Business processing method, system, apparatus, device, medium, and program product

Abstract

This invention provides a business processing method, system, apparatus, device, medium, and program product. The business processing method includes: acquiring multiple business data points for a target business item and determining a counting rule corresponding to the target business item. The counting rule is obtained based on the classification and configuration of sample data for the target business item, and includes multiple matching sub-rules and counting sub-rules. The method further includes: determining target business data that matches a target matching sub-rule, and determining a target data group corresponding to the target matching sub-rule based on the target business data. The target matching sub-rule can be any matching sub-rule. The method also includes: matching the target data group with the counting sub-rules, recording the number of matches, and updating the counting information corresponding to the target business item based on the number of matches. Finally, the method includes: generating business processing information based on the counting information and sending the business processing information to a business processing terminal. This achieves automatic counting of business items based on business data, reducing the burden of manual statistics.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a business processing method, system, apparatus, device, medium and program product. Background Technology

[0002] In today's highly competitive market environment, companies need to conduct accurate statistics and analysis of their business activities. As companies expand and their business becomes more complex, the requirements for data statistics are also increasing.

[0003] In current corporate practices, business data is still acquired and compiled manually. However, this is not only time-consuming and inefficient, but also prone to errors, especially when dealing with complex business projects, where the risk of data entry errors is even higher. Furthermore, statistics staff must spend considerable time and effort communicating and coordinating with various departments to ensure the accuracy of business data, further exacerbating their workload.

[0004] Therefore, there is an urgent need for a business processing method that reduces manual operations. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a business processing method. One or more embodiments of the present invention also relate to a business processing system, a business processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, to address the technical deficiencies existing in the prior art.

[0006] According to a first aspect of the present invention, a business processing method is provided, comprising:

[0007] Obtain multiple business data for the target business item and determine the counting rules corresponding to the target business item. The counting rules are obtained based on the classification and configuration of the sample data of the target business item, and include multiple matching sub-rules and counting sub-rules.

[0008] Determine the target business data that matches the target matching sub-rule, and determine the target data group corresponding to the target matching sub-rule based on the target business data, wherein the target matching sub-rule is any matching sub-rule;

[0009] Match the target data group with the counting sub-rules, record the number of matches, and update the counting information corresponding to the target business item based on the number of matches;

[0010] Business processing information is generated based on the counting information and then sent to the business processing terminal.

[0011] According to a second aspect of the present invention, a business processing system is provided, comprising:

[0012] The data end is used to send multiple business data for the target business item to the counting end;

[0013] The counting end is used to acquire multiple business data of the target business item and determine the counting rules corresponding to the target business item. The counting rules are obtained by classifying and configuring the sample data of the target business item. The counting rules include multiple matching sub-rules and counting sub-rules. For the target matching sub-rule, the target business data that matches the matching sub-rule is determined, and the target data group corresponding to the target matching sub-rule is determined based on the target business data. The target data group is determined to match the counting sub-rule, the number of matching is recorded, and the counting information corresponding to the target business item is updated. Based on the counting information, business processing information is generated and sent to the business processing end.

[0014] The business processing end is used to receive business processing information sent by the counting end and perform business processing based on the business processing information.

[0015] According to a third aspect of the present invention, a business processing apparatus is provided, comprising:

[0016] The acquisition module is configured to acquire multiple business data of the target business item and determine the counting rules corresponding to the target business item. The counting rules are obtained based on the classification configuration of the sample data of the target business item and include multiple matching sub-rules and counting sub-rules.

[0017] The determination module is configured to determine the target business data that matches the target matching sub-rule, and to determine the target data group corresponding to the target matching sub-rule based on the target business data, wherein the target matching sub-rule is any matching sub-rule;

[0018] The counting module is configured to match the target data group with the counting sub-rules, record the number of matches, and update the counting information corresponding to the target business item based on the number of matches.

[0019] The sending module is configured to generate business processing information based on the counting information and send the business processing information to the business processing terminal.

[0020] According to a fourth aspect of the present invention, a computing device is provided, comprising:

[0021] Memory and processor;

[0022] The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions, which, when executed by the processor, implement the steps of the above-described business processing method.

[0023] According to a fifth aspect of the present invention, a computer-readable storage medium is provided that stores a computer program / instructions, which, when executed by a processor, implement the steps of the above-described business processing method.

[0024] According to a sixth aspect of the present invention, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described business processing method.

[0025] One embodiment of the present invention implements the acquisition of multiple business data of a target business item and the determination of the corresponding counting rules for the target business item. The counting rules are obtained based on the classification and configuration of sample data of the target business item, and include multiple matching sub-rules and counting sub-rules. The system identifies target business data that matches a target matching sub-rule and determines a target data group corresponding to the target matching sub-rule based on the target business data. The target matching sub-rule can be any matching sub-rule. The system matches the target data group with the counting sub-rules, records the number of matches, and updates the counting information corresponding to the target business item based on the number of matches. Business processing information is generated based on the counting information and sent to the business processing terminal. By automating the acquisition and processing of business data and using pre-set counting rules to reduce manual operations, the system solves the problems of low efficiency and error-proneness in traditional manual data collection methods. Through the combined application of matching sub-rules and counting sub-rules, the system can automatically identify and process datasets that meet specific conditions, reducing the need for communication and coordination between the statistics department and various business departments, thereby reducing the workload of staff and improving the accuracy and timeliness of data statistics. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the structure of a business processing system provided in one embodiment of the present invention;

[0027] Figure 2 This is a flowchart of a business processing method provided in one embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of a business processing procedure provided in one embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of the user interface of a business processing system provided in one embodiment of the present invention;

[0030] Figure 5 This is a schematic diagram illustrating a business data matching and counting process provided in one embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram illustrating the principle of a counting rule provided in one embodiment of the present invention;

[0032] Figure 7 This is a schematic diagram of the user interface of another business processing system provided in one embodiment of the present invention;

[0033] Figure 8 This is a schematic diagram of the structure of a business processing device provided in one embodiment of the present invention;

[0034] Figure 9 This is a structural block diagram of a computing device provided in one embodiment of the present invention. Detailed Implementation

[0035] Numerous specific details are set forth in the following description to provide a full understanding of the invention. However, the invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0036] The terminology used in one or more embodiments of the present invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of the invention. The singular forms “a,” “the,” and “the” used in one or more embodiments of the invention and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” used in one or more embodiments of the invention refers to and includes any or all possible combinations of one or more associated listed items.

[0037] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of the present invention, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0038] Furthermore, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0039] TO: In this invention, it generally refers to transport order.

[0040] VIN: Vehicle Identification Number.

[0041] In the current context of globalization and digitalization, the logistics industry, as a vital component of the national economy, is undergoing unprecedented transformation. With the deepening development of supply chain management concepts, enterprises are increasingly emphasizing logistics efficiency and service quality, striving to enhance their competitiveness by optimizing logistics networks and improving information technology levels. As a crucial branch of logistics, whole-vehicle logistics plays a particularly critical role, directly impacting the efficiency of product flow between manufacturers and end consumers.

[0042] Faced with ever-increasing service demands and a complex operating environment, traditional logistics financial management methods are increasingly proving inadequate. This is particularly true for companies involved in large-scale full-truckload (FTL) deliveries, where monthly billing processes and supplier payment settlements often rely on manual management. This approach is not only inefficient but also prone to errors. Specifically, finance personnel need to meticulously verify various service items, fee standards, and settlement methods, collaborating with auditors from different business departments to confirm the consistency between payment items and contract terms. This tedious and time-consuming process increases the likelihood of errors and also adds to the workload of staff.

[0043] Taking a large automobile manufacturer as an example, its vehicle logistics business covers warehousing and transportation tasks across the country, involving warehouses in multiple provinces and various transportation methods. The company's logistics finance personnel need to process dozens of contracts and thousands of terms each month to ensure smooth payment for service projects with more than ten suppliers. In this situation, manually completing the receiving operations in the financial system becomes extremely complex, urgently requiring a more efficient and accurate method to replace the existing process, thereby improving overall operational efficiency.

[0044] To address the aforementioned problems, this invention provides a business processing method. This invention also relates to a business processing system, a business processing device, a computing device, a computer-readable storage medium, and a computer program product, which will be described in detail in the following embodiments.

[0045] See Figure 1 , Figure 1This is a schematic diagram of a business processing system according to an embodiment of the present invention. The business processing system includes a data terminal, a counting terminal, and a business processing terminal. The data terminal is used to send multiple business data points for a target business item to the counting terminal. The counting terminal is used to acquire the multiple business data points for the target business item and determine the counting rule corresponding to the target business item. The counting rule is obtained based on the classification and configuration of sample data for the target business item. The counting rule includes multiple matching sub-rules and counting sub-rules. For a target matching sub-rule, the target business data matching the matching sub-rule is determined, and a target data group corresponding to the target matching sub-rule is determined based on the target business data. The target data group is determined to match the counting sub-rule, the number of matches is recorded, and the counting information corresponding to the target business item is updated. Business processing information is generated based on the counting information and sent to the business processing terminal. The business processing terminal is used to receive the business processing information sent by the counting terminal and perform business processing based on the business processing information.

[0046] Applied to this business processing system, the collaborative work of the data terminal, counting terminal, and business processing terminal enables automated acquisition and processing of business data, reducing the need for manual operations. The data terminal collects and transmits business data for target business items to the counting terminal. The counting terminal, based on pre-defined counting rules, automatically identifies and processes datasets that meet the criteria, filters relevant business data through matching sub-rules, and uses counting sub-rules to count the number of matches for these data, thereby updating the counting information. Subsequently, the counting terminal generates business processing information based on the updated counting information and sends it to the business processing terminal. Finally, the business processing terminal receives this processing information and executes the corresponding business processing. This entire process effectively avoids the inefficiency and frequent errors of traditional manual statistical methods, while also reducing the workload of communication and coordination between the statistics and business departments, and improving the accuracy and speed of data processing.

[0047] See Figure 2 , Figure 2 This is a flowchart of a business processing method provided in an embodiment of the present invention, which specifically includes the following steps.

[0048] Step 202: Obtain multiple business data of the target business item and determine the counting rules corresponding to the target business item. The counting rules are obtained based on the classification and configuration of the sample data of the target business item, and include multiple matching sub-rules and counting sub-rules.

[0049] Among them, the target business item refers to a specific business activity or project that needs to be statistically analyzed and counted, while the business data refers to all data records related to the business item. The counting rules are a set of logical rules designed to ensure the accuracy of data statistics, including but not limited to matching sub-rules and counting sub-rules.

[0050] In practical applications, the system first retrieves multiple business data related to the target business item from the data source. For example, the system needs to collect all data records related to a certain business item, such as order number, operation timestamp, and operation type. Then, the system determines which data should be included in the statistics based on predefined counting rules. These counting rules are derived from the analysis of historical sample data of the target business item and can be used to distinguish different types of data and classify them. For example, the system might identify each business transaction based on the order number and determine the nature of the operation based on the operation type. The counting rules include multiple matching sub-rules, each corresponding to a specific data feature, such as order number or operation type. When a piece of business data meets a certain matching sub-rule, it is classified into the corresponding category. Furthermore, the counting sub-rules define how to count based on the matching results, such as recording the number of operations of each type. One implementation method for retrieving multiple business data for the target business item is to directly obtain the real-time data stream through a database query interface; another implementation method is to periodically download historical data files in batches from the database. Once business data is collected and categorized according to matching sub-rules, the system can count the categorized data according to counting sub-rules and generate counting information.

[0051] In one specific embodiment of the present invention, assuming the target business item is the "car wash" service in a service order, the system first retrieves all the latest car wash service order data from the service management system. This data includes the order number, operation type (e.g., "normal washing," "normal washing (without drying)," etc.), and operation timestamp for each order. Next, the system determines which data should be included in the statistics based on pre-set counting rules. For example, the counting rules might stipulate that only orders in the completed state are counted in the statistics. Matching sub-rules may include identifying each order based on its order number and determining the type of car wash service based on its operation type. Counting sub-rules define how to count based on the matching results, such as recording the number of car wash service orders of each type. The system retrieves car wash service order data in real time through a database query interface or periodically downloads historical data files containing all car wash service orders from the database. Once the car wash service order data is collected and categorized according to the matching sub-rules, the system can count the categorized data according to the counting sub-rules to generate statistical information for the car wash service orders.

[0052] Step 204: Determine the target business data that matches the target matching sub-rule, and determine the target data group corresponding to the target matching sub-rule based on the target business data, wherein the target matching sub-rule is any matching sub-rule.

[0053] In this context, target business data refers to specific data records related to the target business item, while target matching sub-rules refer to one or a group of rules selected from multiple matching sub-rules for identifying target business data. Target data groups refer to the set of data that meets specific conditions and is filtered out through matching sub-rules.

[0054] In practical applications, the system first filters out target business data that matches the target matching sub-rules. For example, if the target matching sub-rule distinguishes different service items based on order type, the system will check the order type field in each business data entry and identify data that matches the specific type. For each business data entry, the system checks whether it meets the conditions of the target matching sub-rule; only when the data fully meets these conditions will it be considered target business data. Once the target business data is determined, the next step is to categorize this data into the corresponding target data groups. For example, if the target matching sub-rule defines different types of "car wash" service (such as "normal wash," "normal wash (without drying)," etc.), the system will categorize all data belonging to the "car wash" service into their respective types. The system can achieve this by querying field values ​​in the database, such as checking the "service type" field in the service order table and grouping the data according to the field value. For determining the target business data that matches the target matching sub-rule, one approach is to traverse all business data records and check each one to see if it matches the target matching sub-rule; another approach is to use a database query language (such as SQL) to quickly locate and extract the data that matches the rule.

[0055] In one specific embodiment of the present invention, assuming the target matching sub-rule is "car wash service type is 'normal cleaning'", the system first retrieves all business data related to car wash services from the service order database, then checks the "service type" field of each data entry to find all service records marked as "normal cleaning". The system identifies these records as target business data and categorizes them into target data groups of the "normal cleaning" type. For example, the system will retrieve all service orders containing "normal cleaning" and organize this order information into a data group. In this way, all service orders that conform to the "normal cleaning" matching sub-rule will be collected together, forming a clear data group, which facilitates subsequent counting and processing.

[0056] Furthermore, the matching sub-rule is configured with a matching data identifier; determining the target business data that matches the target matching sub-rule includes: parsing multiple business data to obtain the business data identifier of each business data; and filtering the target business data whose business data identifier is the same as the matching data identifier from the multiple business data.

[0057] Among them, the matching sub-rule refers to the rule used to identify and classify business data, and the matching data identifier is a unique identifier defined according to the matching sub-rule to identify a specific type of business data; the business data identifier refers to the unique identifier in the business data used to distinguish different business items.

[0058] In practical applications, the system first needs to parse multiple business data sets and extract the business data identifier from each set. For example, if the business data includes fields such as order number, operation type, and operation timestamp, the system needs to extract identifiers to distinguish different business items, such as order number or operation type. Next, the system will filter out business data sets whose business data identifiers match the matching data identifiers. This means that the system will compare the identifier of each business data set with the matching data identifiers defined in the matching sub-rules. Only when they match will the business data set be considered the target business data set. For example, if the matching sub-rules define different types of the "car wash" service (such as "normal wash," "normal wash (without drying)," etc.), and the matching data identifiers for these types are "WASH_DRY" and "WASH_NO_DRY" respectively, then the system will check the operation type field in each business data set and filter out data sets with identifiers of "WASH_DRY" and "WASH_NO_DRY." For parsing multiple business data in a step, one approach is to extract the business data identifier through a database query statement; another approach is to use a data processing script to parse the identifier from the raw data.

[0059] In one specific embodiment of the present invention, it is assumed that the matching sub-rules define two transportation modes: "TO-by VIN" and "TO-by by distance," with matching data identifiers of "VIN_TRANSPORT" and "DISTANCE_TRANSPORT," respectively. The system first retrieves all relevant business data from the transportation order database, and then extracts the operation type field from each piece of business data as the business data identifier. Next, the system filters out business data with the identifiers "VIN_TRANSPORT" and "DISTANCE_TRANSPORT." For example, the system checks the operation type field in each order data, identifies order data with the identifier "VIN_TRANSPORT," and determines these as target business data. Similarly, the system also filters out data with the identifier "DISTANCE_TRANSPORT" and determines it as target business data. In this way, all transportation orders that conform to the two matching sub-rules, "TO-by VIN" and "TO-by by distance," are filtered out separately, forming their respective target business data sets.

[0060] Based on this, by parsing business data and filtering out target business data that matches the matching data identifier, the system can effectively identify and classify different business items, reduce misjudgments and omissions, and improve the accuracy and efficiency of data processing. The system can automatically complete data parsing and filtering, providing accurate data support for subsequent counting and business processing.

[0061] Furthermore, there are multiple target business data; determining the target data group corresponding to the target matching sub-rule based on the target business data includes: when the target matching sub-rule is of type and , aggregating multiple target business data to obtain the target data group corresponding to the target matching sub-rule; when the target matching sub-rule is of type or , using any target business data as the target data group corresponding to the target matching sub-rule.

[0062] Here, target business data refers to specific data records related to target business items, target matching sub-rules refer to rules used to identify and classify this business data, and target data groups refer to data sets that meet specific conditions and are filtered out through matching sub-rules. "AND type" indicates that multiple conditions must be met simultaneously, while "OR type" indicates that only one condition needs to be met.

[0063] In practical applications, the system first determines the target data group corresponding to the target matching sub-rule based on the target business data. When the target matching sub-rule is "and type", the system aggregates all target business data that match this type of rule to form a target data group. This means that the system needs to ensure that each piece of target business data meets all matching conditions; only when all conditions are met will these data be grouped into the same target data group. For example, if the target matching sub-rule defines "car wash service type is 'normal cleaning' and operation time is in the morning", then the system will check each piece of business data, and only data with the service type "normal cleaning" and operation time in the morning will be aggregated together to form a target data group.

[0064] On the other hand, when the target matching sub-rule is "OR type", the system will treat any target business data that meets at least one condition as part of the target data group. This means that as long as the target business data meets any one of the matching conditions, it can be treated as a separate target data group. For example, if the target matching sub-rule defines "car wash service type is 'normal wash' or 'normal wash (without drying)'", then the system will check each business data, and data with a service type of "normal wash" or "normal wash (without drying)" can be treated as independent target data groups respectively.

[0065] For the step "When the target matching sub-rule is of type 'OR', aggregate multiple target business data to obtain the target data group corresponding to the target matching sub-rule", one implementation is to extract data records that meet all conditions through a database query statement; another implementation is to use a data processing script to check whether each piece of data meets all matching conditions, and then merge the data that meets the conditions into a single dataset. For the step "When the target matching sub-rule is of type 'OR', use any target business data as the target data group corresponding to the target matching sub-rule", one implementation is to use a logical "OR" operation in the database query to extract data records that meet the conditions; another implementation is to use a data processing script to check whether each piece of data meets at least one condition, and if so, record that data separately as the target data group.

[0066] In one specific embodiment of the present invention, assuming the target matching sub-rule defines "service order type 'normal cleaning' and operation time in the morning" as a "S" type rule, the system will check all service order data, find data that simultaneously meets both conditions, and aggregate these data together to form a target data group. For example, the system will retrieve all service orders marked as "normal cleaning" and performed in the morning, and organize these order information into a data group. Alternatively, if the target matching sub-rule defines "service order type 'normal cleaning' or 'normal cleaning (without drying)'" as an "OR" type rule, the system will check all service order data, find data that meets either condition, and treat each such data entry as a separate part of the target data group. For example, the system will retrieve all service orders marked as "normal cleaning" or "normal cleaning (without drying)" and record these orders as independent target data groups.

[0067] Based on this, by classifying target business data and determining target data groups according to the type of target matching sub-rules ("AND type" or "OR type"), the system can effectively organize data, facilitating further statistical analysis. This process not only improves the accuracy and efficiency of data processing but also reduces the need for manual operations, ensuring the reliability and timeliness of data statistics. The system can automatically complete data classification and organization, providing structured data support for subsequent business processing.

[0068] Step 206: Match the target data group with the counting sub-rules, record the number of matches, and update the counting information corresponding to the target business item based on the number of matches.

[0069] The target data group refers to the set of data that meets specific conditions and is filtered by the matching sub-rules. The counting sub-rules are a set of rules used to define how to count the data in the target data group. The number of matches refers to the number of data items in the target data group that meet the counting sub-rules. The counting information refers to the result data obtained by counting the number of matches.

[0070] In practical applications, the system applies counting sub-rules to each piece of data in the target data group to check if it meets the counting criteria. For example, if a counting sub-rule specifies a method for counting the number of services of a certain type, the system will check each piece of data in the target data group to see if it belongs to that service type. For each piece of data that meets the counting sub-rule, the system records a match, and these match counts are accumulated. For example, for the "normal cleaning" type in car wash service orders, the system will check all order data categorized under this type, and record a match for each piece of data that meets the definition of "normal cleaning". After all data has been checked, the system updates the counting information based on the recorded match counts. For example, the system will count the number of all orders that meet the "normal cleaning" type and save this number as part of the counting information. One way to match the target data group with the counting sub-rules is to iterate through each piece of data in the target data group and compare it with each counting sub-rule; another way is to use an efficient algorithm (such as a hash table) to quickly find data that meets the counting sub-rules.

[0071] In one specific embodiment of the present invention, assuming the target data group consists of all service orders marked "normal cleaning," the system first checks whether each piece of data in the target data group conforms to the counting sub-rule of "normal cleaning." The system verifies each piece of data one by one whether it is a service order of the "normal cleaning" type; if so, a match is recorded. For example, the system will retrieve all service orders containing "normal cleaning" and record a match for each such order. Once all data has been checked, the system will count the total number of orders that conform to the "normal cleaning" type and save this number as part of the counting information. For example, the system will display that the total number of "normal cleaning" type orders is 50 and save this counting information for subsequent business processing.

[0072] Furthermore, the counting sub-rule includes matching data group identifiers; determining whether a target data group matches the counting sub-rule and recording the number of matches, including: parsing at least one data group to obtain at least one target data group identifier; determining whether the matching data group identifier matches the target data group identifier and recording the number of matches.

[0073] Among them, the counting sub-rules refer to a set of rules used to define how to count based on the data in the target data group, the matching data group identifier is a unique identifier defined according to the counting sub-rules and used to identify a specific type of data group; the target data group identifier is a unique identifier extracted from the target data group and used to distinguish different data groups.

[0074] In practical applications, the system first needs to parse at least one data group and extract at least one target data group identifier. For example, if a data group includes fields such as order type and operation timestamp, the system needs to extract identifiers to distinguish different data groups, such as order type. Next, the system determines whether the matching data group identifier in the counting sub-rule matches the extracted target data group identifier. This means that the system compares each data group identifier with the matching data group identifier defined in the counting sub-rule one by one; only when they match is the data group considered a successful match. For example, if the counting sub-rule defines the matching data group identifier for the "normal cleaning" type as "WASH_DRY", then the system will check the order type field in each data group and filter out the data groups with the identifier "WASH_DRY". Each time a matching data group is found, the system records a match count. One way to parse at least one data group to obtain at least one target data group identifier is to extract the data group identifier through a database query statement; another way is to use a data processing script to parse the identifier from the raw data.

[0075] In one specific embodiment of the present invention, it is assumed that the counting sub-rule defines the matching data group identifier of the "normal cleaning" type as "WASH_DRY", and the system has already determined multiple target data groups according to the matching sub-rule. The system first parses these data groups, extracting the order type field of each data group as the target data group identifier. Then, the system compares the order type field of each data group with the matching data group identifier "WASH_DRY" one by one. For example, the system checks the order type field of each data group; if the order type is "WASH_DRY", it records a match. In this way, the system can count the number of all successfully matched data groups, i.e., the number of matches. For example, if the system finds 10 data groups with the order type "WASH_DRY", then the system will record the number of matches as 10.

[0076] Based on this, by parsing data groups and determining whether their identifiers match the identifiers of matching data groups in the counting sub-rules, the system can accurately count the number of data groups that meet specific conditions. This process not only improves the accuracy and efficiency of data statistics but also reduces the need for manual operation, ensuring the reliability and timeliness of the counting information. The system can automatically complete the parsing of data groups and the recording of matching counts, providing accurate data support for subsequent business processing.

[0077] Further, there is at least one target data group; determining that the matching data group identifier matches the target data group identifier and recording the number of matches includes: in the case of the counting sub-rule being of type and , determining that the target data group identifier of at least one target data group matches the matching data group identifier and recording the number of matches as one; in the case of the counting sub-rule being of type or , determining that the target data group identifier of any target data group matches the matching data group identifier and recording the number of matches as the number of target data groups.

[0078] The target data group identifier is a unique identifier extracted from the target data group to distinguish different data groups, while the matching data group identifier is a unique identifier defined according to the counting sub-rules to identify a specific type of data group. "AND type" means that multiple conditions must be met simultaneously, while "OR type" means that only one condition needs to be met.

[0079] In practical applications, the system first needs to determine whether the matching data group identifier matches the target data group identifier and record the number of matches. When the counting sub-rule is "and type", the system will check whether the target data group identifier of at least one target data group is completely identical to the matching data group identifier. This means that the system needs to ensure that all target data group identifiers are exactly the same as the matching data group identifiers. Only when all target data group identifiers are completely identical to the matching data group identifiers will the system record a match. For example, if the counting sub-rule defines the matching data group identifier for "normal cleaning" as "WASH_DRY", and the system has already determined multiple target data groups according to the matching sub-rule, then the system will check the identifiers of these data groups. If the identifiers of all data groups are "WASH_DRY", then a match is recorded.

[0080] On the other hand, when the counting sub-rule is of the "OR type," the system checks whether the target data group identifier of any target data group matches the matching data group identifier, and records the number of matches as the number of target data groups. This means that as long as one target data group identifier matches a matching data group identifier, the system will record a match. For example, if the counting sub-rule defines the matching data group identifiers for "Normal Cleaning" and "Normal Cleaning (No Drying)" as "WASH_DRY" and "WASH_NO_DRY" respectively, and the system has already identified multiple target data groups according to the matching sub-rule, then the system will check the identifier of each data group. If any data group has the identifier "WASH_DRY" or "WASH_NO_DRY," then a match is recorded. Ultimately, the number of matches recorded by the system will be the sum of the number of all data groups that meet the conditions.

[0081] For the step "When the counting sub-rule is of type 'and', determine that at least one target data group identifier matches the matching data group identifier, and record the match count as one," one implementation method is to check whether all data group identifiers are consistent with the matching data group identifier using a database query statement; another implementation method is to use a data processing script to verify whether each data group identifier matches the matching data group identifier one by one, and record a match if all match. For the step "When the counting sub-rule is of type 'or', determine that the target data group identifier of any target data group matches the matching data group identifier, and record the match count as the number of target data groups," one implementation method is to use a logical "or" operation during database query to extract data group identifiers that meet the conditions; another implementation method is to use a data processing script to check whether each data group identifier meets at least one matching condition, and record a match if it does.

[0082] In one specific embodiment of the present invention, it is assumed that the counting sub-rule defines the identifier of the matching data group for "normal cleaning" as "WASH_DRY", and the system has already determined multiple target data groups according to the matching sub-rule. If the counting sub-rule is of the "AND" type, the system will check whether the identifiers of all target data groups are "WASH_DRY". If all match, a match is recorded. If the counting sub-rule is of the "OR" type, the system will check whether the identifier of each target data group is "WASH_DRY". If any data group has the identifier "WASH_DRY", a match is recorded. For example, if the system finds that 8 out of 10 data groups have the identifier "WASH_DRY", then 8 matches are recorded.

[0083] Based on this, by matching the identifiers of target data groups and recording the number of matches according to the type of the counting sub-rule ("AND type" or "OR type"), the system can accurately count the number of data groups that meet specific conditions. This process not only improves the accuracy and efficiency of data statistics but also reduces the need for manual operation, ensuring the reliability and timeliness of the counting information. The system can automatically complete the matching of data group identifiers and the recording of the number of matches, providing accurate data support for subsequent business processing.

[0084] Step 208: Generate business processing information based on the counting information and send the business processing information to the business processing terminal.

[0085] Among them, counting information refers to the result data obtained after matching and counting processing, business processing information refers to the data or instructions generated based on the counting information to guide subsequent business processing, and business processing end refers to the application or system that receives and executes business processing information.

[0086] In practical applications, the system first generates business processing information based on the counting information obtained in the previous step. For example, if the counting information indicates that there are 50 orders of the "normal cleaning" type, the system will generate information indicating the number of orders of this type, such as "Total number of normal cleaning orders is 50." The generated business processing information can include, but is not limited to, statistical data, reports, and instructions, which will be used to guide subsequent business processing operations. After generation, the system sends the business processing information to the business processing end. For example, the system can pass the business processing information to the business processing end through API calls, message queues, email notifications, etc. After receiving this information, the business processing end can further process the data, such as generating reports, updating database records, triggering other business processes, etc. For the step of generating business processing information based on counting information, one implementation method is to write a script to automatically parse the counting information and format it into business processing information; another implementation method is to use existing reporting tools to convert the counting information into a visual report format.

[0087] In one specific embodiment of the present invention, it is assumed that the counting information shows a total of 50 orders of the "normal cleaning" type. The system will generate a business processing message indicating the number of orders of this type, such as "Total number of normal cleaning orders: 50". Then, the system sends this business processing message to the business processing terminal. For example, the system can send the information to the business processing module through an internal API. After receiving the information, the business processing module may further process the data, such as updating the order status, generating reports, or storing the information in a database for subsequent analysis. In addition, the system can also send this information to relevant personnel via email notification so that they can be informed of the business progress in a timely manner.

[0088] Furthermore, before acquiring multiple business data of the target business item and determining the counting rule corresponding to the target business item, the method further includes: acquiring multiple sample business data corresponding to the target business item based on the business item identifier of the target business item; acquiring classification information corresponding to the target business item; classifying each sample business data corresponding to the target business item based on the classification information to obtain multiple sample data groups and recording the identifier of each sample data group as the matching data group identifier; and for the target sample data group, recording the data identifier of each sample data in the target sample data group as the matching data identifier.

[0089] Among them, the business item identifier of the target business item is an identifier used to uniquely identify a specific business item; the sample business data refers to representative business data records used to analyze and configure counting rules; the classification information refers to information used to distinguish different business data categories; the sample data group refers to the set formed after grouping the sample business data through the classification information; the matching data group identifier is a unique identifier used to identify a specific sample data group; and the matching data identifier is a unique identifier used to identify a specific data item in the sample data group.

[0090] In practical applications, before acquiring multiple business data points for a target business item and determining the corresponding counting rules, the system needs to perform a series of preparatory steps. First, the system acquires multiple sample business data points related to the target business item based on its business item identifier. For example, if the target business item is a "car wash" service, the system will select several "car wash" service order records from historical records as sample business data. Next, the system acquires the classification information corresponding to the target business item. This information defines how to classify the sample business data. For example, classification information may include fields such as service type, operation time, and operation location. The system classifies the sample business data according to this classification information, grouping data with similar characteristics into the same group to form multiple sample data groups. For example, the system might group "normal cleaning" and "normal cleaning (without drying)" service orders into different sample data groups based on service type. The system also records the identifier of each sample data group as a matching data group identifier for subsequent use. For example, the system assigns a unique identifier "WASH_DRY" to the "normal cleaning" sample data group.

[0091] In addition, the system needs to record the data identifier of each specific data item in each sample data group as a matching data identifier. For example, the system will record the order number as a matching data identifier for each "normal cleaning" service order. In this way, the system can determine which data items should be included in the matching scope of the counting rules based on these identifiers.

[0092] In one specific embodiment of the present invention, assuming the target business item is a "car wash" service, the system first obtains multiple sample business data related to this business item, such as several "car wash" service order data, based on the business item identifier of the "car wash" service. Then, the system obtains the classification information corresponding to the "car wash" service, such as service type, operation time, and other fields. The system classifies the sample business data according to this classification information; for example, it groups "normal cleaning" and "normal cleaning (without drying)" service orders into different sample data groups and assigns a unique identifier to each sample data group, such as "WASH_DRY" and "WASH_NO_DRY". Next, the system records the data identifier of each specific data item in each sample data group as a matching data identifier; for example, the system records the order number of each "normal cleaning" service order as a matching data identifier. In this way, the system can determine which data items should be included in the matching scope of the counting rules based on these identifiers.

[0093] Based on this, by acquiring sample business data using the business item identifier of the target business item and classifying the sample business data according to classification information, the system can provide the necessary preparation for subsequent counting rule configuration. This process not only ensures the rationality and accuracy of the counting rules but also simplifies the complexity of subsequent data processing and improves the efficiency and reliability of data statistics. The system can automatically complete the classification and identification recording of data, providing accurate data support for subsequent business processing.

[0094] Furthermore, after acquiring multiple business data of the target business item and determining the counting rule corresponding to the target business item, the method also includes: recording the data identifier of the business data when the business data does not match the matching sub-rule; generating feedback information based on the data identifier and sending the feedback information to the management terminal; and updating the counting rule in response to the rule update instruction sent by the management terminal.

[0095] Among them, data identifier refers to the identifier used to uniquely identify each piece of business data, feedback information refers to the prompt or warning information generated based on unmatched business data, management terminal refers to the application or system that receives feedback information and manages and updates the counting rules, and rule update instruction refers to the instruction issued from the management terminal to modify or optimize the counting rules.

[0096] In practical applications, after acquiring multiple business data points for a target business item and determining the corresponding counting rules, the system also needs to handle some special cases. When business data does not match a matching sub-rule, the system records the data identifiers of this data. For example, if a piece of business data fails the verification of a matching sub-rule, the system records a unique identifier for this data, such as an order number or service record ID. Next, the system generates feedback information based on the recorded data identifiers and sends this feedback information to the management end. The feedback information typically includes basic information about the unmatched data and the reason for the mismatch, so that managers can understand the specific situation and take appropriate measures. For example, the system might generate a notification containing the order number of the unmatched data and the reason for the mismatch, and send it to relevant managers via email or an internal messaging system.

[0097] Furthermore, upon receiving feedback, the management system can send rule update commands to update the counting rules based on the actual situation. For example, if the same type of data mismatch occurs repeatedly, administrators may decide to adjust the matching sub-rules to make them more accurate or more inclusive. Upon receiving the rule update command, the system will modify the counting rules accordingly. For instance, the system may expand the conditions of the matching sub-rules or add a new matching sub-rule to cover more business scenarios.

[0098] In one specific embodiment of the present invention, suppose that when processing "car wash" service orders, the system finds that some order data does not conform to existing matching sub-rules, for example, the data format or field content of some orders does not meet expectations. At this time, the system records the data identifiers of these unmatched orders, such as the order number, and generates feedback information containing the order number and the reason for the mismatch, and then sends this information to the management terminal. For example, the system might send a message: "Order number 123456 is not matched because the service type field is empty." After receiving the feedback information, the management staff can issue a rule update instruction according to the specific situation, such as adjusting the matching conditions of the service type field to accept empty values ​​or other non-standard formats. After receiving the update instruction, the system will adjust the counting rules to correctly handle similar situations in the future.

[0099] Based on this, by recording business data that does not match the sub-rules and generating feedback information to send to the management end, the system can promptly identify and correct problems in the counting rules. This process improves the accuracy and flexibility of data processing, reduces data omissions or errors caused by incomplete rules, and enhances the system's adaptability and reliability. The system can automatically complete the recording of data identifiers and the generation and transmission of feedback information, providing support for subsequent rule updates.

[0100] One embodiment of the present invention implements the acquisition of multiple business data of a target business item and the determination of the corresponding counting rules for the target business item. The counting rules are obtained based on the classification and configuration of sample data of the target business item, and include multiple matching sub-rules and counting sub-rules. The system identifies target business data that matches a target matching sub-rule and determines a target data group corresponding to the target matching sub-rule based on the target business data. The target matching sub-rule can be any matching sub-rule. The system matches the target data group with the counting sub-rules, records the number of matches, and updates the counting information corresponding to the target business item based on the number of matches. Business processing information is generated based on the counting information and sent to the business processing terminal. By automating the acquisition and processing of business data and using pre-set counting rules to reduce manual operations, the system solves the problems of low efficiency and error-proneness in traditional manual data collection methods. Through the combined application of matching sub-rules and counting sub-rules, the system can automatically identify and process datasets that meet specific conditions, reducing the need for communication and coordination between the statistics department and various business departments, thereby reducing the workload of staff and improving the accuracy and timeliness of data statistics.

[0101] Taking the business processing method provided by this invention in the context of a contractual transaction between an enterprise and its supplier as an example. See also... Figure 3 As shown, Figure 3 This is a schematic diagram of a business processing procedure provided by an embodiment of the present invention. The processing procedure includes a rule maintenance stage and a counting stage. The rule maintenance stage includes settlement object mapping maintenance, used to define the mapping relationship between settlement objects and business items, ensuring that fees can be correctly allocated to the corresponding entities during settlement; contract maintenance, used to manage contract terms with suppliers, including service content, fee standards, and settlement methods, for accurate subsequent fee calculation; order maintenance and business item matching rule maintenance, used to record and manage the status and related information of each order, ensuring that the processing of each order conforms to the predetermined business process. The counting stage includes data system capturing behavioral data to automatically collect raw data related to the business from different data sources; business item matching, used to determine which business items the captured behavioral data matches; and behavioral data matching with matching rules, used to verify whether the collected behavioral data conforms to the pre-set matching rules, thereby determining whether it should be included in a specific business item, and the business item counting result. Finally, the business item counting result will be sent to the business processing end for business processing.

[0102] See Figure 4 As shown, Figure 4 This is a schematic diagram of the user interface of a business processing system according to an embodiment of the present invention. See also... Figure 4As shown in (a), the business item management interface includes multiple business item information. Users can add and delete business items in the business item management interface through the delete and create controls in the upper right corner.

[0103] See Figure 4 As described in (b) above. When the user clicks... Figure 4 After creating the control, the business item details interface will look like this. Figure 5 As shown, in the contract row, users can select a specific contract ID, contract item, and time range from the dropdown menu to define the scope of the business item. In the details section, users can add controls, new data groups, and business data within those data groups.

[0104] See Figure 5 As shown, Figure 5 This is a schematic diagram illustrating a business data matching and counting process according to an embodiment of the present invention. After acquiring business data at the data end, the business data is matched according to business data matching rules, specifically satisfying the attribute conditions of business items, satisfying the attribute conditions of data groups, and satisfying the attribute conditions of business items. Data identifiers and corresponding matching and counting logics are recorded. For example, the matching logic for business data 1 is business item 1—data group A—business data 1, and the counting logic for business data 1 is business data 1—data group A—business item 1. The matching logic for business data 2 is business item 2—data group B—business data 2, and the counting logic for business data 1 is business data 2—data group B—business item 2. The specific logic of the business data technology is as follows: determine if the business data or its combination satisfies the requirement to construct a data group; if so, if the data group or its combination satisfies the requirement to construct a business item, increment the count by 1; and finally, generate a counting bill based on the business item count.

[0105] See Figure 6 As shown, Figure 6 This is a schematic diagram illustrating the principle of a counting rule according to an embodiment of the present invention. See also: Figure 6 As shown in (a), the counting rule is to increment the business item count by 1:

[0106] Data Group 1: Business Data 1 AND Business Data 2

[0107] AND data group 2: business data 3 AND business data 4.

[0108] See Figure 6 As shown in (b), the counting rule is to increment the business item count by 1:

[0109] Data Group 1: Business Data 1 AND Business Data 2

[0110] OR Data Group 2: Business Data 3 AND Business Data 4.

[0111] See Figure 6As shown in (c), the counting rule is to increment the business item count by 1:

[0112] Data Group 1: Business Data 1 AND Business Data 2

[0113] AND data group 2: business data 3 OR business data 4.

[0114] See Figure 6 As shown in (d), the counting rule is to increment the business item count by 1:

[0115] Data Group 1: Business Data 1 OR Business Data 2

[0116] OR data group 2: business data 3 OR business data 4.

[0117] See Figure 6 As shown in (e), the counting rule is to increment the business item count by 1:

[0118] Data Group 1: Business Data 1 OR Business Data 2

[0119] AND data group 2: business data 3 OR business data 4.

[0120] See Figure 6 As shown in (f), the counting rule is to increment the business item count by 1:

[0121] Data Group 1: Business Data 1 AND Business Data 2

[0122] OR Data Group 2: Business Data 1 AND Business Data 3.

[0123] See Figure 7 As shown, Figure 7 This is a schematic diagram of the operation interface of another business processing system provided in one embodiment of the present invention. Taking the business item as the transportation of products from location A to location C or from location B to location C under a contract as an example, data group 1 represents outbound from location A OR outbound from location B, and data group 2 represents inbound from location C.

[0124] Corresponding to the above method embodiments, the present invention also provides a business processing apparatus embodiment. Figure 8 A schematic diagram of a service processing apparatus according to an embodiment of the present invention is shown. Figure 8 As shown, the device includes:

[0125] The acquisition module 802 is configured to acquire multiple business data of the target business item and determine the counting rule corresponding to the target business item. The counting rule is obtained based on the classification configuration of the sample data of the target business item and includes multiple matching sub-rules and counting sub-rules.

[0126] The determination module 804 is configured to determine the target business data that matches the target matching sub-rule, and to determine the target data group corresponding to the target matching sub-rule based on the target business data, wherein the target matching sub-rule is any matching sub-rule;

[0127] The counting module 806 is configured to match the target data group with the counting sub-rules, record the number of matches, and update the counting information corresponding to the target business item based on the number of matches.

[0128] The sending module 808 is configured to generate business processing information based on the counting information and send the business processing information to the business processing terminal.

[0129] Optionally, the matching sub-rule is configured with a matching data identifier, and the determination module 804 is further configured to parse multiple business data to obtain the business data identifier of each business data; and filter the target business data whose business data identifier is the same as the matching data identifier from the multiple business data.

[0130] Optionally, there are multiple target business data. The determining module 804 is further configured to aggregate multiple target business data to obtain the target data group corresponding to the target matching sub-rule when the target matching sub-rule is of type and ; and to take any target business data as the target data group corresponding to the target matching sub-rule when the target matching sub-rule is of type or .

[0131] Optionally, the counting sub-rule includes a matching data group identifier, and the counting module 806 is further configured to parse at least one data group to obtain at least one target data group identifier; determine that the matching data group identifier matches the target data group identifier, and record the number of matches.

[0132] Optionally, there is at least one target data group, and the counting module 806 is further configured to determine that the target data group identifier of at least one target data group matches the matching data group identifier when the counting sub-rule is of type and, and record the number of matches as one; when the counting sub-rule is of type or, determine that the target data group identifier of any target data group matches the matching data group identifier, and record the number of matches as the number of target data groups.

[0133] Optionally, the business processing device further includes a classification module, configured to acquire multiple sample business data corresponding to the target business item based on the business item identifier of the target business item; acquire classification information corresponding to the target business item; classify each sample business data corresponding to the target business item based on the classification information to obtain multiple sample data groups and record the identifier of each sample data group as the matching data group identifier; and for the target sample data group, record the data identifier of each sample data in the target sample data group as the matching data identifier.

[0134] Optionally, the business processing device further includes an update module, configured to record the data identifier of the business data when the business data does not match the matching sub-rule; generate feedback information based on the data identifier and send the feedback information to the management terminal; and update the counting rule in response to the rule update instruction sent by the management terminal.

[0135] Applied to this business processing device, the acquisition module 802 automatically acquires multiple business data points for the target business item and determines the corresponding counting rules. Next, the determination module 804 filters out the target business data that conforms to the preset matching sub-rules, forming a target data group. Subsequently, the counting module 806 performs matching processing on the target data group according to the counting sub-rules, records the result of each match, and updates the counting information of the target business item accordingly. Finally, the sending module 808 generates business processing information based on the updated counting information and sends it to the business processing terminal. This series of modular operation processes effectively automates the processing of business data, reduces the inefficiency and error risks of traditional manual statistical methods, and lowers the workload of coordination between the statistics and business departments, thereby improving the overall accuracy and efficiency of data processing.

[0136] The above is an illustrative scheme of a business processing apparatus according to this embodiment. It should be noted that the technical solution of this business processing apparatus and the technical solution of the business processing method described above belong to the same concept. For details not described in detail in the technical solution of the business processing apparatus, please refer to the description of the technical solution of the business processing method described above.

[0137] Figure 9 A structural block diagram of a computing device 900 according to an embodiment of the present invention is shown. The components of the computing device 900 include, but are not limited to, a memory 910 and a processor 920. The processor 920 is connected to the memory 910 via a bus 930, and a database 950 is used to store data.

[0138] The computing device 900 also includes an access device 940, which enables the computing device 900 to communicate via one or more networks 960. Examples of such networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the Internet. Access device 940 may include one or more of any type of wired or wireless network interface (e.g., network interface card (NIC)), such as IEEE 802.11 Wireless Local Area Network (WLAN) interface, Wi-MAX (Worldwide Interoperability for Microwave Access) interface, Ethernet interface, Universal Serial Bus (USB) interface, cellular network interface, Bluetooth interface, and Near Field Communication (NFC).

[0139] In one embodiment of the present invention, the above-described components of the computing device 900 and Figure 9 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 9 The illustrated block diagram of the computing device is for illustrative purposes only and is not intended to limit the scope of the invention. Those skilled in the art can add or replace other components as needed.

[0140] The computing device 900 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 900 can also be a mobile or stationary server.

[0141] The processor 920 is used to execute the following computer program / instruction, which, when executed by the processor, implements the steps of the above-mentioned business processing method.

[0142] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the computing device embodiments are basically similar to the business processing method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the business processing method embodiments.

[0143] An embodiment of the present invention also provides a computer-readable storage medium storing a computer program / instructions that, when executed by a processor, implement the steps of the above-described business processing method.

[0144] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the computer-readable storage medium embodiment is described simply because it is substantially similar to the business processing method embodiment; relevant parts can be referred to in the description of the business processing method embodiment.

[0145] An embodiment of the present invention also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the above-described business processing method.

[0146] The above is an illustrative scheme of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the above-described business processing method belong to the same concept. For details not described in detail in the technical solution of the computer program product, please refer to the description of the technical solution of the above-described business processing method.

[0147] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0148] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added or removed according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0149] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments of the present invention.

[0150] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0151] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments of the present invention. These embodiments are selected and specifically described to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A service processing method characterized by, include: Obtain multiple business data of a target business item and determine the counting rule corresponding to the target business item. The counting rule is obtained based on the classification configuration of the sample data of the target business item and includes multiple matching sub-rules and counting sub-rules. Determine the target business data that matches the target matching sub-rule, and determine the target data group corresponding to the target matching sub-rule based on the target business data, wherein the target matching sub-rule is any matching sub-rule; The target data group is matched with the counting sub-rule, the number of matches is recorded, and the counting information corresponding to the target business item is updated based on the number of matches. Based on the counting information, business processing information is generated and sent to the business processing terminal.

2. The method of claim 1, wherein, The matching sub-rule is configured with a matching data identifier; The determination of the target business data that matches the target matching sub-rule includes: Parse the multiple business data to obtain the business data identifier of each business data; Filter the target business data from the multiple business data sets whose business data identifier is the same as the matching data identifier.

3. The method of claim 1, wherein, The target business data is multiple; the step of determining the target data group corresponding to the target matching sub-rule based on the target business data includes: When the target matching sub-rule is of type , multiple target business data are aggregated to obtain the target data group corresponding to the target matching sub-rule; When the target matching sub-rule is of type OR, any target business data will be used as the target data group corresponding to the target matching sub-rule.

4. The method according to claim 1, characterized in that, The counting sub-rule includes a matching data group identifier; determining that the target data group matches the counting sub-rule and recording the number of matches includes: Parse the at least one data group to obtain at least one target data group identifier; Determine that the matching data group identifier matches the target data group identifier, and record the number of matches.

5. The method according to claim 4, characterized in that, The target data group is at least one; the step of determining that the matching data group identifier matches the target data group identifier and recording the number of matches includes: In the case of the counting sub-rule being of type , it is determined that the target data group identifier of the at least one target data group matches the matching data group identifier, and the number of matches is recorded as one. When the counting sub-rule is of type OR, determine that the target data group identifier of any target data group matches the matching data group identifier, and record the number of matches as the number of target data groups.

6. The method according to claim 1, characterized in that, Before acquiring multiple business data for the target business item and determining the counting rule corresponding to the target business item, the method further includes: Based on the business item identifier of the target business item, obtain multiple sample business data corresponding to the target business item; Obtain the classification information corresponding to the target business item; Based on the classification information, the sample business data corresponding to the target business item are classified to obtain multiple sample data groups and the identifier of each sample data group is recorded as the matching data group identifier. For a target sample data group, the data identifier of each sample data in the target sample data group is recorded as the matching data identifier.

7. The method according to claim 1, characterized in that, After acquiring multiple business data points for the target business item and determining the counting rule corresponding to the target business item, the method further includes: If the business data does not match the matching sub-rule, record the data identifier of the business data; Feedback information is generated based on the data identifier and sent to the management terminal; In response to the rule update command sent by the management terminal, the counting rule is updated.

8. A business processing system, characterized in that, include: The data end is used to send multiple business data for the target business item to the counting end; The counting terminal is used to acquire multiple business data of the target business item and determine the counting rule corresponding to the target business item. The counting rule is obtained based on the classification configuration of the sample data of the target business item. The counting rule includes multiple matching sub-rules and counting sub-rules. For a target matching sub-rule, the target business data that matches the matching sub-rule is determined, and the target data group corresponding to the target matching sub-rule is determined based on the target business data. Determine if the target data group matches the counting sub-rule, record the number of matches, and update the counting information corresponding to the target business item; generate business processing information based on the counting information, and send the business processing information to the business processing terminal; The service processing terminal is used to receive service processing information sent by the counting terminal and perform service processing based on the service processing information.

9. A business processing device, characterized in that, include: The acquisition module is configured to acquire multiple business data of a target business item and determine the counting rule corresponding to the target business item. The counting rule is obtained based on the classification configuration of the sample data of the target business item and includes multiple matching sub-rules and counting sub-rules. The determination module is configured to determine the target business data that matches the target matching sub-rule, and to determine the target data group corresponding to the target matching sub-rule based on the target business data, wherein the target matching sub-rule is any matching sub-rule; The counting module is configured to match the target data group with the counting sub-rule, record the number of matches, and update the counting information corresponding to the target business item based on the number of matches. The sending module is configured to generate service processing information based on the counting information and send the service processing information to the service processing terminal.

10. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer programs / instructions, and the processor is used to execute the computer programs / instructions, which, when executed by the processor, implement the steps of the business processing method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, It stores a computer program / instruction that, when executed by a processor, implements the steps of the business processing method according to any one of claims 1-8.

12. A computer program product, characterized in that, Includes a computer program / instruction that, when executed by a processor, implements the steps of the business processing method according to any one of claims 1-8.

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