Dynamic data analysis method, device and equipment under multi-level rule mutual exclusion and medium

Abstract

This invention relates to the field of data analysis technology, and discloses a dynamic data analysis method, apparatus, device, and medium under multi-level rule mutual exclusion. The method includes: concatenating multiple data sets obtained in advance from a target merchant into first order information; classifying the data within the order information according to preset multiple feature value types to obtain order classification data; filtering the order classification data using a multi-level decision tree to obtain second order information; calculating a first feature value of the target order based on the second order information and preset rate rule text; if the first feature value is less than or equal to a preset feature threshold, using the first feature value as the order feature value of the target order; if the first feature value is greater than the feature threshold, calculating a second feature value of the target order based on the first order information and rate rules, and using the minimum value between the first and second feature values ​​as the order feature value of the target order. This invention can improve the accuracy of existing order feature value analysis.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a dynamic data analysis method, apparatus, device, and medium under multi-level mutually exclusive rules. Background Technology

[0002] Order feature values ​​are key parameters in ticketing distribution. For example, in airline ticketing, based on multi-dimensional feature parameters such as route, airline, ticketing date, flight date, fare type, and ticket status, combined with pre-configured billing rules and promotional strategies, a rule engine analyzes different order feature values ​​(such as service value-added fees, service profit values, and order month-on-month price changes) under specific business scenarios (such as invoicing, refunds, changes, cancelled tickets, ancillary services, and ancillary service cancellations). The analyzed order features are then used for business decision-making and optimization.

[0003] However, most existing order feature value analysis methods analyze order-related data based on decentralized rule configurations and single-dimensional sequential judgment logic to obtain order feature values. If the analysis involves cascading data modules such as independent time window matching, airline whitelist filtering, route rule verification, and business status determination, or if each judgment dimension is configured independently, existing methods can lead to difficulties in coordinating global rule conflicts. For example, the lack of a priority determination mechanism when promotional service fees and contract service fees coexist can easily result in double-counting or omissions. Therefore, existing analysis methods may lead to low accuracy in order feature value analysis. Summary of the Invention

[0004] This invention provides a dynamic data analysis method, apparatus, equipment, and medium under multi-level rule mutual exclusion, the main purpose of which is to solve the problem of low accuracy in existing order feature value analysis.

[0005] Firstly, to achieve the above objectives, the present invention provides a dynamic data analysis method under multi-level rule mutual exclusion, comprising: The order ticket number, customer information, transaction parameters, and status parameters of the target order obtained in advance from the target merchant are concatenated into the first order information; The data in the order information is classified according to a variety of preset feature value types to obtain order classification data corresponding to each feature value type; Obtain a multi-level decision tree pre-constructed based on the order issuance rules provided by the target merchant, and use the multi-level decision tree to filter the order classification data to obtain second order information that conforms to the order issuance rules; The first feature value of the target order is calculated based on the second order information and the preset rate rule text; If the first feature value is less than or equal to a preset feature threshold, then the first feature value is used as the order feature value of the target order; If the first feature value is greater than the feature threshold, then the second feature value of the target order is calculated based on the first order information and the rate rule, and the smaller value between the first feature value and the second feature value is taken as the order feature value of the target order.

[0006] Secondly, the present invention also provides a dynamic data analysis device under multi-level rule mutual exclusion, comprising: Data stitching module: stitches together the order ticket number, order customer information, order transaction parameters and order status parameters of the target order obtained in advance from the target merchant into the first order information; Data classification module: Classifies the data in the order information according to multiple preset feature value types, and obtains the order classification data corresponding to each feature value type; Data filtering module: Obtains a multi-level decision tree pre-constructed based on the order issuance rules provided by the target merchant, and uses the multi-level decision tree to filter the order classification data to obtain second order information that conforms to the order issuance rules; Feature value analysis module: Calculates the first feature value of the target order based on the second order information and the preset rate rule text. If the first feature value is less than or equal to the preset feature threshold, the first feature value is used as the order feature value of the target order. If the first feature value is greater than the feature threshold, the second feature value of the target order is calculated based on the first order information and the rate rule, and the minimum value between the first feature value and the second feature value is used as the order feature value of the target order.

[0007] Thirdly, the present invention also provides an electronic device, the electronic device comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the dynamic data analysis method under multi-level rule mutual exclusion described above.

[0008] Fourthly, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the dynamic data analysis method under multi-level rule mutual exclusion described above.

[0009] In this embodiment of the invention, on the one hand, a multi-level decision tree transforms the multi-level and complex ticketing rules of the target merchant into a hierarchical decision-making mechanism, solving the problem of complex and chaotic calculations of massive rules, avoiding chaotic calculations of conflicting rules, and improving the accuracy of the final order feature value analysis. On the other hand, through the calculation and analysis of dual feature values, the optimal analysis of the calculation results is achieved, avoiding situations where the order feature values ​​calculated under a single condition do not conform to the real-time scenario, further improving the accuracy of order feature value analysis. In summary, this method comprehensively improves the accuracy of existing order feature value analysis. Attached Figure Description

[0010] 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.

[0011] Figure 1 This is a schematic diagram of an application environment for a dynamic data analysis method under multi-level rule mutual exclusion in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a dynamic data analysis method under multi-level rule mutual exclusion provided in an embodiment of the present invention; Figure 3 A functional block diagram of a dynamic data analysis device under multi-level rule mutual exclusion provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements a dynamic data analysis method under multi-level rule mutual exclusion according to an embodiment of the present invention; Figure 5 This is another structural schematic diagram of an electronic device that implements a dynamic data analysis method under multi-level rule mutual exclusion, according to an embodiment of the present invention.

[0012] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. 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] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0015] It should be noted that the terms "first," "second," etc., used in this disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0016] This application provides a dynamic data analysis method under multi-level rule mutual exclusion. The execution subject of the dynamic data analysis method under multi-level rule mutual exclusion includes, but is not limited to, at least one of the electronic devices that can be configured to execute the device provided in this application, such as a server and a terminal. In other words, the dynamic data analysis method under multi-level rule mutual exclusion can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0017] The dynamic data analysis method under multi-level rule mutual exclusion of this invention can be applied to, for example, Figure 1In this application environment, 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 standalone server or a server cluster consisting of multiple servers. The invention will now be described in detail through specific embodiments.

[0018] Reference Figure 2 The diagram shown is a flowchart illustrating a dynamic data analysis method under multi-level rule mutual exclusion provided in an embodiment of the present invention. In this embodiment, the dynamic data analysis method under multi-level rule mutual exclusion includes: S1. Combine the order ticket number, order customer information, order transaction parameters, and order status parameters of the target order obtained in advance from the target merchant into the first order information.

[0019] In this embodiment of the invention, the target merchant can be any individual or group organization that can provide specific services to the outside world, such as a physical airline ticket seller or a B2B e-commerce merchant.

[0020] In detail, with the authorization of the target merchant, information such as the order ticket number and customer information (customer ID, etc.) corresponding to the target order can be obtained from the target merchant. For example, with the authorization of the ticket seller, data such as the order ticket number, order transaction parameters (order discount, contract discount, order price, etc.) and order status parameters (unpaid, paid, pending ticketing, ticketed, etc.) corresponding to the ticket seller's ticket order can be collected.

[0021] In this embodiment of the invention, each order has multiple types of order data, and each type of order data is also quite large. Therefore, in order to achieve quick processing of the order data in the future, the obtained order ticket number, order customer information, order transaction parameters and order status parameters can be concatenated to generate first order information that corresponds to the target order and contains the above-mentioned multiple types of parameters.

[0022] In this embodiment of the invention, the step of concatenating the order ticket number, order customer information, order transaction parameters, and order status parameters of the target order obtained in advance from the target merchant into the first order information includes: The order identifier is obtained by concatenating the order number and the order customer information using preset logical operators; Using the order identifier as the query key, the order transaction parameters and order status parameters corresponding to the order identifier are extracted from the preset target merchant database; Extract the field values ​​belonging to the time category from the order transaction parameters to obtain the time parameter set, and extract the field values ​​belonging to the business category from the order transaction parameters to obtain the business parameter set; The time parameter set, the business parameter set, the order ticket number, and the order customer information are concatenated to obtain the first order information.

[0023] In this embodiment of the invention, the preset logic symbols include characters such as "&& (AND)," "v (OR)," and "!" (NOT) that identify connection logic.

[0024] In detail, the target merchant database is a database that stores various orders and corresponding order information within the target merchant. When the order ticket number and the order customer information are connected into an order identifier using a preset logical operator, the order identifier can be used as a query key to extract the order transaction parameters and order status parameters corresponding to the order identifier from the target merchant database.

[0025] After obtaining the above types of parameters, the time parameter set, business parameter set, order ticket number and order customer information can be concatenated into the first order information using simple connection symbols (such as "-" and "&").

[0026] For example, in the air ticket business, the passenger's ticket order number and passenger's name / ID number are combined as a unique order identifier. Using the order identifier, time parameters such as ticketing date, international flight date, and query time can be retrieved, as well as order transaction parameters such as international / domestic route information, corresponding airline information, and fare type, and order status parameters such as ticket number (ticketing / refund / rescheduling). The data from different dimensions are then merged to form the first order information containing all order information, so as to enable quick processing of subsequent order data.

[0027] S2. Classify the data in the order information according to the preset multiple feature value types to obtain the order classification data corresponding to each feature value type.

[0028] In this embodiment of the invention, since the first order information contains multiple types of data related to the target order, in order to achieve accurate processing of different types of data, the data in the first order information can be classified according to multiple preset feature value types, thereby determining the data corresponding to each feature value type in the first order information, and improving the pertinence and accuracy of subsequent processing.

[0029] In detail, the feature value type can be determined according to the actual situation of the target order. For example, when the target order is a travel ticketing order (such as an air ticket order), the feature value type can include time parameter type (airplane departure time, check-in time, etc.), business parameter type (base price, extra baggage fee, discount coefficient, etc.) and status parameter type (unpaid, paid, pending ticketing, ticketed, etc.).

[0030] In this embodiment of the invention, classifying the data within the order information according to preset multiple feature value types to obtain order classification data corresponding to each feature value type includes: Retrieves regular expressions corresponding to various preset feature value types; The regular expression is used to traverse and match within the order information to obtain the feature data segment corresponding to each feature value type within the order information; Select any feature data segment as the target data segment, and extract the data within a preset length range before and after the target data segment in the order information; The extracted data is encapsulated together with the target data segment into order classification data of the corresponding feature value type of the target data segment, until the order classification data corresponding to each feature value type is obtained.

[0031] In detail, data segments corresponding to various feature value types can be obtained from the order information using pre-set regular expressions. For example, taking the precise time "year-month-day hour-minute-second" on a flight ticket as an example, its corresponding regular expression in String data format can be as follows: StringtimeRegex="^((([0-9]{3}[1-9]|[0-9]{2}[1-9][0-9]{1}|[0-9]{1}[1-9][0-9]{2}|[1-9][0 -9]{3})-(((0

[13578] |1

[02] )-(0[1-9]|

[12] [0-9]|3

[01] ))|((0

[469] |11)-(0[1-9]|

[12] [0-9]|30) )|(02-(0[1-9]|[1][0-9]|2[0-8]))))|((([0-9]{2})(0

[48] |

[2468]

[048] |

[13579]

[26] )|((0

[48] |[ 2468]

[048] |

[3579]

[26] )00))-02-29))\\s+([0-1]?[0-9]|2[0-3]):([0-5][0-9]):([0-5][0-9])$"; boolean flag = Pattern.matches(timeRegex, "year-month-day hour:minute:second").

[0032] The order information can be searched and matched using regular expressions of the form described above, ultimately yielding data segments corresponding to the time parameter type.

[0033] Furthermore, in certain special cases, the data extracted by the regular expression may not be complete. For example, when it is necessary to use a regular expression to extract the discount coefficient data field from the business parameter type in the order information, the discount coefficient data is not only contained in its numerical value itself, but also needs to be combined with the context and semantics to determine the actual coefficient value or the conditions under which the coefficient value is valid (e.g., when the first preset condition is met, the discount coefficient is 0.8, and when the second preset condition is met, the discount coefficient is 0.6; if only the data fields of "discount coefficient 0.8" and "discount coefficient 0.6" are extracted, it will lead to confusion in subsequent data analysis).

[0034] Therefore, after extracting the feature data segments corresponding to each feature value type using regular expressions, this embodiment of the invention also needs to extract the context of each feature data segment within the order information to obtain other data within a preset length range before and after it, thereby improving the accuracy of the order classification data corresponding to each feature value type in the final classification.

[0035] S3. Obtain a multi-level decision tree pre-constructed based on the order issuance rules provided by the target merchant, and use the multi-level decision tree to filter the order classification data to obtain second order information that conforms to the order issuance rules.

[0036] In this embodiment of the invention, in order to achieve targeted analysis of order classification data corresponding to multiple different feature value types and improve the accuracy of the analysis results, the first order information can be filtered by a pre-constructed multi-level decision tree to obtain the second order information that conforms to the order issuance rules.

[0037] In detail, the order issuance rules can be a variety of texts containing order issuance rules (such as texts that record various order issuance information such as order discount conditions, discount amount, and time interval for enjoying the discount) provided in advance by the target merchant.

[0038] Specifically, a multi-level decision tree can be constructed based on the content of the order issuance rules text mentioned above. Then, the constructed multi-level decision tree can be used to perform hierarchical filtering and analysis of the first order information, thereby improving the accuracy of data analysis.

[0039] In this embodiment of the invention, the construction steps of the multi-level decision tree include: Obtain the order processing rules of the target merchant; The regular expression is used to iterate and match within the order issuance rule, and the matching result is used as the matching data field corresponding to each feature value type within the order issuance rule. According to the feature value type of the regular expression corresponding to the matching data field, the matching data field is classified to obtain matching data fields corresponding to different feature value types; Using a preset conditional function, each matching data field with the same feature value type is converted into a decision node, and the decision nodes are then concatenated into a sub-decision tree; By cascading the sub-decision trees corresponding to all feature value types, a multi-level decision tree is obtained.

[0040] In detail, the step of using the regular expression to traverse and match within the order issuance rules is the same as the step of using the regular expression to traverse and match within the order information in S2, and will not be repeated here.

[0041] Specifically, the data results matched from the order issuance rules (i.e., the data fields corresponding to the regular expressions of various feature value types in the order issuance rules) can be used as the matching data fields corresponding to each feature value type in the order issuance rules. The matching data fields can be classified according to the feature value type of the regular expression corresponding to them, so that decision tree nodes for different feature value types can be constructed according to the classification results. This will enable the final decision tree to have stronger data analysis capabilities.

[0042] In this embodiment of the invention, the conditional function can be any function with conditional judgment function, such as the common IF function, or a composite function composed of the IF function and the AND function.

[0043] For example, the matching data field obtained by traversing and matching within the order issuance rules using regular expressions is: a data field representing the time range {June 1st to June 3rd}. If the order placement time of the target order is... Then, the matching field can be converted into a decision node for time interval judgment using the IF function: in, This refers to the data field representing the time range {June 1st to June 3rd}.

[0044] In this embodiment of the invention, matching data fields with the same feature value type are converted into decision nodes, and the converted decision nodes are spliced ​​together to form a sub-decision tree (which has the ability to analyze fields with specific feature value types). Then, all sub-decision trees are cascaded into a multi-level decision tree with multiple layers, so that each layer in the multi-level decision tree can perform targeted analysis on data with different feature value types, thereby improving the accuracy of data analysis.

[0045] In this embodiment of the invention, the step of using the multi-level decision tree to filter the order classification data to obtain second order information that conforms to the order issuance rules includes: Identify the hierarchical data type of each level within the multi-level decision tree; The hierarchical data types are sorted according to the top-down hierarchical order of the multi-level decision tree to obtain the sorted data types; Calculate the matching degree between each hierarchical data type and the feature value type corresponding to each order category data, and select the order category data corresponding to the feature value type with a matching degree greater than a preset threshold as preprocessing data; The preprocessed data is sorted according to the sorting data type to obtain sorted data; The multi-level decision tree is used to analyze the sorted data according to the data order within the sorted data to obtain the second order information that conforms to the order issuance rules.

[0046] In detail, the hierarchical data type of each level in the multi-level decision tree is the feature value type corresponding to the sub-decision tree to which the decision tree node of that level belongs when constructing the decision tree.

[0047] The data types of different levels in the multi-level decision tree can be sorted in a top-down order. This sorting can then be used to sort the order classification data that needs to be processed, ensuring that the sorted data matches the data order of the decision tree. This allows the multi-level decision tree to process the order classification data content in a targeted manner according to different levels.

[0048] Specifically, algorithms with similarity calculation functions, such as Euclidean distance algorithm and cosine distance algorithm, can be used to calculate the matching degree between each level data type and the feature value type corresponding to each order category data, and then determine which level of the multi-level decision tree the order category data belongs to through the matching degree.

[0049] For example, with a preset threshold of 0.9, the order classification data includes data A and data B. The matching degree between data A and layer X in the multi-level decision tree is calculated to be 0.98, and the matching degree between data A and layer Y in the multi-level decision tree is 0.65; the matching degree between data B and layer X in the multi-level decision tree is 0.73, and the matching degree between data B and layer Y in the multi-level decision tree is 0.92. Therefore, data A is selected as the preprocessed data for layer X in the multi-level decision tree, and data B is selected as the preprocessed data for layer Y in the multi-level decision tree.

[0050] In this embodiment of the invention, the second order information is the set of all data in the order category data that can pass through all nodes of the multi-level decision tree. For example, the order category data contains the data: "Purchased on June 1, 2026, the ticket purchaser Xiaoming is 8 years old". When the multi-level decision tree contains decision node one: the date range is from May 25, 2026 to June 3, 2026, and decision node two: the age range is from 1 to 10 years old; then through the decision tree filtering, the data fields of "June 1, 2026" and "8 years old" in the order category data pass through all nodes of the multi-level decision tree and are used as the second order information that conforms to the order issuance rules.

[0051] By sorting data and applying targeted processing with multi-level decision trees, the accuracy of data processing can be improved and erroneous judgments can be reduced.

[0052] S4. Calculate the first feature value of the target order based on the second order information and the preset rate rule text.

[0053] In this embodiment of the invention, the preset rate rule can be a data text pre-given by the target merchant, containing various fee calculation rules such as basic fee values, discount conditions, discount coefficients, and additional billing items.

[0054] In this embodiment of the invention, calculating the first feature value of the target order based on the second order information and the preset rate rule text includes: Convert the second order information into search terms; Based on the search terms, a search is performed within the rate rule text to generate a search path for the second order information; Select the calculation parameters on the retrieval path within the rate rule text, and calculate the first feature value of the target order based on the calculation parameters.

[0055] In detail, the second order information can be concatenated using preset logical connectors to obtain search terms, and then the search terms can be used to search within the rate rule text to obtain the search path corresponding to the search terms within the rate rule text.

[0056] Specifically, the retrieval path is the retrieval trajectory of rate rule text data. For example, if the second order information is a flight ticket from B to C purchased at time A, then the retrieval path is: base price of the flight ticket from B to C >> infrastructure and fuel costs of the flight ticket from B to C >> baggage fees of the flight ticket from B to C >> discount coefficient of the flight ticket from B to C at time A.

[0057] In this embodiment of the invention, through the above retrieval, all parameters related to the target order when the target merchant issues the target order can be obtained from the rate rule text. Then, based on the calculation parameters on the retrieval path, the order feature value (such as the price value of the target order) of the target order under the condition of the second order information can be calculated.

[0058] Due to unforeseen events in real-world application scenarios, the rules for order issuance may change in real time. Therefore, in order to process the second order information more accurately, this invention can also add a step of administrator confirmation or adjustment after obtaining the calculation parameters.

[0059] In this embodiment of the invention, after selecting the calculation parameters on the retrieval path within the rate rule text, the method further includes: The calculated parameters are sent to a preset administrator device, and the parameter correction content returned by the administrator device is obtained. The calculation parameters are numerically corrected according to the parameter correction content.

[0060] In detail, the administrator device can be the electronic device where the target merchant's human service is located. After obtaining the calculation parameters, the calculation parameters can be sent to the administrator through the electronic device, so that the administrator can confirm or make corrections according to the actual situation, thereby avoiding changes in order conditions under sudden events and making the accuracy of the final calculated order feature values ​​higher.

[0061] S5. Determine whether the first feature value is greater than the preset feature threshold.

[0062] In this embodiment of the invention, the feature threshold may be a target value given by the technical personnel or expert group of the target merchant based on experience. For example, when the order feature value is a value representing the service fee of the target order, the feature threshold may be the sum of the basic cost and labor cost of the service corresponding to the target order, or the sum of the basic cost of the service corresponding to the target order and the expected price of the order customer, or the highest / average market price of the service corresponding to the target order, etc.

[0063] If the first feature value is less than or equal to a preset feature threshold, then execute S6 and use the first feature value as the order feature value of the target order; In this embodiment of the invention, the calculated first feature value can be compared with the feature threshold to determine whether the current calculation result is reasonable.

[0064] For example, when the order feature value is a numerical value representing the service fee of the target order, and the feature threshold is the average market price of the service corresponding to the target order, if the first feature value is less than or equal to the preset feature threshold, it indicates that it is within a reasonable range, and the first feature value can be used as the order feature value of the target order.

[0065] Furthermore, to prevent the order feature value from deviating too much from the reasonable range, this solution can also set a difference adjustment mechanism. That is, when the order feature value is a value representing the service fee of the target order, and the feature threshold is the average market price of the service corresponding to the target order, if the first feature value is less than or equal to the preset feature threshold, and the difference between the first feature value and the feature threshold is greater than X, then the first feature value is adjusted to: feature threshold - X; and vice versa, to prevent the calculated order feature value from deviating too much from the reasonable expectation (i.e., the feature threshold).

[0066] If the first feature value is greater than the feature threshold, then S7 is executed: calculate the second feature value of the target order based on the first order information and the rate rule, and take the minimum value between the first feature value and the second feature value as the order feature value of the target order.

[0067] In this embodiment of the invention, the step of calculating the second feature value of the target order based on the first order information and the rate rule is the same as step S4, which calculates the first feature value of the target order based on the second order information and the preset rate rule, and will not be described again here.

[0068] In a practical application scenario of the present invention, when the order feature value is a value representing the service fee of the target order, the calculated first feature value and the second feature value can be compared, and the smaller of the two values ​​can be selected as the order feature value of the target order.

[0069] Alternatively, the calculated first and second feature values ​​can be sent to a preset backend administrator client to obtain selection instructions from the backend administrator, and the corresponding values ​​can be selected as the order feature values ​​of the target order according to the selection instructions from the backend administrator.

[0070] In this embodiment of the invention, on the one hand, a multi-level decision tree transforms the multi-level and complex ticketing rules of the target merchant into a hierarchical decision-making mechanism, solving the problem of complex and chaotic calculations of massive rules, avoiding chaotic calculations of conflicting rules, and improving the accuracy of the final order feature value analysis. On the other hand, through the calculation and analysis of dual feature values, the optimal analysis of the calculation results is achieved, avoiding situations where the order feature values ​​calculated under a single condition do not conform to the real-time scenario, further improving the accuracy of order feature value analysis. In summary, this method comprehensively improves the accuracy of existing order feature value analysis.

[0071] like Figure 3 The diagram shown is a functional block diagram of a dynamic data analysis device under multi-level rule mutual exclusion provided in an embodiment of the present invention.

[0072] In this embodiment of the disclosure, a dynamic data analysis device under multi-level rule mutual exclusion is provided, which corresponds one-to-one with the dynamic data analysis method under multi-level rule mutual exclusion described in the above embodiments. For example... Figure 3 As shown, the dynamic data analysis device 100 under multi-level rule mutual exclusion can be installed in an electronic device. According to its functions, the dynamic data analysis device 100 under multi-level rule mutual exclusion includes a data splicing module 101, a data classification module 102, a data filtering module 103, and a feature value analysis module 104. Detailed descriptions of each functional module are as follows: Data splicing module 101: splices the order ticket number, order customer information, order transaction parameters and order status parameters of the target order obtained in advance from the target merchant into the first order information; Data classification module 102: Classifies the data in the order information according to a variety of preset feature value types to obtain order classification data corresponding to each feature value type; Data filtering module 103: Obtains a multi-level decision tree pre-constructed based on the order issuance rules provided by the target merchant, and uses the multi-level decision tree to filter the order classification data to obtain second order information that conforms to the order issuance rules; Feature value analysis module 10: Calculates the first feature value of the target order based on the second order information and the preset rate rule text. If the first feature value is less than or equal to the preset feature threshold, the first feature value is used as the order feature value of the target order. If the first feature value is greater than the feature threshold, the second feature value of the target order is calculated based on the first order information and the rate rule, and the minimum value between the first feature value and the second feature value is used as the order feature value of the target order.

[0073] In this invention, the specific limitations of a dynamic data analysis device under multi-level rule mutual exclusion can be found in the above-described limitations of the dynamic data analysis method under multi-level rule mutual exclusion, and will not be repeated here. Each module in the aforementioned dynamic data analysis device under multi-level rule mutual exclusion 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 operations corresponding to each module.

[0074] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 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 stored 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 server-side functions or steps of a dynamic data analysis method under multi-level rule mutual exclusion.

[0075] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 5 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 stored 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 client-side functions or steps of a dynamic data analysis method under multi-level rule mutual exclusion.

[0076] 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: The order ticket number, customer information, transaction parameters, and status parameters of the target order obtained in advance from the target merchant are concatenated into the first order information; The data in the order information is classified according to a variety of preset feature value types to obtain order classification data corresponding to each feature value type; Obtain a multi-level decision tree pre-constructed based on the order issuance rules provided by the target merchant, and use the multi-level decision tree to filter the order classification data to obtain second order information that conforms to the order issuance rules; The first feature value of the target order is calculated based on the second order information and the preset rate rule text; If the first feature value is less than or equal to a preset feature threshold, then the first feature value is used as the order feature value of the target order; If the first feature value is greater than the feature threshold, then the second feature value of the target order is calculated based on the first order information and the rate rule, and the smaller value between the first feature value and the second feature value is taken as the order feature value of the target order.

[0077] In the several embodiments provided by this invention, it should be understood that the disclosed devices and apparatuses can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0078] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0079] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0080] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.

[0081] The readable storage medium of the present invention stores a computer program, which, when executed by a processor of an electronic device, can perform the following: The order ticket number, customer information, transaction parameters, and status parameters of the target order obtained in advance from the target merchant are concatenated into the first order information; The data in the order information is classified according to a variety of preset feature value types to obtain order classification data corresponding to each feature value type; Obtain a multi-level decision tree pre-constructed based on the order issuance rules provided by the target merchant, and use the multi-level decision tree to filter the order classification data to obtain second order information that conforms to the order issuance rules; The first feature value of the target order is calculated based on the second order information and the preset rate rule text; If the first feature value is less than or equal to a preset feature threshold, then the first feature value is used as the order feature value of the target order; If the first feature value is greater than the feature threshold, then the second feature value of the target order is calculated based on the first order information and the rate rule, and the smaller value between the first feature value and the second feature value is taken as the order feature value of the target order.

[0082] 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.

[0083] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0084] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).

[0085] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0086] Those skilled in the art will understand that all or part of the processes in 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, and when executed, it can include the processes of the embodiments of the above methods. Furthermore, 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.

[0087] 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.

[0088] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0089] 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.

[0090] 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.

Claims

1. A method for dynamic data analysis under multi-level rule exclusion, characterized in that, The method comprises: splicing order ticket number, order customer information, order transaction parameters and order state parameters of a target order previously obtained from a target merchant into first order information; classifying data in the order information according to a plurality of preset characteristic value types to obtain order classification data corresponding to each characteristic value type; obtaining a multi-level decision tree previously constructed according to an order issuing rule provided by the target merchant, and filtering the order classification data by using the multi-level decision tree to obtain second order information meeting the order issuing rule; calculating a first characteristic value of the target order based on the second order information and a preset rate rule text; if the first characteristic value is less than or equal to a preset characteristic threshold, taking the first characteristic value as an order characteristic value of the target order; if the first characteristic value is greater than the characteristic threshold, calculating a second characteristic value of the target order based on the first order information and the rate rule, and taking the minimum value between the first characteristic value and the second characteristic value as the order characteristic value of the target order.

2. The method of claim 1, wherein the dynamic data analysis under multi-level rule exclusion is characterized by, The splicing of the order ticket number, the order customer information, the order transaction parameters and the order state parameters of the target order previously obtained from the target merchant into the first order information comprises: character connection of the order ticket number and the order customer information by using a preset logic symbol to obtain an order identifier; extraction of order transaction parameters and order state parameters corresponding to the order identifier from a preset target merchant database by taking the order identifier as a query key; extraction of field values belonging to a time category in the order transaction parameters to obtain a time parameter set, and extraction of field values belonging to a business category in the order transaction parameters as a business parameter set; data splicing of the time parameter set, the business parameter set, the order ticket number and the order customer information to obtain the first order information.

3. The method of claim 1, wherein the dynamic data analysis under multi-level rule exclusion is characterized by, The classification of data in the order information according to a plurality of preset characteristic value types to obtain order classification data corresponding to each characteristic value type comprises: obtaining regular expressions corresponding to the plurality of preset characteristic value types; iterative matching in the order information by using the regular expressions to obtain characteristic data segments corresponding to each characteristic value type; target data segment by target data segment, data within a preset length range before and after the target data segment in the order information is intercepted; encapsulation of the intercepted data and the target data segment as order classification data of the characteristic value type corresponding to the target data segment until order classification data corresponding to each characteristic value type is obtained.

4. The method of claim 3, wherein the plurality of rules are dynamically analyzed under the dynamic data analysis method in the multi-level rule exclusion. The construction steps of the multi-level decision tree comprise: obtaining an order issuing rule of the target merchant; iterative matching in the order issuing rule by using the regular expressions, and taking matching results as matching data fields corresponding to each characteristic value type in the order issuing rule; classification of the matching data fields according to characteristic value types of the regular expressions corresponding to the matching data fields to obtain matching data fields corresponding to different characteristic value types; convert each matching data field with the same characteristic value type into a decision node by using a preset condition function, and splice the decision nodes into a sub-decision tree; concatenate the sub-decision trees corresponding to all characteristic value types to obtain a multi-level decision tree.

5. The method of claim 4, wherein the dynamic data analysis under multi-level rule exclusion is characterized by, The filtering of the order classification data by using the multi-level decision tree to obtain second order information meeting the order issuing rule includes: identifying the hierarchical data types of each level in the multi-level decision tree; sorting the hierarchical data types in a top-down order of the multi-level decision tree to obtain sorted data types; calculating the matching degree between each hierarchical data type and the characteristic value type corresponding to each order classification data, and selecting the order classification data corresponding to the characteristic value type with a matching degree greater than a preset threshold as preprocessed data; sorting the preprocessed data according to the sorted data types to obtain sorted data; using the multi-level decision tree to analyze the sorted data according to the data order in the sorted data to obtain second order information meeting the order issuing rule.

6. The method of claim 1, wherein the dynamic data analysis under multi-level rule exclusion is characterized by, The calculation of the first characteristic value of the target order based on the second order information and a preset rate rule text includes: converting the second order information into a search term; searching in the rate rule text according to the search term to generate a search path of the second order information; selecting a calculation parameter on the search path in the rate rule text, and calculating the first characteristic value of the target order according to the calculation parameter.

7. The method of claim 6, wherein the dynamic data analysis under multi-level rule exclusion is characterized by, After selecting the calculation parameter on the search path in the rate rule text, the method further includes: sending the calculation parameter to a preset administrator device and obtaining parameter correction content returned by the administrator device; numerically correcting the calculation parameter according to the parameter correction content.

8. A dynamic data analysis device under multi-level rule exclusion, characterized in that, The device includes: a data splicing module that splices the order ticket number, order customer information, order transaction parameters and order state parameters of a target order previously obtained from a target merchant into first order information; a data classification module that classifies the data in the order information according to a plurality of preset characteristic value types to obtain order classification data corresponding to each characteristic value type; a data filtering module that obtains a multi-level decision tree previously constructed according to an order issuing rule provided by the target merchant, and filters the order classification data by using the multi-level decision tree to obtain second order information meeting the order issuing rule; a characteristic value analysis module that calculates a first characteristic value of the target order based on the second order information and a preset rate rule text, and if the first characteristic value is less than or equal to a preset characteristic threshold, takes the first characteristic value as the order characteristic value of the target order, and if the first characteristic value is greater than the characteristic threshold, calculates a second characteristic value of the target order based on the first order information and the rate rule, and takes the minimum value of the first characteristic value and the second characteristic value as the order characteristic value of the target order.

9. An electronic device, comprising: The electronic device includes: at least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the dynamic data analysis method under multi-level rule mutual exclusion 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 dynamic data analysis method under multi-level rule mutual exclusion as described in any one of claims 1 to 7.

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