority, facilitating subsequent storage and retrieval.

[0176] The order attribute information in the final review results is extracted and compared with the trigger condition expressions in the rule configuration scheme to calculate the matching degree. During the matching degree calculation, the values ​​of each field in the order attribute information are compared item by item with the condition constraints in the trigger condition expression. The proportion of fields that meet the conditions is calculated out of the total number of trigger condition fields, and a weighted summary is performed based on the condition weights to obtain the matching metric between the current order and the rule configuration scheme. When the matching metric exceeds a preset matching threshold, the rule configuration scheme is considered highly relevant to the current order scenario. The rule configuration scheme and the matching metric are then pushed to the reviewers for manual confirmation to ensure the accuracy of the rule configuration and business compliance. The pushed content also includes a readable description of the trigger condition expression and a preview of the execution action, facilitating quick understanding of the rule content and judgment by the reviewers.

[0177] Upon receiving confirmation from the reviewer, the rule configuration scheme is converted into executable review rules. The conversion process includes syntax validation and compilation of the trigger condition expressions to generate rule objects that can be directly executed in the review engine, and assigning a unique rule identifier and version number to each rule object. The generated executable review rules are stored in the rule library of the target review rule version, and priority sorting and conflict detection are performed against existing review rules of that version to ensure that new rules do not logically contradict existing rules. After rule storage is complete, operation instructions are executed sequentially according to the order of the operation instructions in the execution queue. Each operation instruction reads the corresponding execution parameters and calls the corresponding function interface of the review engine to complete the specific order review operation, until all operation instructions in the execution queue have been executed.

[0178] The completed operation instructions, operation time, and operation results are encapsulated into operation log nodes. Each operation log node includes the operation instruction identifier, operation type, execution parameter snapshot, precise timestamp of the operation, and the result status after execution. These operation log nodes are appended to the version switching causal chain, ensuring that the chain not only records the historical evolution of audit rule version changes but also fully preserves the execution trajectory of each order audit operation. Subsequently, during multi-hop path reasoning, operation log nodes can participate in inference calculations as new causal relationship nodes, providing richer historical evidence for version adaptability assessment and previous version backtracking, continuously enhancing the traceability and causal analysis capabilities of audit rule version management.

[0179] A second aspect of this invention provides an order approval system with intelligent control and versioning engine, comprising:

[0180] The data acquisition unit is used to acquire data on orders to be reviewed, including order identifiers and order attribute information;

[0181] The version inference unit is used to extract the historical review record sequence corresponding to the order identifier, identify the review rule version switching event and business scenario characteristics, calculate the gradient of scenario feature changes before and after the switching event to construct causal relationship nodes, connect them in time sequence to form a version switching causal relationship chain, and determine the target review rule version through multi-hop path inference.

[0182] The semantic matching unit is used to semantically match order attribute information with the target review rule version to generate preliminary review results;

[0183] The threshold adjustment unit is used to adjust and verify the preliminary review conclusion based on the dynamic review threshold, and generate the verified review result.

[0184] The adaptation and switching unit is used to extract the decision feature vector of the audit result after verification and the rule feature vector of the target audit rule version, project them onto the multi-dimensional semantic space to calculate the spatial overlap metric as the version adaptation degree. When the version adaptation degree is lower than the preset adaptation threshold, the previous version is obtained from the version switching causal relationship chain to perform the version switching and generate the final audit result.

[0185] The audit execution unit is used to perform order audit operations based on the final audit results, and to add the final audit results as new causal nodes to the version switch causal relationship chain.

[0186] A third aspect of the present invention provides an electronic device, comprising:

[0187] processor;

[0188] Memory used to store processor-executable instructions;

[0189] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0190] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0191] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An order review method using an intelligent control and versioning engine, characterized in that: include: Obtain data on orders awaiting review, including order identifiers and order attribute information; Extract the historical review record sequence corresponding to the order identifier, identify the review rule version switching event and business scenario characteristics, calculate the gradient of scenario feature changes before and after the switching event to construct causal relationship nodes, connect them in time sequence to form a version switching causal relationship chain, and determine the target review rule version through multi-hop path reasoning. Semantic matching of order attribute information with the target review rule version generates preliminary review results; Adjust and verify the preliminary review conclusions based on the dynamic review thresholds, and generate the verified review results. Extract the decision feature vector of the audit result after verification and the rule feature vector of the target audit rule version, project them into the multi-dimensional semantic space to calculate the spatial overlap metric as the version adaptability. When the version adaptability is lower than the preset adaptability threshold, obtain the previous version from the version switching causal relationship chain and perform the version switching to generate the final audit result. The order review operation is performed based on the final review result, and the final review result is added as a new causal link node to the version switch causal relationship chain.

2. The method according to claim 1, characterized in that, Extract the historical review record sequence corresponding to the order identifier, identify the review rule version switching event and business scenario characteristics, calculate the gradient of scenario feature changes before and after the switching event to construct causal relationship nodes, connect them in chronological order to form a version switching causal relationship chain, and determine the target review rule version through multi-hop path reasoning, including: Extract the audit rule version switching event from the historical audit record sequence, obtain the order volume fluctuation, audit pass rate change and resource load intensity corresponding to the switching time as business scenario features, and calculate the difference vector norm of the business scenario features before and after the audit rule version switching event as the gradient of scenario feature change. The version identifier, switching time, business scenario characteristics, and the gradient of changes in scenario characteristics are encapsulated as causal relationship nodes, and connected end to end in the order of switching time to form a causal relationship chain for version switching. The current business scenario features are obtained by acquiring the order volume fluctuation range, the change in the approval rate, and the resource load intensity corresponding to the current order. Starting from the first node of the version switching causal relationship chain, the cosine similarity between the current business scenario features and the business scenario features stored in the causal relationship node is calculated at each causal relationship node. Based on cosine similarity, a multi-path voting mechanism is used to determine the version of the target review rule.

3. The method according to claim 2, characterized in that, Based on cosine similarity, the target review rule version is determined through a multi-path voting mechanism, including: When the cosine similarity is greater than the preset matching threshold, the causal association node is marked as the scene matching node, the path hop count from the first node to the scene matching node is recorded, and the scene matching nodes with the path hop count greater than the preset hop count threshold are selected to form a candidate node set. Extract the version identifiers stored in the matching nodes of each scenario in the candidate node set, count the frequency of each version identifier in the candidate node set as the version vote count, and select the version identifier with the largest version vote count as the target review rule version.

4. The method according to claim 1, characterized in that, The preliminary review conclusion is adjusted and verified based on the dynamic review threshold, and the verified review result includes: Extract order risk scores from the preliminary review results and construct a constrained dataset including order identifiers, risk scores, and preliminary review conclusions; The dynamic review thresholds are divided into rejection threshold ranges, suspected threshold ranges, and approval threshold ranges to determine the threshold range type to which the order risk score belongs; When an order's risk score falls within the rejection threshold range and the initial review conclusion is "pass," the review conclusion for that order is adjusted to "reject" and the reason for adjustment is marked as "threshold exceeding the limit." When an order's risk score falls within the pass threshold range and the initial review conclusion is "reject," the review conclusion for that order is adjusted to "pass" and the reason for adjustment is marked as "threshold regression," generating the review result after the conclusion adjustment. From the revised review results, select orders whose review conclusions have been adjusted, and extract the transaction amount, user credit rating, and historical number of defaults of the adjusted orders to form a verification feature group; The verification feature group is matched with the preset abnormal feature template in multiple dimensions, and the matching score is calculated. When the matching score is lower than the preset security verification threshold, it is determined that the adjustment decision has the risk of misjudgment. The review conclusion of the order is restored to the preliminary review conclusion and a manual review mark is added. When the matching score is not lower than the security verification threshold, the adjusted review conclusion is maintained and a post-verification review result is generated.

5. The method according to claim 1, characterized in that, Extract the decision feature vector of the verified review result and the rule feature vector of the target review rule version, project them onto the multi-dimensional semantic space to calculate the spatial overlap metric as the version fit. When the version fit is lower than the preset fit threshold, retrieve the preceding version from the version switching causal relationship chain and perform the version switch to generate the final review result, including: Extract audit decision information from the verified audit results and form a decision feature vector; Extract rule information from the target review rule version and form a rule feature vector; Project the decision feature vector and the rule feature vector onto the multidimensional semantic space respectively, calculate the intersection volume and the union volume in the multidimensional semantic space, and use the ratio of the intersection volume to the union volume as the spatial overlap metric. The spatial overlap metric is compared with the preset adaptation threshold as the version adaptation degree. When the version compatibility is lower than the preset compatibility threshold, the preceding version of the target review rule version is obtained from the version switching causal relationship chain. The spatial overlap measure between the decision feature vector and the rule feature vector of the preceding version in the multidimensional semantic space is calculated as the preceding version compatibility. When the preceding version compatibility is higher than the version compatibility, the target review rule version is switched to the preceding version. The switched preceding version is used to perform rule matching on the verified review results. Generate the final review results.

6. The method according to claim 5, characterized in that, The preceding versions of the target review rule version can be obtained from the version switch causal chain, including: Initialize the version compatibility record table and store the target review rule version and version compatibility in the version compatibility record table; Obtain the preceding version of the target review rule version from the causal relationship chain of version switching, extract the rule information of the preceding version and form the rule feature vector of the preceding version; Project the feature vector of the previous version rule onto the multidimensional semantic space, calculate the intersection volume and union volume of the decision feature vector and the feature vector of the previous version rule, and use the ratio of the intersection volume to the union volume as the fit degree of the previous version. Store the compatibility between the previous version and the previous version in the version compatibility record table; Determine whether the previous version has a previous version in the version switching causal relationship chain. If a previous version exists, use the previous version as the current previous version and repeat the rule information extraction and adaptation calculation. Otherwise, terminate the backtracking. Select the version with the highest compatibility score from the version compatibility record table as the optimal version, and switch the target review rule version to the optimal version.

7. The method according to claim 1, characterized in that, The order review process based on the final review results includes: Obtain the textual description of the requirement, extract keywords of business intent, constraints and operation goals to construct semantic triples, encode the semantic triples with word vectors, input the encoding results into a multi-layer attention network to extract semantic features, and identify the requirement type and map it to the operation type through context association calculation; Execution parameters are extracted based on operation type, combined to generate operation instruction sequence, the strength of dependency relationship between each operation instruction is calculated, and the execution queue is formed by sorting according to the strength of dependency relationship. Extract operation type and execution parameters from operation instruction sequence, convert constraint conditions into trigger condition expressions, encapsulate operation type and execution parameters into execution actions, and combine trigger condition expressions and execution actions to form rule configuration scheme; Extract the order attribute information from the final review result and calculate the matching degree with the trigger condition expression in the rule configuration scheme. When the matching degree exceeds the preset matching threshold, push it to the reviewer for confirmation. After receiving the confirmation instruction, the rule configuration scheme is converted into an executable audit rule and stored in the target audit rule version. The operation instructions are executed sequentially according to the execution queue to complete the order audit operation. The completed operation instructions, operation time, and operation results are encapsulated into operation log nodes and appended to the version switching causal relationship chain.

8. An order approval system with intelligent control and versioning engine, used to implement the method as described in any one of claims 1-7, characterized in that, include: The data acquisition unit is used to acquire data on orders to be reviewed, including order identifiers and order attribute information; The version inference unit is used to extract the historical review record sequence corresponding to the order identifier, identify the review rule version switching event and business scenario characteristics, calculate the gradient of scenario feature changes before and after the switching event to construct causal relationship nodes, connect them in time sequence to form a version switching causal relationship chain, and determine the target review rule version through multi-hop path inference. The semantic matching unit is used to semantically match order attribute information with the target review rule version to generate preliminary review results; The threshold adjustment unit is used to adjust and verify the preliminary review conclusion based on the dynamic review threshold, and generate the verified review result. The adaptation and switching unit is used to extract the decision feature vector of the audit result after verification and the rule feature vector of the target audit rule version, project them onto the multi-dimensional semantic space to calculate the spatial overlap metric as the version adaptation degree. When the version adaptation degree is lower than the preset adaptation threshold, the previous version is obtained from the version switching causal relationship chain to perform the version switching and generate the final audit result. The audit execution unit is used to perform order audit operations based on the final audit results, and to add the final audit results as new causal nodes to the version switch causal relationship chain.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

Images