Ticket order issuing control method and device, equipment and medium

Abstract

The application relates to the technical field of big data processing, and discloses a ticket order issuing control method, device, equipment and medium.The method comprises the following steps: acquiring operation rule data of a plurality of sales platforms of a target product and order data to be processed generated by each platform, and extracting order pressing rules of each platform from the operation rule data; determining an order pressing strategy of the order to be processed according to the order pressing rules, executing permission locking on the order to be processed through the strategy, and generating a pressed order; matching a purchase price comparison configuration of the pressed order, screening candidate lists of purchase places; sorting settlement prices of the candidate purchase places in ascending order, taking a purchase place corresponding to the lowest price in the first place as a target ticket issuing place, and generating a ticket issuing instruction; completing order ticket issuing according to the ticket issuing instruction and generating a result, and finally updating a product global sales rule library according to the ticket issuing result. The application can improve the accuracy of ticket order data processing in the whole process of ticket operation management and control.

Description

Technical Field

[0001] This invention relates to the field of big data processing technology, and in particular to a method, apparatus, equipment and medium for controlling ticket order issuance. Background Technology

[0002] With the large-scale and platform-based development of online ticketing services across all categories (transportation, performances, sports events, cultural tourism, etc.), ticketing operations need to address the complex management requirements of multiple platforms, business lines, and scenarios. Core processes such as refunds and changes, fee calculations, anomaly appeals, and work order processing urgently require a flexible, multi-dimensional priority control, and automated adaptation system of operational rules to achieve standardized processes, controllable anomalies, and efficient handling. Therefore, to meet the needs of large-scale intelligent operation of all categories of ticketing, it is necessary to uniformly configure and intelligently manage ticketing operation rules across all scenarios to improve the accuracy of data processing throughout the entire ticketing process.

[0003] In the international air ticketing business, existing models mostly rely on manual configuration of rules for ticketing time limits, refunds, changes, and abnormal feedback based on a single platform, airline, or cabin class. There is no unified priority control system, and there is no standardized adaptation logic for flight change processing, NO position, and abnormal feedback of price changes. Refund and change surcharges are calculated based on fixed templates only. Automatic ticketing lacks multi-dimensional loss threshold verification. Rules are prone to conflict under cross-route and multi-policy scenarios, parameters cannot accurately match business scenarios, and process nodes lack linkage control, resulting in low accuracy of data processing when issuing, refunding, or changing international air tickets.

[0004] Current practices for managing ticketing operations across all categories often employ a single-scenario, fixed-parameter configuration model for order data processing. Rules for refunds, changes, cost calculations, and appeal deadlines are manually set based on single dimensions such as platform or business line. This relies on manual input of instructions and parameter validation, lacking a unified priority control logic. Furthermore, there are no standardized adaptation mechanisms for anomaly feedback, price increase rules, and loss thresholds. Cross-scenario rule adaptability is poor, parameters are prone to conflict, automated processing relies solely on fixed templates and cannot dynamically match, process nodes lack precise linkage, and data statistics and risk control judgments are based on a single dimension, resulting in low accuracy in ticketing order data processing during the entire ticketing operation management process. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and medium for controlling ticket order issuance, in order to solve the problem of low accuracy in ticket order data processing during the entire ticketing process operation and management.

[0006] Firstly, a method for controlling ticket order issuance is provided, the method comprising: Obtain operational rule data of the target product on multiple sales platforms, as well as pending order data generated by the target product on the multiple sales platforms; Extract the order pressure rules corresponding to the multiple sales platforms from the operational rule data; The order-pressing parameters in the pending order data are identified according to the order-pressing rules, and the order-pressing strategy corresponding to the pending order data is matched from the preset operation rule library based on the order-pressing parameters. The pre-trained order pressure analysis model is used to select order data that conforms to the order pressure strategy from the order data to be processed, and thus obtain the order pressure orders; The system filters out purchase price comparison configurations that match the order pressure parameters in the pre-set price comparison rule library, and uses the purchase price comparison configurations to filter the purchase location of the order pressure, generating a candidate purchase location list. Obtain the settlement price feature value corresponding to each procurement location in the candidate procurement location list, determine the procurement location corresponding to the minimum settlement price feature value as the target invoicing procurement location, and generate an invoicing instruction for the pending order data based on the target invoicing procurement location. The ticketing instruction is used to execute an order ticketing operation on the pending order data to generate a ticketing result.

[0007] Secondly, a ticketing order issuance control device is provided, the device comprising: The data acquisition module is used to acquire the operational rule data of the target product on multiple sales platforms, as well as the pending order data generated by the target product on the multiple sales platforms. The order pressure rule extraction module is used to extract the order pressure rules corresponding to the multiple sales platforms from the operational rule data; The order pressure strategy matching module is used to identify the order pressure parameters in the order data to be processed according to the order pressure rules, and to match the order pressure strategy corresponding to the order data to be processed from the preset operation rule library based on the order pressure parameters. The order selection module is used to select order data that conforms to the order-pressing strategy from the order data to be processed using a pre-trained order-pressing analysis model, so as to obtain the order-pressing orders. The procurement location filtering module is used to filter out procurement price comparison configurations that match the order pressure parameters in the pre-set price comparison rule library, and use the procurement price comparison configuration to filter the procurement location of the order pressure to generate a candidate procurement location list; The ticketing instruction generation module is used to obtain the settlement price feature value corresponding to each procurement location in the candidate procurement location list, determine the procurement location corresponding to the minimum settlement price feature value as the target ticketing procurement location, and generate the ticketing instruction for the pending order data according to the target ticketing procurement location. The order ticketing module is used to perform order ticketing operations on the pending order data through the ticketing instruction, and generate ticketing results.

[0008] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described ticket order issuance control method.

[0009] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described ticket order issuance control method.

[0010] The solution implemented by the above-mentioned ticketing order issuance control method, device, equipment, and medium can obtain multi-platform operation rules and pending order data through the client, extract order-holding rules to identify order-holding parameters, determine corresponding strategies, and lock orders. Then, according to preset price comparison rules, the procurement location is filtered, the lowest settlement price is selected as the ticketing procurement location, and a ticketing instruction is generated to complete the ticketing and update the full-domain sales rule library. In this invention, innovative contents such as multi-dimensional rule configuration, priority control, automated ticketing, and anomaly calculation are integrated to achieve unified adaptation of multi-platform rules, intelligent order control, and accurate price comparison ticketing. The entire ticketing process is automated, the rule system is dynamically optimized, manual intervention and parameter errors are reduced, and operational efficiency and control capabilities are improved. It can effectively improve the accuracy of ticketing order data processing during the entire ticketing process operation and control. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of an application environment for a ticketing order issuance control method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a ticket order issuance control method according to an embodiment of the present invention; Figure 3 yes Figure 2 A flowchart illustrating a specific implementation method of step S3; Figure 4 yes Figure 2 A flowchart illustrating a specific implementation of step S4; Figure 5 This is a schematic diagram of a ticketing order issuance control device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 7This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0013] It should be noted that in the technical solutions disclosed in this invention, the acquisition of user information (personal image data (e.g., facial videos or pictures, facial feature videos or pictures, etc.) and personal privacy information (e.g., name, ID number, occupation, address, etc.)) is all completed with the user's knowledge and consent, and the acquisition of the relevant user information is legal and compliant.

[0014] 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, not all, of the embodiments of the present invention. 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.

[0015] The ticketing order issuance control method provided in this embodiment of the invention can be applied to, for example, Figure 1 In this application environment, the client communicates with the server via a network. The server can obtain multi-platform operation rules and pending order data from the client, extract order pressure rules to identify order pressure parameters, determine corresponding strategies, and lock orders. Then, it filters the procurement location according to preset price comparison rules, sorts and selects the procurement location with the lowest settlement price, generates a ticketing instruction to complete the ticketing, and updates the full-domain sales rule library. This invention integrates innovative content such as multi-dimensional rule configuration, priority control, automated ticketing, and anomaly calculation, achieving unified adaptation of multi-platform rules, intelligent order management, and accurate price comparison ticketing. It automates the entire ticketing process, dynamically optimizes the rule system, reduces manual intervention and parameter errors, and improves operational efficiency and risk control capabilities. It can solve the problem of low accuracy in ticketing, refunds, and anomaly calculation for all categories of tickets. The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a separate server or a server cluster composed of multiple servers. The invention will be described in detail below through specific embodiments.

[0016] Please see Figure 2 As shown, Figure 2 A flowchart illustrating a ticketing order issuance control method provided in an embodiment of the present invention, the method comprising the following steps: S1. Obtain the operational rule data of the target product on multiple sales platforms, as well as the pending order data generated by the target product on the multiple sales platforms.

[0017] In this embodiment of the invention, the target product can be a standardized strategy carrier built based on a full-category ticketing business scenario, carrying pricing strategies and revenue calculation logic. It is typically a strategy unit integrating ticketing revenue rules and sales adaptation conditions. The sales platform can be an online transaction channel providing ticketing sales services for ticketing transaction scenarios, typically a transaction carrier carrying ticket order flow and user ticket purchase behavior. The operational rule data can be configuration data for various business rules related to ticketing control, refund control, change control, appeal control, refund / change surcharges, fee calculation, loss limits, and work order processing configured on multiple sales platforms for the target product. This includes specific fields such as rule configuration dimensions, rule priority order, various business time limits, operation permission on / off status, fee ratio, loss threshold, and maximum number of appeals. For example, it may include normal ticketing time limits and refund service fee ratios.

[0018] In detail, obtaining operational rule data can be achieved by traversing the operational rule configuration repository associated with multiple sales platforms, retrieving the original operational rule data that is fully compatible with each attribute dimension of the target product, and using a field mapping parsing method to structurally decompose the original operational rule data, separating subdivided rule data such as ticketing operation rules, refund operation rules, rescheduling operation rules, appeal operation rules, fee management rules, ticketing time limit rules, and automatic ticketing loss threshold rules. Invalid rule data that does not match the applicable scenario of the target product is eliminated through dimension verification. The valid rule data of each sales platform that has passed verification is aligned and integrated by attribute dimensions to form a standardized set of operational rule data for the target product across multiple sales platforms.

[0019] In this embodiment of the invention, the pending order data can be a collection of order information generated by multiple sales platforms for the target product that has not yet completed the entire process of ticketing transactions. It is usually structured data containing information on the ticket purchaser, ticketing service, transaction amount, order status, operation time limit, and channel source. For example, pending ticketing order data containing information on the ticket purchaser, ticket seat, order payment status, and ticketing processing time limit.

[0020] In detail, the process of obtaining pending order data involves constructing an order matching dimension index based on the business line, product type, service provider, itinerary, seat class, ticket price type, and sales platform identifier corresponding to the target product. A data retrieval request is initiated to multiple sales platforms through an order interface polling mechanism to obtain the original order data streams from each platform. A dimension field matching method is used to compare the original order data streams with the order matching dimension index item by item to filter the original order data belonging to the target product. Duplicate records, invalid records, and records not in a pending state are removed from the original order data through data cleaning and filtering to retain valid pending order data. Finally, a unified field format encapsulation method is used to organize the valid pending order data into a set of pending order data for the target product across multiple sales platforms.

[0021] Based on multi-dimensional attribute indexing, the system accurately matches and retrieves operational rule data from multiple sales platforms corresponding to the target product. Through an integrated process of interface polling, dimension filtering, and data cleaning, the system synchronously collects pending order data for the target product across multiple sales platforms, achieving synchronous integration and acquisition of cross-platform rule data and order data.

[0022] By simultaneously acquiring operational rule data and pending order data for target products across multiple sales platforms, compliance verification and process processing of pending orders can be directly performed based on the matching operational rules. This significantly improves the efficiency of cross-platform ticketing order processing and reduces operational errors and compliance anomalies caused by differences in rules across different sales platforms. For example, it avoids ticketing processing losses caused by discrepancies in platform ticketing deadlines or conflicting refund and surcharge rules. Furthermore, by unifying the order data format and rule execution standards across multiple platforms, it simplifies the cross-platform operation and management process for all ticketing categories. For instance, it synchronously adapts to the platform rules and pending orders for airline, railway, and performance tickets, achieving standardized and efficient management of ticketing order data processing in multi-scenario ticketing operation and control.

[0023] S2. Extract the order pressure rules corresponding to the multiple sales platforms from the operation rule data.

[0024] In this embodiment of the invention, the order pressure rule can be a combination of various rules in the operation rule data used to constrain order processing time limits, restrict order operation conditions, control order abnormal feedback and appeal requests, and set automatic order processing loss thresholds. These rules include specific rules such as the time limit for backfilling in the ticketing process, the time limit for refunding in the ticketing process, the limit on the number of appeals, and the automatic ticketing loss threshold.

[0025] In this embodiment of the invention, step S2 includes: Identify the business line dimensions of the multiple sales platforms based on the operational rule data, and determine the rule configuration dimensions of the multiple sales platforms based on the business line dimensions; The rule configuration dimension is used to parse the ticketing time limit parameter, ticketing requirement parameter, and anomaly feedback parameter in the operation rule data to obtain the rule parsing factor; The rule parsing factors are sorted according to a preset priority sorting rule to obtain a rule factor sorting set; The rule factor sorting set is aggregated and encapsulated according to the rule configuration dimension to generate the order pressure rules corresponding to the multiple sales platforms.

[0026] In detail, the operational rules data extracts the sales platform association identifier through field matching and dimension mapping, locates the corresponding business line classification information, and then matches and locks the associated dimension items configured in the rules based on the business line classification information.

[0027] Business line dimension typically refers to the operational branch categories within a sales platform, divided according to product type or service process, such as domestic air ticket business line, international air ticket business line, train ticket business line, or insurance business line. Rule configuration dimension typically refers to the multi-layered matching conditions and their priority order on which an operational rule takes effect. For example, for the domestic air ticket business line of a sales platform, its rule configuration dimensions could be, in order: route (e.g., Beijing-Shanghai), excluded routes (e.g., Guangzhou-Shenzhen), airline (e.g., Air China), product type (e.g., economy class), and policy group (e.g., promotional policy group). Applicable ticketing time limits or anomaly feedback rules are matched level by level, with the route having the highest priority and the policy group being the last.

[0028] The extraction operation iterates through the platform-related fields within the operational rule data, using precise field value matching to filter out the business line identifier data corresponding to each sales platform. Null values ​​and abnormal redundant data are removed from the business line identifier data to complete the business line dimension identification operation. The identified business line identifier data is then paired with a preset dimension mapping table using key-value pairs. Based on the pairing results, matching rule configuration attribute items are retrieved from a preset dimension library to complete the rule configuration dimension determination operation.

[0029] Specifically, the rule configuration dimension performs parsing processing on the ticketing time limit parameters, ticketing requirement parameters, and exception feedback parameters in the operation rule data through field alignment, parameter matching, and logical decomposition, and generates rule parsing factors.

[0030] Ticketing time limit parameters typically refer to constraint values ​​that control the ticketing operation to be completed within a specified time window. For example, the ticketing time limit type can be set to the latest ticketing time provided by the interface or fixed at 2 hours before departure. For a certain airline and a certain class of service, and when the ticketing time is within 24 hours of departure, the ticketing time limit after order placement can be set to 30 minutes. Ticketing requirement parameters typically refer to the switch conditions or value ranges that determine what operations are allowed to be performed during ticketing. For example, whether downgrading is allowed (e.g., allowing downgrading from business class to economy class), whether price reduction is allowed (e.g., allowing price reductions but the price adjustment range for the same class cannot exceed 10% of the face value), ticketing profit range (e.g., automatic ticketing can be performed if calculated per person between 50 and 200 yuan), and whether secondary backfilling is allowed (e.g., allowing ticket number to be refilled after an error failure). Anomaly feedback parameters typically refer to the rule values ​​that define whether the operator can report or appeal to the platform when specific problems occur during the ticketing process (such as no seats available, price changes, or other technical anomalies), and the timeframe within which feedback must be completed. For example, the time limit for reporting price changes (NO) could be set to 2 hours after the order is generated, while indicating whether the situation is appealable (if yes). The time limit for other anomaly feedback could be set to 1 hour and not appealable. Rule parsing factors can be standardized data units formed after parsing, used to carry the logic of ticketing rules.

[0031] The parsing operation aligns the attribute fields of the rule configuration dimension with the parameter fields of the operation rule data one-to-one, uses format validation to remove redundant characters and invalid format content in the parameter data, performs time value extraction and interval validity validation on the ticketing time limit parameter, performs permission determination and constraint condition extraction on the ticketing requirement parameter, and performs feedback permission identification and time limit value parsing on the abnormal feedback parameter. The parsing results of the three types of parameters are integrated and encapsulated according to the attribute structure of the rule configuration dimension to form a standardized rule parsing factor.

[0032] Furthermore, by retrieving preset priority configuration information and performing weight matching and order adjustment, the rule parsing factors are sorted to generate a sorted set of rule factors.

[0033] Preset priority ranking rules typically refer to the order in which multiple matching dimensions take effect under the same business scenario. For example, in ticketing requirement rules, the system sorts the rule parsing factors in descending order: cabin class first, then route, then excluded routes, then airline, then excluded airlines, and finally policy type. In another example, in normal ticketing time limit rules, the system sorts the rules in the following order: cabin class first, then airline, and finally policy type. When the same order matches multiple rules, only the rule ranked first takes effect. The rule factor ranking set can be an ordered data set formed by arranging the rule parsing factors according to priority.

[0034] The sorting operation can be performed by retrieving a preset priority configuration table containing priority weight values ​​from local storage, and extracting the priority attribute field content corresponding to each rule parsing factor. The priority attributes of the rule parsing factors are then numerically matched with the weight values ​​in the preset priority configuration table. A bubble sort algorithm is used to rearrange the matched rule parsing factors according to their weights from high to low, removing duplicate rule parsing factor data generated during the sorting process. The rearranged rule parsing factors are then sequentially packaged and integrated to form an ordered set of rule factors.

[0035] In one embodiment, firstly, a complete dimension hierarchy chain is extracted from the rule configuration dimension. This chain, ordered from highest to lowest priority, includes platform, store, business line, policy type, airline, excluded airline, route, excluded route, and cabin class (taking ticketing rules as an example). Secondly, each rule factor record in the rule factor sorting set is traversed, and the dimension values ​​such as platform identifier, store identifier, and business line identifier carried by the record are read. Using the dimension hierarchy chain as the key-value structure, rule factor records with the same combination of dimension values ​​are grouped into the same dimension combination group. Next, for each dimension combination group, a nested dictionary object is constructed using the sorted rule factor records within the group. The highest priority dimension serves as the first-level key, the next-level dimension as the second-level key, and so on, until the last-level dimension serves as the rule factor content array corresponding to the value. Then, the nested dictionary object is serialized, converting the rule factor content array on each leaf node into a pressure order rule data block that conforms to the platform interface requirements. This pressure order rule data block at least includes the specific values ​​of the ticketing time limit parameter, ticketing requirement parameter, and exception feedback parameter. Finally, the order-pressing rule data blocks generated by combining all dimensions are aggregated according to the platform identifier to form an order-pressing rule mapping table with the sales platform as the primary key, thus completing the generation of order-pressing rules corresponding to the multiple sales platforms.

[0036] For example, in the air ticket sales scenario, the "third-party e-commerce platform - flagship store - international business line" dimension can be identified based on operational rule data, and the rule configuration dimension can be determined as platform + store + business line + airline + cabin class. The ticketing time limit parameters (30 minutes for ticketing within 24 hours before departure), ticketing requirement parameters (no downgrading, profit range ≥ 10 yuan), and anomaly feedback parameters (15 minutes for feedback for NO position) are parsed, sorted by priority (cabin class > airline > policy type), and aggregated and encapsulated into a congestion rule for that international flight, used for automatic ticketing decisions.

[0037] S3. Identify the order-pressing parameters in the pending order data according to the order-pressing rules, and match the order-pressing strategy corresponding to the pending order data from the preset operation rule library based on the order-pressing parameters.

[0038] In this embodiment of the invention, the order-holding parameters typically refer to key values ​​or status indicators extracted from the pending order data to determine whether to postpone ticketing for the order. Examples include: the calculated ticketing profit for the order is 30 yuan (lower than the minimum profit range of 50 yuan per person set in the order-holding rules); the current time of the order is less than 5 minutes before the latest ticketing deadline; or the predicted loss for ticket cancellation associated with the order reaches 40% of the ticket price (exceeding the maximum loss range for cancellations and changes set in the order-holding rules by 20%). In another example, the order-holding parameters can also be the abnormal feedback status marked on the order (e.g., the NO position feedback has exceeded the 2-hour time limit and is not appealable). Based on these parameters, it is decided to transfer the order to the manual processing queue instead of automatic ticketing.

[0039] In detail, the system retrieves order-holding rule data that is fully compatible with the dimensions of the pending order data through dimensional matching methods based on platform, store, business line, policy type, airline, cabin class, and route. Configuration items in the rule data that are irrelevant to the pending order data are removed. Order feature data corresponding to ticketing profit, estimated refund / change loss, remaining ticketing time limit, downgrade / price reduction execution authority, and automatic ticketing loss threshold are extracted from the pending order data using precise field mapping. The extracted order feature data is then compared item by item with the threshold standards and restrictions in the order-holding rules, and permission compliance is verified to determine whether the order feature data triggers the built-in delay ticketing judgment conditions. The combination of rule thresholds, restrictions, and order feature values ​​corresponding to triggering the delay ticketing judgment conditions is determined as the order-holding parameters corresponding to the pending order data.

[0040] In this embodiment of the invention, the preset operation rule base typically refers to a structured database that pre-configures and stores all operation rule data (including ticketing rules, refund rules, rescheduling rules, appeal rules, and refund / rescheduling surcharge rules, etc.). For example, this database stores specific rule entries indexed by platform, store, business line, policy type, airline, route, etc., such as a ticketing profit range of 50 to 200 yuan per person, a refund rejection time limit of 2 hours, and a rescheduling completion time limit of 4 hours. The order-holding strategy typically refers to an order processing plan that is automatically matched and executed when the order-holding parameters of an order to be processed are triggered. For example, for an order with a ticketing profit of 30 yuan, the matched order-holding strategy could be "transfer to manual ticketing and prohibit automatic backfilling". For an order with a refund loss prediction value reaching 40% of the ticket price, the matched order-holding strategy could be "suspend refund processing and trigger an abnormal feedback appeal process".

[0041] In this embodiment of the invention, reference is made to Figure 3 As shown, the step of matching the order-pressing strategy corresponding to the order data to be processed from the preset operation rule base based on the order-pressing parameters includes: S31. Extract abnormal data records that meet the preset abnormal threshold from the order data to be processed according to the order pressure parameters; S32. Match the abnormal data records with the order-holding rules in the preset operation rule library, and generate a delay time window for the pending order data according to the order-holding rules; S33. Obtain the order conversion rate and expected revenue of the pending order data within the postponement time window, and calculate the priority weight of the postponement time window based on the order conversion rate and expected revenue. S34. Extract the order fulfillment time point from the order data to be processed, align the priority weight with the order fulfillment time point on the time axis, and determine the start time node and end time node of the postponement of ticket issuance. S35. Generate a pressure strategy corresponding to the pending order data based on the start time node and the end time node.

[0042] In detail, the data of the orders to be processed is verified and compared according to the order pressure parameters to filter out the data that touches the preset abnormal judgment criteria.

[0043] Preset abnormal thresholds usually refer to pre-set threshold values ​​used to determine whether a certain value in order data belongs to an abnormal record. For example, the lower limit threshold of the ticketing profit range can be set to 50 yuan per person. When the ticketing profit calculated for an order is 30 yuan (lower than 50 yuan), it is judged as an abnormal data record.

[0044] By precisely mapping fields, order parameters are associated with data items in the pending order data, such as ticketing profit, refund / change loss, ticketing time limit, and automatic ticketing loss threshold. A numerical comparison method is used to determine the compliance of the associated order data items against preset anomaly thresholds. Order data items exceeding or falling below the preset anomaly thresholds are retained through data filtering and retention. These retained order data items are then integrated to form anomaly data records in the pending order data that meet the preset anomaly thresholds.

[0045] Specifically, feature matching is performed between abnormal data records and order-holding rules in the preset operation rule library, and a time interval for temporarily suspending ticketing is generated based on the matched order-holding rules.

[0046] The preset operation rule library can be a set of rules storing rules for ticketing control, exception handling, loss limits, and time constraints. The order-holding rule can be a set of execution standards for temporarily suspending ticketing for abnormal orders within the preset operation rule library. The suspension time window typically refers to a duration generated based on the order-holding rule matched with the abnormal data record. During this duration, the pending order is temporarily suspended and does not execute automatic ticketing, automatic refund, or automatic rescheduling operations. For example, when an order is marked as an abnormal data record because its ticketing profit of 30 yuan is lower than the preset abnormal threshold of 50 yuan, the matched order-holding rule could be "transfer to manual ticketing," and the corresponding suspension time window could be set to 30 minutes. That is, it waits for manual intervention within 30 minutes, and after the timeout, it is transferred to manual processing or automatically rolled back.

[0047] By precisely aligning dimensions, the abnormal data records are compared item by item with the trigger conditions, dimensional restrictions, and parameter standards of the order-related store business line information against the pre-defined order-holding rules in the operational rule library. A rule-matching filter is then used to identify order-holding rules that perfectly match the abnormal data records. The postponement duration, time calculation base, and time fluctuation coefficient parameters from the matching order-holding rules are extracted through rule parameter parsing. Using time logic calculation methods, the start and end times for postponing ticket issuance for pending order data are calculated based on the extracted rule parameters. These calculated start and end times are then combined to form the postponement time window for the pending order data.

[0048] Furthermore, by collecting the order conversion rate and estimated revenue value corresponding to the pending order data within the postponement time window, the priority weight of the postponement time window is calculated by combining the two values.

[0049] The order conversion rate typically refers to the estimated percentage of pending orders that, within a deferred time window, are successfully ticketed (or refunded, rescheduled) and settled after manual intervention or automatic retries. For example, based on historical data of similar orders, if an order is held back for 30 minutes due to a profit margin below a threshold, the probability of successful ticketing after manual intervention and requoting is 65%. This 65% is the order conversion rate. The expected revenue typically refers to the estimated profit that a pending order could generate if successfully converted within the deferred time window. For example, based on the profit range per person set in the order-holding rules (e.g., 50 to 200 yuan) and the estimated ticket price of the current order, if the order is successfully ticketed, the expected profit is 120 yuan. This 120 yuan is the expected revenue.

[0050] The time interval corresponding to the postponement time window is located by locking the time dimension. Real-time data collection is used to extract the number of completed orders and the total number of orders within this time interval from the order statistics module. The order conversion rate is obtained by dividing the number of completed orders by the total number of orders using a numerical division method. The expected revenue is calculated by combining the order face value, rebate ratio, and additional revenue parameters using a revenue model. A weighted calculation algorithm is then used to substitute the order conversion rate and expected revenue into a preset weighted calculation formula, and the formula outputs a corresponding quantitative score as the priority weight for the postponement time window.

[0051] Furthermore, the relevant time points for order fulfillment are extracted from the pending order data, and the priority weights are aligned with the order fulfillment time points on a unified time axis. Based on the alignment results, the start and end time points corresponding to the postponement of ticket issuance are determined.

[0052] The order fulfillment time point can be the critical time node at which a ticketing order needs to be completed and fulfilled. The start time point can be the specific time when the ticketing suspension process begins. The end time point can be the specific time when the ticketing suspension process terminates and normal ticketing operations resume.

[0053] Order fulfillment time data is extracted from the fulfillment configuration field of the order data to be processed using precise field parsing. A time axis coordinate mapping method is used to match and align the quantified scores corresponding to priority weights with the time coordinates corresponding to the order fulfillment time points on a unified time axis. The start execution time of the delayed ticketing process is calculated by combining the priority weight scores, order fulfillment time points, and preset delay duration parameters using a time offset calculation method. The end execution time of the delayed ticketing process is determined by using a time overlay calculation method based on the start time node and the preset delay duration parameter. The calculated start execution time is determined as the start time node for delayed ticketing, and the calculated end execution time is determined as the end time node for delayed ticketing.

[0054] In addition, the start and end times are associated with unique identifiers in the pending order data. A strategy template is used to populate the bound start and end times into the standard template for the ticketing order consolidation strategy. A time logic validation method is used to verify the compliance of the start time being earlier than the end time in the template. After validation, the corresponding order consolidation strategy for the pending order data is generated.

[0055] For example, in an international flight scenario, abnormal data records with a flight change probability exceeding 80% are extracted from pending orders. These records are then matched against the "Typhoon Warning Order Delay Rule" in the operations rule library to generate a 24-hour delay window. The weighted priority of order conversion rate and expected revenue within the window is calculated. Combined with the flight departure time (fulfillment time), the delay ticketing start point is determined to be immediately after the typhoon warning is issued, and the end point is 2 hours before departure. This generates an order delay strategy for orders with high abnormal flight change rates.

[0056] S4. Using a pre-trained order pressure analysis model, select order data that conforms to the order pressure strategy from the order data to be processed to obtain the order pressure orders.

[0057] In this embodiment of the invention, the pre-trained order-holding analysis model typically refers to a machine learning model pre-trained using historical order samples (including features such as ticketing profit, refund / change losses, abnormal feedback status, and whether the order was ultimately held up). This model can output a prediction result on whether the input order data needs to be temporarily suspended and the priority of the suspension. For example, if the model inputs an order (ticketing profit of 30 yuan, 10 minutes remaining until the latest ticketing deadline, NO position feedback has timed out), the output probability of order holding up is 0.85. The order data for the order-holding strategy typically refers to those order records in the order data that meet the conditions defined by the order-holding strategy and are thus selected into the order-holding queue. For example, orders that meet the conditions of ticketing profit being less than 50 yuan and expected revenue greater than zero within the suspension time window, or orders that meet the conditions of refund losses exceeding 20% ​​of the ticket price and order conversion rate being less than 30%. The order being held back can be an order that, after the order data to be processed is locked according to the order holding strategy, presents a form in which the order operation permission is restricted, the ticketing process is temporarily suspended, the corresponding ticketing product strategy matching process continues to advance, and the associated ticketing resources are in a reserved state.

[0058] In this embodiment of the invention, reference is made to Figure 4 As shown, the training steps of the pre-trained single-item analysis model include: S41. Extract various operational attributes from preset historical refund and modification orders, encode and convert each operational attribute to obtain an initial sample set; S42. Assign weights to each operational attribute in the initial sample set according to the preset priority configuration, and perform time-series partitioning on the assigned initial sample set to obtain a training subset and a validation subset. S43. Obtain a multi-branch time-series classification network adapted to the preset order pressure warning task, initialize the weights of the multi-branch time-series classification network based on the operation rule data to obtain an initial network model, define the order pressure classification loss function of the initial network model, and match the optimizer and learning rate scheduling strategy corresponding to the order pressure classification loss function. The training subset is input into the initial network model for forward propagation calculation. Based on the calculation results and the training subset, the order classification loss value is calculated. Based on the order classification loss value, the parameters of the initial network model are updated by backpropagation. The validation subset and the preset early stopping mechanism are combined to perform multiple rounds of iterative training to obtain the pre-trained order analysis model.

[0059] In detail, the process involves filtering valid historical order data for refunds and changes, extracting order-related operational rule fields, standardizing and encoding the fields, and integrating the encoded data to construct an initial sample set from historical order data.

[0060] The preset historical order data can be past order data that has completed the entire process of ticketing, refunding, changing, and appealing, at the platform, store, and business line levels. For example, it could be all order data for changing in a store throughout a certain year. Various operational attributes can be configuration fields of various platform operational rules bound to the order data, such as the platform service start time in ticketing rules, the refund feedback time limit in refund rules, and the maximum number of appeals in appeal rules. The initial sample set can be a structured dataset used for rule validation or model training after encoding and conversion, such as a dataset containing fields such as service time, feedback time limit, and number of appeals for each order after encoding.

[0061] The extraction operation connects the order database and the platform's operation rules database, using the unique order number as the matching identifier. It iterates through historical order data rows, reading and filtering each row for ticketing, refund, rescheduling, and appeal-related rule fields. Invalid data rows with missing fields or incorrect formats are removed, thus accurately extracting operational attributes. The encoding conversion operation employs differentiated processing methods for different types of operational attribute fields. Time-limited fields (such as refund refill time limit and rescheduling completion time limit) are normalized to minutes. Boolean fields (such as whether a second refill is possible or whether a downgrade is possible) use 0 / 1 binary mapping. Enumerated fields (such as ticketing time limit type and refund sub-category) are constructed using a field mapping dictionary to complete the numerical conversion, achieving standardized encoding of operational attributes. The encoded operational attribute fields are then horizontally concatenated along the order dimension to generate a single structured sample. All structured samples are aggregated to form a standardized dataset, constructing the initial sample set.

[0062] By retrieving the platform's operational rule priority configuration table, constructing a priority-weight mapping relationship, and sorting and splitting samples by order timestamp, the allocation of operational attribute weights and the temporal splitting of the sample set are completed.

[0063] The preset priority configuration can be a hierarchical sorting rule defined in the platform's operation rules for various operational attributes such as ticketing, refunds, and changes. For example, in the normal ticketing time limit rule, the cabin class has a higher priority than the airline, and the airline has a higher priority than the policy type; in the refund and fare increase rule, the cabin class has a higher priority than the base fare, and the base fare has a higher priority than the route. The training subset can be a set of sample data used for model rule fitting and parameter learning after time-series partitioning. The validation subset can be a set of sample data used for verifying the model rule adaptation effect after time-series partitioning.

[0064] The weight assignment operation reads the priority ranking list of operational attributes for each business line within the platform's operational rules document, establishes a one-to-one mapping dictionary between priority levels and weight values ​​in the 0-1 range, iterates through the operational attribute fields of each sample in the initial sample set, matches the corresponding weight values ​​according to the mapping dictionary, and writes them into the sample's new weight field, thus completing the precise binding of weights for each operational attribute. The time-series partitioning operation parses the order generation timestamp field associated with each sample in the initial sample set, calls the quicksort algorithm to perform a global sorting of all samples in ascending order of timestamp, sets a fixed time node as a partitioning threshold, and groups all samples with timestamps earlier than the threshold into a training subset, and all samples with timestamps later than the threshold into a validation subset, thus completing the precise partitioning of the sample set along the time-series dimension.

[0065] The initial network model construction and basic parameter configuration for training are completed by retrieving the network structure adapted to the order pressure early warning task, initializing network weights using operational rule data, constructing an order pressure classification loss function, and matching the corresponding optimizer and learning rate scheduling strategy.

[0066] The pre-set order backlog warning task can be a business task that identifies the risk of order backlog and avoids order timeouts or losses based on the platform's refund and change operation rules. The multi-branch temporal classification network can be a classification neural network adapted to the temporal characteristics of refunds and changes, containing multiple feature processing branches. The network typically includes time-series branches for ticketing rules, refund rules, change rules, and appeal rules. The temporal sequence is represented by the sequence of operational attributes sorted by order timestamp within each branch. For example, the ticketing rule time-series branch arranges attribute sequences such as platform service time and ticketing time limit by hour, while the refund rule time-series branch arranges attribute sequences such as refund return time limit and refund completion time limit by day. The initial network model can be a multi-branch temporal classification network instance that has completed weight initialization and has not undergone iterative training. The order backlog classification loss function can be a mathematical function that measures the deviation between the network's predicted order backlog risk category and the actual category, such as the cross-entropy loss function, used to calculate the error between the network's predicted order backlog and no backlog results and the actual results. An optimizer can be an algorithmic tool used to update network weights and minimize the loss function, such as the Adam optimizer, which adjusts the weight parameters of each branch of the initial network model to reduce single-prediction errors. A learning rate scheduling strategy can be a rule that dynamically adjusts the step size of weight updates during training, such as the cosine annealing learning rate scheduling strategy, used to adapt different step sizes at different stages of model training to improve convergence.

[0067] The acquisition of the multi-branch time-series classification network can be achieved by retrieving network structure configuration files in the model library that are labeled and adapted to the order pressure warning task, parsing the number of branches, the network layer structure of each branch, and the time-series sequence length parameters within the configuration file, and extracting the complete structural definition of the multi-branch time-series classification network. The weight initialization operation can divide the operational rule data into subsets of ticketing rules, refund rules, rescheduling rules, and appeal rules, inputting each subset into the corresponding network branches. Using the Xavier uniform initialization algorithm, an initial weight matrix is ​​generated according to the numerical distribution range of each rule field and assigned to the neurons in each layer of the network. The definition of the order pressure classification loss function can be based on the binary classification requirement of the order pressure warning task, constructing a cross-entropy loss function. The function input is set as the order pressure probability vector and the true order pressure label vector output by the network, and the output is the prediction deviation value, clearly defining the calculation logic and parameter range of the loss function. The matching operation filters optimizers suitable for the temporal classification task by traversing the optimizer algorithm library, prioritizing the matching of the Adam optimizer, and traversing the learning rate scheduling strategy library to match the cosine annealing learning rate scheduling strategy. It establishes the binding relationship between the optimizer, the learning rate scheduling strategy, the initial network model, and the single classification loss function, thus completing the parameter matching configuration.

[0068] The pre-training of the single-order analysis model is completed by using a multi-round iteration method, including forward propagation of the training subset, loss value calculation, reverse parameter update, validation subset verification, and early stopping mechanism control.

[0069] The calculation results can be the predicted probability or classification result of the backlog risk of the training subset samples output by the initial network model. For example, the probability of backlog risk for a sample of refunded tickets is 0.78, and the probability of no backlog risk is 0.22. The backlog classification loss value can be a numerical value that quantifies the deviation between the network prediction result and the true label of the training subset, such as the error value of 0.19 calculated by the cross-entropy loss function, used to reflect the accuracy of the model prediction. The preset early stopping mechanism can be an iteration termination rule that monitors the performance of the validation subset and avoids model overfitting. For example, a rule can be set to stop training if the validation set loss does not decrease for 4 consecutive rounds, or a rule can be set to terminate the iteration if the validation set accuracy does not improve for 3 consecutive rounds.

[0070] The time-series operational attribute data of ticketing, refunds, changes, and appeals for each sample in the training subset are imported into the corresponding branches of the initial network model in fixed batches to complete the targeted input of feature data. The forward propagation computation involves each branch of the initial network model sequentially performing convolution operations to extract local rule features, pooling operations to compress feature dimensions, and fully connected operations to fuse multi-branch features, outputting the order risk prediction result for each sample, thus completing the forward propagation computation process. The order risk classification loss value is calculated by calling the bound order risk classification loss function, substituting the prediction results output by the forward propagation and the actual order risk labels labeled in the training subset into the function formula, calculating the deviation for each sample, and then summing the results to obtain the overall order risk classification loss value. The backpropagation parameter update can utilize a matched optimizer to perform chain derivatives on the order risk classification loss value, solving for the gradients of the weight parameters of each layer of each branch of the initial network model, and backpropagating the parameter values ​​according to the update step size determined by the learning rate scheduling strategy, thus completing the iterative update of the model parameters. The training process iterates through multiple rounds, performing input, forward propagation, loss calculation, and backpropagation parameter update operations. After each iteration, a validation subset is input into the current model to complete the prediction and calculate the validation set loss and accuracy. The system matches the preset early stopping mechanism trigger conditions in real time. When the conditions are met, the iteration process is terminated, and the final pre-trained single-item analysis model is output.

[0071] In this embodiment of the invention, the step of inputting the training subset into the initial network model for forward propagation calculation, and calculating the single classification loss value based on the calculation result and the training subset, includes: The training subset is calculated using a pre-set forward relay algorithm within the initial network model to obtain calculation results, which include priority features, multi-business process time limit features, profit and loss features, abnormal state features, and platform service time features. A hierarchical weight matrix for the pending orders is constructed based on a preset priority ranking relationship. The priority features are then mapped into the hierarchical weight matrix to obtain the priority weight coefficients corresponding to the pending orders. The remaining time limit ratio of each business link is calculated based on the time limit characteristics of the multi-business links and the preset benchmark time limit of each business link. The remaining time limit ratio is corrected in combination with the platform service time characteristics to obtain the time delay coefficient of each business link. The coefficient with the largest value among the time delay coefficients is selected as the target coefficient of the order being pushed back. The target coefficient is multiplied by the priority weight coefficient to obtain the priority matching loss component. Based on the profit and loss characteristics and the preset ticketing loss threshold, calculate the profit deviation value and loss deviation value of the delayed order, and sum the squares of the profit deviation value and the loss deviation value to obtain the deviation loss component. The abnormal status characteristics and the preset abnormal feedback time limit are used to calculate the abnormal non-feedback ratio of the pending orders. The abnormal non-feedback ratio is then multiplied by the preset appeal success rate influence factor to obtain the abnormal loss component. The priority matching loss component, the deviation loss component, and the anomaly loss component are multiplied by the preset corresponding business global weights to obtain three weighted loss components. The weighted loss components are then added together to obtain the order classification loss value of the pre-trained order analysis model.

[0072] In detail, the feature calculation is completed and the calculation results containing multiple features are output by calling the forward propagation algorithm built into the initial network model, inputting the operational attribute data of the training subset, and extracting five types of core features in different dimensions.

[0073] The pre-defined forward propagation algorithm can be a forward data flow calculation algorithm adapted to multi-branch temporal classification networks and extracting features hierarchically according to operational rules. For example, it could include a forward propagation algorithm with a priority feature extraction layer, a time-limit feature fusion layer, and a multi-dimensional feature output layer. Priority features can be weighted numerical features generated based on the platform's operational rule priority configuration, such as the ranking weight feature in the refund / price increase rule where seat priority is higher than base fare, and base fare priority is higher than route priority. Time-limit features across multiple business processes can be time-limited numerical features related to the entire process of ticketing, refunds, changes, and appeals, such as standardized numerical features for normal ticketing time limits, refund feedback time limits, change response time limits, and time limits after appeal rejection. Profit / loss features can be numerical features related to the revenue and loss thresholds of ticketing, refunds, and changes, such as quantitative numerical features for ticketing profit range, refund / change loss range, and automatic ticketing loss threshold. Abnormal status features can be binary marker features related to abnormal events in ticketing, refunds, and changes, such as 0 / 1 markers for NO position anomalies, price change anomalies, and other anomalies. Platform service time characteristics can be standardized numerical characteristics of the platform's refund and change customer service time periods, such as the normalized numerical characteristics of the start time of ticketing platform services, the end time of refund platform services, and the time periods of change platform services.

[0074] First, by retrieving the core configuration file of the initial network model, the network layer structure, parameter dimensions of each branch, and feature mapping logic of the preset forward propagation algorithm within the file are parsed to complete the loading and activation of the algorithm module. Next, the priority configuration data, multi-business segment time limit data, profit / loss threshold data, abnormal state labeling data, and platform service time data of each sample in the training subset are mapped to the corresponding feature branches of the forward propagation algorithm. The algorithm performs weight normalization on the priority data, time-series encoding on the time limit data, threshold quantization on the profit / loss data, binarization mapping on the abnormal data, and time-segment standardization on the service time data. Finally, the feature vectors of each branch are fused through a fully connected layer to output structured numerical results corresponding to the priority features, multi-business segment time limit features, profit / loss features, abnormal state features, and platform service time features, generating complete calculation results.

[0075] The priority weight coefficient of the pending order is generated by extracting the priority of the platform operation rules, constructing a hierarchical weight matrix, and matching feature mapping values.

[0076] The preset priority ranking relationship can be a hierarchical rule of multi-dimensional operational attributes defined in the platform's operation rules. For example, the priority for ticketing rules is route > excluded routes > airline > product type; the priority for refund and surcharge rules is cabin class > base fare > route. The hierarchical weight matrix can be a two-dimensional numerical matrix constructed according to priority levels. Rows and columns correspond to different priority levels, and cells store the weight value for the corresponding priority range. For example, rows can be set as first-level priority and second-level priority, and columns as third-level priority and fourth-level priority weight matrices. The priority weight coefficient can be a single-order-specific weight value obtained after priority feature mapping, such as a weight value of 0.85 obtained after mapping a certain pending order.

[0077] The construction of the hierarchical weight matrix can be achieved by reading the priority ranking list of each business line in the platform's operation rules document, dividing the matrix into dimensions according to priority levels, determining the mapping relationship between the matrix's row and column dimensions and the hierarchy, setting the weight value range of matrix cells from 0 to 1, filling in the weight values ​​corresponding to different priority combinations, and generating the hierarchical weight matrix. The mapping operation can be performed by parsing the hierarchy identifier corresponding to the priority features of the order, traversing the hierarchical weight matrix to match the corresponding hierarchy cells, extracting the cell weight values, performing a weighted operation with the priority feature values, and outputting the priority weight coefficients.

[0078] Priority matching loss components are generated by calculating the ratio of remaining time limits for each business segment, adjusting the ratio based on platform service time characteristics, filtering the maximum delay coefficient, and multiplying it with the priority weight coefficient.

[0079] The preset baseline time limits for each business process can be the standard processing time limits set in the platform's operation rules for ticketing, refunds, changes, and appeals, such as the normal time limit for ticketing, the time limit for refund feedback, the time limit for change response, and the time limit for appeal feedback. The remaining time limit ratio can be the normalized ratio of the current remaining processing time for each business process to the corresponding baseline time limit, such as a ratio of 0.5 when there are 2 hours remaining for refunds and a baseline time limit of 4 hours. The time limit delay coefficient can be a value reflecting the degree of delay risk for each business process after adjustments based on platform service hours, such as a corrected value of 0.78 obtained by amplifying the remaining ratio of 0.6 during non-service hours. The target coefficient can be the risk indicator value with the largest value among the time limit delay coefficients for each business process, such as a value of 1.3 selected when the ticketing delay coefficient is 1.3, the refund coefficient is 1.1, and the change coefficient is 0.9. The priority matching loss component can be obtained by multiplying the target coefficient and the priority weight coefficient. It is a value used to measure the deviation between the priority and the delay risk matching. For example, the value of 1.105 is obtained by multiplying 1.3 and 0.85.

[0080] The calculation of the remaining time limit ratio can be achieved by iterating through the remaining time data of ticketing, refunding, rescheduling, and appeals in the time limit features of multiple business links, retrieving the preset benchmark time limit configuration table, and performing a floating-point division operation by dividing the remaining time by the benchmark time limit. The correction operation can be achieved by parsing the time period markers corresponding to the platform service time features, keeping the remaining time limit ratio unchanged within the service period, and multiplying the remaining time limit ratio outside the service period by a preset amplification factor (such as 1.2) to complete the numerical scaling, and outputting the time limit delay coefficient of each link. The selection operation can be achieved by iterating through the time limit delay coefficient values ​​of all business links, calling the maximum value filtering algorithm to complete the numerical comparison, and extracting the time limit delay coefficient with the largest value as the target coefficient. Finally, the floating-point multiplication operation interface is called to multiply the target coefficient by the priority weight coefficient corresponding to the delayed order, and output the final priority matching loss component.

[0081] The deviation loss component is generated by retrieving a preset ticketing loss threshold, calculating profit deviation and loss deviation based on profit and loss characteristics, and summing the squares of the two types of deviations.

[0082] The preset ticketing loss threshold can be the maximum acceptable loss value for automatic ticketing set by the platform's store level, including loss thresholds for individuals, flight segments, and specific airlines and cabin classes. The profit deviation value can be the difference between the actual profit of a delayed order and the platform's defined profit range for ticketing. The loss deviation value can be the difference between the actual loss amount of a delayed order and the preset ticketing loss threshold. The deviation loss component can be the combined deviation value obtained by adding the square of the profit deviation value and the square of the loss deviation value. For example, a profit deviation value of 20 and a loss deviation value of -30 correspond to a deviation loss component of 1300.

[0083] First, the actual profit and loss values ​​of the pending orders are extracted from the profit and loss characteristics. A preset invoicing loss threshold configuration file is retrieved. The profit deviation value is obtained by subtracting the upper or lower limit of the invoicing profit range from the actual profit, and the loss deviation value is obtained by subtracting the corresponding preset invoicing loss threshold from the actual loss. Next, the square of the profit deviation value is calculated using a power function, performing a power-law operation with an exponent of 2 on both the profit and loss deviation values ​​to obtain the squared profit deviation value. Finally, a floating-point addition calculation interface is called to add the squared profit deviation value and the squared loss deviation value, outputting the final deviation loss component.

[0084] An abnormal loss component is generated by analyzing abnormal state characteristics, retrieving preset abnormal feedback time limits to calculate the non-feedback ratio, matching the appeal success rate influencing factors and multiplying them.

[0085] The preset anomaly feedback time limit can be the standard feedback duration set in the platform's operation rules for NO positions, price changes, and other anomalies. For example, a 24-hour feedback time limit for NO / price change anomalies and a 12-hour feedback time limit for other anomalies. The anomaly non-feedback ratio can be the normalized ratio of the non-feedback time after an order anomaly occurs to the corresponding anomaly feedback time limit. For example, a ratio of 0.75 is obtained when the anomaly occurs for 18 hours and the feedback time limit is 24 hours. The preset appeal success rate impact factor can be the appeal difficulty weight value corresponding to different anomaly types. For example, an impact factor of 1.2 for NO / price change anomalies and 0.8 for other anomalies. The anomaly loss component can be a value that measures the risk of non-feedback anomalies by multiplying the anomaly non-feedback ratio by the impact factor. For example, a value of 0.9 is obtained by multiplying 0.75 by 1.2.

[0086] First, the system parses the anomaly type identifier and anomaly occurrence timestamp from the anomaly status characteristics. It then retrieves the preset anomaly feedback time limit for the corresponding anomaly type from the platform's rule base. The system subtracts the anomaly occurrence time from the current system time to obtain the non-feedback duration. Finally, it performs a floating-point division operation by dividing the non-feedback duration by the feedback time limit to generate the anomaly non-feedback ratio. Next, it retrieves the preset appeal success rate impact factor mapping table, matches the impact factor value corresponding to the anomaly type, and calls the floating-point multiplication operation interface to multiply the anomaly non-feedback ratio by the matched impact factor value, outputting the final anomaly loss component.

[0087] The order classification loss value is synthesized by retrieving the business global weights corresponding to the three types of loss components, performing weighted calculations on a component-by-component basis, and summarizing the weighted results.

[0088] The preset global weights for the corresponding business operations can be the global importance weight values ​​set by the platform for the three risk dimensions of priority matching, profit deviation, and abnormal feedback. For example, the global weight for priority matching is 0.4, the global weight for deviation is 0.35, and the global weight for abnormal feedback is 0.25. The three weighted loss components can be the corrected loss values ​​obtained by multiplying the three original loss components by their corresponding global weights.

[0089] The multiplication operation retrieves the platform's global weight configuration file, matches the corresponding global weight values ​​according to the type identifiers of the three loss components: priority matching, deviation, and anomaly. It then calls a floating-point multiplication interface to precisely multiply each original loss component with the matched global weight, generating three weighted loss components. The addition operation calls a floating-point addition interface to sequentially accumulate the values ​​of the priority matching weighted loss component, the deviation weighted loss component, and the anomaly weighted loss component, outputting the order classification loss value of the pre-trained order analysis model.

[0090] In this embodiment of the invention, updating the parameters of the initial network model through backpropagation based on the single-classification loss value includes: Using the order classification loss value of the pre-trained order analysis model, the gradient information of each network layer of the order analysis model is calculated, wherein the gradient information includes the gradient update value and the gradient update direction; Based on the priority weight coefficient corresponding to the order, the learning rate of the gradient information is dynamically adjusted to obtain the adjusted gradient parameters; The basic network parameters of the initial network model are updated according to the gradient update direction corresponding to the gradient update value in the adjusted gradient parameters.

[0091] In detail, by calling the backpropagation chain derivative algorithm, substituting the single classification loss value, and solving the parameter gradient layer by layer, gradient information containing the gradient update value and gradient update direction is obtained.

[0092] The gradient update value can be the quantized magnitude of the adjustment required for the weights or bias parameters of each network layer in the model. For example, the gradient update value for the weights of the convolutional layer in the ticketing rules branch is 0.03, and the gradient update value for the bias parameters of the fully connected layer in the appeal rules branch is 0.008. The gradient update direction can be an indicator of the positive or negative trend of the parameter adjustment in each network layer of the model. A positive sign indicates that the parameter needs to be increased, and a negative sign indicates that the parameter needs to be decreased. For example, the gradient update direction for the weights of the pooling layer in the refund rules branch is negative, and the gradient update direction for the weights of the fully connected layer in the rescheduling rules branch is positive.

[0093] The gradient information of the network layers can be calculated by passing the single classification loss value into the model backpropagation calculation module, traversing the network layers in reverse order from the output layer to the input layer, performing chain-like partial derivative calculation on the weight parameters and bias parameters of each layer, determining the absolute value of the partial derivative as the gradient update value of the corresponding parameter, and determining the positive or negative sign of the partial derivative as the gradient update direction of the corresponding parameter, and summing up the gradient update values ​​and gradient update directions of all network layer parameters to generate complete gradient information.

[0094] The adjusted gradient parameters are generated by retrieving the priority weight coefficient of the pending orders, scaling the base learning rate, and correcting the gradient values ​​parameter by parameter.

[0095] The adjusted gradient parameters can be the combination of gradient update values ​​and directions corresponding to the parameters of each network layer of the model after adapting the priority weight coefficient and dynamic learning rate. For example, when the priority weight coefficient is 0.8 and the base learning rate is 0.01, the gradient update value of the branch is corrected from 0.05 to 0.004, while the direction remains unchanged.

[0096] First, the system retrieves the data storage node for the priority weight coefficients of the current order, parses the floating-point values ​​for precise reading. Next, it uses a scaling multiplication interface to multiply the preset base learning rate by the priority weight coefficients, calculating the dynamic learning rate adapted to the current order. Then, it corrects the parameter entries for each network layer in the gradient information, multiplying the gradient update value of each parameter by the dynamic learning rate, while maintaining the original positive / negative sign of the gradient update direction, thus completing the single-parameter gradient information correction. Finally, it summarizes the corrected gradient update values ​​and directions for all network layers, encapsulates them into a standardized parameter structure, and outputs the final adjusted gradient parameters.

[0097] The process of updating the basic network parameters of the initial network model involves loading the adjusted gradient parameters from memory, parsing the gradient update values ​​and direction identifiers for each network branch related to ticketing, refunds, rescheduling, and appeals, and establishing an index mapping between network layer IDs, parameter types (weights / biases), and gradient information. Next, the basic network parameter storage area of ​​the initial network model is traversed, and the basic network parameters to be updated in each layer are precisely matched according to the index mapping. Then, for parameters with positive gradient update directions, a floating-point addition operation is called to add the basic network parameter value to the corresponding gradient update value; for parameters with negative gradient update directions, a floating-point subtraction operation is called to subtract the basic network parameter value from the corresponding gradient update value, generating new parameter values. Finally, the generated new parameter values ​​are written back to the parameter storage location of the corresponding network layer in the initial network model, performing a parameter persistence overwrite operation to replace the original basic network parameters with the new parameter values, completing the parameter update.

[0098] In detail, the operation of selecting order data that meets the order-pressing strategy from the order data to be processed using the pre-trained order-pressing analysis model is completed in the following way.

[0099] For each order record in the pending order data, features (including platform identifier, store identifier, business line, policy type, airline, route, cabin class, departure time, and remaining ticketing time) are extracted one by one according to the model input format, and these features are concatenated into an input vector. This input vector is then fed into a pre-trained order pressure analysis model, which performs forward propagation calculations (passing sequentially through the input layer, multiple fully connected layers and activation functions in the shared underlying network, fully connected layers and activation functions in the three task layers, and finally fused using weighted coefficients to obtain the target order pressure anomaly index). The target order pressure anomaly index value corresponding to the order is read from the model's output layer. The anomaly index threshold configured in the preset order pressure strategy is read from memory. A numerical comparison operation is performed to determine whether the target order pressure anomaly index is greater than or equal to the anomaly index threshold. If 0.85 ≥ 0.6, the order is determined to meet the order pressure strategy. For all orders that meet the order pressure strategy, the system copies the complete data of each order record from the original pending order data table and appends it one by one to a data structure named "Order Pressure Queue". After traversing and filtering all pending order data, the final pending order is the total number of order records stored in the pending order queue.

[0100] For example, in the scenario of placing a deposit on international flights, abnormal feature parameters (such as historical price reduction range and number of days until departure) of the flight price reduction prediction information are extracted, and the predicted success rate of placing a deposit is calculated to be 12%. This is compared with the preset 10% threshold in the deposit strategy, and the result is that it exceeds the threshold. The associated "allow deposit" locking rule is selected, the order ticketing permission is extracted and marked as "temporarily locked", the locking mark is written into the order lock status field, a deposit order is generated, and the automatic ticketing is triggered by the price reduction window.

[0101] S5. Select the purchase price comparison configuration that matches the order pressure parameters in the preset price comparison rule library, and use the purchase price comparison configuration to filter the purchase location of the order pressure to generate a candidate purchase location list.

[0102] In this embodiment of the invention, the preset price comparison rule base typically refers to a data set that pre-stores price comparison logic between different procurement channels (such as multiple suppliers or different policy groups). This set includes matching conditions such as the platform, store, business line, airline, route, cabin class, and changeover threshold applicable to each rule. For example, when the absolute value of the price difference between two suppliers does not exceed a certain amount, the supplier with the lower price is directly used for ticketing without triggering a changeover operation. The procurement price comparison configuration typically refers to a set of specific price comparison parameters selected from the preset price comparison rule base and applicable to the current pending order. For example, the configuration matched according to the airline, route, and cabin class of the order may include a switch for "changeover threshold is a certain amount" and "whether changeover is allowed when downgrading cabin class or price" (e.g., allow changeover). In another example, the procurement price comparison configuration may also include additional conditions such as "tolerance range for price difference caused by exchange rate fluctuations" (e.g., fluctuations not exceeding 3%) and "prioritizing suppliers that provide free baggage allowance".

[0103] In detail, the pre-set price comparison rule base stores the price comparison priority, procurement channel threshold, cost verification standard, order attribute matching conditions, and risk control constraint parameters associated with the order lock status of all ticket categories. By parsing the structured data fields of the orders, the order status identifier, itinerary attributes, procurement requirements, cost constraints, and risk control level data of the pending orders are obtained. The attribute data of the pending orders are matched with the rule entries in the pre-set price comparison rule base according to priority, channel, cost, and risk control dimensions. Rule entries in the pre-set price comparison rule base that do not meet the requirements of the pending orders are removed, and rule entries that match perfectly are retained. From the matching rule entries, price comparison execution parameters, procurement channel screening conditions, price calculation logic, cost control standards, and order matching constraints are extracted and combined to form a procurement price comparison configuration adapted to the pending orders.

[0104] In this embodiment of the invention, the candidate procurement location list can be a set of procurement location entries that meet the requirements of ticket procurement compliance, cost optimization, and ticketing compatibility after the procurement location screening is completed for pending orders based on the procurement location screening conditions, procurement location permission constraints, procurement location cost adaptation standards, and procurement location ticketing compliance requirements in the procurement price comparison configuration.

[0105] In this embodiment of the invention, the step of using the procurement comparison configuration to filter the procurement locations of the delayed orders and generate a candidate procurement location list includes: The travel information of the delayed orders is analyzed to obtain a travel feature sequence. The passenger information of the delayed orders is statistically analyzed to obtain a passenger type weight distribution. Based on the trip feature sequence and the passenger type weight distribution, identify the business strategy tag corresponding to the delayed order; The business strategy tags are used to match the pre-bound matching rules of each procurement location in the procurement price comparison configuration, and procurement locations that do not meet the matching rules are filtered out to obtain a set of procurement locations that are suitable for the scenario. The historical average profit of each procurement location in the scenario-adapted procurement location set is sorted by the difference between the expected profit of the pending order and the procurement location. A preset number of procurement locations with the smallest difference are selected as the candidate procurement location list.

[0106] In detail, the structured features of the pending orders are extracted by breaking down the trip data and combined in order. The proportion of each category is calculated by statistically analyzing the passenger category data of the pending orders, thus completing the feature processing of the trip and passenger dimensions of the pending orders.

[0107] Trip information can be ticket-related data including departure and arrival points, trip type, trip date, transfer points, and cabin class contained in the pending order. Trip feature sequence can be an ordered combination of features formed after parsing the ticket-related trip information according to fixed dimensions. Passenger information can be ticket-related passenger data including passenger age, passenger identity type, number of passengers, and passenger nationality contained in the pending order. Passenger type weight distribution can be a weighted distribution formed by calculating the proportion of each category after statistically analyzing the ticket-related passenger information by type.

[0108] The itinerary information of delayed orders is decomposed by field splitting and dimension extraction. Feature data such as departure point, arrival point, itinerary type, itinerary date, transit node, and cabin class are extracted. The feature data are arranged in a preset dimension order to form an itinerary feature sequence. Passenger information of delayed orders is classified and statistically analyzed by classification counting and proportion calculation. Passenger age, passenger identity type, and number of passenger categories are counted. The proportion of each category to the total number of passengers is calculated. The combined proportion values ​​form the passenger type weight distribution.

[0109] Specifically, by comparing the features of the trip feature sequence and adapting the numerical values ​​of the passenger type weight distribution, the system matches the preset strategy label rules to determine the ticketing business strategy label corresponding to the delayed order.

[0110] Business strategy tags can be used to identify the product type, distribution rules, ticketing constraints, and profit calculation methods that are applicable to ticketing orders.

[0111] The feature data of departure point, arrival point, trip type and cabin class in the trip feature sequence are compared with the standard entries of the trip feature in the preset strategy label matching library at the field level by mapping the feature dimensions one by one. The proportion of each category in the passenger type weight distribution is compared with the passenger weight standard threshold in the preset strategy label matching library at the numerical level by using the weight threshold precise matching method. When the comparison result and the verification result meet the preset conditions at the same time, the corresponding ticketing business strategy label is retrieved from the preset strategy label matching library.

[0112] Furthermore, through two-way matching and verification between business strategy tags and the binding rules of each procurement location within the procurement price comparison configuration, procurement locations that do not meet the rule constraints are eliminated, forming a set of procurement locations suitable for ticketing order scenarios with locked status.

[0113] Matching rules can be pre-defined adaptation conditions, permission constraints, cost standards, and ticketing compliance requirements corresponding to ticketing business strategy tags, set for each procurement location in the procurement comparison configuration. The scenario-adapted procurement location set can be a combination of procurement location entries that meet the needs of ticketing order scenarios, procurement permissions, cost control, and ticketing requirements after being verified by business strategy tags and matching rules.

[0114] By mapping the business strategy tags item by item using the tag attributes, the classification identifiers and adaptation features of the tags are precisely matched at the field level with the matching rules pre-bound to each procurement location in the procurement price comparison configuration. Procurement locations that do not meet the constraints are removed by the rule non-compliance elimination method. All procurement locations that have passed the matching are combined by the standard item aggregation method to obtain the scenario-adapted procurement location set.

[0115] Furthermore, by calculating the difference between the historical average profit amount of each procurement location in the scenario-adapted procurement location set and the expected profit amount of the pending orders, the procurement locations are sorted from smallest to largest, and a specified number of procurement locations with the smallest difference are selected to form a candidate procurement location list.

[0116] Historical average profit can be the average profit generated by ticketing locations within a fixed historical period from completed ticketing orders. Expected profit can be the projected profit from locked-in ticketing orders, calculated based on business strategy, itinerary characteristics, passenger type, and procurement costs. Preset quantity can be a threshold number of candidate procurement locations pre-set during the ticketing procurement screening process.

[0117] By summing and averaging historical order profits, the total profit of each procurement location within a specified historical period is calculated in the statistical scenario-adapted procurement location set. This total profit is then divided by the total number of completed procurement orders within that period to obtain the historical average profit amount. The expected profit amount is calculated using business strategy tags and cost parameters, combined with order travel characteristics, passenger type weights, procurement costs, and the sales benchmark price. The absolute value difference is calculated by subtracting the historical average profit amount from the expected profit amount of the order backlog for each procurement location and taking the absolute value. All procurement locations are then sorted in ascending order of their difference values. Finally, a predetermined number of procurement locations with the smallest differences are selected from the sorted results and aggregated to form a candidate procurement location list.

[0118] Furthermore, structured data analysis based on itinerary and passenger dimensions ensures that business strategy tags match the true attributes of orders. For example, train ticket orders can be accurately matched with business strategy tags corresponding to child discounts and group discounts, eliminating strategy matching deviations. Two-way verification of business strategy tags and procurement location matching rules eliminates procurement locations that do not meet ticketing authority, cost control, and compliance requirements. For example, for performance ticket orders, procurement locations that do not meet group procurement qualifications are eliminated, effectively reducing compliance anomalies in ticket procurement. Procurement locations are filtered by sorting based on profit margins, narrowing the gap between actual and expected profits. For example, for bus ticket orders, the procurement location with the smallest profit margin is selected, stabilizing the revenue level of ticket order procurement. This also simplifies the procurement location screening process, shortens the decision-making time for pending orders, and improves the overall efficiency of ticket procurement across all categories.

[0119] S6. Obtain the settlement price feature value corresponding to each procurement location in the candidate procurement location list, determine the procurement location corresponding to the minimum settlement price feature value as the target invoicing procurement location, and generate the invoicing instruction for the pending order data according to the target invoicing procurement location.

[0120] In this embodiment of the invention, the settlement price characteristic value typically refers to a set of numerical attributes obtained from candidate procurement locations (such as different suppliers or policy groups) for comparing procurement costs. Examples include the face value of each procurement location, surcharges, platform handling fees, and the actual payment amount after currency conversion. In another example, the settlement price characteristic value may also include the refund / change service fee corresponding to that procurement location and the price difference tolerance within the conversion threshold.

[0121] In one embodiment, the process can be achieved by traversing the candidate procurement location list (e.g., the list includes procurement location A, procurement location B, and procurement location C), and for each procurement location entry in the list, calling the locally cached procurement location price database query interface using a preset procurement location identifier (e.g., supplier number "SUP_001"). The query involves passing in the procurement location identifier and matching parameters such as the airline, route, and cabin class of the pending order. The database then returns raw data fields for that procurement location, including the ticket price, additional taxes and fees, platform handling fees, applicable exchange rate, and refund / change service fees. These raw data fields are then extracted according to a preset settlement price feature value template (containing five dimensions: ticket price, taxes and fees, handling fees, exchange rate, and refund / change fees), forming a five-dimensional vector as the settlement price feature value for that procurement location. Finally, all settlement price feature values ​​for all procurement locations are stored in a new feature value array in the original list order, completing the retrieval operation.

[0122] In this embodiment of the invention, the minimum settlement price feature value typically refers to the set of feature values ​​with the smallest value selected from the settlement price feature values ​​of each procurement location in the candidate procurement location list according to a preset comparison benchmark. The target ticketing procurement location typically refers to the procurement location corresponding to the minimum settlement price feature value, and the ticketing channel of that procurement location will be used to complete the final ticketing operation.

[0123] In this embodiment of the invention, the ticketing instruction typically refers to a standardized command string automatically generated based on the target ticketing purchase location and information such as the airline, route, and cabin class of the order to be processed, used to trigger the external ticketing system to perform ticketing operations. For example, "PNR location code is ABC123, use the interface of purchase location A to perform ticketing operations, and the time limit for filling in the ticket number is 2 hours before departure."

[0124] In this embodiment of the invention, the step of generating the ticketing instruction for the pending order data based on the target ticketing purchase location includes: Identify the ticketing channel type corresponding to the target ticketing purchase location, and match the instruction template corresponding to the ticketing channel type from the preset instruction template library; Read the preset field mapping relationship in the instruction template, and extract the target ticketing element that matches the field mapping relationship from the order data to be processed; Based on the field mapping relationship, the target ticketing element is filled into the target placeholder of the instruction template to generate an instruction to be packaged. The instruction to be encapsulated is protocol-encapsulated to generate a ticketing instruction for the order data to be processed.

[0125] In detail, by retrieving the association mapping data between the target ticket purchase location and the ticketing channel type, and matching the template ownership identifier in the preset instruction template library, the instruction template that is suitable for the ticketing channel type corresponding to the target ticket purchase location is determined.

[0126] Ticketing channel types can be categorized based on ticketing supplier type, procurement authority scope, and business line attributes. The preset instruction template library can be a standardized collection of templates storing operation instructions specific to each ticketing channel across all ticketing categories. Instruction templates can be standardized operation instruction format text adapted to specific ticketing channels.

[0127] The identification operation involves traversing the pre-stored purchase location-ticketing channel type mapping data table within the ticketing system to extract the channel type field value corresponding to the target purchase location, thereby accurately locating the ticketing channel type. The matching operation involves character matching of the identified channel type field value with the channel type tags of each template in the preset instruction template library, filtering out instruction templates with matching tags, and completing the matching and retrieval of instruction templates.

[0128] Specifically, by reading the pre-set field association rules in the instruction template, the core ticketing information items that match the field association rules are filtered and extracted from the ticketing order data to be processed.

[0129] The pre-defined field mapping relationship can be the correspondence rule between the ticketing order data fields and the ticketing operation execution fields pre-set in the instruction template. The target ticketing element can be the core order information items required to complete the ticketing operation for all categories of tickets, such as the airline information, cabin class information, and sales date information required for air ticketing, and the scenic spot information, entry date information, and ticket purchaser information required for scenic spot ticketing.

[0130] The read operation parses the text content of the instruction template by calling the storage interface of the instruction template, and extracts the pre-configured field-corresponding association rule data. The extraction operation iterates through all fields of the order data to be processed, performs character comparison between the order field names and the order fields in the pre-defined field mapping relationship, filters out the order field data with matching comparison results, and integrates the filtered field data into the target ticketing elements adapted for the ticketing operation.

[0131] Furthermore, based on the preset field mapping relationship, the target ticketing elements are filled into the corresponding target placeholder positions in the instruction template to generate a standardized ticketing instruction that has not undergone encapsulation processing.

[0132] The target placeholder can be a pre-set reserved mark position in the instruction template for filling in various core elements of ticketing. The instruction to be encapsulated can be a ticketing operation instruction text adapted to the corresponding ticketing channel that has completed the filling of ticketing elements but has not yet undergone data encapsulation and transmission processing.

[0133] The filling operation matches the correspondence between the target ticketing element and the target placeholder by reading the field mapping relationship, and replaces the target placeholder mark in the instruction template with the actual data of the target ticketing element using a precise text character replacement method. The generation operation performs field integrity verification and format specification verification on the instruction text after character replacement, removes redundant whitespace characters and illegal characters in the text, and forms a packaged instruction that conforms to the execution standard of the ticketing channel.

[0134] Furthermore, according to the ticketing communication protocol standard adapted to the ticketing channel, the protocol format type corresponding to the instruction to be encapsulated is determined. At the beginning of the instruction to be encapsulated, a message header consisting of the protocol version number, the unique order identifier, and the instruction type identifier is concatenated. A JSON format conversion tool is used to convert the text content of the instruction to be encapsulated into key-value pair structure data that meets the protocol requirements. The MD5 algorithm is used to calculate the checksum of the entire data of the converted instruction to be encapsulated and concatenate it to the end of the instruction. The protocol encapsulation process of data format regularization and checksum concatenation is completed, and the ticketing instruction corresponding to the order data to be processed is generated.

[0135] In addition, the system automatically identifies and matches the ticketing channel type based on the target ticketing purchase location, accurately calls the instruction template based on the preset instruction template library, automatically extracts order ticketing elements and accurately fills placeholders through preset field mapping relationships, and generates ticketing instructions in a standardized manner by combining protocol encapsulation. The entire process of ticketing instructions is automated, standardized and normalized, from channel matching, element extraction, template filling to protocol encapsulation.

[0136] S7. Perform an order ticketing operation on the pending order data using the ticketing instruction to generate a ticketing result.

[0137] In this embodiment of the invention, the ticketing result can be standardized execution data generated after the order ticketing operation is executed according to the ticketing instruction. This data includes ticketing status, ticketing voucher information, settlement data, and compliance verification results. The ticketing result typically covers the status determination of whether the order ticketing is successful or unsuccessful, the valid ticketing voucher code, the settlement amount matching the ticketing product and operation rules, and the compliance verification conclusion of the ticketing process.

[0138] In detail, a rule-matching algorithm is used to retrieve ticketing instructions that match the order data to be processed, extracting parameters such as ticketing time limit, ticketing channel, compliance verification threshold, and profit range constraint parameters contained within the ticketing instructions. Field validation logic is used to perform integrity checks on the ticketing itinerary information, passenger information, sales channel information, and product matching information within the order data to be processed, removing missing, conflicting, or invalid fields that do not comply with operational rules. Based on the ticketing instruction parameters and the validated order data, a ticketing request is initiated to the designated ticketing channel through a supplier interface call mechanism. Ticketing operations such as class reduction, price reduction, and class change constraints within the ticketing operation rules are executed, including class locking, ticket voucher generation, and settlement amount calculation, simultaneously completing ticketing compliance and profit threshold verification. The status data after ticketing execution, ticket voucher code data, settlement amount data, and compliance verification result data are structured and integrated to generate a ticketing result containing ticketing status, ticket voucher, settlement data, and verification conclusion.

[0139] In this embodiment of the invention, after executing the order ticketing operation on the order data to be processed through the ticketing instruction and generating the ticketing result, the method further includes: The ticket issuance result is decomposed into feature quantization to obtain the execution feature vector, profit and loss feature vector, and constraint feature vector; The execution feature vector, the profit and loss feature vector, and the constraint feature vector are compared with the benchmark parameter vector in the preset product full-domain sales rule library to perform deviation quantification calculation and obtain the rule deviation value. The rule parameters in the global sales rule base are updated using the rule deviation value.

[0140] In detail, the multi-dimensional data in the ticketing results are split into dimensions and numerically processed through feature quantization transformation to form execution feature vector, profit and loss feature vector and constraint feature vector.

[0141] The execution feature vector can be numerical feature data of the ticketing operation process execution status, operation time, interface call results, and ticket voucher generation status. The profit and loss feature vector can be quantitative feature data of the ticketing settlement amount, rebate income, ticketing cost, and profit generated by the ticketing operation. The constraint feature vector can be standardized feature data of the operational rule thresholds, product matching constraints, channel permission restrictions, and compliance verification results followed by the ticketing operation.

[0142] A multi-dimensional feature extraction model iterates through all data fields in the ticketing results, classifying and grouping the data fields according to three dimensions: execution process, profit and loss calculation, and rule constraints. Numerical mapping rules are then used to convert the classified data fields into standardized values. A vector concatenation algorithm is then used to combine the standardized values ​​of the execution process category into an execution feature vector, the standardized values ​​of the profit and loss calculation category into a profit and loss feature vector, and the standardized values ​​of the rule constraints category into a constraint feature vector.

[0143] Specifically, the deviation quantization operation method is used to perform dimensional matching and numerical difference calculation on the execution feature vector, profit and loss feature vector, constraint feature vector and the benchmark parameter vector in the preset product full-domain sales rule library, and output the rule deviation value.

[0144] The pre-configured product-wide sales rule base is a standardized set of rule data that is pre-configured and centrally stored, covering product strategy parameters, ticketing operation constraints, price calculation standards, compliance verification rules, inventory control logic, and channel permission restrictions for all ticketing scenarios. This pre-configured product-wide sales rule base typically includes ticketing product matching rules, ticketing time limit thresholds, price reduction constraints, settlement amount calculation basis, and inventory update trigger conditions. It supports the entire process of rule invocation and dynamic updates for ticketing orders, product deployment, compliance verification, and data settlement. The benchmark parameter vector is a standardized numerical vector formed by integrating pre-set execution process standard indicators, profit and loss accounting standard indicators, and constraint rule standard indicators within the product-wide sales rule base. The rule deviation value is a quantitative result of the degree of deviation between the three types of feature vectors and the benchmark parameter vector in corresponding dimensions, used to reflect the magnitude of the difference between the ticketing execution data and the standard rule parameters.

[0145] The vector dimension alignment logic precisely aligns the data dimensions of the execution feature vector, profit and loss feature vector, and constraint feature vector with the corresponding data dimensions of the benchmark parameter vector. The Euclidean distance calculation model is used to perform difference calculation, square accumulation and square root operation on the aligned same-dimensional values. The calculation results are converted into standardized values ​​with a unified range through numerical normalization processing to form a regular deviation value that reflects the degree of data deviation.

[0146] Furthermore, by using rule deviation values ​​to drive targeted correction, the rule parameters within the global sales rule base are numerically adjusted to achieve dynamic updates of the rule parameters.

[0147] Rule parameters can be standardized numerical indicators from the overall sales rule base that control the ticketing process, calculate ticketing profits and losses, and constrain compliant operations, such as ticketing time limit thresholds, ticketing profit ranges, and the proportion of restrictions on downgrading and price reductions.

[0148] The deviation dimension matching logic establishes a precise correspondence between the rule deviation value and the rule parameter of the same dimension in the global sales rule library. A numerical compensation algorithm performs addition and subtraction operations on the corresponding rule parameter based on the rule deviation value to correct the value. A valid range verification logic determines whether the corrected rule parameter is within a preset compliant value range. An overwrite mechanism replaces the original rule parameter value in the global sales rule library with the compliant corrected value, thus updating the rule parameter value.

[0149] Furthermore, the ticketing results are decomposed into quantifiable features to generate execution feature vectors, profit and loss feature vectors, and constraint feature vectors. This enables the standardized quantification and transformation of ticketing execution data, profit and loss data, and constraint data. By quantifying the deviation between the three types of feature vectors and the benchmark parameter vector, rule deviation values ​​are generated. This accurately locates the numerical differences between the ticketing data and the sales rules. The rule deviation values ​​are used to complete the dynamic numerical update of rule parameters within the entire sales rule library, thus constructing a closed-loop system in which ticketing data drives the autonomous optimization of sales rules.

[0150] It can improve the processing accuracy of ticketing data and the flexibility of rule control. By decomposing discrete data such as ticketing status, settlement amount, and compliance verification results into standardized feature vectors through feature quantification, it can transform ticketing success status, ticketing settlement amount, and downgrade constraints into corresponding feature vectors. By quickly locking rule deviations through deviation quantification calculation, the rule parameters can be updated using the rule deviation value to dynamically adapt to changes in ticketing sales scenarios, ensuring the compliance of the ticketing process, the accuracy of profit and loss accounting, and improving the overall efficiency of ticketing product sales and ticketing operations across the entire domain.

[0151] As can be seen, the above solution integrates innovative features such as multi-dimensional rule configuration, priority control, automated ticketing, and anomaly calculation. It achieves unified adaptation of rules across multiple platforms, intelligent order management and accurate price comparison for ticketing, automates the entire ticketing process, dynamically optimizes the rule system, reduces manual intervention and parameter errors, and improves operational efficiency and risk control capabilities. It can solve the problem of low accuracy in ticketing, refunds, and anomaly calculation for all types of tickets.

[0152] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0153] In one embodiment, a ticketing order issuance control device 100 is provided, which corresponds one-to-one with the ticketing order issuance control method described in the above embodiments. For example... Figure 5As shown, the ticketing order issuance control device 100 includes a data acquisition module 101, a congestion rule extraction module 102, a congestion strategy matching module 103, a congestion order selection module 104, a purchase location filtering module 105, a ticket issuance instruction generation module 106, and an order issuance module 107. Detailed descriptions of each functional module are as follows: The data acquisition module 101 is used to acquire the operation rule data of the target product on multiple sales platforms, as well as the pending order data generated by the target product on the multiple sales platforms. The order pressure rule extraction module 102 is used to extract the order pressure rules corresponding to the multiple sales platforms from the operation rule data; The order pressure strategy matching module 103 is used to identify the order pressure parameters in the order data to be processed according to the order pressure rules, and to match the order pressure strategy corresponding to the order data to be processed from the preset operation rule library based on the order pressure parameters. The order selection module 104 is used to select order data that conforms to the order-pressing strategy from the order data to be processed using a pre-trained order-pressing analysis model, so as to obtain the order-pressing order. The procurement location filtering module 105 is used to filter out procurement price comparison configurations that match the order pressure parameters in the pre-set price comparison rule library, and use the procurement price comparison configuration to filter the procurement location of the order pressure to generate a candidate procurement location list; The ticketing instruction generation module 106 is used to obtain the settlement price feature value corresponding to each procurement location in the candidate procurement location list, determine the procurement location corresponding to the minimum settlement price feature value as the target ticketing procurement location, and generate a ticketing instruction for the pending order data based on the target ticketing procurement location. The order ticketing module 107 is used to perform an order ticketing operation on the pending order data through the ticketing instruction, and generate a ticketing result.

[0154] In one embodiment, the order pressure strategy matching module 103, when executing the order pressure strategy corresponding to the order data to be processed, matched from the preset operation rule library based on the order pressure parameters, is used to: Extract abnormal data records that meet the preset abnormal threshold from the order data to be processed based on the order parameters; The abnormal data records are matched with the order-holding rules in the preset operation rule library, and a delay time window for the pending order data is generated according to the order-holding rules. Obtain the order conversion rate and expected revenue of the pending order data within the postponement time window, and calculate the priority weight of the postponement time window based on the order conversion rate and expected revenue. Extract the order fulfillment time point from the order data to be processed, align the priority weight with the order fulfillment time point on the time axis, and determine the start and end time points for postponing ticket issuance; The order-pressing strategy corresponding to the pending order data is generated based on the start time node and the end time node.

[0155] In one embodiment, the order selection module 104, during the training step of the pre-trained order analysis model, is used to: Extract various operational attributes from preset historical refund and modification orders, encode and convert each operational attribute to obtain an initial sample set; According to the preset priority configuration, weights are assigned to each operational attribute in the initial sample set, and the initial sample set after assignment is divided into training subsets and validation subsets. A multi-branch time-series classification network adapted to a preset order pressure warning task is obtained. The weights of the multi-branch time-series classification network are initialized based on the operation rule data to obtain an initial network model. The order pressure classification loss function of the initial network model is defined, and the optimizer and learning rate scheduling strategy corresponding to the order pressure classification loss function are matched. The training subset is input into the initial network model for forward propagation calculation. Based on the calculation results and the training subset, the order classification loss value is calculated. Based on the order classification loss value, the parameters of the initial network model are updated by backpropagation. The validation subset and the preset early stopping mechanism are combined to perform multiple rounds of iterative training to obtain the pre-trained order analysis model.

[0156] In one embodiment, the order selection module 104, when performing forward propagation calculation by inputting the training subset into the initial network model and calculating the order classification loss value based on the calculation result and the training subset, is further configured to: The training subset is calculated using a pre-set forward relay algorithm within the initial network model to obtain calculation results, which include priority features, multi-business process time limit features, profit and loss features, abnormal state features, and platform service time features. A hierarchical weight matrix for the pending orders is constructed based on a preset priority ranking relationship. The priority features are then mapped into the hierarchical weight matrix to obtain the priority weight coefficients corresponding to the pending orders. The remaining time limit ratio of each business link is calculated based on the time limit characteristics of the multi-business links and the preset benchmark time limit of each business link. The remaining time limit ratio is corrected in combination with the platform service time characteristics to obtain the time delay coefficient of each business link. The coefficient with the largest value among the time delay coefficients is selected as the target coefficient of the order being pushed back. The target coefficient is multiplied by the priority weight coefficient to obtain the priority matching loss component. Based on the profit and loss characteristics and the preset ticketing loss threshold, calculate the profit deviation value and loss deviation value of the delayed order, and sum the squares of the profit deviation value and the loss deviation value to obtain the deviation loss component. The abnormal status characteristics and the preset abnormal feedback time limit are used to calculate the abnormal non-feedback ratio of the pending orders. The abnormal non-feedback ratio is then multiplied by the preset appeal success rate influence factor to obtain the abnormal loss component. The priority matching loss component, the deviation loss component, and the anomaly loss component are multiplied by the preset corresponding business global weights to obtain three weighted loss components. The weighted loss components are then added together to obtain the order classification loss value of the pre-trained order analysis model.

[0157] In one embodiment, the order selection module 104, when performing backpropagation to update the parameters of the initial network model based on the order classification loss value, is further configured to: Using the order classification loss value of the pre-trained order analysis model, the gradient information of each network layer of the order analysis model is calculated, wherein the gradient information includes the gradient update value and the gradient update direction; Based on the priority weight coefficient corresponding to the order, the learning rate of the gradient information is dynamically adjusted to obtain the adjusted gradient parameters; The basic network parameters of the initial network model are updated according to the gradient update direction corresponding to the gradient update value in the adjusted gradient parameters.

[0158] In one embodiment, the ticketing instruction generation module 106, when executing a ticketing instruction to generate the pending order data based on the target ticketing purchase location, is used to: Identify the ticketing channel type corresponding to the target ticketing purchase location, and match the instruction template corresponding to the ticketing channel type from the preset instruction template library; Read the preset field mapping relationship in the instruction template, and extract the target ticketing element that matches the field mapping relationship from the order data to be processed; Based on the field mapping relationship, the target ticketing element is filled into the target placeholder of the instruction template to generate an instruction to be packaged. The instruction to be encapsulated is protocol-encapsulated to generate a ticketing instruction for the order data to be processed.

[0159] In one embodiment, the order ticketing module 107, after executing the order ticketing operation on the order data to be processed through the ticketing instruction and generating the ticketing result, is used for: The ticket issuance result is decomposed into feature quantization to obtain the execution feature vector, profit and loss feature vector, and constraint feature vector; The execution feature vector, the profit and loss feature vector, and the constraint feature vector are compared with the benchmark parameter vector in the preset product full-domain sales rule library to perform deviation quantification calculation and obtain the rule deviation value. The rule parameters in the global sales rule base are updated using the rule deviation value.

[0160] This invention provides a ticketing order issuance control device that integrates innovative features such as multi-dimensional rule configuration, priority control, automated ticketing, and anomaly calculation. It achieves unified adaptation of rules across multiple platforms, intelligent order management, and accurate price comparison for ticketing. It automates the entire ticketing process, dynamically optimizes the rule system, reduces manual intervention and parameter errors, and improves operational efficiency and risk control capabilities. It can solve the problem of low accuracy in ticketing, refunds, and anomaly calculation for all types of tickets.

[0161] Specific limitations regarding the ticket order issuance control device can be found in the limitations of the ticket order issuance control method described above, and will not be repeated here. Each module in the aforementioned ticket order issuance control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0162] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a ticket order issuance control method on the server side.

[0163] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a ticket order issuance control method on the client side.

[0164] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Obtain operational rule data of the target product on multiple sales platforms, as well as pending order data generated by the target product on the multiple sales platforms; Extract the order pressure rules corresponding to the multiple sales platforms from the operational rule data; The order-pressing parameters in the pending order data are identified according to the order-pressing rules, and the order-pressing strategy corresponding to the pending order data is matched from the preset operation rule library based on the order-pressing parameters. The pre-trained order pressure analysis model is used to select order data that conforms to the order pressure strategy from the order data to be processed, and thus obtain the order pressure orders; The system filters out purchase price comparison configurations that match the order pressure parameters in the pre-set price comparison rule library, and uses the purchase price comparison configurations to filter the purchase location of the order pressure, generating a candidate purchase location list. Obtain the settlement price feature value corresponding to each procurement location in the candidate procurement location list, determine the procurement location corresponding to the minimum settlement price feature value as the target invoicing procurement location, and generate an invoicing instruction for the pending order data based on the target invoicing procurement location. The ticketing instruction is used to execute an order ticketing operation on the pending order data to generate a ticketing result.

[0165] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Obtain operational rule data of the target product on multiple sales platforms, as well as pending order data generated by the target product on the multiple sales platforms; Extract the order pressure rules corresponding to the multiple sales platforms from the operational rule data; The order-pressing parameters in the pending order data are identified according to the order-pressing rules, and the order-pressing strategy corresponding to the pending order data is matched from the preset operation rule library based on the order-pressing parameters. The pre-trained order pressure analysis model is used to select order data that conforms to the order pressure strategy from the order data to be processed, and thus obtain the order pressure orders; The system filters out purchase price comparison configurations that match the order pressure parameters in the pre-set price comparison rule library, and uses the purchase price comparison configurations to filter the purchase location of the order pressure, generating a candidate purchase location list. Obtain the settlement price feature value corresponding to each procurement location in the candidate procurement location list, determine the procurement location corresponding to the minimum settlement price feature value as the target invoicing procurement location, and generate an invoicing instruction for the pending order data based on the target invoicing procurement location. The ticketing instruction is used to execute an order ticketing operation on the pending order data to generate a ticketing result.

[0166] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0167] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0168] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0169] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0170] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for controlling the issuance of ticketing orders, characterized in that, The method includes: Obtain operational rule data of the target product on multiple sales platforms, as well as pending order data generated by the target product on the multiple sales platforms; Extract the order pressure rules corresponding to the multiple sales platforms from the operational rule data; The order-pressing parameters in the pending order data are identified according to the order-pressing rules, and the order-pressing strategy corresponding to the pending order data is matched from the preset operation rule library based on the order-pressing parameters. The pre-trained order pressure analysis model is used to select order data that conforms to the order pressure strategy from the order data to be processed, and thus obtain the order pressure orders; The system filters out purchase price comparison configurations that match the order pressure parameters in the pre-set price comparison rule library, and uses the purchase price comparison configurations to filter the purchase location of the order pressure, generating a candidate purchase location list. Obtain the settlement price feature value corresponding to each procurement location in the candidate procurement location list, determine the procurement location corresponding to the minimum settlement price feature value as the target invoicing procurement location, and generate an invoicing instruction for the pending order data based on the target invoicing procurement location. The ticketing instruction is used to execute an order ticketing operation on the pending order data to generate a ticketing result.

2. The ticketing order issuance control method as described in claim 1, characterized in that, The step of matching the order-pressing strategy corresponding to the pending order data from the preset operation rule base based on the order-pressing parameters includes: Extract abnormal data records that meet the preset abnormal threshold from the order data to be processed based on the order parameters; The abnormal data records are matched with the order-holding rules in the preset operation rule library, and a delay time window for the pending order data is generated according to the order-holding rules. Obtain the order conversion rate and expected revenue of the pending order data within the postponement time window, and calculate the priority weight of the postponement time window based on the order conversion rate and expected revenue. Extract the order fulfillment time point from the order data to be processed, align the priority weight with the order fulfillment time point on the time axis, and determine the start and end time points for postponing ticket issuance; The order-pressing strategy corresponding to the pending order data is generated based on the start time node and the end time node.

3. The ticketing order issuance control method as described in claim 1, characterized in that, The training steps of the pre-trained single-item analysis model include: Extract various operational attributes from preset historical refund and modification orders, encode and convert each operational attribute to obtain an initial sample set; According to the preset priority configuration, each operational attribute in the initial sample set is weighted and assigned a value. The initial sample set after the weighting is then divided into training subsets and validation subsets. A multi-branch time-series classification network adapted to a preset order pressure warning task is obtained. The weights of the multi-branch time-series classification network are initialized based on the operation rule data to obtain an initial network model. The order pressure classification loss function of the initial network model is defined, and the optimizer and learning rate scheduling strategy corresponding to the order pressure classification loss function are matched. The training subset is input into the initial network model for forward propagation calculation. Based on the calculation results and the training subset, the order classification loss value is calculated. Based on the order classification loss value, the parameters of the initial network model are updated by backpropagation. The validation subset and the preset early stopping mechanism are combined to perform multiple rounds of iterative training to obtain the pre-trained order analysis model.

4. The ticketing order issuance control method as described in claim 3, characterized in that, The step of inputting the training subset into the initial network model for forward propagation calculation, and calculating the single classification loss value based on the calculation result and the training subset, includes: The training subset is calculated using a pre-set forward relay algorithm within the initial network model to obtain calculation results, which include priority features, multi-business process time limit features, profit and loss features, abnormal state features, and platform service time features. A hierarchical weight matrix for the pending orders is constructed based on a preset priority ranking relationship. The priority features are then mapped into the hierarchical weight matrix to obtain the priority weight coefficients corresponding to the pending orders. The remaining time limit ratio of each business link is calculated based on the time limit characteristics of the multi-business links and the preset benchmark time limit of each business link. The remaining time limit ratio is corrected in combination with the platform service time characteristics to obtain the time delay coefficient of each business link. The coefficient with the largest value among the time delay coefficients is selected as the target coefficient of the order being pushed back. The target coefficient is multiplied by the priority weight coefficient to obtain the priority matching loss component. Based on the profit and loss characteristics and the preset ticketing loss threshold, calculate the profit deviation value and loss deviation value of the delayed order, and sum the squares of the profit deviation value and the loss deviation value to obtain the deviation loss component. The abnormal status characteristics and the preset abnormal feedback time limit are used to calculate the abnormal non-feedback ratio of the pending orders. The abnormal non-feedback ratio is then multiplied by the preset appeal success rate influence factor to obtain the abnormal loss component. The priority matching loss component, the deviation loss component, and the anomaly loss component are multiplied by the preset corresponding business global weights to obtain three weighted loss components. The weighted loss components are then added together to obtain the order classification loss value of the pre-trained order analysis model.

5. The ticketing order issuance control method as described in claim 3, characterized in that, The backpropagation update of the parameters of the initial network model based on the single classification loss value includes: Using the order classification loss value of the pre-trained order analysis model, the gradient information of each network layer of the order analysis model is calculated, wherein the gradient information includes the gradient update value and the gradient update direction; Based on the priority weight coefficient corresponding to the order, the learning rate of the gradient information is dynamically adjusted to obtain the adjusted gradient parameters; The basic network parameters of the initial network model are updated according to the gradient update direction corresponding to the gradient update value in the adjusted gradient parameters.

6. The ticketing order issuance control method as described in claim 1, characterized in that, The ticketing instruction that generates the pending order data based on the target ticketing purchase location includes: Identify the ticketing channel type corresponding to the target ticketing purchase location, and match the instruction template corresponding to the ticketing channel type from the preset instruction template library; Read the preset field mapping relationship in the instruction template, and extract the target ticketing element that matches the field mapping relationship from the order data to be processed; Based on the field mapping relationship, the target ticketing element is filled into the target placeholder of the instruction template to generate an instruction to be packaged. The instruction to be encapsulated is protocol-encapsulated to generate a ticketing instruction for the order data to be processed.

7. The ticketing order issuance control method as described in claim 1, characterized in that, After executing the order ticketing operation on the order data to be processed through the ticketing instruction and generating the ticketing result, the method further includes: The ticket issuance result is decomposed into feature quantization to obtain the execution feature vector, profit and loss feature vector, and constraint feature vector; The execution feature vector, the profit and loss feature vector, and the constraint feature vector are compared with the benchmark parameter vector in the preset product full-domain sales rule library to perform deviation quantification calculation and obtain the rule deviation value. The rule parameters in the global sales rule base are updated using the rule deviation value.

8. A ticketing order issuance control device, characterized in that, The device includes: The data acquisition module is used to acquire the operational rule data of the target product on multiple sales platforms, as well as the pending order data generated by the target product on the multiple sales platforms. The order pressure rule extraction module is used to extract the order pressure rules corresponding to the multiple sales platforms from the operational rule data; The order pressure strategy matching module is used to identify the order pressure parameters in the order data to be processed according to the order pressure rules, and to match the order pressure strategy corresponding to the order data to be processed from the preset operation rule library based on the order pressure parameters. The order selection module is used to select order data that conforms to the order-pressing strategy from the order data to be processed using a pre-trained order-pressing analysis model, so as to obtain the order-pressing orders. The procurement location filtering module is used to filter out procurement price comparison configurations that match the order pressure parameters in the pre-set price comparison rule library, and use the procurement price comparison configuration to filter the procurement location of the order pressure to generate a candidate procurement location list; The ticketing instruction generation module is used to obtain the settlement price feature value corresponding to each procurement location in the candidate procurement location list, determine the procurement location corresponding to the minimum settlement price feature value as the target ticketing procurement location, and generate the ticketing instruction for the pending order data according to the target ticketing procurement location. The order ticketing module is used to perform order ticketing operations on the pending order data through the ticketing instruction, and generate ticketing results.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the ticketing order issuance control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the ticketing order issuance control method as described in any one of claims 1 to 7.

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